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Poxviruses subvert the host immune response by producing immunomodulatory proteins , including a complement regulatory protein . Ectromelia virus provides a mouse model for smallpox where the virus and the host's immune response have co-evolved . Using this model , our study investigated the role of the complement system during a poxvirus infection . By multiple inoculation routes , ectromelia virus caused increased mortality by 7 to 10 days post-infection in C57BL/6 mice that lack C3 , the central component of the complement cascade . In C3−/− mice , ectromelia virus disseminated earlier to target organs and generated higher peak titers compared to the congenic controls . Also , increased hepatic inflammation and necrosis correlated with these higher tissue titers and likely contributed to the morbidity in the C3−/− mice . In vitro , the complement system in naïve C57BL/6 mouse sera neutralized ectromelia virus , primarily through the recognition of the virion by natural antibody and activation of the classical and alternative pathways . Sera deficient in classical or alternative pathway components or antibody had reduced ability to neutralize viral particles , which likely contributed to increased viral dissemination and disease severity in vivo . The increased mortality of C4−/− or Factor B−/− mice also indicates that these two pathways of complement activation are required for survival . In summary , the complement system acts in the first few minutes , hours , and days to control this poxviral infection until the adaptive immune response can react , and loss of this system results in lethal infection .
Poxviruses remain a threat to the human population despite the eradication decades ago of naturally circulating variola virus , the causative agent of smallpox . Smallpox , with its up to 30% mortality rate , could devastate the large unvaccinated population if released accidentally or by bioterrorists [1] . Closely related monkeypox virus has also emerged as a human pathogen [2] . To understand the virulence of smallpox , investigators have turned to related poxviruses like ectromelia virus ( ECTV ) , the causative agent of mousepox . Variola virus and ECTV have a narrow host-range and cause significant morbidity and mortality [3] , [4] . The numerous available mousepox-susceptible and -resistant mouse strains allow the components of the protective immune response to poxviruses to be dissected in the natural host . Disease severity varies among inbred mouse strains , and comparisons of these strains have elucidated factors essential for survival . Mice naturally acquire ECTV via cutaneous abrasions , which is mimicked experimentally with footpad inoculation [4] . Through this route , ECTV infection is 100% lethal in susceptible strains ( BALB/c , DBA/2 , and A/J ) but asymptomatic in the resistant C57BL/6 strain . The C57BL/6 strain has a stronger TH1 type cytokine response and a more robust cytotoxic lymphocyte response than susceptible strains [5] . Lethal infection occurs in C57BL/6 mice that lack CD8+ T cells [6] , [7] , B cells [7] , [8] , macrophages [6] , natural killer ( NK ) cells [9] , [10] , interferon ( IFN ) -γ [11]–[13] , IFN α/β receptor [13] , perforin [14] , [15] , and granzyme A or B [16] . Survival , therefore , requires both the adaptive and innate immune response . The innate immune system defends the host during the early phase of an infection and shapes the adaptive response [17]–[19] . The complement system is an essential component of the innate immune system , and evidence from human disease and animal models implicates complement as a critical part of host defense against several virus families [20]–[24] . The complement system consists of cell-surface and serum proteins that interact to destroy invading microorganisms and infected host cells [19] , [25]–[27] . Three distinct pathways activate this cascade: classical , lectin , and alternative ( pathway diagram in the results section ) . Antibody binding to antigen triggers the classical pathway . Mannan-binding lectin ( MBL ) and related proteins recognize repetitive carbohydrate motifs on pathogens and infected cells to initiate the lectin pathway [28] . Spontaneously activated C3 initiates the alternative pathway , especially if deposited on surfaces deficient in regulatory proteins [29] . The alternative pathway also serves as a positive feedback loop by forming additional C3 convertases from the C3b produced by any pathway . All three pathways converge at the step of C3 cleavage to C3a and C3b , and they share a common terminal pathway that generates the C5a anaphylatoxin and the membrane attack complex ( MAC ) . Complement system activation can exert multiple antiviral effects [25] , [27] . Opsonization of the virion may block attachment or promote destruction by phagocytosis . The MAC disrupts the membrane integrity of the virion or infected cells . The anaphylatoxin cleavage products , C3a and C5a , attract and activate proinflammatory and immune effector cells [30] . Finally , complement activation induces and instructs the adaptive response and augments the neutralizing activity of antibody [18] , [31]–[33] . To evade these antiviral activities , viruses use multiple strategies to hinder complement activation [26] , [27] , [34] . In their large double-stranded DNA genomes , poxviruses encode factors that modify the immune response [35] . Study of immunomodulatory molecules has provided insights into viral pathogenesis and revealed novel facets of the host's immune response [36]–[39] . Variola virus , monkeypox virus , and ECTV each produce an orthologous complement regulatory protein that has structural and functional homology to host proteins [33] , [40]–[45] . Loss of this complement regulatory protein may account for the reduced virulence seen in the West African vs . Congo basin strains of monkeypox virus [45] , [46] . The limits of the monkeypox animal models , however , have made this a difficult hypothesis to test . Loss of the complement regulatory protein affects local lesion size of cowpox and vaccinia virus , but these are non-lethal infection models [33] , [47] . Additionally , an incomplete understanding of the role of complement during poxviral infections has complicated the investigation into how these proteins enhance virulence . Complement influences poxviral infections , but an essential role for survival has not been demonstrated . One study described increased inflammation at the inoculation site of cowpox virus in C5−/− mice; however , no mortality occurred in these mice [48] . Additionally , an allele for genetic resistance to ECTV mapped to the chromosomal region containing C5 [49] . Using complement-deficient mice , the mousepox model offers an opportunity to characterize the role of this system during infection in the natural host . Use of a model where the host and pathogen have co-evolved is particularly important given the species specificity of many poxviruses and of complement proteins , regulators , and receptors [3] , [50] . In this study , we focused on the role of C3 , the complement cascade's central component . Resistant C57BL/6 mice that genetically lack C3 inadequately control ECTV infection and have increased morbidity , viral burdens , and mortality . Our in vitro and in vivo evidence suggests that the complement system neutralizes ECTV early in infection and contributes to survival .
The route of infection influences the interaction between poxviruses and the host [51] . Half of the 16 mutant vaccinia viruses assessed using two routes of inoculation , ear pinna or intranasal , had a detectible phenotype by only one route . ECTV infections of C57Bl mice by the intranasal , intraperitoneal , or intravenous routes result in severe disease and mortality , while the footpad and intradermal routes cause minimal disease [52] . To examine the role of complement in vivo , wild-type and C3−/− mice were infected by three routes: footpad , ear pinna , and intranasal . Approximately 95% ( 54 of 57 ) of the wild-type mice survived when inoculated with 40 , 000 pfu of ECTV , the highest dose employed in the footpad infections ( Figure 1A ) . In contrast , C3−/− mice had about 90% mortality at that dose . They also had significantly increased mortality ( P<0 . 0001 ) at lower doses , even when inoculated with only 4 pfu . The median time to death increased as the dose decreased from 7 days at 40 , 000 pfu to 9 , 10 , or 13 days at the lower doses of 4 , 000 , 400 , or 4 pfu , respectively . The C3−/− mice also showed increased morbidity over the course of the infection . Unlike the wild-type mice on day 7 post-infection with 40 , 000 pfu , the C3−/− mice displayed clinical signs of infection , including fur ruffling and hunchbacked posture . Consistent with these observations , C3−/− mice lost more weight at 400 and 40 , 000 pfu than wild-type ( Figure 1B ) . The few surviving C3−/− mice at the 400 pfu dose required ∼3 additional weeks compared to the wild-type mice to return to their initial weight . All surviving mice in Figure 1A were held for at least 40 days to monitor recovery , and a subset of C3−/− mice ( n = 4 at 400 pfu ) were held to day 119 post-infection . The mice that survived the acute illness recovered weight steadily and showed no signs of relapse . The ear pinna studies used a dose of 700 pfu to mimic the low inoculum thought to transmit the natural poxvirus infection [2] . The infection caused 72% mortality in the C3−/− mice ( 28 of 39 ) ( Figure 1C ) , compared to 25% in the wild-type mice ( 6 of 24 , P = 0 . 0002 ) . The surviving C3−/− mice lost less weight and recovered to the initial weight earlier if inoculated by the ear pinna compared to the footpad route ( Figure 1B and 1D ) . In contrast to the increased morbidity and mortality observed , C3 deficiency caused no gross differences in the primary lesion; C3−/− and wild-type mice had similar levels of footpad swelling or necrosis at the ear pinna inoculation site ( data not shown ) . To examine the role of C3 in intranasal infection , the dose was lowered to 100 pfu due to the increased susceptibility of the wild-type mice with this route . C3 deficiency increased the mortality rate from 40% to 80% ( P<0 . 0001 , Figure 1E ) . Similar to the other routes , the surviving C3−/− mice had more severe disease than wild-type , as they lost more weight and took longer to recover ( Figure 1F ) . ECTV replicates at the inoculation site and in the draining lymph node to generate the primary viremia that infects the spleen and liver [4] . Virus released from these target organs causes a secondary viremia , which seeds distal sites like the skin , generating the characteristic pox lesions . To begin to dissect how C3 contributed to protection against ECTV , we examined viral burden in two key tissues , the spleen and liver . Wild-type and C3−/− mice were inoculated in the footpad with either 400 or 40 , 000 pfu , and then spleen and liver tissue were collected on day 7 post-infection . All animals had detectible virus in either the spleen or liver . At the two doses , the C3−/− mice had a 1–2 log higher mean titer than wild-type mice in both tissues ( Figure 2A and 2B ) . In wild-type mice , both doses produced similar maximal tissue titer; however , the higher dose increased the uniformity of the group and , thereby , increased the mean titer . At the 40 , 000 pfu dose , the splenic viral burden in the C3−/− mice was ∼150-fold higher ( P = 0 . 0002 , Figure 2A ) . Reducing the dose to 400 pfu resulted in ∼25-fold lower viral titer in the C3−/− mice , yet it was still ∼25-fold higher than the wild-type controls ( P = 0 . 03 ) . In contrast , both doses produced similar liver titers in the C3−/− mice . The lower dose revealed an 80-fold increase in the liver titer of the C3−/− mice compared to the wild-type mice ( P = 0 . 01 , Figure 2B ) , while the higher dose showed less of a difference between the strains ( 15-fold ) due to the increased titer in the wild-type mice . Illustrative of the impact of C3 , the C3−/− mice at 400 pfu had higher titers than the wild-type mice given 40 , 000 pfu , a 100-fold more virus . These increases in viral titer prompted further exploration of how C3 deficiency impacts viral spread . C3 could control viral replication early at the inoculation site by directly inactivating free virus or by recruiting inflammatory cells through release of anaphylatoxins . The lack of C3 in the blood to neutralize or opsonize the virus could also result in greater viremia , thereby producing the higher titers observed in the target organs on day 7 . Alternatively , C3's well-established ability to facilitate induction of antibody and T cell responses could explain the observed difference [21] , [22] , [53]–[57] . To elucidate when the infections in the C3−/− and wild-type mice diverge , we inoculated via the ear pinna route and examined the viral burden in the blood , spleen , and liver on days 2 , 4 , 7 , and 10 post-infection . The ear pinna route was selected for further analysis because it is a cutaneous route of inoculation that mimics a natural infection of the epithelium where complement may promote containment . Using whole blood enables an unbiased detection of all virus , whether free in the plasma or in infected cells . Quantitative PCR was employed to detect viral DNA in blood on days 2 , 4 , and 7 ( Figure 2E ) . A few day 2 samples contained viral DNA , but most were below the detection limit . The C3−/− mice had 2 . 0- and 2 . 5-fold higher levels of viral DNA than wild-type mice had on days 4 and 7 ( P = 0 . 004 and 0 . 03 , respectively ) . Despite the low levels of viremia on day 2 , infectious virus was present in the spleen of over 70% of the C3−/− mice ( 13 of 18 ) compared to 28% of the wild-type mice ( 5 of 18 , P = 0 . 006 , Figure 2C ) . By day 7 post-infection , the C3−/− mice had 45-fold higher viral titers in the liver ( P = 0 . 01 , Figure 2D ) , and there was also a similar trend in the spleen ( 6-fold , P = 0 . 09 ) . The wild-type mice regained weight starting on day 10 ( Figure 1D ) , and by then over 80% had cleared the virus from the spleen or liver ( 9 of 11 , Figure 2C and 2D ) . In contrast , less than half of the C3−/− mice survived to day 10 ( Figure 1C ) , and of these , over 75% had ongoing infection of the spleen and liver ( 7 of 9 , P = 0 . 008 and 0 . 01 , respectively ) . In summary , C3 deficiency resulted in earlier dissemination to spleen and in higher peak titers in the liver . The viral infection also continued to day 10 in the C3−/− when it had been cleared by most wild-type mice . In susceptible mouse strains , ECTV causes extensive hepatic and splenic necrosis [58] , [59] . We compared C3−/− and wild-type mice for histopathological changes in the liver on days 4 , 7 , and 10 post-infection . On day 4 , the liver histopathology appeared normal in 4 of 5 wild-type and 3 of 4 C3−/− mice ( data not shown ) . By day 7 , all animals had a diffuse lymphocytic infiltrate in addition to discrete inflammatory foci ( Figure 3 ) . These lesions varied in size and were smaller and less frequent in the wild-type ( Figure 3A and 3B ) compared to the C3−/− mice ( Figure 3C and 3D ) . They often occurred near the portal triad , and some contained areas of coagulative necrosis . An inflammatory infiltrate encircled the discrete necrotic foci ( Figure 3B and 3C ) and bordered the areas of bridging necrosis ( Figure 3D ) . In contrast to the liver , no major differences were observed in the spleen at this time ( data not shown ) . Using blinded samples , we counted the necrotic and non-necrotic foci and evaluated the location and severity of the necrosis in the liver ( Figure 4 ) . There were prominent differences between the C3−/− and wild-type mice relative to the number inflammatory foci and in the degree of necrosis . The C3−/− mice had twice as many total foci ( 8 vs . 18 per field , P = 0 . 02 , Figure 4A ) and 5-fold more foci containing regions of necrosis ( 3 vs . 15 per field , P = 0 . 03 , Figure 4B ) . The majority of inflammatory foci contained necrotic areas in two-thirds of the C3−/− mice compared to only one-fifth of the wild-type mice ( Figure 4C ) . The C3−/− mice had larger foci with more extensive necrosis ( P = 0 . 02 , Figure 4D ) . Most wild-type mice had small foci with either no necrosis or only piecemeal necrosis ( 0 and 1 on necrosis severity scale , Figure 3A and 3B , respectively ) . In contrast , the C3−/− mice had confluent areas of necrosis that coalesced into bands of bridging necrosis ( 2 and 4 on the necrosis severity scale , Figure 3C and 3D , respectively ) . Given that necrosis most frequently occurred in zone 1 of the liver , it likely originated there and then extended into zones 2 and 3 ( Figure 4E ) . The increased hepatic necrosis in the C3−/− mice resulted in higher levels of liver enzymes , aspartate aminotransferase ( AST ) and alanine aminotransferase ( ALT ) , in the serum on day 7 ( P = 0 . 008 , 0 . 0503 , respectively , Figure 4F ) . The AST and ALT levels positively correlated with the viral burden ( Figure 4G ) . Most C3−/− mice died between day 7 and 10 ( Figure 1C ) . Two C3−/− mice that were sacrificed on day 10 had ∼5–7 inflammatory foci per field , while the 5 wild-type mice had only occasional foci ( data not shown ) . At this time point , infectious ECTV persisted in the C3−/− mice; whereas , wild-type mice had cleared the infection ( Figure 2D ) . To explore the interaction between C3 and ECTV in vivo , we examined how mouse complement affects ECTV virions in vitro . Purified intracellular mature ECTV was incubated with either EDTA-treated plasma or sera from naïve C57BL/6 mice . Infectious virus was detected as plaques on a BS-C-1 monolayer . EDTA-treated plasma was reconstituted with a buffer containing calcium and magnesium to allow for complement activation . Reconstituted wild-type plasma neutralized approximately 90% of the virus ( Figure 5A , P<0 . 001 ) . Heat inactivation or buffer lacking calcium and magnesium abolished neutralization . Wild-type sera concentrations of 10 , 25 , or 50% neutralized 70–80% of the ECTV ( Figure 5B , P<0 . 0001 ) . These observations implicate the complement system in neutralizing ECTV . To further define if complement neutralized ECTV , sera from mice genetically deficient in a complement component or antibody were used in this assay ( Figure 4C–4G ) . The neutralizing activity was reduced by ∼50% with deficiency of either C3 or C4 ( Figure 5C ) . However , mixing C3−/− and C4−/− sera produced results equivalent to wild-type sera . This requirement for C4 for full ECTV neutralization was further dissected . The C1q subunit of C1 interacts with antibody to trigger the classical pathway . MBL , a C1q analog , initiates the lectin pathway . MBL A−/− x MBL C−/− , C1q−/− , and antibody-deficient ( μMT ) sera were compared ( Figure 5D ) . μMT or C1q−/− sera only partially neutralized ECTV , comparable to C4−/− sera . Conversely , wild-type levels of neutralization occurred independent of MBL A and C . These data suggest that natural antibody activated the classical complement pathway to neutralize ECTV . Further analysis revealed three key points relative to natural antibody . First , heat-inactivated wild-type sera behaves like buffer alone , which indicates that natural antibody alone lacks neutralizing activity; instead , complement activity was required to neutralize ECTV ( Figure 5A and 5G ) . Second , heat-inactivated wild-type sera , as a source of natural antibody , restored the neutralizing activity of μMT sera ( Figure 5F ) . Consistent with this finding , μMT or heat-inactivated wild-type serum did not effectively neutralize ECTV independently , but they did so in combination . Third , the modest but significantly greater neutralization in the normal compared to heat-inactivated μMT sera suggests that antibody-independent ( alternate pathway ) complement activation also occurred . C3b deposited by any pathway interacts with factor B ( FB ) and factor D ( FD ) to generate the alternative pathway C3 convertase , which amplifies C3b production . Alternative pathway activation itself likely explains the neutralization observed in the μMT , C1q−/− , or C4−/− sera . Interestingly , FB−/− or FD−/− sera neutralized less ECTV than wild-type sera ( Figure 5E ) , which indicates that the alternative pathway enhanced complement-mediated neutralization initiated by the classical pathway . C3b could neutralize ECTV by directly preventing attachment to or entry into the cell or by disrupting the virion's membrane through formation of the C5 convertase and the MAC . C5 initiates the terminal pathway that forms the MAC , and no lytic activity occurs in the absence of C5 . C5−/− sera from C57BL/10 mice were used to define the contribution of the MAC to neutralization ( Figure 5G ) . C5−/− sera neutralized a significant portion of virus ( P<0 . 001 ) , however , less than C5+/+ sera ( P<0 . 05 ) . These findings suggest that opsonization by C4b and C3b mediated most of the neutralization; although , the MAC also contributed . To conclude , these findings demonstrate that naïve wild-type mouse sera neutralized ECTV . We propose that natural antibodies bound to ECTV and triggered the classical pathway . This led to C4b deposition , formation of the C3 convertase , and C3b deposition on the virus . The alternative pathway amplified the C3b placed on the virion by the classical pathway . Most ECTV neutralization occurred through opsonization by C4b and C3b , with a minor contribution from the MAC . Both the classical and alternative pathways contributed to ECTV neutralization in vitro . To examine the importance of each pathway in vivo , we compared C4−/− and FB−/− mice to C3−/− and wild-type mice . We challenged C4−/− mice via the ear pinna route and monitored survival and weight loss . Over 90% of the C4−/− mice succumbed to the infection ( P<0 . 0001 , Figure 6A ) . The C4−/− and C3−/− mice had comparable mortality and weight loss ( Figures 6C and 1D ) . Intranasal ECTV infection also produced similar results in the C3−/− , C4−/− , or FB−/− mice . Each complement-deficient strain had a higher mortality rate compared the wild-type mice ( P<0 . 0001 ) , and there were no significant differences among the three strains ( Figure 6B ) . The complement-deficient strains also lost weight at a similar rate ( Figures 6D and 1F ) . Thus , control of ECTV in vivo required both the alternative and classical pathways , analogous to the in vitro results . Complement poses a barrier to the systemic spread of pathogens , particularly through the bloodstream [17] . The major role of complement could be to neutralize ECTV recognized by natural antibody . Our prior experiments established that B cell-deficient μMT mice challenged with a high dose of ECTV by the footpad route all died early in infection ( 94% by day 8 ) ( Figure 6E ) . Their early death suggests that B cells contribute to survival prior to the rise of specific antibody on day 7 [5] . Based on our in vitro data and the data of others [60] , [61] , we hypothesized that natural antibody contributes to early protection . Consequently , providing μMT mice with natural antibody should prolong their survival . Based on the work of Ochsenbein et al . [61] , μMT mice infected with a high dose of ECTV were treated with naïve sera from either μMT or wild-type mice ( Figure 6F ) . Treatment with wild-type sera increased the median day of death from 7 to 9; however , sera lacking natural antibodies ( μMT ) had no effect . On day 8 post-infection , over half of the mice receiving wild-type sera outlived both other groups and 16 of 17 mice from the prior experiments ( Figure 6G ) . Thus , natural antibody delayed , but did not prevent , lethal ECTV infection in μMT mice .
We investigated the impact of complement deficiency using the ECTV mouse model . Deficiency of C3 , C4 , or FB resulted in acute lethal infection , establishing a requirement for multiple complement pathways in host defense against this pathogen . Specifically , C3 deficiency permitted ECTV to disseminate earlier , reach a higher titer in the target organs , and induce greater liver damage . Consistent with these in vivo results , naïve mouse sera neutralized ECTV infectivity in vitro , and sera lacking either classical or alternative pathway components had decreased activity . Several lines of evidence indicate that natural antibody initiated the classical complement cascade in the wild-type mouse . Substantial neutralization occurred in sera without lytic activity , which points to opsonization as the predominant mechanism of neutralization . Based on these results , we propose that natural antibody binds viral antigen to activate the classical pathway , followed by engagement of the alternative pathway's feedback loop to opsonize the virus . The ECTV model system provides several advantages for analyzing the role of complement in poxviral pathogenesis . First , the mouse-specific pathogen ECTV has coevolved with and causes severe disease in the natural host , analogous to variola virus in humans . Second , the role of complement and the pathways involved can now be more rigorously dissected in vivo and in vitro with the availability of complement-deficient mice . Additionally , the in vitro experiments employed sera from the same strains used to characterize the effect of complement deficiency in vivo , and the neutralizing capacity in vitro paralleled the in vivo mortality . Third , viral pathogenesis , morbidity , and mortality can be assessed by multiple routes of infection and across a range of viral inoculum to demonstrate a broad requirement for complement . Complement-deficient mice succumbed to acute ECTV infection with the majority of deaths occurring between days 6–10 . Based on time to death following footpad inoculation , C3 deficiency resembled immunodeficiencies of other important components of the antiviral response , specifically CD8+ T cells [6] , [7] , NK cells [9] , [10] , and IFN-γ [12] . In contrast , mice deficient in CD4+ T cells [6] , CD40 , or CD40 ligand ( CD154 ) [7] survive the acute phase but do not clear the virus . The CD40−/− and CD154−/− mice ultimately die ∼4 to 8 weeks post-infection . This differs from surviving C3−/− mice , which recovered and did not show signs of ongoing illness for up to 4 months of observation . The early death of the complement-deficient mice highlights the complement system's essential contribution to survival during the first few days of infection . To characterize how complement protects the host from lethal infection , we analyzed the impact of C3 deficiency on the kinetics of viral spread . ECTV replication at the inoculation site and in the draining lymph node produces a viremia that seeds the primary target organs , the liver and spleen [4] . Several observations from this study increase our understanding of complement's role in controlling poxviral infection . First , as early as day 2 , C3 deficiency allowed for greater spread of ECTV from the inoculation site to the spleen . This indicates that complement is a key player in the initial hours of infection , likely to control ECTV at the inoculation site . Second , we detected higher levels of viral DNA in the blood on days 4 and 7 . Consistent with our in vitro data , these results establish that C3−/− mice poorly control viral dissemination through the bloodstream . This higher viremia could result from increased replication in tissues and/or decreased clearance of virus from the bloodstream . Third , the liver viral titers on day 7 were ∼50-fold higher in the C3−/− mice . The greater viremia likely produced more extensive infection , but a delayed adaptive immune response may also have contributed to this observation . The viral titer correlated with serum levels of ALT , which suggests that ECTV caused hepatic necrosis either directly through lytic infection or indirectly through the antiviral immune response . An inflammatory infiltrate surrounded the necrosis in the C3−/− mice , which contrasts with susceptible Balb/c mice where necrosis occurs in the absence of a lymphocytic infiltration [62] . In summary , we propose that mice lacking C3 have reduced ability to control ECTV locally and in the bloodstream , leading to higher levels of infection and greater tissue damage in the liver . Complement could delay viral dissemination by opsonizing and thereby neutralizing virions at the inoculation site or in the circulation and by promoting the inflammatory response including the recruitment of phagocytic cells . To assess if complement could directly neutralize ECTV , we examined the interaction between purified ECTV and mouse complement in vitro . Naïve plasma or sera neutralized ECTV in a complement-dependent manner , even at a concentration as low as 10% . Sera from mice deficient in specific complement components demonstrated that maximal neutralization required both the classical and alternative pathways . μMT sera , lacking antibody , resembled the sera deficient in the classical pathway components , C1q or C4 , and addition of a natural antibody source restored neutralization activity . Opsonization led to neutralization of the majority of virus; however , the modest but significant difference between the C5−/− and C5+/+ sera indicates that the MAC contributed to viral damage . Interestingly , no complement component deficiency tested fully abolished neutralization . The residual activity suggests that the classical and alternative pathways functioned independently , likely because both C4b and C3b opsonized and , consequently , neutralized ECTV . However , the system was most effective when the two pathways and the MAC worked cooperatively . The reconstitution of the neutralization activity in the μMT sera with heat-inactivated wild-type sera suggests that natural antibody is important in the neutralization process . Consistent with this observation and prior studies with other viruses [60] , [61] , natural antibody passively transferred into μMT mice lengthened survival during the acute infection . In our experiments , most μMT mice died early , with 100% mortality by day 9 at the highest inoculum . The mice that survived the acute infection eventually died at ∼2 months post-infection . Our findings differ from prior studies , which described mortality at either 2–4 weeks [8] or 2 months [7] post-infection . More of our mice survived the acute infection at the lower doses . This suggests that the observed discrepancy could be secondary to differences in the viral stock or dose , as both differed among the three groups . The death of the μMT mice , despite natural antibody treatment , indicates that B cells help control the infection by additional mechanisms . Our in vitro experiments provide a model for understanding the fate of the viral inoculum in our in vivo experiments , since they both used the same stock of purified intracellular mature virus ( IMV ) . To understand the spread of infection , a second infectious form must be considered . During viral replication in the host cell , extracellular enveloped virus ( EEV ) is produced by enveloping the IMV with an additional unique membrane derived from the Golgi complex and late endosomal compartment [63] . In studies of vaccinia virus , the host's complement regulators , present in the outermost membrane , protect the EEV from human and rabbit complement; in contrast , the IMV is sensitive to complement [64] . Our study builds on this observation by determining the contribution of each complement activation pathway to the neutralization of IMV infectivity , and it implicates natural antibody as the primary initiating factor [61] . We also show that natural antibody by itself is ineffective but requires augmentation by the complement system to neutralize ECTV . Additionally , the neutralization observed with vaccinia virus and ECTV points to the IMV form being inherently susceptible to complement-mediated neutralization . The relative importance of IMV vs . EEV during infection in vivo is not well established . However , the IMV's sensitivity to complement neutralization suggests that ECTV likely travels through areas featuring efficient complement activation , such as the blood stream , in the EEV form or within infected cells . At extravascular sites , where complement levels are lower than in circulation , infected cells may produce sufficient soluble poxviral complement regulatory protein to protect the IMV . Most poxvirus disease models initiate infection with the complement-sensitive IMV . If complement activity in the mouse behaves as it does in vitro , then inoculated ectromelia IMV should be recognized by natural antibody and coated with C4b and C3b , resulting in neutralization of viral infectivity at the site of injection and inhibition of spread . This line of reasoning could explain why the mortality increases in the wild-type mice as the invasiveness of the route decreases [52] . Percutaneous inoculation would likely result in neutralization , while application to the mucosal membranes might enable ECTV to enter host cells before being neutralized by complement . Once internalized , ECTV produces its regulatory protein and EEV to evade complement and propagate the infection . Additionally , based on the in vitro data , complement deficiency would greatly limit this initial neutralization , which likely contributes to the early spread and greater mortality observed in the complement-deficient mice . A sub-neutralizing concentration of complement opsonins could target the virion for immune adherence and phagocytosis in vivo , particularly in blood with its high complement levels . Furthermore , the liver sinusoids are lined with Kupffer cells bearing CRIg ( Complement Receptor of the Ig superfamily ) , which mediates phagocytosis of C3-opsonized pathogens [65] . Indeed , the liver clears over 95% of intravenously administered ECTV from the circulation within 5 min of injection [66] . In the following hour , most of the viral antigen in the liver becomes undetectable by immunofluorescence , and viral infectivity decreases by over 90% . This rapid removal suggests that the virus has been recognized as foreign and tagged for immune adherence and destruction . Opsonization by complement followed by uptake via the recently described CRIg provides a mechanistic explanation for these important observations made nearly five decades ago [66] . These observations influence the interpretation of poxviral infections initiated with an IMV-rich inoculum by the intravenous route . The liver's Kupffer cells may sequester most of the inoculated virus within minutes and destroy much of it within an hour , thereby inhibiting systemic dissemination . Not only is the dose effectively reduced by ∼10-fold , but the neutralized IMV also provides the immune system with an immediate source of antigen . These issues have particular relevance for the monkeypox and variola virus non-human primate models that commonly use the intravenous route to test vaccines for human use [67]–[72] . The early mortality of the C3−/− , C4−/− , and FB−/− mice demonstrates an essential role for the classical and alternative pathways in the initial stages of poxvirus infection . Despite equivalent mortality levels , further analysis may reveal different functions for each complement pathway in vivo , as such differences exist in the immune response to other viruses [23] . The similarity between the in vivo mortality and in vitro serum neutralization experiments suggests that complement neutralizes ECTV and thereby limits its spread . Undoubtedly , complement deposition triggers other effector functions , such as recruiting inflammatory cells , promoting phagocytosis , and priming the adaptive immune response . The precise contribution of each of these to protection in vivo remains unexplored . However , these experiments establish that complement is essential to the immune response to poxviruses . It accounts for why the virus encodes a potent complement regulatory protein . A virus lacking this regulator would be at risk for greater host complement activity and attenuation . This is consistent with the theory that loss of this regulator contributes to the reduced virulence of some strains of monkeypox virus [46] . To conclude , the complement system is critical for slowing down viral spread and decreasing tissue titers and damage .
Plaque-purified Moscow strain ECTV was propagated in murine L929 cells . Intracellular mature viral stocks were purified through a sucrose cushion as described [73] and titrated on BS-C-1 cells , an African green monkey kidney cell line [74] . Both cell lines were cultured in Dulbecco's modified Eagle's media ( DMEM , BioWhittaker , Walkersville , MD ) supplemented with 10% heat-inactivated fetal calf serum ( FCS , HyClone , Logan , UT ) , 2 mM L-glutamine , and antibiotics . The following strains on a C57BL/6 background were acquired: C3−/− [56] , [75] and FB−/− [76] , [77] from H . Molina , Washington University Medical School; C4−/− [78] from M . Carroll , Harvard Medical School; B cell-deficient μMT [79] from H . W . Virgin , Washington University Medical School; C1q−/− [80] from M . Botto , Imperial College School of Medicine; FD−/− [81] from Y . Xu , University of Alabama , Birmingham; and MBL A−/− x MBL C−/− ( B6 . 129S4-Mbl1tm1Kata Mbl2tm1Kata/J ) and wild-type from Jackson Laboratories . The C5+/+ and C5−/− C57BL/10 mice ( B10 . D2-Hc1 H2d H2-T18c/nSnJ , B10 . D2-Hc0 H2d H2-T18c/oSnJ ) were also obtained from Jackson Laboratories . Age-matched mice of both sexes were used in the footpad and ear pinna studies ( 6–11 weeks-old ) and the μMT survival experiments ( 10–11 weeks-old ) . Male mice were used in the intranasal ( 8–12 weeks ) and sera transfer ( 10–12 weeks ) studies . Some wild-type and μMT mice used in the footpad studies were purchased from Jackson Laboratories . The rest of the mice were bred at Washington University in a specific pathogen-free facility . The animals were transported to the biohazard suite at Saint Louis University at least a week prior to infection . All experiments were performed following the animal care guidelines of the two institutions . Mice were inoculated with 10 µl ECTV diluted in PBS to the indicated dose using a 29 gauge insulin syringe into the ear pinna and hind footpad or a 20 µl pipettor for the intranasal route . Mice were anesthetized for inoculation using CO2/O2 for the footpad route and ketamine/xylazine for the ear pinna and intranasal routes . Individual mice were marked by ear punching or shaving . After infection and before sacrifice in the mortality studies , mice were manipulated only to obtain weights . Serum was collected from surviving animals at the end of the experiment . The survival curves include only animals that generated an antiviral antibody response , which was detected by ELISA in >95% of the mice [82] . In the passive transfer experiment , mice received intraperitoneally 1 ml of wild-type or μMT C57BL/6 sera on day −1 and 0 . 5 ml every two days starting on day 0 . Blood was collected via cardiac puncture . Spleen and liver tissues were harvested aseptically , frozen immediately on dry ice , and stored at −70°C . Tissues were homogenized in PBS-1% FCS to ∼10% ( weight/vol ) using 1 ml glass homogenizers . They were frozen and thawed three times , sonicated , and titrated on BS-C-1 monolayers [74] . DNA was isolated from whole blood collected in EDTA microtainer tubes ( BD , Franklin Lakes , NJ ) using the High Pure PCR Template Preparation Kit ( Roche ) . The kit's whole blood protocol was used with the following modifications . The 40 µl Proteinase K , 200 µl Binding Buffer , 150 µl PBS , and 50 µl of whole blood in EDTA were added sequentially and then vortexed . The incubation at 70°C was extended to 12 min . The sample was applied to the column by centrifugation at 8 , 000 g for 2 min and eluted in 50 µl . Quantitative PCR was performed on viral DNA using Power SYBR Green PCR Master Mix on a 7500 Real Time PCR System ( Applied Biosystems , Foster City , CA ) [83] . The primers ( 10 pmol ) SP028 ( GTAGAACGACGCCAGAAT AAGAATA , 5′ at 120627 bp ) and SP029 ( AGAAGATATCAGACGATCCACAATC , 5′ at 120462 bp ) were used to amplify 165 bp of gene EV107 . The amplification product cloned into a plasmid vector ( pGEM-T , Promega ) was used as a standard to estimate copies of DNA/µl in blood . Three to four wells were used for each sample . Tissue samples were fixed in 10% buffered formalin , embedded in paraffin , sectioned , and stained with hematoxylin and eosin by the Digestive Diseases Research Core Center , Washington University . The number of inflammatory foci and the magnitude of tissue necrosis were evaluated in blinded samples . Inflammatory foci in a 10× visual field were counted for ∼7 fields/mouse liver . AST and ALT levels were measured in samples of frozen sera by the Department of Comparative Medicine at Saint Louis University using a standard clinical analyzer . Mouse EDTA plasma and sera were collected on ice from male C57BL/6 mice in microtainer tubes ( BD ) , separated by centrifugation , and then pooled , aliquoted , and frozen at −70°C . Plasma and sera were diluted on ice into GVB± Ca++/Mg++ ( #B102 , B103 , Complement Technology , Tyler , TX ) or GVB without Ca++/Mg++ , respectively , to 2× the desired final concentration ( vol/vol ) . Purified ECTV was sonicated and diluted in PBS ( without Ca++/Mg++ ) to ∼5×104 pfu/ml . A 1∶10 dilution in the buffer used to dilute the complement source , GVB±Ca++/Mg++ , produced a final concentration of ∼5 pfu/µl . An equal volume of virus ( 30 µl≈150 pfu ) was added rapidly to the diluted complement at RT . Samples were vortexed , centrifuged for 5 sec , and incubated at 37°C for 60–90 min . Samples were diluted by addition of 700 µl of DMEM-2% FCS , vortexed , and applied to BS-C-1 monolayers in 6-well plates . After 1 hr , 3 ml/well of 37°C overlay media ( 1% carboxymethylcellulose in culture media ) was added . After 3–5 days , the cells were fixed with 1 ml/well of an 11% formaldehyde/ 0 . 13% crystal violet/ 5% ethanol solution for over 1 hr , rinsed , and dried . The number of plaques was scored visually using a light box . The EDTA plasma or sera data were normalized to the buffer only control or heat-inactivated sera , respectively . All statistical analysis was performed using GraphPad Prism software version 5 . 01 ( GraphPad Software , San Diego , CA ) . The survival curves were analyzed by the log-rank test . The Mann-Whitney test was used to determine the statistical significance of the viral titers , viremia , liver histology , and liver enzymes . Either 1-way ANOVA followed by Tukey multiple comparisons test or 2-way ANOVA was used for the analysis of the complement neutralization assays . | As one of the most successful pathogens ever , smallpox caused death and disfigurement worldwide until its eradication in the 1970s . The complement system , an essential part of the innate immune response , protects against many pathogens; however , its role during smallpox infection is unclear . In this study , we investigated the importance of the complement system in mousepox infection as a model for human smallpox disease . We compared mice with and without genetic deficiencies in complement following infection by multiple routes with ectromelia virus , the causative agent of mousepox . Deficiencies in several complement proteins reduced survival of ectromelia infection . Sera from these same complement-deficient mice also have reduced ability to neutralize ectromelia virus in vitro . In complement-deficient mice , ectromelia virus disseminated from the inoculation site earlier and produced higher levels of virus in the bloodstream , spleen , and liver . The increased infection in the liver resulted in greater tissue damage . We hypothesize that the complement-deficient mice's reduced ability to neutralize ectromelia virus at the inoculation site resulted in earlier dissemination and more severe disease . We have demonstrated that surviving ectromelia virus infection requires the complement system , which suggests that this system may also protect against smallpox infection . | [
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"infect... | 2008 | Surviving Mousepox Infection Requires the Complement System |
Errors in protein synthesis , so-called phenotypic mutations , are orders-of-magnitude more frequent than genetic mutations . Here , we provide direct evidence that alternative protein forms and phenotypic variability derived from translational errors paved the path to genetic , evolutionary adaptations via gene duplication . We explored the evolutionary origins of Saccharomyces cerevisiae IDP3 - an NADP-dependent isocitrate dehydrogenase mediating fatty acids ß-oxidation in the peroxisome . Following the yeast whole genome duplication , IDP3 diverged from a cytosolic ancestral gene by acquisition of a C-terminal peroxisomal targeting signal . We discovered that the pre-duplicated cytosolic IDPs are partially localized to the peroxisome owing to +1 translational frameshifts that bypass the stop codon and unveil cryptic peroxisomal targeting signals within the 3’-UTR . Exploring putative cryptic signals in all 3’-UTRs of yeast genomes , we found that other enzymes related to NADPH production such as pyruvate carboxylase 1 ( PYC1 ) might be prone to peroxisomal localization via cryptic signals . Using laboratory evolution we found that these translational frameshifts are rapidly imprinted via genetic single base deletions occurring within the very same gene location . Further , as exemplified here , the sequences that promote translational frameshifts are also more prone to genetic deletions . Thus , genotypes conferring higher phenotypic variability not only meet immediate challenges by unveiling cryptic 3’-UTR sequences , but also boost the potential for future genetic adaptations .
Latent , promiscuous protein functions serve as starting points for evolving new functions , thus resolving the evolutionary ‘catch’ of no new trait can evolve unless it already exists and can confer an immediate survival benefit [1 , 2 , 3] . Along the same veins , it has been proposed that other forms of molecular infidelity , such as transcriptional and translational errors , may also underlie the evolution of new protein traits [4 , 5 , 6] . Indeed , these so-called ‘phenotypic mutations’ yield protein variability from an unmutated gene and are up to 105 times more frequent than genetic mutations [7 , 8 , 9] . Phenotypic mutations may thus bridge the crucial and relatively long time gap between the appearance of a new challenge and the emergence and fixation of changes in genotype , i . e . , evolutionary adaptations as often manifested in new , paraloguous genes . To this date , however , no direct evidence exists for a phenotypic mutation paving the path to a genetic , evolutionary adaptation . Gene duplication is the source of new paralogs , including proteins with new activities or new subcellular localizations . However , different mechanisms may underlie the emergence of new functions via gene duplication . The first proposed mechanism , now known as Ohno’s model , or neo-functionalization , is initiated by duplication as a random event generating a redundant gene copy that acquires mutations under no selection . If and when a new function becomes beneficial , and if the drifted copy happens to provide this new function , the duplicated gene becomes under selection thus giving rise to a new paralog [10] . The discovery of multi-functional proteins prompted alterative models by which gene duplication follows rather than precedes the emergence of new functions . By these models , ‘gene sharing’ [11] , sub-functionalization [12] , or more explicitly , ‘divergence before duplication’ [13 , 14] , the new function initially develops in the original , pre-duplicated gene . Mutations , that are largely neutral with respect to the primary , original function , may give rise to latent , promiscuous functions , which , in turn , may become under selection if and when needed [15 , 16] . The new function therefore becomes under selection alongside the original one , giving rise to a bi- or multi-functional protein ( gene sharing ) . Duplication may occur at a much later stage , thus allowing the two functions to be split between two paraloguous genes ( sub-functionalization ) . The current literature primarily addresses how new binding , regulatory or enzymatic functions evolve via duplication , thus providing ample evidence for divergence via multifunctional ancestors [17 , 18 , 19 , 20 , 21] . However , protein function relates not only to what a protein does but also to where it functions and with which partners . In eukaryotic cells , for example , proteins localize to different subcellular compartments to perform their designated functions . Indeed , about a third of duplicate protein pairs derived from the yeast whole-genome duplication ( WGD ) that occurred along the lineage leading to S . cerevisiae localize to different subcellular compartments . However , the evolutionary mechanisms underlying the divergence of gene paralogues with new subcellular localizations remain largely unknown [22] . Divergence before duplication , and a subsequent sub-functionalization to two paralogues , demands the appearance of the new trait within the ancestral , pre-duplicated gene while maintaining its original function [14 , 23 , 24 , 25] . In the case of localization , this means dual localization , a phenomenon that is in fact well recorded [26 , 27 , 28] . Amongst other mechanisms , the partial expression of protein forms carrying targeting signal sequences may occur via alternative splicing or transcriptional/translational errors [29 , 30 , 31] . To this date , however , no particular example exists whereby a phenotypic mutation led to the divergence of a new paralog in a recently diverged species . To study the history of evolution of new protein localizations , we sought to examine duplicate gene pairs that derived from the yeast WGD and diverged in their cellular localizations . Of the potential candidates listed in the literature [22 , 32 , 33] , one gene stood out in having a clear-cut selectable phenotype , and hence being amenable to laboratory evolution experiments . Saccaromyces cerevisiae IDP3 is an NADP-dependent isocitrate dehydrogenase that following the WGD diverged towards peroxisomal localization . Peroxisomes are ubiquitous eukaryotic subcellular compartments where oxidative reactions occur , most notably the degradation of fatty acids via β-oxidation . IDP3 is selectively essential for yeast growth on unsaturated fatty acids as main carbon source , providing the reducing agent NADPH in peroxisome for the β-oxidation of these fatty acids such as petroselinate [34] . S . cerevisiae has three differently compartmentalized IDP paralogues: mitochondrial IDP1 , cytosolic IDP2 and peroxisomal IDP3 . While the divergence of IDP1 is an ancient event , IDP2 and IDP3 derived from the WGD and share >77% sequence identity . Indeed , in species that diverged prior to the WGD , apart from IDP1 , a single IDP copy exists corresponding to cytosolic IDP2 . We thus examined the evolutionary mechanisms that underlie the divergence of the ancestral cytosolic IDP2 to give the newly localized peroxisomal paralogue IDP3 .
Peroxisomal proteins are transported from the cytosol , most commonly , as with IDP3 , via a carboxy-terminal peroxisomal targeting signal . This signal , dubbed PTS1 primarily comprises a tripeptide motif: ( S/A/C ) - ( K/R/H ) - ( L/M ) -*; whereby * represents a stop codon [35] . While additional C-terminal residues affect targeting efficiency , the last 3 C-terminal residues are most crucial for peroxisomal targeting [36] . We first analyzed the IDP gene sequences from S . cerevisiae-related species that diverged before and after the WGD . The pre-WGD species posses only a cytosolic IDP2 gene with no PTS1 signature , whereas the post-WGD species all have a peroxisomal IDP3 paralogue with a C-terminal PTS1 as well as a cytosolic IDP2 gene ( Table 1 ) . By this generally-accepted analysis [22] , the ancestral IDP2 was presumably localized to the cytosol , and following the WGD , IDP3 neo-localized to the peroxisome by acquiring a PTS1 while IDP2 remained a cytosolic isozyme . To examine how IDP3’s new , peroxisomal localization diverged from a cytosolic IDP2 , we replaced the coding and regulatory regions of S . cerevisiae IDP3 with those of IDP2 , and measured the effects on yeast growth in a petroselinate containing medium . A ΔIdp3 strain was constructed from a wild-type strain that spontaneously adapted to growth on petroselinate . The wild-type IDP2 and modified IDP2 with addition of IDP3’s PTS1 at its C-terminus ( IDP2+CKL ) were cloned into a chromosomal plasmid and transformed into the ΔIdp3 strain . Whilst wild-type IDP2 failed to complement the ΔIdp3 growth on petroselinate , IDP2+CKL gave an IDP3-like growth phenotype ( S1A Fig ) , as previously studied [37] . Like-wise , relative to the PTS1 , the divergence of upstream regulatory elements is minor , as indicated by the same petroselinate growth when IDP3’s promoter region was replaced with IDP2’s ( S1B Fig ) . These results suggest that acquisition of the PTS1 motif may have been necessary , and possibly even sufficient , to support divergence of IDP3 from IDP2 . It also appears that other changes in IDP3’s open reading frame , and changes in its regulatory regions , were less critical . We thus focused on unraveling the evolutionary origin of PTS1 motif , that is , when and how IDP3’s peroxisomal signal peptide emerged . By the classical Ohno’s model , the key steps towards divergence occur after duplication , and initially as drift , namely not under adaptive selection [10] . Nonetheless , we searched the C-termini and 3’-UTR sequences immediately after the stop codon of the pre-duplication IDP2s , attempting to identify possible starting sequences from which a PTS1 motif may have evolved via few mutations . We discovered intact , putative PTS1 motifs including an adequate stop codon located shortly after the original stop codon , in the 3’-UTRs . Putative PTS1s were found in 4 out of 5 of Saccaromycetaceae species that are phylogenetically closest to S . cerevisiae but not in more distant species including Candida ( so-called CTG fungi group; the bottom clade in Fig 1A ) . However , unlike previously discovered cryptic PTS1 motifs [30 , 31 , 38] , these cryptic PTS1 motifs relate not to the enzyme’s coding frame but to a +1 frameshift ( Fig 1B ) . Accordingly , when the cryptic PTS1 was revealed in the coding frame by a single nucleotide deletion upstream to the stop codon , A . gossypii IDP2 ( A . gos IDP2 ) enabled growth of the ΔIdp3 strain on petroselinate ( Fig 2A ) . Regulated translational frameshifts are known , but they typically occur at long homorepeats such as 8A that are not observed in the coding sequences prior to the stop codons of the pre-duplication IDP2s . Do , then , the cryptic PTS1 motifs within the 3’-UTR regions comprise relics of ancestral PTS1 motifs that were non-functionalized; or do they still encode a functional peroxisomal targeting signal and are thereby maintained under selection for dual localization ? The latter seems likely given that sequences that perfectly match a functional PTS1 ( Table 1 and Fig 1B ) are found in 4 out of 5 of the pre-WGD Saccaromycetaceae species . To investigate the possibility that the pre-duplication IDP2 genes partially produce a peroxisomal isoform carrying a PTS1 via a transcriptional or translation frameshift , we tested whether wild-type A . gos IDP2 can enable petroselinate growth of the S . cerevisiae ΔIdp3 strain . Indeed , growth on petroselinate could be observed with wild-type A . gos IDP2 at about half the rate observed with S . cerevisiae’s original , peroxisomal IDP3 , and mutating the cryptic PTS1 within the 3’-UTR abolished the growth ( Fig 2A ) . Thus , a peroxisomal IDP isoform carrying the PTS1 motif seems to be co-expressed alongside the original , cytosolic form . How are the cryptic PTS1 recruited in the coding region ? Homonucleotide repeats show consistently higher tendency for slippage of RNA polymerases , and the ribosome , thus inducing phenotypic frameshift mutations [39 , 40 , 41] . Indeed , a 6T repeat exists shortly before the original stop codon of A . gos IDP2 and within a highly diverged segment of the C-terminus ( S . cerevisiae Leu403 is the last conserved position in IDP alignments; Fig 1B ) . Accordingly , silent mutations replacing 2 out of the 6 T within this repeat gave no complementation ( Fig 2A ) . We further examined whether the phenotypic frameshift at the 6T repeat is due to a transcriptional or translational error . Total RNA from S . cerevisiae expressing A . gos IDP2 and grown on petroselinate was extracted . The cDNA derived from mRNAs of A . gos IDP2 gene was amplified by RT-PCR and cloned for sequencing . In randomly picked 36 clones , all carried a 6U site , corresponding to the original gene’s 6T sequence , and no other sequence changes were detected along 500 bp flanking IDP2’s stop-codon . The phenotypic frameshift is therefore caused by translational errors , consistently with the fact that they are ~10-fold more frequent than transcriptional errors [9] . Overall , these results suggest that the A . gossypii IDP2 partially produces an alternative isoform carrying a PTS1 motif via translational error that bypasses the stop codon and unveils the cryptic , frame-shifted PTS1 , and thus exhibiting peroxisomal IDP activity that enables growth on petroselinate . We further validated the coexistence of two isoforms of A . gos IDP2 by mass spectrometry . Alongside the expected A . gos IDP2 gene product , a higher mass form corresponding to the predicted frame-shifted product at the 6T repeat including the C-terminal AKL was observed with ~30% of the total mass ( Fig 2B ) . Peroxisomal targeting by the cryptic PTS1 was also observed by fluorescent cell imaging . Red fluorescent protein ( mCherry ) was C-terminally tagged with the C-terminal fragment of A . gos IDP2 containing the cryptic PTS1 in the 3’-UTR . We observed clear punctate co-staining with the peroxisomal marker protein Pex14 fused to GFP when the cryptic PTS1 was revealed by the frameshift ( one T deletion in the 6T repeat ) . On the other hand , wild-type A . gos IDP2 fragment fused to mCherry was primarily visualized in the cytosol , yet with also weak , punctate co-staining with the peroxisomal marker , likely indicating dual localization ( Fig 2C ) . We subsequently tested the cryptic PTS1 motifs of the other pre-duplication IDP2 genes . The C-terminus of S . cer IDP2 was replaced with the C-termini of 5 different pre-duplication IDP2 genes , including their 3’UTRs ( Fig 1B ) . As expected , K . lactis IDP2 that contains no cryptic PTS1 showed no growth complementation . Three pre-duplication IDP2s containing the cryptic PTS1 appeared to partially express peroxisomal isoforms at a level similar to A . gos IDP2 , although only A . gos IDP2 has a >3 bp long homorepeat site ( S2A Fig ) . In contrast , K . waltii IDP2 ( K . wal IDP2 ) failed to show complementation despite the existence of the cryptic PTS1 motif . Nonetheless , growth rate on petroselinate was significantly enhanced when the cryptic motif was revealed by a genetic frameshift ( one nucleotide deletion before the stop codon; S2B Fig ) . We subsequently tested complementation with the full length of K . wal IDP2 open-reading-frame including 150 bp downstream after the stop codon . The full-length K . wal IDP2 showed complementation although with lower growth rates than the other pre-duplication IDP2s . Thus , although K . waltii IDP2 appears to have a functional cryptic PTS1 , unveiling it by a phenotypic frameshift seems to be dependent on having the broader context of the K . waltii gene and not just the region around the stop-codon , as was the case with the other pre-duplication IDP2 genes . The frequency of ribosomal slippage may therefore depend on the secondary structure of mRNA , as well as on environmental factors that regulate translational fidelity in the host organism [42 , 43] . Following the above findings , and a report that appeared while this work was ongoing on cryptic peroxisomal targeting of two cytosolic enzymes [30] , we performed a systematic computational search of the 3’-UTR regions of four closely related post-WGD Saccharomyces genomes: S . cerevisiae , S . paradoxus , S . bayanus and S . mikatae ( S1 and S3 Tables ) . The search was based on PTS1 motifs containing all possible variation of amino acids ( ( S/A/C/E/I/H/Q ) - ( K/R/H ) - ( L/F ) -stop ) from 20 peroxisomal proteins in the Saccaromyces genome database . We searched for the motif starting up to 30 bp downstream the stop codon in all frames . PTS1-like motifs were found in around 1% of total genes of the genomes . However , about 40% of these were interrupted by another stop codon ( S1 Table ) . Further , only a small number of these potentially cryptic motifs were found in more than one species , suggesting that these motifs are under functional selection . We thus focused on few interesting candidates that are conserved among the post-WGD species , and foremost on pyruvate carboxylase 1 ( PYC1 ) —a cytosolic enzyme converting pyruvate to oxaloacetate . The NADPH used for peroxisomal β-oxidation could be produced from pyruvate by a putative pathway that includes four enzymes: PYC: pyruvate carboxylase; CIT: citrate synthase; ACO: aconitase; and finally IDP: isocirate dehydrogenase . Although not established as a peroxisomal NADPH providing pathway , this reaction sequence comprises part of the TCA cycle . Among these , two enzymes have known peroxisomal paralogues in S . cerevisiae: CIT2 and IDP3 . The other two , PYC and ACO , are thought to act in the cytosol and mitochondria , respectively , thus demanding the shuttle of their substrates and products to and from the peroxisome ( Fig 3A ) [44] . We identified , however , a cryptic PTS1-like motif ( SHL* ) in the PYC1 genes of all four Saccharomyces species . The motif of S . cerevisiae PYC1 is located at 11 bp downstream from the stop codon , in a +1-shifted frame , and predicted as a weak motif by the PTS1 predictor [45] ( S3A Fig ) . We examined the functionality of the PTS1-like motif of S . cer PYC1 by tagging S . cer IDP2 at the C-terminus with the C-terminal fragment of S . cer PYC1 ( the last 11 amino acids and the 3’UTR ending with SHL* ) . The PYC1 motif showed functional targeting when recruited within the coding frame via a single nucleotide deletion , as indicated by ΔIdp3 complementation for growth on petroselinate , while not functional with the native sequence ( Fig 3B ) . Peroxisomal localization was also observed by fluorescent imaging with mCherry C-terminally-tagged with the 3’UTR motif revealed by a single nucleotide deletion ( S3B Fig ) . These results suggest that this motif is relevant for peroxisomal targeting of S . cer PYC1 via phenotypic errors . At a minimum , our results indicate that a duplicated S . cer PYC1 is within a single genetic mutation from becoming a functional peroxisomal paralog , or perhaps that PYC1 was dually localized in the past . Our computational search did not identify consensus PTS1 motifs ( ( S/A/C/E/I/H/Q ) - ( K/R/H ) - ( L/F ) -stop ) in ACO genes . However , upon a closer look we identified a PTS1-like motif ( -NKF* ) located at +1-shifted frame shortly after the stop codon of S . cer ACO2 ( S4A Fig ) . This motif gave very weak functional targeting when inserted in-frame at the C-terminus of IDP2 and tested for growth on petroselinate ( S4B Fig ) . However , in a continuous passage culture , the slow growth was dramatically accelerated and eventually matched the growth rate of IDP3 ( Fig 3C ) . Sequencing of randomly chosen clones from the petroselinate culture identified a single nucleotide exchange that occurred spontaneously , converting NKF* to NKL* and thus yielding a stronger targeting signal ( S4C Fig ) . The rapid fixation of this mutation demonstrates the ease by which the latent ACO2 motif can further evolve to yield an efficient targeting signal . How do phenotypic mutations , e . g . the slippage in pre-duplication IDP2s , become eventually ‘imprinted’ via a genetic mutation , thus leading to evolutionary adaptation as observed in the extant , peroxisomal IDP3s ? Homonucleotide repeats of 3–8 bases are prone to phenotypic , transcriptional/translational errors as exemplified here with A . gossyppii IDP2 ( Fig 2B ) . However , homorepeats are also highly prone to genetic , frame-shifting InDels ( insertions and deletions ) . In fact , these two phenomena are strongly correlated: the longer the homorepeat , the higher is the frequency of both phenotypic and genetic frameshifts ( see Ref . [39] and references therein ) . However , apart from A . gossyppii IDP2 , the 3 other pre-duplication IDP2 genes have no homorepeats of >3 bases length in the region before the stop-codon ( Fig 1B ) . We therefore sought to identify the sites of slippage that unveil the cryptic PTS1 sequences , and to also establish whether the very same sites also comprise hotspots for genetic deletion mutations that result in the exclusive expression of a peroxisomal form . In fact , we began our exploration with the latter—namely , we sought to identify hotspots for genetic , single base deletions that may occur upstream to K . waltii IDP2’s stop-codon and result in its cryptic PTS1 becoming in-frame ( K . waltii IDP2 was the most poorly bypassed pre-duplication IDP2; and , as mentioned above , has no >3 bases repeats in its C-terminus; Fig 1B ) . We randomly mutated the segment of 100 bases around K . waltii IDP2’s stop-codon , transformed the mutated gene library to the S . cerevisiae ΔIdp3 strain and selected the transformed yeast cells for growth on petroselinate . After 200 hours , the culture’s growth rate dramatically increased ( Fig 4A ) . The selected pool was analyzed by sequencing seven randomly chosen clones . We identified 3 different single base deletions that all occurred within a stretch comprising 3 repeats of 3 bases each just before the stop codon ( AAATCCCAAA; Fig 4B ) . To examine whether the phenotypic frameshifts occur within the very same stretch , we applied the same test applied to validate the 6T repeat as the site of ribosomal slippage in A . gossyppii IDP2 . Namely , we introduced silent mutations at each of the 3 deletion sites ( AAA TCC CAA A; in bold , the sites of silent mutations; Fig 4B ) and examined whether the frequency of slippage , as reflected by the rate of growth on petroselinate , would be reduced . Indeed , silent mutations in the two deletion sites that are closer to the stop-codon showed a marked inhibition of growth , and the triple mutant showed effectively no growth ( Fig 4C ) . It therefore appears that the phenotypic mutations leading to cryptic peroxisomal localization in the cytosolic IDP2s are readily ‘immortalized’ via genetic deletion mutations that occur within the very same site .
Taken together , our results show that S . cerevisiae IDP3 diverged from an ancestral , pre-duplicated gene that , although primarily localized to the cytosol , had the capacity for peroxisomal localization via a phenotypic mutation—a frameshift induced by translational slippage . We appear to be witnessing all the putative intermediates along this evolutionary trajectory . Specifically , S . cerevisiae IDP3 , and the other post-duplication IDP3s , all have a ‘legitimate’ in-frame PTS1 . The pre-duplication cytosolic IDP2s in Saccaromycetaceae species have a cryptic PTS1 within their 3’-UTR regions that are unveiled by translational and/or transcriptional errors . Further , the contemporary S . cerevisiae PYC1 and ACO2 genes appear to contain cryptic PTS1 signals in their 3’-UTRs that are readily revealed by a single genetic deletion mutation . Indeed , our findings also suggest that four enzymes , that together comprise a putative pathway providing NADPH for peroxisomal β-oxidation , either have a known peroxisomal paralogue ( CIT and IDP ) , or have been partially localized to the peroxisome in the past or are evolving towards peroxisomal localization ( PYC and ACO ) . Finally , our laboratory evolution experiments confirm that the pre-duplication IDP2s carrying cryptic PTS1 sequences readily evolve via genetic mutations to yield ‘legitimate’ peroxisome-targeted genes . We also conclude that IDP3’s mechanism of divergence does not fit Ohno’s model , namely , neo-functionalization/localization . Rather , IDP3’s peroxisomal targeting emerged in the ancestral Saccaromycetaceae species long before the IDP gene was duplicated to give the newly diverged IDP3 . As shown here , 4 out of the 5 pre-duplication IDP2s in Saccaromycetaceae species are dually localized via a cryptic PTS1 whilst the more distant species do not possess such cryptic PTS1s ( Fig 1 ) . Thus , IDPs represent a clear case of divergence via ‘gene sharing’ [11 , 21] and of ‘divergence before duplication’ [13 , 14] . Following duplication , the ancestral dual , cytosol-peroxisome localization function was split between two paralogous genes , thus representing a case of sub-functionalization/localization [22 , 24] . The latter evolutionary mechanisms rely on one gene executing multiple functions , and accordingly on weak trade-off—namely , that mutations that endow the newly emerging function do not abolish the original function . Weak trade-offs , i . e . , ‘something for nothing’ , at least at the early stages of evolution , is a key feature that makes divergence before the duplication a far more plausible scenario than Ohno’s model [46] . Indeed , the assumption of tradeoffs underlies Ohno’s model—the existence of a redundant copy relieved from the burden of selection enables mutations to freely accumulate , including mutations that undermine the original function [47] . Our analysis indicated a ratio of ~1:3 of the peroxisome-cytosol isoforms in A . gossyppii IDP2 ( Fig 2B ) . Thus , a reduction of ~25% in the levels of the cytosolic IDP is enough to shift from no growth on petrosalinate to a growth rate that is only half of that observed with the ‘legitimate’ peroxisomal IDP3 ( Fig 2A ) . However , growth levels comparable to wild-type were afforded only upon a genetic mutation that leads to exclusive peroxisomal targeting . Assuming that cytosolic IDP is essential , such a mutation could only follow duplication . IDPs’ divergence prior to duplication was driven by transcriptional /translational errors that result in dual localization to both the cytosol and peroxisome from a single gene . Dual localization is commonly observed ( see also refs [27 , 28 , 48 , 49] . Specifically , stop-codon read-through , or alternative splicing , were previously shown to mediate the dual cytosol-peroxisome targeting of several glycolytic enzymes in various yeast species [30] . However , there is no evidence indicating that these genes duplicated and diverged into ‘legitimate’ peroxisomal paralogues , as is the case with the pre-duplication IDP2s . Divergence before duplication may apply to localization signals other than PTS1 . Most protein localizations , such as to the endoplasmic reticulum , mitochondria , or chloroplasts , are mediated by N-terminal target signals that are ~20 amino acids long . Translational errors can also produce various N-terminal isoforms from a single mRNA owing to alternative translation initiation sites ( “leaky scanning” ) [50] thus enabling dual targeting [27 , 29 , 51] . Foremost , our results provide unequivocal evidence that phenotypic mutations led to the evolution of new traits [43 , 52] . Noise and infidelity in general , and transcriptional and translational errors specifically , may comprise a “look-ahead” effect [6] thus underlining the phenotype of the yet to emerge duplicated , diverged gene [5] . The rate of phenotypic mutations is >105-fold higher than genetic mutations , and may be further enhanced under stress due to the malfunction of translational fidelity [42] , or under the yeast prionic state ( [PSI+] ) , thus promoting phenotypic diversity that mediates survival in challenging environments [53] . Further , although phenotypic mutations are not inherited as such , the capacity to induce them is inherited via DNA sequences that favor slippage , as manifested , for example , in the 6T repeat inducing dual localization of A . gossyppii IDP2 . Finally , our results indicate another intriguing aspect of phenotypic mutations—the same gene context is prone to both phenotypic mutations ( transcriptional/translational errors ) and genetic mutations ( Fig 4 ) . Thus , selection for dual localization , hence favoring gene sequences whereby slippage downstream the stop-codon occurs at relatively high frequency ( as seems to be the case in the pre-duplication IDP2 genes ) also creates a hotspot for a genetic mutation . In this manner , a coincidental error becomes a ‘frozen accident’ under selection , as well as a hotspot for evolutionary adaptation .
All strains were derived from By4741 ( MATa , his3Δ1 , leu2Δ0 , met15Δ0 , ura3Δ0 ) [54] . The strains used are listed in S2 Table . To obtain the wild-type strain harboring a selection marker ( WT; idp3:: IDP3-kanMX4 ) , the IDP3 open reading frame ( ORF ) of the By4741 genome was replaced with the IDP3 gene fusing to kanMX4 cassette by homologous recombination [55] . This strain ( WT ) was subjected to serial passage culture in the petroselinate containing medium ( 1% yeast extract , 2% Bacto-peptone , 0 . 2% Tween-40 , 0 . 1% petroselinate ) containing G418 ( 200 μg/ml ) ) until spontaneously adapting and exhibiting higher growth rate . The IDP3 knockout strain ( ΔIdp3; idp3:: hphNT1 ) was constructed from this adapted strain , whereby the locus containing the IDP3 gene—the kanMX4 cassette was replaced with a hpnNT1 cassette by homologous recombination . The kanMX4 cassette was PCR amplified from a pBS7 vector ( Yeast resource center ) . The IDP3 gene—kanMX4 fusion was constructed as follows: The IDP3 gene , including the ORF and 150 bps downstream , was PCR amplified from genomic DNA . The amplified gene was introduced into the pBS7 vector containing kanMX4 cassette by using the SmaI /BglII sites . The IDP3 gene—the kanMX4 cassette was thus PCR amplified from the sub-cloned pBS7 vector . The hpnNT1 cassette was amplified from pRS41H plasmid [56] using primers franking the 5’ end of IDP3’s ORF and the 3’ end of the kanMX4 cassette . DNA fragments encompassing the coding region , plus the 500 bps upstream ( 5’ ) and 150 bps downstream regions ( 3’ ) of the various IDP genes ( S . cerevisiae IDP3 , IDP2 , A . gossypii IDP2 , and K . waltii IDP2 ) were amplified from genomic DNA . For testing the effects of the coding vs . 5’-UTR ( promoter ) and 3’-UTR ( cryptic PTS1s ) , the coding and non-coding fragments were separately amplified and combined by assembly PCR . These assembled fragments were subcloned by SmaI/NotI sites into pRS41K [56] , a centromere-based plasmid for single-copy expression in yeast , generating: pRS41K-ScIDP3Pro/ScIDP3/ScIDP3Ter , pRS41K-ScIDP2Pro/ScIDP3/ScIDP3Ter , pRS41K-ScIDP3Pro/ScIDP2+CKL/ScIDP3Ter , pRS41K-ScIDP3Pro/ScIDP2/ScIDP2Ter , pRS41K-ScIDP3Pro/AgIDP2/AgIDP2Ter , pRS41K-ScIDP3Pro/KwIDP2/KwIDP2Ter; whereby ScIDP3pro and ScIDP2pro are 500 bp upstream regions of S . cerevisiae IDP3 and IDP2 , respectively; ScIDP2 , ScIDP2+CKL , ScIDP3 , AgIDP2 , and KwIDP2 are coding region; ScIDP2ter , ScIDP3ter , AgIDP2ter , and KwIDP2ter are 150 bp downstream regions of S . cerevisiae IDP3 , IDP2 , A . gossypii IDP2 , and K . waltii IDP2 respectively: e . g . ScIDP2Pro/ScIDP3/ScIDP3Ter represents the assembled fragment of the upstream region of ScIDP2 , ScIDP3 coding region , and the downstream region of ScIDP3 . To introduce mutations ( shown with underbars in primers sequences below ) in the PTS1 , or in the polyT region of A . gossypii IDP2 , site-directed mutagenesis was performed by using pRS41K-ScIDP3Pro/AgIDP2/AgIDP2Ter plasmid as a template with primer sets ptsdel_f ( 5’-GAAAAAGCAAGCATAATTATAGCCTAGGCTGCCT-3’ ) and ptsdel_r ( 5’-AGGCAGCCTAGGCTATAATTATGCTTGCTTTTTC-3’ ) for cPTS1 mutation , delt_f ( 5’-GGCTACAAGCGTCTTTTTGTGAATAAGAAAAAGC-3’ ) and delt_r ( 5’-GCTTTTTCTTATTCACAAAAAGACGCTTGTAGCC-3’ ) for single T deletion on the polyT , and tsyn_f ( 5’-GGCTACAAGCGTCTCTTCTGTGAATAAGAAAAAG-3’ ) and tsyn_r ( 5’-CTTTTTCTTATTCACAGAAGAGACGCTTGTAGCC-3’ ) for silent mutations on the polyT , generating pRS41K-ScIDP3Pro/AgIDP2ΔAKL/AgIDP2Ter , pRS41K-ScIDP3Pro/AgIDP2Δt/AgIDP2Ter , and pRS41K-ScIDP3Pro/AgIDP2+silent /AgIDP2Ter , respectively . Replacement of the C-terminus of S . cer IDP2 with the C-termini and 3’-UTRs of various genes was performed using Leu403 as the 5’ crossover point ( as it comprises the last conserved residue in yeast IDPs ) , and the stop codon as the 3’ crossover . The peroxisome targeting potential of cryptic PST1 sequences was tested by measuring the growth rates of ΔIdp3 strain complemented with various IDP genes in the petroselinate containing medium . The IDP constructs tested for targeting ( native and chimeras alike ) were cloned into a chromosomal pRS41K plasmid and transformed to the ΔIdp3 strain . Cells were first grown on YPD media ( 1% yeast extract , 2% Bacto-peptone , 2% Glucose ) for at least 18 hours , and then used to inoculate into the YP-petroselinate medium ( 1% yeast extract , 2% Bacto-peptone , 0 . 2% Tween-40 , 0 . 1% petroselinate ) at an initial OD600 0 . 1 . Growth was monitored by absorbance at 600 nm ( error bars represent standard deviations of three independent cultures ) . The 100 bp region centered around the stop codon of K . waltii IDP2 was randomly mutated by error-prone PCR using GeneMorph II random mutagenesis kit ( Agilent technologies , CA ) and integrated by MEGAWHOP cloning [57] into the IDP2 encoding plasmid pRS41K-ScIDP3Pro/KwIDP2/KwIDP2Ter ( construction details above ) . Sequencing indicated an average mutation rate of ~1 mutation per gene . The plasmid library was transformed to the ΔIdp3 strain . Cells were cultured for 300 hours in the YP-petroselinate medium as described above . The resulting culture with increased growth rates was plated on YPD plates and randomly chosen colonies were analyzed by DNA sequencing . Silent mutations were introduced in the plasmid pRS41K-ScIDP3Pro/KwIDP2/KwIDP2Ter by site-direct mutagenesis . These plasmids were used to transform the ΔIdp3 strain and test growths on petroselinate . Note that the ΔIdp3 strain used here was derived from another adaptation experiment where the ΔIdp3 strain complemented with K . waltii IDP2 gene ( pRS41K-ScIDP3Pro/KwIDP2/KwIDP2Ter ) was cultured in the YP-petroselinate medium until spontaneously adapting . IDP sequences were obtained from the Fungal Orthogroups Repository [58] . Sequence alignment was created by MUSCLE [59] . Maximum likelihood phylogenic trees were created by PhyML [60] based on the yeast species tree [58] by using the JTT substitution matrix . The ΔIdp3 strain transformed by plasmid pRS41K-ScIDP3Pro/AgIDP2/AgIDP2Ter was cultured in the YP media containing petroselinate until the mid-log phase ( OD600 ~0 . 6 ) . One mL culture was centrifuged and the collected cell pellet was subjected to total RNA extraction using total RNA extraction kit ( Epicentre ) . The cDNA of A . gossypii IDP2 was amplified from the total RNA by RT-PCR using gene-specific primers: xhoI_agidp500f ( 5’-ATTGGGTACCCTCGAGAGGACGGGGACAAGTCCAAG-3’ ) and agter_notIr ( 5’-CACCGCGGTGGCGGCCGCAGATATGCTAGACTAGTAATAAATAGACGC-3’ ) . The amplified PCR products were subcloned into plasmid pRS41K using XhoI/NotI . The plasmids were transformed into E . coli . and 36 randomly selected colonies were subjected to DNA sequencing . For expression in S . cerevisiae and purification , A . gossypii IDP2’s ORF plus 150 bps of the 3’-UTR region was amplified by PCR ( from plasmid pRS41K-ScIDP3Pro/AgIDP2/AgIDP2Ter ) with primers encoding an N-terminal Histidine-tag . The DNA fragment was subcloned into plasmid pFA6a-nat [61] using XhoI/SpeI sites , for expression and the strong constitutive TEF2 promoter and ADH1’s terminator . The resulting construct including the promoter and terminator was excised using SacI sites and subcloned into plasmid pRS42H [56] , a multicopy 2μ-based yeast plasmid , generating pRS42H-His:AgIDP2/AgIDP2Ter . This plasmid was transformed into the adaptive wild-type strain ( WT ) . For purification of histidine-tagged protein , transformants were pre-cultivated in YPD for 18 hours at 30°C , transferred to YPD at a starting OD 0 . 05 . The culture was harvested at OD 2 . 2 after 22 hours incubation at 30°C . Harvested cells were resuspended in the two-fold cell volumes of lysis buffer ( 50 mM potassium phosphate ( pH 8 . 0 ) , 300 mM sodium chloride , 2 mM sodium citrate , 10 mM Imidazole , 10% glycerol , 1 mM DTT , 0 . 1% Triton-100 , 1% protease inhibitor cocktail ( Sigma ) ) and lysed by voltex with the same cell volume of glass beads ( 425–600 nm , Sigma; G8772 ) and subsequent sonication . Cell debris was removed by centrifugation for 30 min at 11 , 500 rpm , and the supernatant was passed through a open column containing Ni-NTA resin . The column was washed with 30-fold resin volumes of wash buffer ( 50 mM potassium phosphate ( pH 8 . 0 ) , 300 mM sodium chloride , 2 mM sodium citrate , 20 mM Imidazole , 10% glycerol ) . The proteins were finally eluted with elution buffer ( 50 mM potassium phosphate ( pH 8 . 0 ) , 300 mM sodium chloride , 2 mM sodium citrate , 250 mM Imidazole , 10% glycerol ) . The eluted samples were buffer-exchanged with PBS ( 137 mM sodium chloride , 2 . 7 mM potassium chloride , 1 . 76 mM potassium dihydrogenphosphate , 10 mM disodium hydrogenphosphate , pH 7 . 4 ) containing 10% glycerol and 2 mM sodium citrate and concentrated by ultrafiltration ( Vivaspin 500-10K , GE ) . Typical yield was ~ 0 . 2 mg/L at >90% purity as judged by SDP-PAGE . Microcapillary reverse phase liquid chromatography ( LC ) was performed with a nanoAcquity UPLC system ( nUPLC ) ( Waters Corp . ) , using the HEMA/EDMA ( Hexyl methacrylate/Ethylene glycol dimethacrylate 60/40 v/v ) monolithic column prepared in-house , as previously described [62] . Proteins ( 5μl of 50 ng/mL ) were loaded onto the column and separated using a linear gradient of 20% to 60% solvent B over 40 min , at a flow rate of 10–15 μl/min , at 60°C . Solvent A was water + 0 . 05% formic acid+ 0 . 035% Trifluoroacetic acid and solvent B was acetonitrile + 0 . 05% formic acid+ 0 . 035% Trifluoroacetic acid . The LC eluant was sprayed into a Qstar XL mass spectrometer ( MDS Sciex , Canada ) by means of an electrospray ion ( ESI ) source . The following experimental parameters were used: capillary 5 . 3 kV , declustering potential of 40 V , focusing potential of 200 V , and second declustering potential of 20 V . The covered mass range was 500–5 , 000 m/z . Minimal smoothing and centering parameters were used . Spectra were calibrated using a solution of Reserpine ( 1 μM ) . The experimental masses of both the original and alternative isoforms corresponded to the theoretical values minus 130 Da due to the removal of the initial methionine [63] . Peroxisomal localization was confirmed by mating strains expressing Pex14p fused to GFP and the C-terminal proteins of interest fused to mCherry . The reference haploid strain ( By4742: MATα , his3Δ1 , leu2Δ0 , met15Δ0 , ura3Δ0 ) expressing Pex14p C-terminally tagged with GFP was constructed as described before [64] . The C-terminal fragments from A . gos IDP2 or S . cer PYC1 were inserted to the C-terminus of mCherry in plasmid pbs69_PRX_tdh3_mCherry , derived from pBS35 ( yeast resource center ) , by whole plasmid PCR . The plasmid was digested in the Mfe1 site and integrated into the TDH3 promoter site of the adapted wild-type strain ( WT ) by homologous recombination . The transformed haploid WT strain was selected in the presence of Hygromycin ( 300 μg/ml ) , and analysed for positive RFP signal by fluorescent microscopy . The RFP-tagged strains were then mated with the GFP-tagged reference strain in SD medium lacking Histidine containing Hygromycin , and the resulting diploid strains were visualized by fluorescent microscopy ( Nikon Ecripse Ti , Japan ) . Images were taken using a cooled CCD camera with an exposure time of 40–300 ms and processed using ImageJ ( National Institutes of Health ) . The motif sets used for PTS1 search were built from 20 known peroxisomal genes ( 13 genes containing the canonical PTS1 , 7 genes either containing resembled PTS1 or C-terminus responsible for its localization ) from Saccaromyces Genome Database . Each position in the motif was defined as the union of amino acids that appeared in these genes ( ( S/A/C/E/I/H/Q ) - ( K/R/H ) - ( L/F ) -stop ) , in order to minimize the possibility for false negative . Searched were the motifs starting within the first 30 bps of the 3’ UTR , excluding the one disturbed by an additional stop codon . This search was performed in four genomes ( downloaded from the Saccaromyces Genome Database ) : S . cerevisiae ( S288C reference genome version R64 ) , S . pardoxus ( strain NRRL Y-17217 ) , S . bayanus ( WashU version ) and S . mikatae ( strain IFO1815 ) . For each cryptic PTS1 candidate , 12 amino acids upstream to the PTS1 ( including it ) was extracted and manually scored by using the PTS1 predictor ( http://mendel . imp . ac . at/pts1/ PTS1predictor . jsp ) [45] . The cryptic PTS1 appeared in the coding frame were scored by replacing the original stop codons UAA/UAG and UGA with glutamine and arginine , respectively , according to the known stop codon read-through [65 , 66] . | The rarity of genetic mutations limits the likelihood of adaptation . However , transcriptional and translational errors , so-called phenotypic mutations , are >105-fold more frequent , thus generating protein mutants from unmodified genes . We provide the first evidence that phenotypic mutations paved the path to what later , after gene duplication , became newly compartmentalized enzymes . Thus , gene duplication followed rather than initiated the divergence of this new trait . Our findings also show that translational infidelity and phenotypic variability comprise the origins of evolutionary innovations , and how selection for enhanced phenotypic variability also promotes the appearance of genetic mutations that lead to the very same outcome . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | The Evolutionary Potential of Phenotypic Mutations |
The bacterium Helicobacter pylori can cause peptic ulcer disease , gastric adenocarcinoma and MALT lymphoma . The cell-surface mucin MUC1 is a large glycoprotein which is highly expressed on the mucosal surface and limits the density of H . pylori in a murine infection model . We now demonstrate that by using the BabA and SabA adhesins , H . pylori bind MUC1 isolated from human gastric cells and MUC1 shed into gastric juice . Both H . pylori carrying these adhesins , and beads coated with MUC1 antibodies , induced shedding of MUC1 from MKN7 human gastric epithelial cells , and shed MUC1 was found bound to H . pylori . Shedding of MUC1 from non-infected cells was not mediated by the known MUC1 sheddases ADAM17 and MMP-14 . However , knockdown of MMP-14 partially affected MUC1 release early in infection , whereas ADAM17 had no effect . Thus , it is likely that shedding is mediated both by proteases and by disassociation of the non-covalent interaction between the α- and β-subunits . H . pylori bound more readily to MUC1 depleted cells even when the bacteria lacked the BabA and SabA adhesins , showing that MUC1 inhibits attachment even when bacteria cannot bind to the mucin . Bacteria lacking both the BabA and SabA adhesins caused less apoptosis in MKN7 cells than wild-type bacteria , having a greater effect than deletion of the CagA pathogenicity gene . Deficiency of MUC1/Muc1 resulted in increased epithelial cell apoptosis , both in MKN7 cells in vitro , and in H . pylori infected mice . Thus , MUC1 protects the epithelium from non-MUC1 binding bacteria by inhibiting adhesion to the cell surface by steric hindrance , and from MUC1-binding bacteria by acting as a releasable decoy .
The bacterium Helicobacter pylori can cause peptic ulcer disease , gastric adenocarcinoma and MALT lymphoma [1] . H . pylori is estimated to cause approximately 70% of all gastric cancers , and gastric cancer is the second most common cause of cancer related deaths . H . pylori infection and the H . pylori induced pathologies , chronic atrophic gastritis and gastric cancer , are all associated with an increase in epithelial apoptosis [2] , [3] , [4] . One mechanism by which H . pylori can induce apoptosis is by the delivery of the protein CagA into epithelial cells by a type IV secretion system [5] , [6] . This process subsequently activates multiple intracellular signaling cascades inducing an apoptotic response [5] , [6] that has been suggested to promote gastric carcinogenesis by a compensatory increase in gastric epithelial cell proliferation [4] . Supporting this notion , there are more proliferating cells in inflamed mucosa under H . pylori infestation than in H . pylori free areas of the mucosa [7] . Furthermore , it has been shown that in response to chronic Helicobacter felis infection in mice , bone marrow–derived cells can home to and repopulate the gastric mucosa , replacing dead or exhausted epithelial stem cells and contribute over time to metaplasia , dysplasia , and cancer [8] . Adherence of some H . pylori to the mucosal surface is likely to help the bacterial population remain in the neutral and protected niche under the mucus layer , and help it withstand the continuous mucus washing of the mucosal surface . Adherence of H . pylori is dependent on the expression of bacterial adhesins and cognate host glycans , displayed by glycoproteins and glycosphingolipids in gastric epithelium and also by mucins in the gastric mucus layer [9] , [10] , [11] . Several adhesins have been implicated in H . pylori binding: the Blood group Antigen Binding Adhesin ( BabA ) binds to the fucosylated ABO/Lewis b antigen ( Leb ) , and the Sialic Acid Binding Adhesin ( SabA ) binds to the sialyl-Lewis x ( sLex ) and sialyl-Lewis a antigens ( sLea ) [12] , [13] . In the human stomach , the Leb blood-group antigen is mainly expressed by the surface epithelium and on the MUC5AC secreted mucin [11] . Expression of sialylated Le antigens are common in infected and inflamed gastric mucosa [14] , [15] , [16] . Under the mucus layer , the cell-surface associated mucins are highly expressed glycoproteins on the apical surface of all mucosal epithelial cells . Because of their long filamentous nature , cell surface mucins are likely to be the first point of direct contact between host tissue and organisms that penetrate the secreted mucus layer . MUC1 is the most highly expressed cell surface mucin in the stomach [17] . Most mucins exhibit considerable genetic polymorphism due to variability in their numbers of tandem repeat peptides , which results in proteins of widely divergent lengths . Several studies have linked MUC1 polymorphisms with susceptibility to H . pylori-induced disease , such as gastritis and gastric cancer [18] , [19] , suggesting a direct effect of MUC1 polymorphisms on the development of Helicobacter-associated pathology . We have shown that mice deficient in Muc1 are more susceptible to infection by both H . pylori [20] and Campylobacter jejuni [21] . However , the mechanism by which this mucin limits bacterial pathogenesis has not been elucidated . In this study , we have characterized mechanisms by which MUC1 limits H . pylori colonization and decreases ensuing mucosal pathology .
The human gastric epithelial cell line MKN7 forms a contiguous polarized monolayer ( epithelial resistance 250 ohm/cm2 ) and expresses MUC1 on the apical membrane surface as occurs in normal gastric epithelium ( Figure 1A ) . Using double determinant ELISA we measured MUC1 in MKN7 cells and human gastric juice ( Figure 1B ) , demonstrating that MUC1 is shed from the gastric cell surface in vivo . MUC1 from the MKN7 cells carried the Leb and sLea oligosaccharide structures ( Figure 1C , D ) but not sLex ( Figure 1E ) demonstrating that MKN7 MUC1 possesses ligands for both the SabA and BabA H . pylori adhesins . The glycosylation of MUC1 in gastric juice varied between individuals . Shed MUC1 from all three individuals tested carried Leb ( Figure 1C ) , MUC1 from one individual carried sLex ( Figure 1E ) but none of the three gastric juice samples we investigated carried sLea on MUC1 ( Figure 1D ) . This is consistent with interindividual differences in glycosylation demonstrated for secreted gastric mucins such as MUC5AC [11] . H . pylori strains differ in their expression of the BabA and SabA adhesins and consequently in their ability to bind to the Leb , sLea and sLex carbohydrate structures expressed on gastric mucins [11] , [13] . The J99 wild type strain possesses both adhesins , and bound to the adhesion ligands Leb and sLex ( Figure 2A ) . J99 bound to MUC1 isolated from the human gastric epithelial cell line MKN7 ( Figure 2B ) as well as to MUC1 shed into human gastric juice in both samples that were tested ( Figure 2C ) , as analyzed using a double determinant ELISA . This demonstrates that H . pylori can bind to both cell associated and shed MUC1 . In contrast , when an isogenic J99 double mutant lacking both BabA and SabA was used , no specific attachment of the bacteria to MUC1 was detected ( Figure 2B and C ) . A single J99 SabA deletion mutant resulted in a small non-significant decrease in MUC1 binding and a single J99 BabA deletion mutant showed intermediate MUC1 binding ( Figure 2B ) . This demonstrates that binding to MUC1 occurred via MUC1 oligosaccharides bound by both the BabA and SabA adhesins . Since the healthy stomach normally contains a very low amount of sialylated structures , further binding studies were performed with Leb positive and sLex negative MUC1 obtained from a healthy gastric surgical resection specimen . The BabA positive H . pylori strains P466 and CCUG17875/Leb also bound to MUC1 , whereas the BabA negative strains 75ΔBabA1/A2 , CCUG17874 , 26695 , P1 , P1-140 and M019 did not ( Figure S1 ) . Thus , non-MUC1 binding H . pylori strains also exist . However , 80% of H . pylori strains are BabA positive [12] , and the majority of H . pylori strains thus have the ability to bind to MUC1 . The demonstration that H . pylori binds to MUC1 and that MUC1 can be shed from the epithelial surface indicated that MUC1 could act as a releasable decoy upon bacterial adherence . To determine whether MUC1 release can be triggered simply by particulate binding , in the absence of microbial molecular signalling , we used 1 µm beads coated with either an antibody against the extracellular domain of MUC1 or with an isotype control antibody . Immunohistochemistry of cell layers incubated with the beads demonstrated that the MUC1-antibody-coated beads attached to the cell surface but that they were not endocytosed ( data not shown ) . Adherence of beads coated with the anti-MUC1 antibody to confluent MKN7 epithelial cells was significantly higher at 24 h than at 4 . 5 h and then decreased ( Figure 3A ) . This pattern is consistent with a loss of beads from the cell surface once the MUC1 binding sites on the beads are filled and/or the MUC1 on the epithelial cell surface is depleted . The amount of beads in the conditioned culture supernatant followed the opposite pattern , demonstrating that the decrease of beads attached to the cells is due to release into the supernatant rather than bleaching of the fluorescence ( Figure 3B ) . Furthermore , the amount of cellular MUC1 α-subunit ( extracellular domain ) was decreased in cells treated with anti-MUC1 coated beads compared to isotype control coated beads , as demonstrated by capture ELISA ( Figure 3C ) . This was not due to the adherent beads sterically hindering antibody access to MUC1 on the cell surface , as the same decrease was shown by Western blotting of cell lysates ( Figure 3D ) . MUC1 was also detected in the bead-containing conditioned media from cells treated with anti-MUC1 coated beads , but not with isotype control coated beads ( Figure 3D ) . These results demonstrate that when inert bacterial sized particles bind to MUC1 , the MUC1 extracellular domain is released from the epithelial cell surface . Co-cultures of confluent MKN7 cells and H . pylori were established in microaerobic conditions at 1∶1–1∶20 ( mammalian cell∶bacteria: ) as a model of the interaction between the gastric epithelium and H . pylori that penetrate gastric mucus . Under microaerobic conditions the mammalian cells are not stressed and the H . pylori survive , proliferate and show a closer physiology to that seen in vivo than the typically used aerobic co-cultures [22] . The use of confluent cultures is important in modelling the gastric epithelium to avoid H . pylori binding to the basolateral surface of cells , as occurs with the typically used non-confluent AGS culture system [23] . MUC1 is polarised to the apical membrane in these MKN7 cultures ( Figure 1A ) . After 44 h of co-culture we used flow cytometry to measure the amount of cell surface and total MUC1 within individual viable MKN7 cells . Co-culture with the H . pylori strain J99wt depleted MKN7 cells of approximately 40% of both the total pool of the extracellular domain of MUC1 ( Figure 4A ) and of the cell surface located extracellular domain of MUC1 ( Figure 4B ) . To explore whether H . pylori SabA or BabA adhesin mediated binding to MUC1 was required for depletion of MUC1 we used the J99ΔBabAΔSabA mutant as the single mutants were able to bind MUC1 ( Figure 2B ) . Co-culture with the double adhesin mutant did not deplete cellular or cell surface MUC1 . These results are consistent with decreased MUC1 production , increased degradation in the cell or release of MUC1 following binding of the live MUC1-binding bacteria in a similar manner to that shown with the inert 1 µm beads . Further experiments over 1–24 h showed that the decrease in cellular MUC1 began early in co-culture ( Figure 5A; cellular MUC1 was lower in infected cells at 1 , 6 , 12 and 24 h ) . However , MUC1 mRNA levels increased progressively in culture but were not significantly affected by the presence of H . pylori ( Figure 5B ) . The rapid decrease in MUC1 protein with sustained levels of MUC1 mRNA , suggest that loss of protein rather than decreased MUC1 production is the likely explanation for MUC1 depletion from the cell . Despite the unchanged mRNA levels and the decreasing cellular MUC1 , the amount of free MUC1 in the conditioned medium was significantly lower ( by about 40% ) in infected cultures at 1 , 2 , 4 and 6 h ( Figure 5C ) . Decreased MUC1 in the culture medium in infected cultures suggests that substantial amounts of MUC1 was either bound to the bacteria ( which were removed by centrifugation prior to ELISA ) and/or was degraded . In a further experiment , total and cell surface MUC1 was assessed by flow cytometry after 6 and 24 h of co-culture . After 6 h of co-culture H . pylori strain J99 wt did not affect the level of MUC1 on the cell surface , however , it led to depletion of 44% of cell surface MUC1 after 24 h infection compared to uninfected MKN7 cells ( Figure 5D ) . Bacteria recovered from the culture medium were stained for the presence of MUC1 using an antibody reactive with the extracellular domain . MUC1 was detected bound to H . pylori strain J99wt but not to the J99ΔBabAΔSabA mutant using both flow cytometry ( Figure 5E ) and confocal microscopy ( Figure 5F–I , Figure S2 ) . Binding to H . pylori strain J99wt was greatest at 6h ( Figure 5E ) suggesting the bacteria may be capable of degrading or shedding bound MUC1 . Confocal microscopy showed that MUC1 binding to bacteria was often focally intense rather than evenly distributed on the bacterial surface ( Figures 5F , G ) . Taken together these data are consistent with progressive shedding of MUC1 from the cell surface and binding of shed MUC1 to bacteria dependent on the BabA and SabA adhesins . Release of MUC1 can occur either by ( a ) breakdown of the non-covalent association of the α- and β-subunits , possibly in response to shear stress or conformational changes , and/or ( b ) action of extracellular proteases/sheddases . The proteases ADAM17 and MMP-14 have previously been shown to act as MUC1 sheddases in endometrial cells [24] , [25] . As both of these proteases are expressed by MKN7 cells we repeated co-culture experiments with and without knockdown of expression of either and both ADAM17 and MMP-14 using siRNA . Successful knockdown of mRNA expression of the proteases was achieved over a time period from 8 h ( Figures 6A , B ) to 24 h ( data not shown ) after infection . Cellular MUC1 decreased with infection at 24 h and free MUC1 in the culture medium also decreased at 8 h which is consistent with other experiments ( presented as the proportion of uninfected controls for all siRNA conditions in Figure 6C and D , respectively ) . There were no significant alterations in the amount of cellular MUC1 in either uninfected or infected MKN7 cells following knockdown of ADAM 17 or MMP-14 . However , there were significant increases in the amount of shed MUC1 following knockdown of MMP-14 at 8 but not 24 h of infection , although no changes were seen following knockdown of ADAM17 ( Figure 6D ) . These data indicate that MMP-14 is partially involved in the MUC1 shedding process in MKN7 cells . Transfection of MUC1 siRNAs into MKN7 epithelial cells reduced cell surface MUC1 expression by 80% with the 1∶1 siRNA and by 85% with the 1∶3 siRNA , compared to scrambled siRNA ( Figure 7A ) . H . pylori J99wt , J99ΔCagA or J99ΔBabAΔSabA ( 8×105 CFU H . pylori/well ) were co-cultured with confluent transfected MKN7 monolayers for 0 . 5 , 2 , 4 . 5 and 20 h . While the J99wt and J99ΔCagA strains bound MKN7 cells to a similar level , binding of the J99ΔBabAΔSabA adhesin mutant was reduced at all time points ( as determined by staining the co-cultures with an anti-H . pylori antibody , Figure 7B–E; p<0 . 001 ) . After 30 min of co-culture , binding of J99 ( both with and without the SabA and BabA adhesins ) to MKN7 cells was higher when MUC1 was depleted by siRNA ( Figure 7B ) . By 2 h , and through to 20 h , siRNA knockdown of MUC1 had no significant effect on adhesion to the epithelial cells of the J99wt and J99ΔCagA strains that bind MUC1 via the SabA and BabA adhesins ( see Figure 2 ) . In contrast , MUC1 protection against adhesion of the J99ΔBabAΔSabA strain ( which did not bind MUC1 , see Figure 2 ) to the epithelial cells was maintained to at least 20 h . When considered together with the changes in MUC1 expression shown in Figures 4 and 5 , these results indicate that: ( a ) while binding via the BabA and/or SabA adhesins increases H . pylori adhesion to gastric epithelial cells , these bacteria can bind epithelial cells via mechanisms other than these adhesins; ( b ) during early infection , MUC1 inhibits H . pylori binding to epithelial cells occurring via both adhesin-dependent and -independent mechanisms; ( c ) as infection progresses MUC1 is depleted from the epithelial cell surface of cells infected with H . pylori strains carrying the BabA and SabA adhesins and is no longer effective in inhibiting attachment; and ( d ) in cells that are infected with H . pylori strains that cannot bind to MUC1 , MUC1 continues to be protective against adhesion to the epithelial cells , most likely by steric hindrance of bacterial interactions with non-MUC1 receptors expressed on the epithelial cell surface . To analyze cell death we used flow cytometry in combination with annexinV/7-AAD staining which can distinguish healthy cells , cells in early apoptosis , late apoptosis and necrosis/very late apoptosis . The extent of H . pylori induced apoptosis in MKN7 cells was dependent on the concentration of bacteria in the co-cultures . In MKN7 cells co-cultured for 20 h with 6×105 CFU/mL H . pylori ( MOI 1∶1 ) , J99wt induced 35% early apoptosis , 8% late apoptosis and 5% necrosis as determined by annexinV/7AAD staining . J99ΔCagA induced similar levels of early apoptosis , but no late apoptosis or necrosis suggesting a delayed induction of apoptosis , while J99ΔBabAΔSabA had no affect on the viability of MKN7 cells ( Figure 8A–D , left columns ) . At a 10-fold higher concentration ( 6×106 H . pylori CFU/mL , MOI 10∶1 ) , all the isogenic strains induced more apoptosis and necrosis compared with the lower concentration of H . pylori , albeit in a similar pattern; J99wt induced more cell death than J99ΔCagA , and J99ΔCagA induced more cell death than J99ΔBabAΔSabA ( Figure 8A–D , right panel ) . Thus , both BabA/SabA mediated adhesion to the epithelial cell and CagA contribute to epithelial cell death , but to get the maximal effect of CagA , H . pylori needs to bind to the epithelial cell via the BabA and SabA adhesins . In an experiment to evaluate the importance of MUC1 in H . pylori induced apoptosis , transfection of siRNAs into MKN7 cells reduced MUC1 levels by 93% and 97% with the 1∶1 and 1∶3 sequences respectively , compared to scrambled siRNA . Cultures with depleted MUC1 had fewer healthy cells and more apoptotic cells , irrespective of whether or not they were infected ( Figure 9A–D ) . However , the proportion of necrotic cells was slightly lower in the cells with reduced MUC1 . Finally , to ascertain whether MUC1/Muc1 can also influence H . pylori induced epithelial apoptosis in vivo we analysed apoptosis in wild-type ( Muc1+/+ ) and Muc1−/− mice infected with H . pylori-SS1 as previously described [20] . While no difference was observed 1 week post-infection , Muc1−/− mice had a 3-fold increase in the mean proportion of TUNEL-positive apoptotic cells in their gastric mucosa after 8 weeks of H . pylori infection , compared to Muc1+/+ mice ( Figures 9E and S3 ) . Thus , MUC1/Muc1 protects against apoptosis both in vitro and in a murine H . pylori infection model .
Previously we have shown that mice deficient in Muc1 are more susceptible to infection by H . pylori [20] both with regard to the level of colonisation and the degree of pathology that develops . We have now extended these observations by examining the mechanism by which this mucin can limit infection by H . pylori . Here we demonstrate that MUC1 protects the epithelial cell from both bacterial adhesion and apoptosis . When the pathogen does not bind to MUC1 , the 200–500 nm long extracellular domain of the mucin appears capable of physically distancing the bacteria from the host cell surface , thus sterically inhibiting adhesion to other potential cell surface ligands . When the pathogen does bind to MUC1 , the extracellular domain of the mucin is released from the epithelial surface , thereby acting as a releasable decoy and preventing prolonged adherence . In the process H . pylori are coated with MUC1 via , as we demonstrate here , interactions between the mucin and the BabA and SabA adhesins . We propose that this would further prevent anchorage to the mucosal surface by blocking these key adhesins . Limiting attachment of the bacteria to the epithelial surface would also be expected to reduce pathogenicity , by restricting the functional activity of secretion systems such as that encoded by the Cag pathogenicity island , which delivers proinflammatory mediators into epithelial cells [26] . An isogenic CagA deletion mutant induced less cell death in the polarized MKN7 cultures we established in microaerophilic conditions , consistent with previous studies in other cell lines [27] . However , in our experiments which are the first to test the influence of SabA and BabA adhesins on apoptosis empirically , ablation of both adhesins had a substantially greater impact on H . pylori induced apoptosis than loss of CagA . Thus , both adhesion to the epithelial cell and CagA affect viability , but to get the maximal effect of CagA , H . pylori needs to bind to the epithelial cell via the lectin adhesins . Interestingly , at very high non-physiological bacterial infection densities , similar to those used by most researchers , these nuances were lost . Infection with BabA positive H . pylori strains has been associated with higher lymphocytic infiltration , increased epithelial proliferation , and the presence of glandular atrophy and intestinal metaplasia in human antral biopsies [28] . Similarly , severe neutrophil infiltration and atrophy ( important indicators of more severe pathology ) are associated with the expression of functional SabA [29] . Here , we showed that H . pylori induces apoptosis and necrosis in gastric epithelial cells in a dose dependent manner , and that an isogenic mutant strain lacking the BabA and SabA adhesins had diminished ability to induce cell death . This indicates that these adhesins are major determinants of cell surface binding and that it is the amount of adherent H . pylori that determines the impact on epithelial cell viability . To inhibit bacterial access to the epithelial cells , the mucosal surfaces are covered with a mucus layer primarily composed of secreted mucins . This mucus layer is continuously secreted and transports away trapped material . Both salivary and gastric mucins have a high and specific binding capacity for H . pylori [9] , [10] , [11] , and it is likely that this mucus layer keeps the majority of H . pylori away from the epithelial cell surface . For example , in the human-like rhesus monkey model [30] , monkeys secreting mucins with less H . pylori binding capacity develop higher H . pylori density infections and gastritis [14] . Similarly , humans with primary Sjogren's syndrome , who produce less mucins , have more H . pylori-associated pathology [31] , suggesting that the ability of secreted mucins to bind to H . pylori protects the gastric epithelium . In cultured MKN7 cells we observed a progressive depletion of MUC1 as it was shed off the cell surface . In human patients with chronic gastritis , depletion of the MUC1 extracellular domain α-subunit , but not the transmembrane β-subunit , has been reported [32] . Thus , it is likely that during chronic infection rapid shedding of the extracellular domain of MUC1 occurs in vivo , producing a similar result to that we observed in vitro . We have shown that H . pylori adherence to live gastric epithelial cells is increased when the bacterial strain carries the BabA and SabA adhesins . However , adherence to live epithelial cells occurs even when these adhesins are absent , albeit to a reduced level . In contrast , binding to MUC1 , as we have shown previously for other mucins [10] , [11] , is dependent on the presence of H . pylori adhesins . During the initial contact with gastric cells , MUC1 inhibits adhesion of both MUC1-binding and non MUC1-binding H . pylori to epithelial cells . H . pylori bind to MUC1 isolated from epithelial cells as well as to MUC1 shed into the gastric juice of human patients . In this study we have shown that , in vitro , H . pylori carrying these adhesins caused the gastric epithelial cells to shed MUC1 which bound to the bacteria . Inert bacterial sized beads coated with MUC1 antibodies also caused shedding of MUC1 , demonstrating that MUC1 can act as a releasable decoy following engagement by particulate ligands in a Toll like receptor independent manner . Our demonstration of progressive depletion of cellular MUC1 is consistent with a previous study where MUC1 expression decreased in Kato III cells after 4 h of co-culture with H . pylori ( with unknown adhesion properties ) [33] . Although the study with Kato III cells showed recovery of MUC1 after 24 h of co-culture , the recovery of MUC1 may be because this study added 1000-fold higher concentrations of H . pylori than used in our study , and their H . pylori died during the experiment according to the authors possibly due to the use of aerobic culture conditions [22] , [33] , [34] . We have demonstrated that MMP-14 but not ADAM17 is the relevant protease involved in the endogenous shedding of MUC1 from MKN7 cells . This is consistent with previous reports that found that MUC1 shedding was sensitive to MMP-14 depletion in endometrial cells [24] . However , MMP-14 only partially affected MUC1 shedding during infection and had no influence on MUC1 shedding in non-infected cells , indicating that other factors are involved in MUC1 release . Although cleavage by another unknown MUC1 sheddase cannot be definitively excluded , the most likely scenario is disassociation of the non-covalent interaction between the transmembrane and extracellular domains at the SEA module , a site of cleavage during synthesis found in most cell surface mucins [35] , [36] . Disassociation could occur due to conformational changes in MUC1 following binding or due to shear forces following binding to the highly motile bacteria . Additionally , secreted isoforms of MUC1 expressed via alternative splicing [37] , could potentially be shed without prior H . pylori binding and contribute to the MUC1 pool in the mucus and gastric juice . The pattern of focal binding of MUC1 to bacteria that we observed by confocal microscopy is consistent with binding of MUC1 to regions of the bacteria coming in contact with the cell surface , followed by MUC1 shedding and bacterial detachment . However , this pattern could also arise if the SabA and BabA adhesins are focally expressed or if they are aggregated on the bacterial surface following ligation by MUC1 . MUC1 confers resistance to apoptosis induced by genotoxic drugs , the Campylobacter jejuni cytolethal distending toxin and oxidative stress in vitro [21] , [38] . We found that Muc1−/− mice infected with H . pylori strain SS1 had substantially more apoptotic cells in their gastric mucosa than Muc1+/+ mice . In vitro , cultures with MUC1 knockdown had a higher proportion of apoptotic cells , both when infected and not infected , indicating that epithelial cell apoptosis is down regulated by MUC1 as a general function , not only as a response to stressors . Paradoxically , however , necrosis was lower in cells with reduced MUC1 . A similar increase of damage induced apoptosis and delay of secondary necrosis has previously been described for mono ( ADP-ribosyl ) transferases , which control signal transduction pathways in response to cell damage during cell repair and apoptosis [39] . MUC1 increases ß-catenin levels in the cytoplasm and nuclei of carcinoma cells by blocking its degradation , resulting in an increase in cell proliferation [40] , [41] . Similarly , NF-κB regulates genes that control cell proliferation and cell survival , and NF-κB is constitutively active in some cancers . MUC1 interacts directly with the IκB kinase complex , resulting in degradation of the NF-κB inhibitor IκBα [42] . Thus , high expression of MUC1 , as occurs in the stomach and is commonly found in human cancers , confers increased proliferation and resistance to apoptosis both via ß-catenin and the NF-κB pathway . NF-κB is also the master regulator of inflammatory responses , including responses to H . pylori . In addition to a possible effect on NF-κB , it is possible that MUC1 modulates responses to PAMPs mediated by TLRs . In respiratory cells , MUC1 binding of bacterial flagellin ( TLR5 ligand ) appears to repress TLR5-mediated activation of inflammation [43] , [44] . Thus , in addition to limiting bacterial attachment to the cell surface , MUC1 may modulate inflammatory responses possibly limiting unnecessary responses when bacteria fail to stably bind to the cell surface . Further work is required to fully define the influence of MUC1 on epithelial cell responses to the presence of H . pylori . Human population studies have found associations between MUC1 allele polymorphisms and susceptibility to the development of gastric adenocarcinoma and H . pylori-associated gastritis [18] , [19] . VNTR length polymorphisms have been used to characterize these alleles and short VNTR alleles which encode MUC1 proteins with shorter extracellular glycosylated domains are associated with disease . Our data are consistent with the interpretation that shorter forms of MUC1 are less efficient at sterically inhibiting attachment or acting as releasable decoys , thereby allowing increased bacterial binding to the epithelial surface and exacerbation of pathology . Experiments where cells were transfected with MUC1 expressing differing VNTR lengths suggest that longer alleles are more efficient at binding H . pylori [45] . However , it is possible that these VNTR length polymorphisms are surrogate markers for SNP's encoding functional changes in other domains ( for example , SNPs affecting cytoplasmic domain signaling or cleavage of the extracellular domain ) or promoter polymorphisms affecting the level of MUC1 expression during infection . More comprehensive genetic epidemiological studies are warranted to further define the nature of the MUC1 risk alleles . Our experiments show that MUC1 inhibits the H . pylori binding to epithelial cells that occurs via both the BabA and SabA adhesins and non-adhesin mediated binding . When the pathogen does not bind to MUC1 , the mucin sterically inhibits adhesion to other potential cell surface ligands . When the pathogen does bind to MUC1 , the extracellular domain of the mucin is released from the epithelial surface , thereby acting as a releasable decoy and preventing prolonged adherence . Demonstration of the mechanism by which MUC1 limits gastric H . pylori infection is a model paradigm for elucidation of the function of the family of cell surface mucins which decorate the apical membrane surface of all mucosal epithelial cells , and for exploration of their contribution to preventing infectious and inflammatory disease .
The collection of gastric juice was obtained after written informed consent from patients undergoing upper gastrointestinal endoscopy ( approved by the Mater Health Services' Human Research Ethics Committee , Approval No . 396A ) . All procedures involving animals were reviewed and approved by Institutional animal care and use committees ( University of Melbourne; AEEC No . 03219 ) Dewaxed and rehydrated formalin-fixed sections ( 4 µm ) were treated with 10 mM citric acid , pH 6 at 100°C for 20 min and then with 3% ( v/v ) hydrogen peroxide for 30 min at room temperature . The sections were washed 3 times between all subsequent steps in 0 . 15 M NaCl , 0 . 1 M Tris/HCl buffer ( pH 7 . 4 ) containing 0 . 05% Tween-20 . Non-specific binding was blocked by protein block ( Dako ) for 30 min . The sections were incubated with an anti-MUC1 antibody ( CT2 , gift from Prof . S . Gendler , Scottsdale , USA ) diluted 1∶50 in Antibody Diluent ( Dako ) for 1 h , then incubated with Broad Spectrum Poly HRP Conjugate ( Zymed Laboratories Inc , San Fransisco , USA ) for 10 min and with diaminobenzidine for 10 min . The sections were counterstained with Harris's haematoxylin . Three antibodies against MUC1 were used in this study: The CT2 antibody is against the cytoplasmic tail of MUC1 , whereas the BC2 and BC3 antibodies are against the extracellular domain of MUC1 , and react with a VNTR repeat epitope that is exposed even in fully glycosylated MUC1 [46] . These antibodies all react with mature glycosylated MUC1 on the apical surface of human gastric epithelium [46] . Gastric juice was obtained after informed consent from patients undergoing upper gastrointestinal endoscopy . The gastric juice was adjusted to pH 7 with Tris and mixed with equal volume of 150 mmol/L NaCl , 1% NP40 , 0 . 5% deoxycholic acid , 0 . 1% SDS , 50 mmol/L Tris , pH 7 . 5 containing Complete protease inhibitor ( Roche Diagnostics GmbH , Mannheim , Germany ) ( RIPA buffer ) and incubated for 5 min under agitation at 4°C , centrifuged at 10 , 000 g for 10 min at 4°C and the supernatant used for the binding assays . Cells from the gastric cancer cell line MKN7 ( Riken , Japan ) were lysed in RIPA buffer for 10 min under agitation at 4°C , lysates centrifuged at 10 , 000 g for 10 min at 4°C and the high molecular weight fraction purified from the supernatant using a Microcon centrifugal filter device ( cut off 100 kDa ) . Microtitre plates ( Polysorb , Nunc , Denmark ) were coated overnight at 4°C with either the BC2 antibody or the isotype control antibody 401 . 21 against α-gliadin ( 100 µL/well , 4 µg/mL in phosphate-buffered saline , PBS ) . The plates were then washed 3 times in PBS with 0 . 05% Tween-20 and unbound sites blocked with 1% BSA in PBS for 1 h at room temperature . The wells were then incubated with cell lysate or gastric juice extract diluted 1∶5 in Blocking reagent for ELISA ( Boehringer Mannheim , Germany ) containing 0 . 5% BSA and 0 . 05% Tween-20 ( dilution buffer ) for 2 h with orbital shaking , washed as above , incubated with primary antibody: anti-MUC1 ( clone BC3 ) , anti-Leb ( clone LE2 , Biotest , Dreieich , Germany ) , anti-sialyl-Lea ( clone CA19-9 , NeoMarkers , Freemony , CA , USA ) or anti-sialyl-Lex ( AM3 , gift from Dr C . Hanski , University Medical Center Charite , Berlin , Germany ) diluted to 1 µg/mL , 1∶200 , 1∶1000 and 1∶20 , respectively . The plates were washed and incubated with HRP-conjugated anti-mouse IgM ( Jackson ImmunoResearch Laboratories , Inc , USA ) . HRP activity was determined using 2 , 2′-Azinobis ( 3-ethylbenzothiazoline ) -6-sulphonic acid as a substrate ( 0 . 550 g/L in citrate phosphate buffer pH 4 . 3 ) by measuring absorbance at 405 nm . Assays were performed in triplicate . H . pylori were harvested and washed twice by centrifugation at 2 , 500 g in PBS containing 0 . 05% Tween-20 . 100 µl H . pylori ( OD600 nm = 0 . 90 ) was incubated with 500 ng FITC labelled Leb or sialyl-Lex HSA-conjugates for 30 min in PBS containing 0 . 5% human albumin and 0 . 05% Tween-20 . Fluorescence was measured after washing the bacteria twice . Assays were performed in triplicate . Binding of H . pylori to MUC1 was determined by sandwich ELISA using biotinylated bacteria . Polysorb Microtitre plates ( Nunc , Denmark ) were coated overnight at 4°C with either the BC2 antibody against the extracellular domain of human MUC1 , or the isotype control antibody 401 . 21 ( 100 µL/well , 4 µg/mL in PBS ) . The plates were then washed 3 times in PBS with 0 . 05% Tween-20 and unbound sites blocked with 1% BSA in PBS for 1 h at room temperature . The wells were then incubated with cell lysate or gastric juice extract diluted 1∶5 in Blocking reagent for ELISA ( Boehringer Mannheim , Germany ) containing 0 . 5% BSA and 0 . 05% Tween-20 ( dilution buffer ) for 2 h with orbital shaking , washed as above , then incubated with a suspension of biotinylated SS1 , J99wt or J99ΔBabA/SabA ( OD600 = 0 . 15 diluted 1∶10 in dilution buffer ) for 2 h at 37°C with orbital shaking . The plates were washed and incubated with streptavidin-HRP ( diluted 1∶1000 in dilution buffer ) for 1 h at room temperature and HRP activity determined as above . To get a sufficient amount of MUC1 to analyze binding with a range of H . pylori strains , archived purified mucins from a previously published study on tissue from patients with no history of peptic ulcer disease undergoing elective surgery for morbid obesity was used [11] . MUC1 was isolated from whole gastric wall using isopycnic density gradient centrifugation followed by gel chromatography of the mucin containing fractions to separate MUC1 from the oligomeric mucins as previously described [9] , and analyzed by ELISA [9] . The gastric epithelial cell line MKN7 ( Riken Cell Bank , Japan ) was cultured in RPMI containing 10% FCS , 2 mM L-glutamine , 100 units/mL penicillin G sodium and 100 ug/mL streptomycin . Mucin expression and glycosylation in this cell line has been described previously [22] . For co-culture with H . pylori the medium was changed to antibiotic free medium 20 h prior to infection . The co-culture experiments were performed on confluent MKN7 cells transferred to microaerobic conditions ( 5% O2 , 15% CO2 , 80% N2 ) at the start of the co-culture . MKN7 cell viability is not compromised during these conditions [22] , and the microaerobic conditions are similar to the actual pO2 and pCO2 in tissues of the human body [47] . To assess integrity of monolayer cultures MKN7 cells were cultured on snapwell tissue culture inserts , which were then were mounted in vertical Ussing chambers ( exposed area 1 . 13 cm2 ) . The basolateral side of the membrane was immersed in 115 . 8 mM NaCl , 1 . 3 mM CaCl2 , 3 . 6 mM KCl , 1 . 4 mM KH2PO4 , 23 . 1 mM NaHCO3 , 1 . 2 mM MgSO4 ( KREB ) solution containing 5 . 7 mM Na-pyruvate , 5 . 1 mM Na-L-glutamate 10 mM and D-glucose , whereas the apical compartment was immersed in KREB's solution containing 5 . 7 mM Na-pyruvate , 5 . 13 mM Na-L-glutamate and 10 mM D-mannitol . The solutions were gassed with 95% O2 and 5% CO2 at a temperature of 37°C and pH 7 . 4 throughout the whole experiment . Epithelial resistance ( Rp ) was measured using square-pulse analysis . 5 V , 3 ms pulses were generated by a square pulse generator ( Medimet , Gothenburg , Sweden ) via a current limiting resistor ( 36 kΩ ) connected to a platinum electrode and applied across the sample . H . pylori were grown on Brucella agar supplemented with 10% bovine blood , 2% Vitox ( Oxoid ) , 10 µg/mL vancomycin ( Sigma ) , 5 µg/mL trimethoprim ( Sigma ) and 4 µg/mL amphoteracin B ( Sigma ) for 4 days in 5% O2 and 15% CO2 at 37°C . H . pylori strains J99 wild type ( wt ) bind Leb and sialyl-Lex , CCUG17875/Leb and P466 bind Leb but not sialyl-Lex , the 17875BabA1::kanbabA2::cam-mutant ( 75ΔbabA1A2 ) and CCUG17874 , bind sialyl-Lex but not Leb , whereas the remaining strains; the isogenic J99 adhesion mutant lacking the BabA and SabA adhesins ( J99babA::camsabA::kan [13] , referred to as J99ΔBabAΔSabA ) , 26695 , P1 and P1-140 do not bind sialyl-Lex or Leb [11] ( provided by Prof . Thomas Boren , Umeå University , Sweden ) . The J99 CagA deletion mutant ( J99ΔCagA ) was made by natural transformation with a plasmid containing the cloned CagA from strain 26695 that was knocked out by insertion of the CatGC cassette into the singular Bg/II site in the middle of the cagA gene ( provided by Prof Steffen Backert , Institut fur Medizinische Microbioligie , Magdeburg , Germany ) . Clones were selected after culture on 6 µg chloroamphenicol/mL agar . Disruption of CagA was verified by PCR using the 5′-AAAGGATTGTCCCTACAAGAAGC-3′ and 5′-GTAAGCGATTGCTCTTGCATC sequences . The concentration of H . pylori was estimated by measuring OD600 , and then the amount of CFU in the inoculum was determined by counting colonies from serial dilutions cultured for 5 days . The Multiplicity Of Infection ( MOI , pathogen∶host cell ) was determined based on the CFU of the H . pylori and a density of 1 . 5×105 MKN7 cells/cm2 cells at the day of confluency , which is the day we infected the cells . The bacteria were washed twice in 0 . 2 mol/L carbonate buffer pH 8 . 3 ( 2×109 bacteria/mL ) , 125 µg/mL biotin-XX-NHS was added and the mixture rotated in the dark for 15 min at room temperature . To verify that the BabA adhesin remained functional , adherence to the Leb conjugate was measured ( Figure 2 ) . 105 fluorescent beads ( Fluospheres NeutrAvidin labelled 1 µm microspheres , Molecular probes ) coated with an antibody against the extracellular domain of MUC1 ( BC2 antibody ) or isotype control ( 401 . 21 ) were added to 96 well plates with confluent MKN7 epithelial cells ( N = 9 ) . After incubation for 4 . 5 h , 24 h and 44 h , cultures were washed 3 times with ice cold PBS and fluorescence was quantified in a FLA5100 ( Fujifilm ) . RIPA lysates were subjected to SDS-PAGE in a 4–12% gradient gel . Western blots were cut in the middle ( ∼70 kDa ) and the upper half was stained with an antibody against the MUC1 extracellular domain ( BC2 ) and the lower half was probed for β-actin and detected with fluorescent probes on the Licor Odyssey instrument . Total RNA was prepared using the RNeasy Mini Kit ( Qiagen , Valencia , CA , USA ) . The quantity and quality of the RNA was determined by spectrophotometry ( ND-1000; NanoDrop Technologies Inc . , Wilmington , DE ) . Total RNA ( 1 µg ) from each sample was used for first strand cDNA synthesis using SuperScript™ III reverse transcriptase ( Invitrogen ) following the manufacturer's instructions . Real-time PCR was monitored by SYBR® Green I fluorescence ( Invitrogen ) using Platinum ® Taq DNA-Polymerase ( Invitrogen ) with 3 mM MgCl2 , 0 . 2 µM primers , 200 µM dNTPs , and 0 . 5 U polymerase per reaction ( 25 µl ) under primer-specific conditions . The following experimental protocol for PCR reaction ( 40 cycles ) was performed on a Rotor-Gene 3000 cycler ( Corbett Research , Sydney , Australia ) : denaturation for 15 min at 95°C , followed by 40 amplification cycles at 94°C ( 20 s ) , annealing under primer-specific conditions ( 30 s ) , and extension for 45 s at 72°C . Primers with the following sequences were chosen: GAPDH: forward: 5′- CCTGTACGCCAACACAGTGC -3′ , reverse: 5′- ATACTCCTGCTTGCTGATCC -3′ , annealing temperature 60°C . MUC1: forward: 5′- CCCCTATGAGAAGGTTTCTGC-3′ , reverse: 5′- ACCTGAGTGGAGTGGAATGG -3′ , annealing temperature 60°C . ADAM-17: forward: 5′-ACCTGAAGAGCTTGTTCATCGAG -3′ , reverse: 5′-CCATGAAGTGTTCCGATAGATGTC-3′ , annealing temperature 60°C . MMP-14: forward: 5′-CCATCATGGCACCCTTTTACC-3′ reverse: 5′-TTATCAGGAACAGAAGGCCGG-3′; annealing temperature 60°C . All Primers were obtained from GeneWorks ( Hindmarsh , SA , Australia ) . To confirm the specificity of the amplified DNA , a melting curve was determined at the end of each run . The reaction efficiency was determined with a dilution series of cDNA containing the PCR products . Genes were normalized to the unregulated housekeeping gene GAPDH and the results were expressed as ratio of target gene and GAPDH expression ( arbitrary units ) . Control experiments were also performed to ensure that GAPDH expression was not differentially regulated under the experimental conditions employed . siRNA specific for ADAM17 and MMP-14 as well as scrambled siRNA were chemically synthesized ( Dharmacon Research , Lafayette , USA ) as a mixture of four siRNAs targeting different regions of the same gene to enhance the silencing performance ( 21 mers , SMARTpool ) . MKN 7 cells with a confluence of 70–80% were transfected with either 250 nM of ADAM17 siRNA or MMP 14 siRNA individually or combined both together using Lipofectamine 2000 Reagent ( Invitrogen ) according to the manufacturer's instructions . 250 nM or 500 nM of scrambled control siRNA were used as negative control . After 48 h of transfection , the MKN7 cells were co-cultured with H pylori J99 wild type for a further 8 and 24 h , and the uninfected MKN 7 cells were also cultured for a further 8 and 24 h as a negative control . The MKN7 cells were harvested after 56 and 72 h transfection , the level of knockdown of the ADAM 17 or MMP-14 was detected by quantitative RT-PCR , and the influence of the treatment on MUC1 shedding was monitored by ELISA . 100 nM 2′-hydroxyl DsiRNA against the target sequence NNGUUCAGUGCCCAGCUCUAC ( 1∶1 ) and NNGCACCGACUACUACCAAGA ( 1∶3 ) or siCONTROL non-targeted siRNA#2 ( Dharmacon ) were transfected into MKN7 cells using Lipofectamine 2000 . Four days after transfection the level of knockdown was measured using the median fluorescence intensity determined by flow cytometry ( below ) . The 1∶3 siRNA generally gave higher knockdown than the 1∶1 siRNA . Cell viability was analysed by collecting non-adherent cells together with attached cells harvested with trypsin and counting cell suspensions by flow cytometry on cells stained with 1 µg/mL 7-aminoactinomycin D ( 7AAD ) and Annexin-V-PE Apoptosis Detection Kit I ( BD Pharmingen ) according to the manufacturers instructions . For MUC1 detection , cells harvested with trypsin and stained with 1 µg/mL 7AAD were either stained without fixation ( staining of cell surface structures ) or fixed in 1% paraformaldehyde for 5 min on ice and then permeabilized with 0 . 5% saponin ( intracellular and extracellular staining ) . The cells were then incubated with the anti-MUC1 antibody BC2 or isotype control 401 . 21 at 3 µg/mL in 1% BSA in PBS for 60 min at 4°C and then with anti-mouse antibody conjugated to Alexa fluor 488 ( Invitrogen ) . For detection of intracellular antigens , washes and antibody incubations were performed in the presence of 0 . 5% saponin . After staining , all cells were fixed with 1% paraformaldehyde . The analysis was gated to exclude 7AAD positive cells and assessment of staining was performed on a LSRII Flow Cytometer ( BD Biosciences , San Jose , USA ) using the Diva software ( BD Biosciences , San Jose , USA ) . Bacteria were recovered from the culture medium of MKN7 cells by first sedimenting non-adherent mammalian cells ( 300 g , 5 min ) and then sedimenting bacteria ( 5000 g , 10 min ) . Bacteria were then stained with BC2 or 401 . 21 at 10 µg/mL as above and gated using the SSC and FSC pattern of broth cultured H . pylori . Bacteria prepared and stained for flow cytometry as above were smeared onto charged glass slides , stained with DAPI ( 0 . 1 µg/ml ) for 15 min , washed with PBS , mounted in Prolong Gold ( Invitrogen ) and examined using a Zeiss LSM510 confocal microscope with multitracking detecting DAPI ( excitation 405 nm , detection 420–480 nm ) and FITC ( excitation 488 nm detection , LP 505 nm ) fluorescence separately . H . pylori J99wt , the J99ΔCagA or J99ΔBabAΔSabA were co-cultured at a concentration of 8×105 CFU H . pylori/well in a 96 well plate . Co-cultures were washed 3 times with ice cold PBS and then fixed with 4% paraformaldehyde for 20 min on ice . After washing , wells were blocked for 1 h with 1% BSA in PBS and then incubated with mouse polyclonal anti-Helicobacter antisera [20] . The plates were washed and incubated with HRP-conjugated anti-mouse IgG ( Jackson ImmunoResearch Laboratories , Inc , USA ) . The plates were washed and HRP activity determined using TMB by measuring absorbance at 450 nm . All procedures involving animals were reviewed and approved by Institutional animal care and use committees ( University of Melbourne; AEEC No . 03219 ) . The mouse infection tissue samples were archived material from a previously published study [20] . Female aged matched 129/SvJ wild type and 129/SvJ Muc1−/− mice were infected intra-gastrically once with 107 H pylori suspended in 0 . 1 mL Brain Heart Infusion ( Oxoid ) . The assay was performed according to the manufacturer's instructions ( Roche ) , except that the reaction was diluted 1∶4 in 0 . 1 M sodium cacodylate buffer , pH 7 . 3 to decrease the background . The total number of TUNEL positive cells per 10 randomly selected fields of view in the entire gastric mucosa was counted at 20× magnification . For normally distributed data the Students t-test was used to compare groups . For analyses where a normal distribution could not be demonstrated , including where the number of replicates was low , the non-parametric Mann Whitney U test was used to compare groups . The ANOVA test was used when comparing 3 or more groups , and to ascertain that the multiple testing did not add to the chance of finding statistically significant differences , the Tukey's or Bonferroni's post hoc tests were used . | The bacterium Helicobacter pylori can cause peptic ulcer disease , gastric adenocarcinoma and MALT lymphoma . H . pylori colonize the mucosal surface of the stomach , where adherence helps the bacteria to remain in the neutral and protected niche under the mucus layer , and helps it withstand the continuous mucus washing of the mucosal surface . Adherence is also thought to mediate much of the H . pylori mediated disease . The cell-surface mucin MUC1 is highly expressed on the mucosal surface and limits the density of H . pylori in a murine infection model . We now demonstrate that the majority of H . pylori strains can bind to human MUC1 and that release of MUC1 following binding limits adhesion to the cell surface . Furthermore , MUC1 protects the epithelium from non-MUC1 binding bacteria by acting as a physical barrier to adhesion to other cell surface molecules . Thus , appropriate expression and function of MUC1 is likely to limit development of disease ensuing from chronic H . pylori infection . | [
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"infe... | 2009 | MUC1 Limits Helicobacter pylori Infection both by Steric Hindrance and by Acting as a Releasable Decoy |
Glial cells surround neuronal endings to create enclosed compartments required for neuronal function . This architecture is seen at excitatory synapses and at sensory neuron receptive endings . Despite the prevalence and importance of these compartments , how they form is not known . We used the main sensory organ of C . elegans , the amphid , to investigate this issue . daf-6/Patched-related is a glia-expressed gene previously implicated in amphid sensory compartment morphogenesis . By comparing time series of electron-microscopy ( EM ) reconstructions of wild-type and daf-6 mutant embryos , we show that daf-6 acts to restrict compartment size . From a genetic screen , we found that mutations in the gene lit-1/Nemo-like kinase ( NLK ) suppress daf-6 . EM and genetic studies demonstrate that lit-1 acts within glia , in counterbalance to daf-6 , to promote sensory compartment expansion . Although LIT-1 has been shown to regulate Wnt signaling , our genetic studies demonstrate a novel , Wnt-independent role for LIT-1 in sensory compartment size control . The LIT-1 activator MOM-4/TAK1 is also important for compartment morphogenesis and both proteins line the glial sensory compartment . LIT-1 compartment localization is important for its function and requires neuronal signals . Furthermore , the conserved LIT-1 C-terminus is necessary and sufficient for this localization . Two-hybrid and co-immunoprecipitation studies demonstrate that the LIT-1 C-terminus binds both actin and the Wiskott-Aldrich syndrome protein ( WASP ) , an actin regulator . We use fluorescence light microscopy and fluorescence EM methodology to show that actin is highly enriched around the amphid sensory compartment . Finally , our genetic studies demonstrate that WASP is important for compartment expansion and functions in the same pathway as LIT-1 . The studies presented here uncover a novel , Wnt-independent role for the conserved Nemo-like kinase LIT-1 in controlling cell morphogenesis in conjunction with the actin cytoskeleton . Our results suggest that the opposing daf-6 and lit-1 glial pathways act together to control sensory compartment size .
Sensory organs are the gates through which information flows into the nervous system . In many sensory organs , specialized glial cells form a chemically isolated compartment around neuronal receptive endings [1] , [2] . For example , in the skin , the mechanosensory Pacinian corpuscles consist of an unmyelinated nerve ending that is surrounded by lamellae formed by a modified Schwann glial cell [3] . In the olfactory epithelium , sensory neurons are ensheathed by glia-like sustentacular cells [4] , [5] . In the inner ear , hair cells are surrounded by Deiter's cells , which express the glial marker glial fibrillary acidic protein ( GFAP ) [6]; and in the vertebrate eye , retinal pigmented epithelial cells contact photoreceptor cell cilia [7] . At least in some cases , the integrity of the glial compartment is essential for proper sensory neuron function [8] . Glial compartments also enclose excitatory neuronal synapses in the cerebellum and hippocampus [9] , [10] , and are thought to be important for synaptic function through limiting neurotransmitter diffusion , and regulating levels of synaptic effectors . Despite the prevalence of such glial compartments , little is known about their development . To determine how such compartments form , we turned to the major sense organ of the nematode Caenorhabditis elegans , the amphid . C . elegans has two bilaterally symmetric amphids located in the head [11] . Each amphid consists of 12 sensory neurons , which mediate many of the behavioral responses of the animal , and two glial cells , the sheath and socket glia ( Figure 1A , top ) . Amphid neurons are bipolar , projecting an axon into the nerve ring ( the main neuropil of the animal ) and extending a dendrite anteriorly to the tip of the nose . The two amphid glia also extend anterior processes collateral to the dendrites . At the nose tip , sheath and socket glia form discrete single-cell tubular channels joined by adherens junctions ( Figure 1A bottom ) . The resulting two-cell channel compartment is open to the environment anteriorly and surrounds and isolates the ciliated endings of specific amphid sensory neurons . The socket portion of the channel is lined with cuticle and serves as a conduit for cilia to sample the animal's environment [11] . The sheath glial cell , however , is an active secretory cell [11] , releasing extracellular matrix proteins , required for sensory neuron function , into the sheath glia channel [8] . Previous studies demonstrated that the morphogenesis of this compartment depends on the Patched-related gene daf-6 [12]–[14] , which acts within glia [14] , [15] . Although the primary defects in daf-6 mutants were not characterized , these studies demonstrated that glial compartment formation employs mechanisms shared with the genesis of other tubular structures in the animal , including the vulva and excretory system [14] . Similarly , the C . elegans Dispatched-related protein CHE-14 seems to play important roles in the formation of the amphid sensory compartment and other tubular organs [14] , [16] . Here we demonstrate a primary function for daf-6 in restricting sensory compartment size and show that the conserved MAP kinase LIT-1/NLK acts in counterbalance to DAF-6 to promote compartment expansion . Although LIT-1 is an important component of the Wnt signaling pathway in C . elegans [17] , our studies argue against a role for Wnt in compartment size control . However , the previously characterized LIT-1 activator MOM-4/TAK1 is important for amphid sensory compartment morphogenesis . LIT-1 and MOM-4 co-localize to the amphid sensory compartment , and LIT-1 localization requires its highly conserved carboxy-terminal region . We demonstrate that this C-terminal domain physically interacts with actin and with the Wiskott-Aldrich syndrome protein ( WASP ) , a regulator of actin polymerization [18] . Actin is highly enriched around the amphid pocket , and WASP appears to act in the same pathway as LIT-1 to influence compartment morphogenesis . Our studies reveal two opposing activities , one mediated by DAF-6 , the other by LIT-1 , which , together with glial cytoskeletal proteins , drive sensory compartment morphogenesis .
The amphid sheath glial cell forms a compartment that surrounds the ciliated endings of amphid sensory neurons , constraining them into a tight bundle ( Figure 1A–C ) . Within this bundle , 10 sensory cilia are stereotypically arranged in three successive columns containing 3 , 4 , and 3 cilia , respectively ( Figure 1C; [11] ) . We previously reported the cloning and characterization of daf-6 , a Patched-related gene required for amphid channel morphogenesis [14] . In daf-6 mutant adults , the amphid channel is grossly enlarged , the socket and sheath glia channels are not continuous , and distal portions of sensory cilia are neither bundled nor exposed to the environment ( Figure 1D and 1E ) . At least two interpretations of this phenotype are possible: First , daf-6 might act to open the sheath glia channel at its anterior end . Thus in daf-6 mutants , the channel pocket would form but would remain sealed , and would continuously enlarge as matrix material is deposited . Second , daf-6 might act to constrain the luminal diameter of the sheath glia channel . Thus , in daf-6 mutants , the sheath and socket glia would properly align and form an open compartment , yet without lateral constraints on its size , the sheath channel would expand circumferentially . In this latter model , loss of the sheath-socket junction would be a later secondary defect . To discriminate between these possibilities , we used electron microscopy ( EM ) to follow the development of amphid sensory compartments in wild-type and daf-6 ( e1377 ) mutant embryos . We used high-pressure freezing to fix embryos at several time points between 300 and 450 min post-fertilization , the time period during which the amphid is generated [19] , collected serial sections , and assessed channel morphology . By 380 min , sensory dendrites that have not yet formed cilia are evident in wild-type embryos . The tips of these dendrites are laterally ensheathed by the sheath glial cell , but the sheath cell also forms a cap blocking the anterior portion of the compartment and preventing access of neuronal processes to the socket ( Figure S1 ) . By 400 min , a well-defined amphid primordium is formed in wild-type embryos ( Figures 1F and S1 ) . The sheath glia cap is gone and the open channel is continuous with the socket glia channel . At this stage , the socket channel is devoid of neuronal processes as dendritic tips have yet to extend cilia . Instead , a dense arrangement of filaments traverses the socket channel and forms a link between the tips of the sensory dendrites and the outside of the embryo ( asterisk in Figure 1F ) . These filaments are consistent with an extracellular matrix proposed to anchor dendrites during retrograde extension [20] . Although cilia have not yet formed , structures resembling basal bodies ( the initial sites of cilia construction ) are visible at dendrite endings ( arrow in Figure 1F ) . In daf-6 mutant embryos , the initial stages of amphid development are unperturbed ( n = 3 ) . By 400 min , the sheath and socket channels are aligned and open . Dendrites lacking cilia , but containing basal body-like structures , reside within the sheath channel , while filaments emanating from the dendrite tips and traversing the sheath and socket channels are seen ( Figure 1H ) . However , only slightly later , at 420 min and before cilia have formed , bloating of the amphid sheath channel is apparent , and dendrites begin to unbundle ( Figure 1I , compare to Figure 1G ) . These studies indicate that daf-6 is not required for aligning the sheath and socket channels or for opening the amphid sensory compartment . Rather , daf-6 seems to function in restricting compartment diameter . The abnormal expansion of the amphid sensory compartment in daf-6 mutants suggests that active processes promote compartment expansion and that these processes are balanced by daf-6 activity during development . We surmised that mutations in genes promoting compartment expansion might , therefore , counteract the loss of daf-6 and restore compartment size and function . To identify such genes , we screened for mutants able to generate a normal compartment in the absence of daf-6 function , taking advantage of an easily scored daf-6 mutant defect: the inability to form dauer larvae . Dauer is an alternative developmental state induced by starvation and perception of high concentration of dauer pheromone . Dauer animals are highly resistant to environmental insults and can survive in the presence of 1% sodium-dodecylsulfate ( SDS ) [21] . daf-6 mutants fail to become dauer larvae , presumably due to their sensory deficits [22] , and are thus killed by exposure to SDS . We therefore randomly mutagenized animals homozygous for the strong loss-of-function daf-6 ( e1377 ) allele [14] using ethyl methanesulfonate ( EMS ) , allowed F2 animals to starve , and treated them with SDS . Resistant animals could have suppressed the daf-6 amphid sensory compartment defects or could have constitutively activated a more downstream step in dauer formation . To distinguish between these mutant classes , we examined the ability of amphid sensory neurons to fill with dye provided in the medium . When exposed to a solution of the lipophilic dye DiI , wild-type animals readily take up the dye into exposed amphid neurons . daf-6 animals fail to do so , presumably due to their defective amphid sensory compartments ( Figure S2A–C ) [13] , [23] . From a screen of 60 , 000 mutagenized genomes we identified seven mutants that survived SDS treatment and that dye filled properly . We further characterized one of these daf-6 suppressors , given the allele designation ns132 . As shown in Figure 2A , approximately 40% of ns132; daf-6 ( e1377 ) animals are able to take up dye in at least one amphid . Likewise , the ns132 allele was able to suppress amphid channel defects in another daf-6 mutant , n1543 , supporting the notion that ns132 is a bypass suppressor ( Figure 2A ) . To further confirm the rescue of the daf-6 amphid defects in ns132; daf-6 ( e1377 ) animals , we examined amphid sensory compartments using fluorescence microscopy . We found that cilia in these double mutants projected through a compartment of normal appearance ( Figure 2B , compare to Figure 1D ) . In addition , ns132; daf-6 ( e1377 ) individuals that displayed normal dye filling in one of the two amphids had one amphid channel that resembled a wild-type channel by EM serial reconstruction ( Figure 2C; n = 3 ) . Interestingly , even in rescued amphids , cilia packing was more variable compared to the regular 3∶4∶3 packing observed in wild-type animals , and the amphid sensory compartment was somewhat wider than normal ( Figure 2C , compare to Figure 1C ) , perhaps reflecting a partial suppression of the daf-6 defects . We used single nucleotide polymorphism ( SNP ) mapping and transgenic rescue methods ( Figure S2D ) to identify the gene defective in ns132 animals as lit-1 . lit-1 encodes a Ser/Thr MAP kinase that is highly conserved from C . elegans to mammals . Supporting this assignment , a genomic region containing lit-1 restored dye-filling defects to ns132; daf-6 ( e1377 ) animals ( Figures 2A and S2E ) , as did a transgene in which the lit-1 promoter region ( 2 . 5 kb upstream of the lit-1 start codon ) drives the lit-1 cDNA ( Figure 2A ) . Furthermore , a temperature-sensitive mutation in lit-1 , t1512 , also suppressed the dye-filling defects of daf-6 ( n1543 ) mutants ( Figure 2A ) . Finally , we found that animals containing the ns132 allele have a C-to-T mutation in the coding region of lit-1 , converting codon 437 , encoding glutamine , to a stop codon . This mutation is predicted to result in a truncated LIT-1 protein ( Figure 2D ) lacking the last 26 amino acids of the highly conserved carboxy-terminal ( C-terminal ) domain . To determine in which cells lit-1 functions to regulate compartment development , we first examined its expression pattern by generating animals harboring a transgene in which the lit-1 promoter drives expression of a nuclearly localized dsRed fluorescent protein ( NLS-RFP ) . We found that lit-1 is expressed in amphid sheath glia ( Figure 3A ) , among other cells . In addition , the expression pattern of this reporter partially overlaps with that of ptr-10 ( Figure 3B ) , a gene expressed in ensheathing glia of other sensory organs [24] , suggesting that lit-1 could act in compartment formation in other C . elegans sensory structures as well . Next , we pursued cell-specific rescue experiments to determine in which cells lit-1 can act to regulate compartment morphogenesis . We generated lit-1 ( ns132 ) ; daf-6 ( e1377 ) animals containing a transgene in which a lin-26 promoter fragment drives expression of the lit-1 cDNA in glia , but not neurons , of embryos at the time of amphid sensory compartment formation [25] . We found that transgenic animals were rescued ( Figure 3C ) , supporting the notion that lit-1 can act in glia to regulate compartment morphology . Importantly , expression of the lit-1 cDNA in amphid sensory neurons during the time of amphid morphogenesis ( using the dyf-7 promoter; [20] ) failed to rescue lit-1 ( ns132 ) ; daf-6 ( e1377 ) animals ( Figure 3C ) . To determine whether lit-1 can control amphid sensory compartment structure after compartment formation is complete , we examined lit-1 ( ns132 ) ; daf-6 ( e1377 ) animals expressing the lit-1 cDNA under the control of the sheath glia-specific vap-1 promoter . vap-1 expression begins in late embryos [14] , after the compartment has formed . We found that these transgenic animals were not rescued ( Figure 3C ) , supporting the conclusion that lit-1 is required within amphid sheath glia at the time of amphid morphogenesis to influence compartment formation . Finally , to ascertain whether the kinase activity of LIT-1 is required , we generated a mutant lit-1 cDNA that disrupts the ATP binding domain ( VALKK to VALGK ) and which has been shown to eliminate LIT-1 kinase activity in vitro [17] . lit-1 ( ns132 ) ; daf-6 ( e1377 ) animals carrying a lin-26 promoter::LIT-1 ( K97G ) cDNA transgene still displayed 30% dye filling , similar to controls , suggesting that LIT-1 kinase activity is indeed required for glial compartment morphogenesis ( Figure 3C ) . None of the transgenes used in Figure 3C had an effect on the dye filling of wild-type animals ( n>100 ) . Since daf-6 normally acts to restrict amphid sensory compartment expansion , the observation that lit-1 mutations suppress daf-6 suggests that lit-1 may normally promote compartment growth . Consistent with this idea , the lit-1 ( ns132 ) allele enhances the dye-filling defects of che-14 ( ok193 ) mutants ( Figure 4A ) . CHE-14 protein is similar to the Drosophila and mammalian protein Dispatched , and is important for apical secretion and amphid sensory compartment morphogenesis [16] , suggesting a role in lumen expansion . The enhancement of che-14 defects by lit-1 ( ns132 ) suggests that both genes may be involved in this process . To further test the idea that lit-1 promotes compartment expansion , we examined lit-1 ( ns132 ) single mutants for dye-filling abnormalities; however , no defects were observed ( Figure 4B ) , suggesting that amphid morphology in these animals may be normal . However , two observations suggest that ns132 is a weak allele of lit-1 . First , the ns132 lesion truncates only 26 amino acids from the C-terminus of the LIT-1 protein and leaves the kinase domain intact ( Figure 2D ) . Second , null alleles of lit-1 are embryonic lethal [17] , [26] , whereas ns132 mutants are fully viable . To examine the consequences of more severe defects in lit-1 function , we turned to animals homozygous for the lit-1 ( t1512 ) temperature-sensitive allele . lit-1 ( t1512ts ) animals grow nearly normally at 15°C , but exhibit early embryonic lethality at 25°C [26] . At 20°C , some lit-1 ( t1512ts ) embryos escape lethality and grow to adulthood . We reasoned that in some of these escapers , LIT-1 activity could be low enough to allow us to discern defects in amphid morphogenesis . Indeed , as shown in Figure 4B , nearly 50% of lit-1 ( t1512ts ) adults grown at 20°C exhibit defects in a sensitized amphid dye-filling assay ( this assay was developed to detect weak defects in dye filling; see Experimental Procedures ) . These results suggest that amphid structure , and perhaps compartment morphogenesis , has been perturbed in these mutants . To assess whether compartment morphology is indeed perturbed , we performed serial-section EM on dye-filling defective adult lit-1 ( t1512ts ) animals raised at 20°C ( n = 3 ) . Whereas in wild-type animals a cross-section through the sheath channel immediately posterior to the socket-sheath boundary ( yellow line in Figure 4C ) reveals the stereotypical 3∶4∶3 arrangement of the 10 channel cilia , in lit-1 ( t1512ts ) mutants ( Figure 4D ) , the amphid sensory compartment has a smaller diameter and contains fewer cilia . Fewer cilia are also found in the socket channel in lit-1 ( t1512ts ) animals ( unpublished data ) . Furthermore , in wild-type animals , cross-sections roughly 1 µm posterior to the sheath-socket junction ( blue line in Figure 4C ) reveal a less packed arrangement of cilia that are loosely surrounded by the sheath glia membrane; by contrast , in lit-1 ( t1512ts ) animals the sheath glia is tightly wrapped around individual cilia ( arrowheads in Figure 4D ) , consistent with the idea that compartment diameter is reduced . Importantly , despite the posterior displacement of some cilia in lit-1 ( t1512ts ) animals , the total number of cilia is normal ( blue section in Figure 4D ) . Taken together , the che-14 , dye-filling , and EM studies suggest that lit-1 opposes daf-6 by promoting channel expansion during amphid morphogenesis . The kinase activity of LIT-1 was previously shown to depend on MOM-4/TAK1 , a MAP kinase kinase kinase . MOM-4 increases LIT-1 kinase activity in vitro and mutations in mom-4 interact genetically with mutations in lit-1 during anterior/posterior polarity establishment in early embryos [27] . We therefore tested whether mutations in mom-4 could also suppress the dye-filling defects of daf-6 mutants . While complete loss of mom-4 , like loss of lit-1 , leads to early embryonic lethality , some animals homozygous for a temperature-sensitive allele of mom-4 , ne1539ts , can escape lethality . We found that whereas only 1% of mom-4 ( ne1539ts ) ; daf-6 ( e1377 ) double-mutant escapers grown at 15°C exhibit suppression of the daf-6 dye-filling defect , 18% of surviving animals grown at 20°C can take up dye ( p<10−6 , Chi-squared test; Figure 5A ) . This observation suggests that mom-4 acts similarly to lit-1 in compartment expansion . To test whether mom-4 , like lit-1 , acts within glia to regulate amphid morphogenesis , we constructed mom-4 ( ne1539ts ) ; daf-6 ( e1377 ) double mutants expressing a lin-26 promoter::GFP::mom-4 cDNA transgene . When these animals were grown at 20°C , only 7% filled with dye ( Figure 5A ) , consistent with the hypothesis that mom-4 acts within glia during early amphid morphogenesis , similar to lit-1 . To assess whether mom-4 and lit-1 function in the same pathway to promote channel expansion , we examined dye filling in daf-6 mutants that were also homozygous for both lit-1 ( ns132 ) and mom-4 ( ne1539ts ) alleles . We found that the mom-4; lit-1; daf-6 triple mutant is viable at both 15°C and 20°C and is not suppressed to a greater extent than lit-1; daf-6 double mutants at either temperature ( Figure 5A ) . This result is consistent with the idea that lit-1 and mom-4 function in the same pathway to control channel expansion , similar to their established roles in embryonic cell polarity . The roles of lit-1 and mom-4 in Wnt signaling in C . elegans have been extensively studied [28] , [29] . In this context , MOM-4 activates LIT-1 , which then forms a complex with the β-catenin WRM-1 . The LIT-1/WRM-1 complex phosphorylates the C . elegans TCF/LEF transcription factor POP-1 , resulting in reduction ( but not elimination ) of POP-1 nuclear levels and activation of transcription ( Figure 5B ) [17] , [27] , [30] , [31] . We therefore examined animals containing mutations in Wnt signaling components for defects in dye filling , or for suppression of the daf-6 dye-filling defects . Surprisingly , mutations in Wnt-encoding genes , the C . elegans Wntless homolog mig-14 , required for Wnt protein secretion , Wnt receptors , β-catenins , or pop-1/TCF/LEF , the main LIT-1 target in the Wnt signaling pathway , have no effect on dye filling and show no , or minimal , suppression of daf-6 ( Table S1 ) . Although we cannot eliminate the possibility that multiple redundant Wnt pathways contribute to channel formation and that these operate through LIT-1 targets other than POP-1 , the most parsimonious interpretation of our data is that the MOM-4/LIT-1 kinase module operates independently of Wnt signaling to promote expansion of the amphid glial compartment . To determine where within the amphid sheath glia LIT-1 and MOM-4 are localized , we generated animals expressing either a rescuing GFP::MOM-4 or a rescuing GFP::LIT-1 fusion protein within amphid sheath glia using the T02B11 . 3 amphid sheath promoter [32] . Strikingly , we found that both fusion proteins were tightly associated with the amphid sensory compartment ( Figure 6A and 6B ) . To determine whether LIT-1 localization requires functional mom-4 , we examined localization of the GFP::LIT-1 fusion protein in mom-4 ( ne1539ts ) single mutants at 20°C . GFP::LIT-1 was properly localized in all animals we observed ( n = 44 ) , suggesting that LIT-1 localizes to the sheath channel independently of its regulator . The DAF-6 protein is mislocalized in animals lacking neuronal cilia , accumulating only at the sheath-socket junction rather than along the length of the sheath glia channel [14] . To examine whether LIT-1 also requires cilia to properly localize , we examined animals harboring a loss-of-function mutation in daf-19 , which encodes a transcription factor required for ciliogenesis . Our previous EM studies demonstrated that , despite minor defects , a channel of normal length is generated in these mutants [14] . As shown in Figure 6C , in daf-19 mutants , LIT-1 no longer lines the entire channel , but is restricted to its anterior aspect . Thus , neuronal signals are required for LIT-1 glial localization . The channel localization of LIT-1 raised the possibility that in lit-1 ( ns132 ) mutants , LIT-1 localization might be disrupted . To test this , we expressed GFP-tagged LIT-1 ( Q437Stop ) ( the mutation corresponding to ns132 ) in wild-type animals and examined its localization . While GFP::LIT-1 reproducibly lines the amphid sensory compartment , GFP::LIT-1 ( Q437Stop ) fails to localize in about one-third of animals and is instead diffusely distributed throughout the cell ( Figure 6D and 6G ) . This result suggests that the highly conserved C-terminal region of LIT-1 may be required for compartment localization . In addition , the fraction of animals in which GFP::LIT-1 ( Q437Stop ) is mislocalized ( 31% , Figure 6G ) mirrors the fraction of daf-6 mutants suppressed by the lit-1 ( ns132 ) allele ( Figure 2A ) , raising the possibility that mislocalization may account for the suppression we observed . The observation that GFP::LIT-1 ( Q437Stop ) still localizes to the amphid channel in some animals raised the possibility that the C-terminal 26 amino acids may represent only a portion of the full targeting domain . To test this idea , we generated animals expressing a GFP::LIT-1ΔCt fusion protein in which all sequences downstream of the kinase domain are deleted . We found that in these animals LIT-1 never accumulated at the amphid sensory compartment , and was diffusely distributed throughout the cell ( Figure 6E and 6G ) , demonstrating that the C-terminal domain is necessary for LIT-1 compartment localization . To determine whether the C-terminal domain of LIT-1 is sufficient for channel localization , we generated animals expressing a GFP::LIT-1 C-terminal domain fusion protein . Remarkably , we found that this fusion protein accumulated at the amphid sensory compartment in a pattern identical to that of full-length LIT-1 ( Figure 6F and 6G ) . Previous work showed that LIT-1 also localizes to the cell nucleus [30] , [33] , [34] , and we found this to be the case for amphid sheath glia as well ( Figure S3 ) . However , disruption of the C-terminal domain of LIT-1 does not result in its exclusion from the nucleus ( Figure S3 ) , suggesting that nuclear functions of LIT-1 may not be abrogated in lit-1 ( ns132 ) mutants . Although the C-terminal domain of LIT-1 is highly conserved from C . elegans to mammals , its function is not well studied . Our studies demonstrate that this domain is both necessary and sufficient for LIT-1 localization to the amphid sensory compartment , and suggest that proper localization is important for LIT-1 function in compartment formation . Because of the importance of the LIT-1 C-terminal domain in compartment localization , we used this domain as bait in a yeast two-hybrid screen with the aim of identifying proteins that interact with LIT-1 . From a screen of approximately 106 clones , we identified 26 positive clones ( Table S2 , Figure 7A ) . While some clones were isolated multiple times , others were found only once , suggesting that our screen was not saturated . We were intrigued that 4 of the 26 interacting clones identified encoded the C . elegans actin protein ACT-4 . EM studies of the amphid sheath glia channel had previously shown that the channel is lined by an electron dense subcortical layer ( red arrowheads in Figure 1C ) [13] . A similar layer can be seen in other highly secreting cells such as pancreatic acinar cells and adrenal chromaffin cells . In these cells , this electron dense layer has been demonstrated to be enriched in actin [35] , [36] . To determine whether ACT-4 might be part of the electron-dense subcortical layer near the amphid sensory compartment , we examined animals expressing a GFP::ACT-4 fusion protein in amphid sheath glia . Strikingly , we found that although GFP::ACT-4 was seen throughout the cell , it was highly enriched at the amphid sensory compartment ( Figure 7B ) . We wondered whether other actin proteins also accumulate at the channel and , therefore , generated animals expressing a protein fusion of GFP to ACT-1 . Again , we found increased channel localization ( unpublished data ) , suggesting that actin filaments may be components of the subcortical density . To examine the localization pattern of ACT-4 at higher resolution , we used scanning EM coupled with photo-activated localization microscopy ( PALM ) . In this method , serial sections are imaged by scanning EM and using single-molecule fluorescence of mEos::ACT-4 [37] . Images are then superimposed , using fiduciary markers ( fluorescent gold beads ) , to reveal the subcellular localization of fluorescent proteins . As shown in Figure 7C , at the anterior portion of the amphid channel , where an electron dense subcortical region has been described , mEos::ACT-4 is localized near the sensory compartment membrane ( blue trace ) . mEos::ACT-4 does not localize to the sensory compartment in more posterior sections ( Figure 7D , 2 µm posterior to 7C ) , which should lack the subcortical electron density . These observations support the notion that actin is intimately associated with the glial sensory compartment and that the subcortical density may be composed at least in part of actin . We also found that GFP::ACT-4 was properly localized in lit-1 ( ns132 ) mutants ( n = 50 ) , suggesting that actin accumulates around the sensory compartment independently of lit-1 , and consistent with the possibility that actin may recruit LIT-1 . To test this possibility we tried to disturb GFP::ACT-4 localization by treating the animals with an inhibitor of actin polymerization , cytochalasin D . After a 2 h incubation with 1 mM of the drug , the cell bodies of the sheath glia assumed a rounded morphology , indicative of breakdown of the actin cytoskeleton . However , the sensory compartment localization of neither GFP::ACT-4 nor GFP::LIT-1 was disturbed ( unpublished data ) . This result suggests that the subcortical actin around the amphid channel could be part of a stable structure with a lower turnover rate than the rest of the actin cytoskeleton . Similarly , LIT-1 , MOM-4 , and ACT-4 all localized to the sensory compartment in daf-6 ( n1543 ) mutants ( Figure S4 ) , suggesting that DAF-6 is not involved in recruiting these proteins . In addition to actin , our two-hybrid studies suggested that the LIT-1 C-terminal domain can also bind to the proline-rich region of WSP-1 , the C . elegans homolog of the Wiskott-Aldrich Syndrome Protein ( WASP ) ( Table S2 , Figure 7A ) . Furthermore , we could immunoprecipitate the LIT-1 C-terminal domain using WSP-1 from cultured Drosophila S2 cells co-expressing both proteins ( Figure 7H ) , suggesting that LIT-1 and WSP-1 can interact . Although GFP::WSP-1 expressed in amphid sheath glia is diffusely localized ( unpublished data ) , co-expression with mCherry::LIT-1 revealed partial co-localization ( Figure 7E–G ) , supporting the notion that LIT-1 and WSP-1 may interact in vivo . To determine whether wsp-1 plays a role in amphid morphogenesis , we examined wsp-1 ( gm324 ) mutants , which , unlike actin mutants , are viable [38] . We did not find any defects in dye filling in the single mutant . However , wsp-1 ( gm324 ) suppresses the daf-6 ( n1543 ) dye-filling defects ( Figure 7I ) . Furthermore , daf-6 mutants homozygous for both lit-1 ( ns132 ) and wsp-1 ( gm324 ) were as dye-filling defective as lit-1 ( ns132 ) ; daf-6 ( n1543 ) mutants alone , consistent with the hypothesis that LIT-1 and WSP-1 act in the same pathway . Interestingly , we found that overexpression of a GFP::LIT-1 fusion protein results in abnormal glial morphology ( Figure S5B , compare to Figure S5A ) and distorted sensory compartment morphology ( Figure S5C , compare to Figure 6A ) . This result , together with the genetic and physical interactions between LIT-1 and actin and LIT-1 and WASP , are consistent with the possibility that LIT-1 facilitates glial morphogenesis by regulating actin dynamics .
LIT-1 is the C . elegans homolog of Nemo-like kinase ( NLK ) [39] , a Serine/Threonine kinase originally described in Drosophila [40] . In C . elegans , lit-1 ( loss of intestine ) was first identified for its role in endoderm specification during early embryogenesis [26] . Subsequent work established lit-1 as a component of the Wnt/β-catenin asymmetry pathway that directs many cell fate decisions in C . elegans [28] , [29] . NLK also plays roles in control of the Wnt [41] , [42] , TGFβ [43] , and Notch [44] signaling pathways in vertebrates . Although LIT-1/NLK has been implicated in cell fate determination , we identified lit-1 mutations as suppressors of lesions in daf-6 , a gene that affects morphogenesis of the amphid glial sensory compartment , but not glial cell fate . Indeed , lit-1 single mutants seem to have well-specified amphid components . Furthermore , despite an established connection between lit-1 and the Wnt/β-catenin asymmetry pathway ( a major regulator of cell fate decisions in C . elegans ) , we found no evidence linking Wnt signaling to amphid morphogenesis ( Table S1 ) . These observations are consistent with the idea that the role of lit-1 in sensory organ morphogenesis does not involve cell fate decisions , but instead reflects a novel function in cellular morphogenesis . Within the context of cell fate decisions , LIT-1/NLK often acts by impinging upon the activity of nuclear transcription factors [30] , [43] , [44] . It is unclear whether the role of lit-1 in sensory organ morphogenesis might also involve transcriptional regulation . The C-terminal domain of LIT-1 is required for its role in amphid morphogenesis and for its amphid channel localization , but it is not essential for the ability of LIT-1 to enter the nucleus . This suggests that LIT-1 may exert its primary influence on channel morphogenesis at the channel itself . However , LIT-1 C-terminus can interact not only with cytoskeletal proteins ( actin and WASP ) but also with the transcription factors ZTF-16 and MEP-1 ( Table S2 ) . Thus , while it is likely that sensory compartment localization is important for LIT-1 function , we cannot rule out the possibility that LIT-1 has independent relevant functions in the nucleus . Our results suggest that daf-6 and lit-1 direct the morphogenesis of the sheath glia sensory compartment by exerting opposing influences . In daf-6 mutants , neurons and glia form an amphid primordium in which all components are initially linked and aligned; however , the sensory compartment expands abnormally . Conversely , in lit-1 mutants , the sensory compartment is too narrow . Mutations in lit-1 can correct for the loss of daf-6; thus , lit-1; daf-6 double mutants have relatively normal glial channels . A situation that mimics lit-1; daf-6 double mutants arises in animals with mutations in genes controlling neuronal cilia development . In these animals , channel localization of LIT-1 , as well as DAF-6 , is perturbed . Consistent with the lit-1; daf-6 phenotype , channel formation is only mildly defective in these mutants [14] . The observation that lit-1 loss-of-function mutations suppress daf-6 null alleles argues that lit-1 cannot function solely upstream of daf-6 in a linear pathway leading to channel formation . Our data , however , are consistent with the possibility that daf-6 functions upstream of lit-1 to inhibit lit-1 activity . Alternatively , lit-1 and daf-6 may act in parallel . Our studies do not currently allow us to distinguish between these models . How might DAF-6 restrict the size of glial sensory compartments ? Electron micrographs of the C . elegans amphid reveal the presence of highly organized Golgi stacks near the amphid channel . These images also show vesicles , containing extracellular matrix , that appear to be released by the sheath glia into the channel ( Figure 1A ) [11] . These studies suggest that vesicular secretion may play a role in channel morphogenesis . Interestingly , DAF-6 is related to Patched , a protein implicated in endocytosis of the Hedgehog ligand , and the C . elegans Patched gene ptc-1 is proposed to regulate vesicle dynamics during germ-cell cytokinesis [45] . Furthermore , DAF-6 can be seen in punctate structures , which may be vesicles [14] , and DAF-6 and CHE-14/Dispatched function together in tubulogenesis [14] , [16] , a process hypothesized to require specialized vesicular transport . Together these observations raise the possibility that DAF-6 may restrict amphid sensory compartment expansion by regulating vesicle dynamics in the sheath glia [14] . If indeed DAF-6 controls membrane dynamics , it is possible that LIT-1 , which localizes to and functions at the sheath glia channel , also interfaces with such processes . How might LIT-1 localize to the glial sensory compartment and control vesicle dynamics ? Previous studies suggest that cortical localization of LIT-1 requires it to stably interact with WRM-1/β-catenin [33] , [34] . In the sheath glia , however , we found that wrm-1 is not required for sensory compartment morphogenesis or for LIT-1 localization and that LIT-1 and WRM-1 do not co-localize to the amphid sensory compartment ( unpublished data ) . Instead , we found that LIT-1 physically interacts with actin and that actin is highly enriched around the amphid sensory compartment . Thus , actin might serve as a docking site for LIT-1 . The interaction between LIT-1 and actin may not be passive . Indeed , we showed that LIT-1 also binds to WASP , and mutations in wsp-1/WASP suppress daf-6 similarly to mutations in lit-1 . Furthermore , WASP activity is stimulated by phosphorylation of Serines 483 and 484 [46] , suggesting that LIT-1 , a Ser/Thr kinase , could activate WASP to promote actin remodeling . Remodeling of the cortical actin cytoskeleton plays important roles in several aspects of membrane dynamics [47] . For example , WASP-dependent actin polymerization has a well-established role in promoting vesicle assembly during clathrin-mediated endocytosis [48] . Recent work has demonstrated positive roles for actin polymerization in exocytosis as well [49] , [50] . In pancreatic acinar cells , secretory granules become coated with actin prior to membrane fusion [51] , and in neuroendocrine cells , actin polymerization driven by WASP stimulates secretion [52] . During Drosophila myoblast fusion , actin polymerization , dependent on WASP and WASP interacting protein ( WIP ) , is required for targeted exocytosis of prefusion vesicles [53] , and antibodies against WASP inhibit fusion of purified yeast vacuoles [54] . An attractive possibility , therefore , is that LIT-1 might regulate sensory compartment morphogenesis by altering vesicle trafficking through WASP-dependent actin polymerization . Glial ensheathment is a feature of many animal sensory organs and synapses , and LIT-1 and WASP are highly conserved , suggesting that our studies may be broadly relevant . Interestingly , LIT-1 was recently shown to be required for cell invasion through basement membranes in C . elegans and in metastatic carcinoma cells [55] , processes that require extensive remodeling of the actin cytoskeleton . Our results may , thus , represent a general mechanism for regulating cell shape changes using localized interactions of LIT-1/NLK with cytoskeletal proteins .
See Supporting Information . Animals were washed off NGM plates with M9 buffer , resuspended in a solution of 10 µg/mL of DiI ( 1 , 1′-dioctadecyl-3 , 3 , 3′ , 3′-tetramethylindocarbocyanine perchlorate ) ( Invitrogen D282 ) , and rotated in the dark for 1 . 5 h at room temperature . Animals were then transferred to a fresh NGM plate , anaesthetized with 20 mM sodium azide , and observed using a dissecting microscope equipped with epifluorescence . Animals in which none of the amphid neurons filled with dye were scored as dye-filling defective ( Dyf ) . For the sensitized dye-filling assay , 1 µg/mL of DiI was used , and the incubation time was 15 min . Animals were scored as dye filling defective ( Dyf ) if either one or two amphids failed to fill . See Supporting Information and [37] . Images were acquired using a DeltaVision Image Restoration Microscope ( Applied Precision ) equipped with a 60×/NA 1 . 42 Plan Apo N oil immersion objective ( Olympus ) and a Photometrics CoolSnap camera ( Roper Scientific ) , or an Upright Axioplan LSM 510 laser scanning confocal microscope ( Zeiss ) equipped with a C-Apochromat 40×/NA 1 . 2 objective . Acquisition , deconvolution , and analysis of images from the DeltaVision system were performed with Softworx ( Applied Precision ) ; images from the confocal microscope were acquired and analyzed using LSM 510 ( Zeiss ) . LexA::LIT-1Ct was used as bait in a Y2H screen using the DUALHhybrid kit ( Dualsystems Biotech ) in conjunction with the C . elegans Y2H cDNA library ( Dualsystems Biotech ) , as described by the manufacturer . For the growth assay , cultures growing on Synthetic Complete Dextrose –Tryptophan , –Leucine ( SCD –WL ) plates were resuspended in water to OD660 = 0 . 1 . 5 µL of each culture were seeded on SCD –WL plates and SCD –WL , –Histidine ( H ) plates + 1 mM 3AT ( 3-amino-1 , 2 , 4-triazole ) to select for HIS3 expression . β-galactosidase assay was performed using the yeast β-galactosidase assay kit ( Thermo Scientific ) . Drosophila S2 cells ( Invitrogen ) cultured at 25°C were transfected with FuGene HD ( Roche ) , incubated for 3 d , and lysed in 1 mL of IP buffer ( 60 mM Tris HCl , pH 8 . 0 , 1% Tergitol type NP-40 ( Sigma ) , 10% glycerol , 1×Complete protease inhibitor cocktail ( Roche ) , 1×PhoStop phosphatase inhibitor cocktail ( Roche ) ) . 100 µL of lysate was stored on ice as input . Immunoprecipitation was performed with the remaining lysate for 2 h at 4°C , using goat anti-myc-conjugated agarose beads ( Genetex ) . Immunoprecipitated complexes were released from the beads with 100 µL of sample buffer ( same as IP buffer with the addition of 2% sodium dodecylsulfate ( SDS ) , 0 . 1 M Dithiothreitol ( DTT ) , and 0 . 01% bromophenol blue ) . Samples were analyzed on NuPage 4%–12% Bis-Tris gels ( Invitrogen ) . Immunoblotting was performed using rat monoclonal anti-HA 3F10 coupled to horseradish peroxidase ( HRP ) ( Roche ) , 1∶2 , 000; rabbit polyclonal anti-myc ( AbCam ) , 1∶5 , 000; And goat polyclonal anti-rabbit ( Pierce ) coupled to HRP , 1∶2 , 000 . | The nervous system of most animals consists of two related cell types , neurons and glia . A striking property of glia is their ability to ensheath neuronal cells , which can help increase the efficiency of synaptic communication between neurons . Sensory neuron receptive endings in the periphery , as well as excitatory synapses in the central nervous system , often lie within specialized compartments formed by glial processes . Despite the prevalence of these compartments , and their importance for neuronal function and signal transmission , little is known about how they form . We have used the amphid , the main sensory organ of the worm Caenorhabditis elegans , to investigate glial sensory compartment morphogenesis . We demonstrate that the glia-expressed gene daf-6/Patched-related acts to restrict the size of the sensory compartment , while the Nemo-like kinase lit-1 acts within glia in the opposite direction , to promote sensory compartment expansion . We show that LIT-1 localizes to the sensory compartment through a highly conserved domain . This domain can interact both with actin , which outlines the compartment , and with the regulator of actin polymerization WASP , which acts in the same pathway as lit-1 . We postulate that Nemo-like kinases could have broader roles as regulators of cellular morphogenesis , in addition to their traditional role in regulating the Wnt signaling pathway . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"developmental",
"biology",
"developmental",
"neuroscience",
"biology",
"sensory",
"systems",
"morphogenesis",
"neuroscience"
] | 2011 | Opposing Activities of LIT-1/NLK and DAF-6/Patched-Related Direct Sensory Compartment Morphogenesis in C. elegans |
Replication Protein A ( RPA ) , the major single stranded DNA binding protein in eukaryotes , is composed of three subunits and is a fundamental player in DNA metabolism , participating in replication , transcription , repair , and the DNA damage response . In human pathogenic trypanosomatids , only limited studies have been performed on RPA-1 from Leishmania . Here , we performed in silico , in vitro and in vivo analysis of Trypanosoma cruzi RPA-1 and RPA-2 subunits . Although computational analysis suggests similarities in DNA binding and Ob-fold structures of RPA from T . cruzi compared with mammalian and fungi RPA , the predicted tridimensional structures of T . cruzi RPA-1 and RPA-2 indicated that these molecules present a more flexible tertiary structure , suggesting that T . cruzi RPA could be involved in additional responses . Here , we demonstrate experimentally that the T . cruzi RPA complex interacts with DNA via RPA-1 and is directly related to canonical functions , such as DNA replication and DNA damage response . Accordingly , a reduction of TcRPA-2 expression by generating heterozygous knockout cells impaired cell growth , slowing down S-phase progression . Moreover , heterozygous knockout cells presented a better efficiency in differentiation from epimastigote to metacyclic trypomastigote forms and metacyclic trypomastigote infection . Taken together , these findings indicate the involvement of TcRPA in the metacyclogenesis process and suggest that a delay in cell cycle progression could be linked with differentiation in T . cruzi .
Trypanosoma cruzi is the etiological agent of Chagas disease that infects 8 to 10 million people worldwide . Alternating between mammalian and insect hosts , the parasite faces changing environmental conditions , including thermal shifting , nutritional availability , and osmotic and oxidative stresses ( for review [1] ) . Based on its success to establish chronic infections , one can infer that T . cruzi possesses adaptive mechanisms to respond to environmental changes . A complex life cycle most likely compensates for the variations in extracellular conditions . T . cruzi has four developmental stages , differing in shape , metabolism , replicative and infective capacity . T . cruzi epimastigotes are a non-infective life cycle stage of the parasite that proliferate by binary fission in the guts of Triatoma infestans insects . These epimastigotes then transform into the infective , non-proliferative metacyclic trypomastigotes forms in the insect hindgut . When the insect vector bites a mammalian host , they eliminate the infective forms in their feces . This allows the parasites to penetrate the wounded skin and enter into the mammalian host’s circulatory system . Within the bloodstream , the metacyclic trypomastigotes infect mammalian cells and transform into replicative , spherically shaped amastigotes . Amastigotes proliferate inside the infected cells until they transform into non-replicative trypomastigotes . The life cycle is completed when an insect vector bites an infected mammalian host and takes up trypomastigotes within the blood that then transform into epimastigotes inside the insect gut ( [2] ) . Although it has been previously described that some stressors , such as acidic pH and starvation , trigger the transition from one form to another [3] , the molecular bases involved in this response remain to be elucidated , such as which molecules are sensors or transducers of these differentiation pathways . In other eukaryotes , cell cycle regulation may be a relevant mechanism in the transition from a proliferative to differentiation state of a cell . In vertebrates , inhibition of the cell cycle regulator cyclin dependent kinase ( CDK ) in neuroepithelial cells induces premature differentiation [4] . In the same way , inactivation of regulators of cell cycle , and the DNA metabolism-involved replication protein A ( RPA ) in Drosophila , is required for proper neuroepithelial differentiation into neuroblasts [5] . Therefore , we assessed whether impairment of cell proliferation through cell cycle alteration by RPA reduction could affect the T . cruzi differentiation from a replicative ( epimastigote ) to a non-replicative ( metacyclic trypomastigote ) stage . RPA is the major single-stranded binding protein from eukaryotes and is a fundamental player in DNA metabolism , participating in replication , transcription , and the DNA damage response [6][7][8][9] . RPA is a conserved heterotrimeric complex composed of subunits RPA-1 , RPA-2 and RPA-3 . One of the major structural features of RPA is the presence of the oligonucleotide/oligosaccharide binding folds ( OBF , also called DNA binding domains , DBD , in human RPA ) within the subunits . This OB fold structure consists of beta sheets that form beta-barrel structures that can wrap around ssDNA [10] . In mammals and yeast , RPA-1 is the main subunit responsible for RPA-DNA interaction [11][12] . The major role of RPA-2 is to regulate RPA activity in different DNA processes via its multiple phosphorylation sites . RPA-2 is phosphorylated on multiple N-terminal residues during the cell cycle by cyclin-dependent kinase 1 ( CDK1 ) / cyclin B [13] [14] and in response to DNA damage , when it is hyperphosphorylated by checkpoint kinases , including ATM ( ataxia telangiectasia mutated ) , ATR ( ATM and Rad3-related ) and DNA-PK ( DNA-dependent protein kinase ) [15] . RPA-3 is thought to stabilize the RPA heterotrimer but very little is known about this protein [16] . Unlike other eukaryotes , little is known about RPA in trypanosomatids , protozoa that appear early in the evolution of eukaryotes . In 1992 , Brown and collaborators purified the RPA complex from Crithidia fasciculata and demonstrated that RPA is a nuclear protein that can stimulate DNA polα but cannot replace human RPA in the SV40 DNA replication reaction [17] . In Leishmania amazonensis , LaRPA-1 has relevant structural differences compared with RPA-1 from humans and yeast [18] . In Leishmania major , LmRPA-1 accumulates in the DNA and co-localizes with an LmHus1 homologue after treatment with hydroxyurea ( HU ) and camptothecin , but the interaction of these two proteins has not been confirmed , so trypanosomatids RPA involvement in the repair/damage response remains obscure [19] . Despite this evidence , in Leishmania , only RPA-1 has been studied while RPA from T . cruzi and T . brucei have never been described . Here , we first cloned , expressed and purified RPA-1 and RPA-2 from T . cruzi ( respectively TcRPA-1 and TcRPA-2 ) and showed that this protein complex is indeed a single stranded DNA binding protein that interacts with DNA through the TcRPA-1 subunit . Then , we suggest that TcRPA participates in canonical functions in DNA metabolism , such as replication and repair pathways . Finally , we were able to show that the reduction of TcRPA-2 expression by heterozygous knockout generation slowed down S-phase and consequently cell proliferation . Moreover , while cell growth was impaired in TcRPA-2 heterozygous knockout cells , a better efficiency in differentiation from epimastigote to metacyclic trypomastigote forms and metacyclic trypomastigote infection was observed , suggesting that the delay of cell cycle progression is linked to differentiation in T . cruzi .
A flexible linker containing 21 residues that connect OBF2 and OBF3 on TcRPA-1 and its 20 C-terminal residues do not show homology with any protein DNA-binding structure . Therefore , we created separated models for OBF1/OBF2 of TcRPA-1 ( TcRPA-1-OBF12 model , corresponding to the 1–254 region ) and for OBF3 of TcRPA-1 ( TcRPA-1-OBF3 model , corresponding to the 272–445 region ) . Chain A of the crystal structure of DBD-A and DBD-B of RPA1 from Homo sapiens ( PDB ID 1JMC; [20] ) was selected as the best template for an initial in silico model for TcRPA-1-OBF12 ( score: 342 . 7; E-value: 3e-57; identity: 41% ) . Chain C ( Replication protein A 32 kDa subunit; RPA2 ) of the crystal structure of the human RPA trimerization core ( PDB ID 1L1O; [21] ) was selected for the initial in silico model of TcRPA-1-OBF3 ( score 292 . 4; E-value: 1 . 9e-51; identity: 33% ) . Regarding TcRPA-2 , the OB-fold domain corresponds to the 32–173 region of the protein , and chain B ( Replication protein A 32 kDa subunit ) of the crystal structure of the human RPA trimerization core ( PDB ID 1L1O;[21] ) was selected for the initial in silico model of this region ( called herein as TcRPA-2-OBF model ) ( score 265 . 83; E-value: 2 . 9e-42; identity: 35% ) . The best templates for the TcRPA-1 and TcRPA-2 models were chosen according to data obtained from the profile-based threading method program Phyre2 [22] . Subsequently , these initial models were subjected to molecular dynamics ( MD ) simulations executed by GROMACS ( Groningen Machine for Chemical Simulation ) v . 4 . 5 . 3 [23] in the presence of explicit water molecules . The protonation states of the charged groups were set according to pH 7 . 0 . Counter ions were added to neutralize the system , and the Charmm27 force field [24] was chosen to perform the MD simulations . First , 200 ps of MD simulation with position restraints applied to the protein ( PRMD ) was executed to relax the system gently . Then , 100 and 50 ns of unrestrained MD simulations were performed , respectively to the TcRPA-1 and TcRPA-2 models , to evaluate the stability of the structures . The final in silico models of TcRPA-1-OBF12 , TcRPA-1-OBF3 and TcRPA-2-OBF were deposited into the ModelArchive database [25] with the respective following access codes: ma-azgkp , ma-anz6r and ma-asqiv . Exponentially growing T . cruzi epimastigotes ( Y strain ) were cultured in liver infusion tryptose ( LIT ) medium supplemented with 10% fetal bovine serum at 28°C [26] . The heterozygous knockout lineage ( RPA2+/- ) was cultured in the same conditions as above in the presence of 0 . 5 mg/ml of hygromycin B . In order to induce DNA damage , epimastigote cells were treated in PBS with 75μM of cisplatin for 1 h at 28°C . Cells were then centrifugated and maintained in culture refresh medium for 72 h . T . cruzi metacyclic trypomastigote forms were obtained using an in vitro differentiation assay as previously described [27] . Briefly , epimastigotes ( 5 x 108 ) were harvested by centrifugation at 8 , 500 x g and incubated at 28°C for 2 h in 1 ml of artificial triatomine urine ( TAU , 190 mM NaCl , 17 mM KCl , 2 mM CaCl2 , 2 mM MgCl2 , 8 mM phosphate buffer pH 6 . 0 ) . Thereafter , the parasites were incubated in 25 cm2 culture flasks with 10 ml of TAU3AAG medium ( TAU supplemented with 10 mM L-proline , 50 mM L-glutamate , 2 mM L-aspartate , 10 mM glucose ) for 72 h . The metacyclic trypomastigotes were counted using a Neubauer chamber . To produce the recombinant proteins , TcRPA-1 and TcRPA-2 coding sequences ( accession number: TcCLB . 510901 . 60 and TcCLB . 510821 . 50 , respectively , http://tritrypdb . org/tritrypdb/ ) were amplified by PCR from T . cruzi genomic DNA and inserted into the pGEM-T easy vector ( Promega ) . To produce separately RPA-1 OBF1 , OBF2 , and OBF3 , specific primers were used to amplify each OBF accordingly to aligment of Fig 1 ( OBF1: GCTAGCTGCAACACCCGAGC and AAGCTTTTACGCCAAAGAAATCTGACTCG; OBF2: GCTAGCAAGCAGCGGGAGGTG and AAGCTTTTAAGAGAGCGAAGAAACATCGC; OBF3: GCTAGCTATTTTGACGACATTTCCGC and AAGCTTTTACTGGCGCTTTTCCTCG ) . The amplified three fragments were inserted into pGEM-T easy vector ( Promega ) . After , the TcRPA-1 and TcRPA-2 coding sequences were removed from pGEM-T easy and inserted into pET-28a ( + ) , with a 6XHis-tag to facilitate protein purification , and transformed into E . coli Bl21 ( DE3 ) . Protein expression was induced using 1 mM isopropyl thio-β-d-galactopyranoside ( IPTG ) at 37°C for an additional 3 h . The cells were harvested by centrifugation ( 3 , 200 x g , 10 min , 4°C ) and suspended in lysis buffer ( 50 mM Tris-HCl pH 8 . 0 , 50 mM NaCl , 10 mM EDTA pH 8 . 0 and 1X protease inhibitor cocktail ( Roche ) ) . Then , the cells were disrupted by sonication and incubated with 5U DNase I ( Thermo Fischer Scientific ) , followed by centrifugation ( 18 , 000 x g , 10 min , 4°C ) . Both recombinant proteins were found in the insoluble fraction . The rTcRPA-1 that accumulated in inclusion bodies was solubilized in a buffer containing 20 mM glycine pH 10 . 0 , 20 mM NaCl , 7 M urea and 1 mM β-mercaptoethanol . A pre-purification step before affinity chromatography was necessary to obtain rTcRPA-1 in a purified form [28] . To obtain purified rTcRPA-1 , the bacterial extract was first purified by anion exchange chromatography on a HiTrap column of Q-Sepharose XL . Proteins were eluted with a linear gradient of elution buffer . The elution fraction was then subjected to affinity chromatography . The samples were loaded into a Niquel Sepharose column ( Histrap HP- GE life Sciences ) previously equilibrated with starting buffer ( 20 mM Tris-HCl pH 7 . 0 , 0 . 5 M NaCl , 7 M urea , 71 μl/l β-mercaptoethanol ) . Recombinant proteins were eluted using a linear gradient of 25–500 mM imidazole in elution buffer ( 20 mM Tris-HCl pH 7 . 0 , 0 . 5 M NaCl , 7 M urea and 500 mM imidazole ) . The purified recombinant proteins were subjected to dialysis in renaturing buffer ( 20 mM Tris-HCl pH 7 . 0 and 20 mM NaCl ) at 4°C . Heparin ( 50 μg/ml ) was added to each protein suspension before dialysis to prevent precipitation ( see [28] for details ) . rTcRPA-1 was used to raise polyclonal antibodies as previously described [29] . rTcRPA-2 was submitted to customize specific antibodies ( Proteimax , São Paulo ) . Protein bands were excised from the SDS-polyacrylamide gel and subjected to in-gel trypsin digestion [30] and mass spectrometric analysis by LC-MS/MS . Peptide samples were automatically injected onto a trap column ( 5 cm , 100 μm I . D . x 360 μm O . D . ) packed in-house with Jupiter C-18 10 μm resin ( Phenomex , Torrence , CA , USA ) in tandem with a C-18 analytical column ( 10 cm , 75 μm I . D . x 360 μm O . D . ) packed in-house with Aqua 5 μm resin ( Phenomex , Torrence , CA , USA ) . Peptides were eluted with a linear gradient of 5% to 40% buffer B ( 0 . 1% formic acid in acetonitrile ) over 25 min at a flow rate of 200 nl/min controlled by a nano HPLC system Easy-nLC II ( Thermo , San Jose , CA , USA ) . The eluate was electrosprayed on a LTQ Orbitrap Velos ( Thermo , San Jose , CA , USA ) by an electrospray nano-flow interface with 2 . 0 kV on the capillary . For the MS , the spectrometer was operated in a positive mode , and spectra were acquired in the m/z range of 200–2 , 000 with 60 , 000 resolution at 400 m/z using data dependent acquisition ( DDA ) where the top 10 most intense ions per scan were fragmented by collision-induced dissociation ( CID ) . The minimal signal threshold to trigger a data-dependent scan was set to 5 , 000 cps . The repeat count for dynamic exclusion was set to 1 , and the repeat duration was set to 30 s . The dynamic exclusion duration was set to 15 s and a list size of 500 . MS data were analyzed using Mascot ( version 2 . 4 . 1 ) against the UniProt database restricted to Trypanosoma ( 102 , 903 entries , downloaded on February 13th , 2014 , with a peptide mass tolerance of 10 ppm and fragment mass tolerance of 0 . 5 Da ) . An iodoacetamide derivative of cysteine and the oxidation of methionine were specified in Mascot as fixed and variable modifications , respectively . CD measurements of rTcRPA-1 and rTcRPA-2 were obtained over the spectral ranges of 195–260 nm using a JASCO J-815 spectropolarimeter ( JASCO Spectroscopic Co . , Ltd . , Japan ) equipped with a Peltier thermo-controller . The experiments were performed at 293 K using an optical path length of 0 . 5 nm , a scanning speed of 100 nm/min , a response time of 1 s , a bandwidth of 2 nm and a data pitch of 0 . 5 nm . Twenty spectra were acquired , averaged and corrected for the buffer solution ( baseline ) in the presence and absence of single stranded DNA of 24 bp ( ssDNA24 ) and then normalized to the residual molar ellipticity [θ] . The CD spectra of both buffer and ssDNA24 had negligible signals on the concentrations tested . rTcRPA-1 and rTcRPA-2 were analyzed in buffer containing 20 mM Tris-HCl and 20 mM NaCl , and the effect of single stranded DNA binding on CD spectra was evaluated by the addition of 150 pmol of single stranded DNA of 24 bp to the protein samples . Deconvolution of the CD spectra was performed using the Dichroweb online server [31] with the CDSSTR algorithm and reference set 4 [32] . Single-stranded DNA oligonucleotides were labeled with DIG-11-ddUTP by terminal transferase using a Dig Gel Shift kit ( Roche ) . The gel shift assays were performed using 1 μg of TcRPA-1 or increased concentrations ( 1 , 2 and 4 μg ) of TcRPA-2 or of each truncated mutants , and fixed concentrations of 0 . 155 pmol of labeled oligonucleotide , 0 . 1 μg of poly-L-lysine , 1 μg poly[d ( I-C ) ] , 4 μl of 5X binding buffer ( 100 mM HEPES pH 6 . 0 , 5 mM EDTA , 50 mM ( NH4 ) 2SO4 , 5 mM DTT , Tween 20 1% ( w/v ) , 150 mM KCl ) in a final volume of 20 μl . The samples were maintained at room temperature for 15 min and then applied to a non-denaturing 6% gel ( acrylamide/bis-acrylamide 37 . 5:1 ) run at 80 V in 0 . 25 X TBE buffer . Samples were then transferred onto a nylon membrane at 400 mA for 30 min in 0 . 25 X TBE and were fixed by UV light for 15 min . Detection of labeled oligonucleotides was performed using the Dig Gel Shift kit ( Roche ) , according to the manufacturer's instructions . Exponentially growing epimastigotes were pulsed with 100 μM of EdU ( Click-iT Edu Image Kit , Invitrogen ) for 5 minutes for replication assays or treated with 20 mM of hydroxyurea or UV radiation for DNA damage assays . The cells were pelleted , washed with PBS and fixed with 4% ( v/v ) paraformaldehyde in PBS for 20 min at room temperature . The cells were permeabilized with 0 . 1% Triton X 100 for 5 min and washed three times with PBS . After , the cells were incubated with anti-LmRPA-1 [19] , anti-TcRPA-2 or anti-CPD ( Cosmo Bio ) primary rabbit antibodies for 1 h . Before incubation with anti-CPD , the cells were treated with 2 M HCl for 10 min at 37°C . The coverslips were rinsed three times with PBS and incubated with goat anti-rabbit IgG conjugated to Alexa Fluor 555 for 1 h . After washing the coverslips three times in PBS , the slides were mounted with VectaShield containing DAPI ( Vector ) . For the EdU assay , after permeabilization , the cells were first incubated with a Click-iT reaction cocktail for 30 min at room temperature . Images were acquired through a z-series of 0 . 2 μm using a 100X 1 . 35NA lens and Cell R software in an Olympus IX81 microscope . Images were deconvolved using Autoquant X2 . 1 . T . cruzi epimastigote forms ( 1 x 107 parasites/m1 ) in mid-log phase were harvested and processed to obtain a protein extract as previously described [33] . After , 100 μg of this protein extract was used as the input in IP assays , in conjunction with 10 μg of rabbit anti-TcRPA-2 . The IP assays were performed using Dynabeads Protein A ( Novex by Life Technologies ) , with a crosslinking step to avoid co-elution of the antibody heavy and light chains with TcRPA-1 or TcRPA-2 , according to the manufacturer’s instructions . At the end of the assay , 50% of each IP eluate and 10% of the input were fractionated by 12% SDS-PAGE and transferred to nitrocellulose membranes . The membranes were probed with rabbit anti-TcRPA-1 or anti-TcRPA-2 as primary antibodies . A goat anti-rabbit IgG ( H+L ) HRP conjugate ( Bio-Rad ) was used as a secondary antibody . The reactions were revealed using the ECL western blotting analysis system ( GE Healthcare ) . Flanking sequences of the TcRPA-2 gene were amplified from genomic DNA with a pair of primers: RPA2_KpnI + RPA2_SalI ( 5’ flank , 410 bp ) and RPA2_BamHI + RPA2_XbaI ( 3’ flank , 389 bp ) . The TcRPA-2 flanking fragments were inserted into pTc2KO-hyg , which carries the hygromycin resistance gene ( S1 Fig ) . The construction of this plasmid along with pTc2KO-neo ( with G418 resistance gene ) is described in the supplementary information . The 5’ flanking region was cloned between the KpnI and SalI sites whereas the 3’ flanking region was cloned between the BamHI and XbaI sites , resulting in the recombinant plasmid pTc2KO-RPA2-hyg . The targeting cassette was amplified from pTc2KO-RPA2-hyg with primers RPA2_KpnI and RPA2_XbaI and was used to transfect T . cruzi epimastigote forms as previously described [34] . Briefly , epimastigote forms at 2 x 107/ml were pelleted , washed in PBS and resuspended in an electroporation solution ( 140 mM NaCl , 25 mM HEPES and 0 , 74 mM Na2HPO4 pH 7 , 5 ) . Parasites were transferred into an electroporation cuvette ( 0 . 2 cm gap ) ( 1 . 5 x 108 cells in each one ) followed by addition of 25 μg of the amplicon . Parasites transfected with no DNA were used as a control . After 10 min on ice , the samples were submitted to 2 pulses of 450 V , 500 μF in the Gene Pulser II Apparatus ( Bio-Rad ) . Transfected parasites were cultured in LIT medium supplemented with hygromycin B ( 1 mg/ml ) with passages every 8–10 days , until the death of the control parasites . In order to obtain clones of this lineage , cells were sorted by Flow Citometry and then one cell/well were maintained in LIT medium to start a clone culture . The correct insertion of the targeting cassette into the TcRPA-2 locus was confirmed by PCR from genomic DNA of RPA2 heterozygous knockout parasites using the following primer pairs: EXT5’_f + Hyg_r and EXT3’_r + Hyg_f . The primers EXT5’_f and EXT3’_r are flanking and outside of the site of the recombination cassette . Primer EXT5’_f is located 165 bp upstream of the cassette whereas primer EXT3’_r is located 139 bp downstream of the cassette . LLC-MK2 cells ( Rhesus Monkey Kidney Epithelial Cells ) ( 2x104 cells/well ) were cultured in a 6-well culture plate and maintained in DMEM supplemented with 10% FBS . After two hours , 106 parasites , wild type or knockout , were added to each well . Twenty-four hours later , the medium was removed , and the cells were washed with PBS and fixed with 4% paraformaldehyde in PBS for 20 minutes , stained with eosin methylene blue for 30 minutes and dehydrated by acetone-xylene . The samples were mounted with Entellan and analyzed under a BX51 microscope ( Olympus ) to evaluate the number of infected cells in each group . A total of 100 cells were counted per replicate .
To confirm TcRPA as a single stranded binding protein in T . cruzi , we first analyzed amino acid sequence alignment between TcRPA-1 and TcRPA-2 from T . cruzi with RPA-1 and RPA-2 from other eukaryotes . An analysis of amino acid sequences of RPA-1 from Saccharomyces cereviseae , Ustilago maydis , Tribolium castaneum , Danio rerio , Homo sapiens , and the trypanosomatids Leishmania amazonensis , Trypanosoma brucei , and T . cruzi shows that , despite a few substitutions , the residues involved in ssDNA stacking in the OBF domains of H . sapiens ( [20][21] ) and U . maydis ( [35] ) crystal structures are conserved in the TcRPA-1 amino acid sequence ( Fig 1A—red boxes ) . A peculiar feature that could be observed in this amino acid sequence alignment is the increase in the size of the linkers between the OBF domains of trypanosomatids and fungi compared with metazoan . Trypanosomatid RPA-1 sequences contain an insertion of two residues on a linker between OBF1 and OBF2 ( linker 1 ) . TcRPA-1 , TbRPA-1 , LaRPA-1 , ScRPA-1 and UmRPA-1 contain an insertion of 7–8 residues between OBF2 and OBF3 ( linker 2 ) . The presence of these insertions can promote higher flexibility between OBF domains . Additionally , these insertions are rich in glycine and alanine residues , especially in linker 2 ( Fig 1A ) . These data suggest a higher mobility between OBF domains in RPA-1 from trypanosomatids and fungi compared with metazoan RPA-1 , which may have a more rigid tertiary structure . Finally , C-terminal region of RPA-1 trypanosomatids presents exclusive insertions that can contribute to the flexibility of this region on RPA-1 from these organisms . Regarding TcRPA-2 , an analysis of amino acid sequence alignment containing RPA-2 from H . sapiens , D . rerio , T . castaneum , Saccharomyces cerevisiae U . maydis , L . amazonensis , T . brucei and T . cruzi shows that amino acids involved in DNA stacking on DBD-D of RPA-2 ( according to the crystal structure of RPA-2 from U . maydis complexed to ssDNA ( [35] ) are also majorly conserved in trypanosomatids amino acid sequences ( Fig 1B—red boxes ) . Similar to RPA-1 , T . cruzi and L . amazonensis RPA-2 present the insertion of flexible residues also enriched in glycines and alanines ( Fig 1B , blue box ) ; however , this insertion was found not in the linkers but inside the OBF domain . We then produced recombinants rTcRPA-1 and rTcRPA-2 ( S2 and S3 Figs ) . The circular dichroism spectra of rTcRPA-1 and rTcRPA-2 showed a minimal number of values that varied from approximately 206–208 nm , indicating the presence of alpha-helices and a significant percentage of loops and disordered elements in both protein structures ( Fig 2A and 2B ) . Similar CD results were obtained from full-length LaRPA-1 and truncated mutants of this protein ( [28][18] ) . Deconvolution of the CD spectra showed 14% alpha-helices , 34% beta-sheets and 52% loops/disordered elements for rTcRPA-1 and 15% alpha-helices , 36% beta sheets and 49% loops/disordered elements for rTcRPA-2 . These numbers are quite similar to those predicted for in silico models of TcRPA-1 and TcRPA-2 , suggesting that these recombinant proteins assume the correct structure after refolding . To evaluate the impact of DNA binding on rTcRPA-1 and rTcRPA-2 secondary structures , CD spectra of both proteins were obtained in the presence of single-stranded DNA . The ssDNA increased the secondary structure of rTcRPA-1 because its CD spectra in the presence of ssDNA show an enhancement of the signal from 210 to 230 nm ( Fig 2A and S4 Fig ) . Deconvolution of CD spectra of rTcRPA-1 with ssDNA24 shows a composition of 13% alpha-helices , 39% beta-sheets and 48% loops/disordered elements . In comparison with rTcRPA-1 spectra without ssDNA , the spectra in the presence of this DNA showed a decrease in disordered elements ( 52–48% ) and an increase in beta-sheet secondary structure ( 34–39% ) . Regarding the CD spectra of rTcRPA-2 , ssDNA did not cause any significative modification of the CD spectra of this protein because the CD spectra of rTcRPA-2 in the presence and absence of ssDNA24 were very similar ( Fig 2B ) . These results suggested that only rTcRPA-1 is able to bind ssDNA . To confirm TcRPA-1-DNA interaction , rTcRPA-1 and rTcRPA-2 with ssDNA were analyzed by EMSA assay . Indeed , only rTcRPA-1 was able to form a complex with ssDNA ( Fig 2C and 2D ) . It is important to note , however , that CD and EMSA assays ( Fig 2 ) were performed using refolded recombinant proteins , which can contain structural variations compared with native ones . To further assess the ability of RPA to bind single stranded DNA , we performed molecular modeling of TcRPA-1 and TcRPA-2 OBF domains . These models were performed first for OBF1/OBF2 of TcRPA-1 ( TcRPA-1-OBF12 model , corresponding to the 1–254 region ) and for OBF3 of TcRPA-1 ( TcRPA-1-OBF3 model , corresponding to the 272–446 region ) using Phyre2 software [22] . After , we performed molecular dynamics simulations ( MD ) of these initial models obtained by Phyre2 . The final in silico models of TcRPA-1-OBF12 and TcRPA1-OBF3 were obtained after 100 ns of MD where 96 . 4% and 96 . 0% , respectively , of their residues were in favored and allowed regions of the Ramachandran plot [36] . Both models showed an overall good quality , as evaluated by ProSA-web [37] ( Z-score = -6 . 02 and -5 . 24 , respectively ) . The TcRPA-1-OBF12 in silico model has a similar tertiary structure compared to the DBD-A and DBD-B crystal structures from H . sapiens and U . maydis , especially for DBD-A ( S5A and S5B Fig ) . Both crystal structures present an ssDNA binding channel that is extended in approximately a straight line from DBD-A to DBD-B [20][35] . This ssDNA binding channel is also observed in the TcRPA-1-OBF12 in silico model ( S5C Fig ) , suggesting that ssDNA can bind to OBF1 and OBF2 of TcRPA-1 in a similar way in higher eukaryotes . To investigate this hypothesis , we have generated truncated RPA-1 containing just OBF1 or OBF2 . However , we could observe only interaction of OBF1 with ssDNA by EMSA assay ( Fig 2E and 2F ) . Regarding the TcRPA-1-OBF3 in silico model , the conserved amino acids involved in ssDNA binding are buried inside the protein , most likely blocking ssDNA access to the TcRPA-1 OBF3 binding site ( S5D–S5G Fig ) . Therefore , TcRPA-1 does not seem to be able to bind DNA on OBF3 , in contrast to higher eukaryotes . In fact , recombinant OBF3 was not able to interact with ssDNA in EMSA assay ( Fig 2E and 2F ) . The crystal structure of the RPA complex with ssDNA of U . maydis showed that some regions of the linker between OBF2 and OBF3 of RPA1 is essential for the 8-to-30 nt DNA binding mode transition of RPA , which also includes RPA2 [35] . In this context , the 30-nt DNA binding mode seems to be unlikely to occur on trypanosomatids RPA , due to its inability to bind DNA from OBF3 domains on RPA1 and the unstable structure of the DNA binding site of RPA2 ( see next section ) of these organisms . Thus , the DNA binding mode of the RPA complex in trypanosomatids may involve fewer nucleotides and can be processed in a different way in relation to upper eukaryotes . The Phyre2 software [22] predicted with 100% confidence a OBF domain in the 32–173 region and with 99 . 6% confidence as a winged-helix-loop-helix ( wHLH ) domain in the 205–252 region of TcRPA-2 ( see Fig 1B ) while we found that TcRPA-2 is not able to bind DNA . Therefore , we performed molecular modeling of the OBF domain of TcRPA-2 ( TcRPA-2-OBF model , corresponding to the 32–173 region ) using Phyre2 . After , we performed molecular dynamics simulations ( MD ) of this initial model obtained by Phyre2 . The final in silico model of TcRPA-2-OBF was obtained after 50 ns of MD where 99 . 3% of their residues are in favored and allowed regions of the Ramachandran plot [36] . The TcRPA-2-OBF model also demonstrated an overall good quality , as evaluated by ProSA-web [37] ( Z-score = -5 . 03 ) . The final in silico model of TcRPA-2-OBF adopts a very similar tertiary structure conformation compared to the crystal structures of RPA2 from H . sapiens and U . maydis , showing a similar DNA binding channel with these two proteins ( S6A Fig ) . Amino acid sequence alignment shows that TcRPA-2 has a ten residue insertion rich in flexible residues ( Fig 1B , blue box ) . Mapping the structural location of this insertion in the TcRPA-2-OBF in silico model shows that this insertion is located close to the DNA binding channel ( S6B and S6C Fig ) . In fact , during molecular dynamics ( MD ) simulations , this region presented a high root mean square fluctuation ( r . m . s . f . ) of the main chain , adopting multiple positions during 50 ns of MD simulation , often even blocking the DNA binding channel ( S6D Fig ) . These data suggest that the DNA binding site of trypanosomatid RPA-2 is more structurally unstable than upper eukaryotes , which can promote changes in the DNA binding affinity of RPA-2 from these organisms . Because the DBD-C is involved in the RPA trimerization core in H . sapiens and U . maydis crystal structures , the structural differences observed on OBF-3 of TcRPA-1 raised questions about the formation of an RPA complex in T . cruzi [21] [35] . We performed immunoprecipitation with anti-TcRPA-2 and identified TcRPA-1 ( Fig 3A ) , suggesting the presence of an RPA complex in these cells as found in other eukaryotes . As a single stranded binding protein complex , RPA affects DNA metabolism functions , such as DNA replication and DNA repair . To demonstrate these RPA functions in T . cruzi , we performed three distinct experiments . In the first experiment , cells were pulsed with the thymidine analogue EdU for a short period ( 5 min ) to mark DNA replication factories , and the localization of TcRPA was evaluated . We found that RPA ( detected with both anti-rTcRPA-1 and rTcRPA-2 ) co-localized with replication sites in nuclei , but not in kinetoplast ( Fig 3B ) , most likely stabilizing single stranded DNA during nuclear DNA duplication . Next , we induced replicative stress by HU treatment and analyzed TcRPA-1 and TcRPA-2 nuclear localization by immunofluorescence assays . After HU treatment , the cells were synchronized at the G1/S transition [38] due to inhibition of DNA replication by the depletion of dNTP pools . We found that 40% of cells presented TcRPA-1 and TcRPA-2 in a 2-3-foci pattern and 60% of cells presented TcRPA-1 and TcRPA-2 in a multi-foci pattern contrasting with a dispersed nuclear localization observed in control cells ( Fig 4A ) . Finally , we induced DNA damage through UV irradiation , and immunofluorescence with anti-CPD was used to demonstrate the presence of pyrimidine dimers induced by UV ( Fig 4B ) . TcRPA-1 and TcRPA-2 were found in a punctuated pattern constrained at the nuclear periphery in UV irradiated cells while control cells presented TcRPA dispersed throughout the nuclear space ( Fig 4B ) . In fact , in eukaryotes RPA localization at intra-nuclear foci is observed in response to DNA damage/replication stress , co-localizing with several recombination repair and checkpoint proteins [39] [40] [41] . Therefore , our data strongly suggest the involvement of TcRPA in DNA damage repair . Taken together , these data suggest that RPA has canonical functions in DNA metabolism in T . cruzi . To perturb cell proliferation by DNA metabolic impairment , TcRPA-2 levels were reduced by heterozygous knockout cell generation . The replacement of an RPA-2 allele by insertion of the hygromycin selectable marker was confirmed using specific primers located inside or outside of the recombination cassette ( Fig 5A and 5B ) . Using an anti-rTcRPA-2 antibody , we demonstrated that heterozygous knockout reduced the level of TcRPA-2 expression to 66 . 4% ± 3 . 2 ( Fig 5C and 5D ) . We have also added efforts trying to generate RPA-2 null mutants . However , this mutant was not viable , evidencing that RPA is a fundamental protein for parasite survival . The reduction of TcRPA-2 expression impairs cell growth ( Fig 6A ) . Additionally , the percentage of cells labeled by EdU incorporation is higher in RPA-2 heterozygous knockout cells ( Fig 6B ) , suggesting that S phase is longer when the TcRPA-2 level is reduced . The increment of S phase duration was confirmed by cell cycle analysis ( Fig 6C ) . These data strongly suggest that lacking the TcRPA-2 subunit of the RPA complex slows down DNA replication , strengthening the role of TcRPA in this process . Moreover , a reduction of the cell growth of TcRPA-2 heterozygous knockout cells might be at least in part a consequence of a delay in DNA duplication . We took advantage of the generation of TcRPA2 heterozygous knockout parasites to better investigate the involvement of RPA in DNA damage response in T . cruzi . We used cisplatin to generate monoadducts as well as intra- and inter-strand crosslinks in DNA ( [42] ) . After treatment , we followed the growth culture of wild type and TcRPA2 heterozygous knockout cells . In fact , TcRPA2 heterozygous knockout were not able to recover cell growth even after 72 h of treatment , while growth of wild type cells was observed 24 h after treatment ( Fig 6D ) . This result adds evidence to the hypothesis suggested above that RPA is indeed an important player in DNA damage response . Finally , taking into account that our data show that the reduction in TcRPA-2 level impairs T . cruzi proliferation , we assessed whether TcRPA-2 heterozygous knockout also induced changes in T . cruzi differentiation . First , TcRPA-2 heterozygous knockout and wild type epimastigote forms were submitted to the in vitro metacyclogenesis process . Unlike the effect on T . cruzi proliferation , the reduction of the levels of TcRPA-2 in the heterozygous knockout parasites increased T . cruzi differentiation . In fact , three times more metacyclic trypomastigotes were obtained in heterozygous knockout cells when compared to wild type ( Fig 7A ) . We also generated five different clones and analyzed the level of expression of RPA-2 . All clones expressed the same amount of protein , and three analyzed also demonstrated quite similar growth and ability to differentiate onto metacyclic ( S7 Fig ) . These data evidence that hemi-knockout lineage RPA-2+/- is homogenous . In addition , the percentage of LLC-MK2 cells infected by metacyclic trypomastigote forms from heterozygous knockout parasites ( 37% ) was higher than that from wild type parasites ( 21% ) ( Fig 7B ) . Taken together these data showed that the reduction of TcRPA-2 expression increases the capacity of epimastigote differentiation and metacyclic trypomastigote infection .
In this manuscript , we performed in silico , in vitro and in vivo analysis of T . cruzi RPA-1 and RPA-2 subunits . We could observe , by alignment analysis , that both TcRPA-1 and TcRPA-2 indeed present OB-fold domains , including residues that are important for RPA-DNA interaction in mammalian and fungi RPA [20][35] . However , in vitro analysis demonstrated that TcRPA-1 can interact with single stranded DNA while TcRPA-2 cannot , although we can not exclude the possibility that the RPA-2 tridimensional structure obtained after refolding of insoluble protein could be the cause of lacking of RPA-2-ssDNA interaction . According to our molecular model of the predicted tridimensional structure of TcRPA-1 , OBF1 and OBF2 are the major OB-folds responsible for RPA-1-DNA interactions , forming an open binding channel between these two OB-fold domains . In vitro assay showed interaction only between ssDNA and OBF1 . It is important to note , however , that OBF2 may need OBF1 to increase its affinity for ssDNA as occurs in mammalian cells ( [8] ) . In contrast , OBF3 of TcRPA-1 seems to be unable to interact with DNA because the residues involved in DNA binding are buried within the protein . These results differ from the crystallographic structure of hRPA-1 , where the residues of DBD-C are exposed to solvent and hRPA can interact with DNA via multiple binding modes , which can include DBD-C and also DBD-D ( present in RPA-2 ) in some binding modes [8] . Our molecular model of TcRPA-2 suggests that this protein does not have the capacity to interact with DNA due to an exclusive insertion , present in trypanosomatids RPA-2 inside OBF/DBD-D , which is unstable and adopts multiple positions that very often occlude the DNA binding channel during MD simulations . In conclusion , we suggest that T . cruzi RPA interacts with DNA only through TcRPA-1 . Regarding the third subunit of RPA , we performed a thorough and low stringent in silico search in the local files of annotated proteins and we have found a bona fide candidate for T . cruzi RPA-3 ( TcCLB . 507011 . 150 ) . However , further investigation is necessary in order to demonstrate that it is indeed part of RPA complex . Alignment of TcRPA-1 with fungi and mammalian sequences showed that TcRPA-1 lacks a 70-N domain ( DBD-F ) , important for interaction with proteins involved in DNA damage and checkpoint responses [12] . The lack of a 70-N domain appears to be a common characteristic of trypanosomatids RPA-1 because it has been previously observed in Leishmania [18] and Chrithidia [17] . Despite its absence , we demonstrated here that the pattern of TcRPA localization changes in response to DNA damage induced by UV irradiation . Moreover , TcRPA localization at the nuclear periphery after UV irradiation matches with DNA damage localization at the nuclear periphery observed using an anti-CPD antibody . In addition , the constriction of repair machinery , using LmHus1 as a marker , at the nuclear periphery after genotoxic treatment was previously shown in Leishmania [19] . Moreover , treatment of TcRPA-2 heterozygous knockout parasites with genotoxic compound showed that RPA is indeed involved in DNA damage response , even lacking the 70-N domain . We also demonstrated that TcRPA is involved in DNA replication and the replicative stress response , with canonical functions previously demonstrated in other organisms [6][43][15] [21][44] . In accordance with its involvement in DNA replication , the reduction of TcRPA-2 expression levels via heterozygous knockout cells slowed down S-phase progression and cell growth . TcRPA is also involved in metacyclogenesis in T . cruzi because heterozygous knockout epimastigote cells presented a 3 . 0-fold increased ability to differentiate onto metacyclic trypomastigotes . As cited above , the effect of RPA reduction on differentiation was demonstrated before in Drosophila neuroepithelial differentiation into neuroblasts , where the authors suggest that a delay of cell cycle progression is causally linked with differentiation [5] . Other non-canonical functions of RPA were previously described in Leishmania braziliensis , where RPA-1 is able to bind the untranslated region of HSP70 mRNA , suggesting that RPA from trypanosomatids can also be an RNA-binding protein [45] . Moreover , in silico analysis presented here showed that TcRPA-1 has more flexible linkers that connect OB-fold domains compared to yeast and mammalian RPAs . Because it was recently suggested that the size and flexibility of these linkers are directly involved in RPA function [46] , it is possible that trypanosomatids RPA is indeed involved in additional responses , such as the ones discussed above . Further investigations are necessary to understand how TcRPA could be involved in the differentiation process and also to analyze whether TcRPA participates directly in the metacyclogenesis process or whether the delay of cell cycle progression triggered by TcRPA-2 reduction increases T . cruzi differentiation . It is important to note , however , that there is no obligatory coupling between cellular decisions to proliferate and to differentiate , even though the two behaviors are often controlled at the same time [47] . Because metacyclogenesis in T . cruzi is triggered by a low pH , a shift in temperature and nutrient restriction , it has been proposed that the altered redox state could be a metabolic integrator in cells under multiple stress conditions , triggering or regulating the differentiation process [1] . Therefore , we cannot exclude the possibility that RPA could be one of the molecules participating in the process of identify alteration in the redox state . In fact , the alteration of the redox state can regulate human RPA activity [48] . Our knowledge of the molecules involved in the detection of environmental changes and transduction of these changes triggering T cruzi differentiation is limited . For this reason , increasing the knowledge in the field of the T . cruzi differentiation process , including RPA in this scenario , will be important to provide valuable information regarding new therapeutic targets . | Trypanosoma cruzi is the etiological agent of Chagas disease . During its life cycle , this parasite alternates between proliferative/non-infective forms and forms that are infective but not able to proliferate . Some stressors , such as acidic pH and starvation , trigger the transition from one form to another; however , many molecules involved in this response remain to be identified . Replication Protein A ( RPA ) is a single stranded DNA binding protein involved in many functions of DNA metabolism , such as DNA replication and repair . Although RPA is well characterized in yeast and mammalian cells , nothing is known about this heterotrimer in T . cruzi . Here , we demonstrated that T . cruzi RPA is involved in canonical functions of DNA metabolism . Accordingly , when we reduced the expression levels of subunit 2 of TcRPA ( TcRPA-2 ) the growth of the replicative form of T . cruzi was compromised . Moreover , we observed that the impairment of cell growth is linked with the differentiation process because the reduction of the level of TcRPA-2 increased the capacity of the proliferative epimastigote form to differentiate into an infective metacyclic trypomastigote one . In conclusion , TcRPA has canonical functions in the ancient eukaryote T . cruzi and is also involved in the control of life cycle progression . | [
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... | 2016 | Replication Protein A Presents Canonical Functions and Is Also Involved in the Differentiation Capacity of Trypanosoma cruzi |
Cutaneous beta-papillomaviruses are associated with non-melanoma skin cancers that arise in patients who suffer from a rare genetic disorder , Epidermodysplasia verruciformis ( EV ) or after immunosuppression following organ transplantation . Recent studies have shown that the E6 proteins of the cancer associated beta human papillomavirus ( HPV ) 5 and HPV8 inhibit NOTCH and TGF-β signaling . However , it is unclear whether disruption of these pathways may contribute to cutaneous HPV pathogenesis and carcinogenesis . A recently identified papillomavirus , MmuPV1 , infects laboratory mouse strains and causes cutaneous skin warts that can progress to squamous cell carcinoma . To determine whether MmuPV1 may be an appropriate model to mechanistically dissect the molecular contributions of cutaneous HPV infections to skin carcinogenesis , we investigated whether MmuPV1 E6 shares biological and biochemical activities with HPV8 E6 . We report that the HPV8 and MmuPV1 E6 proteins share the ability to bind to the MAML1 and SMAD2/SMAD3 transcriptional cofactors of NOTCH and TGF-beta signaling , respectively . Moreover , we demonstrate that these cutaneous papillomavirus E6 proteins inhibit these two tumor suppressor pathways and that this ability is linked to delayed differentiation and sustained proliferation of differentiating keratinocytes . Furthermore , we demonstrate that the ability of MmuPV1 E6 to bind MAML1 is necessary for papilloma formation in experimentally infected mice . Our results , therefore , suggest that experimental MmuPV1 infection in mice will be a robust and useful experimental system to model key aspects of cutaneous HPV infection , pathogenesis and carcinogenesis .
Papillomaviruses ( PVs ) represent a large , diverse group of DNA viruses that infect squamous epithelia of many animals . Genetically , these viruses are grouped into genera based on diversity of their major capsid protein . Biologically , PVs can be stratified according to the type of epithelium that they can productively infect , either mucosal or cutaneous tissue . Infections with cutaneous human PVs ( HPVs ) is associated with a wide range of pathologies from asymptomatic infection to benign warts , actinic keratosis to squamous cell carcinomas [1] . Two HPV types in particular , beta-HPV5 and beta-HPV8 , were found to be associated with lesions and tumors in patients suffering from a rare hereditary disease , epidermodysplasia verruciformis ( EV ) [2] . A majority of these patients harbor genetic mutations in TMC6 ( EVER1 ) or TMC8 ( EVER2 ) genes on chromosome 17 , which encode putative transmembrane channel proteins that may be involved in cellular zinc and calcium homeostasis [3 , 4] . HPV-associated warts in EV patients have a high risk for progression to squamous cell carcinomas ( SCC ) , and these tumors are positive for viral DNA [5] . Further studies provided evidence that SCCs that arise in immunosuppressed individuals such as organ transplant patients are also associated with cutaneous HPV infections [6 , 7] . HPV-associated SCCs often arise in sun-exposed areas of the skin , implicating UV exposure as a key risk factor for malignant progression [8] . However , beta-HPV sequences are not maintained in every cancer cell . The association of cutaneous HPV infections with SCC in immunocompetent patients is less clear . One study showed a positive serological connection between HPV8 and SCC [9] , but other studies have not shown a link [10] . This has led to a gradated model of beta-HPV association with human skin cancers . Cancer development is highly associated in the case of EV patients , correlated in the case of immune suppression , and only slightly or sporadically associated in immune competent patients . However , in transgenic mouse models skin restricted expression of the beta-HPV oncogenes E6 and E7 are capable of tumorigenesis suggesting that oncogene expression may play an important role [11–13] . In order to assess HPV contributions to skin carcinogenesis , it is important to define the effects of cutaneous HPVs on host cell pathways . UV exposure is an important risk factor for skin cancer , and several reports suggest that cells expressing cutaneous HPV E6 proteins can tolerate or survive UV exposure and UV-induced DNA damage better than normal cells . It has been shown that HPV5 E6 can inhibit apoptotic cell death in response to UV through degradation of the pro-apoptotic BCL2 family member , BAK1 [14 , 15] . Moreover , activation of the ATM/ATR kinases that play an important role in UV induced DNA damage signaling is also inhibited in cutaneous HPV E6 expressing keratinocytes , and this has been linked to E6 mediated EP300/CREBBP degradation [16 , 17] . Beyond modulation of the cellular UV response , cutaneous HPVs have additional oncogenic activities , and our group along with several others discovered that HPV8 E6 can bind to MAML1 and inhibit NOTCH signaling [18–20] . NOTCH is an important driver of keratinocyte differentiation , and defects in the pathway are highly associated with cutaneous SCCs [21 , 22] . Additionally , HPV5 E6 was shown to interact with SMAD3 causing its destabilization and subsequently inhibiting TGF-β signaling [23] . SMAD2 and SMAD3 are TGF-β receptor associated factors that are activated by phosphorylation upon TGF-β1 ligand binding and translocate to the nucleus . They form complexes with non-receptor associated SMADs and transcriptional co-activators to bind and activate expression of TGF-β target genes . TGF-β also plays an important , albeit complicated role in skin carcinogenesis , as it has both tumor suppressive and tumor promoting activities; loss of cytostatic TGF-β signaling is important during the early phase of carcinogenic progression , whereas TGF-β signaling may drive late stage carcinogenic events including invasion and metastasis through activation of the epithelial to mesenchymal transition ( EMT ) [24 , 25] . In addition to understanding the effects of beta-HPV oncogenes on host pathways , it would be beneficial to have a robust model system to study viral pathogenesis and oncogenesis . One principal hurdle in investigating HPV pathogenesis and oncogenesis has been the difficulty to study these viruses in appropriate experimental model systems . The exquisite host specificity of papillomaviruses has precluded experimental infections of heterologous hosts and the viral life cycle cannot be fully studied in conventional tissue culture experiments . Organotypic “raft” culture systems have been developed to study aspects of the productive life cycle of high-risk mucosotropic HPVs [26] , but cutaneous HPVs such as HPV5 and HPV8 have not been studied in this system . Moreover , raft cultures do not faithfully recapitulate the steady state physiology of the skin , and thus they cannot be used to examine long-term persistent HPV infections . To circumvent some of these issues , researchers has resorted to growing HPV genome expressing human keratinocytes in implantation chambers on the backs of immunodeficient mice [27] or culturing them in the renal capsule [28] . The recent isolation of a mouse PV , MmuPV1 , from warts developed in a colony of NMRI-Foxn1nu/Foxn1nu ( nude ) mice [29] has provided an important breakthrough and now allows PV pathogenesis studies in a genetically tractable animal model . Infectious MmuPV1 quasivirions that are synthesized in vitro can be used for experimental infections that result in warts [30] . Alternatively , circularized viral genomes directly applied to scarified skin regions also lead to wart formation [31] . Recent reports have shown that lesions arising due to MmuPV1 infection have malignant potential and in some cases , can progress to SCCs [31] . Moreover , experimental MmuPV1 infection causes papillomas associated with SCC in UVB-irradiated immunocompetent strains of mice . Importantly , these studies showed that MmuPV1 mediated tumor formation was a consequence of UV-induced immunosuppression [32] . Given these biological similarities to cutaneous HPVs , there is an exciting probability that MmuPV1 is a biologically relevant animal papillomavirus model that will be useful in determining whether and how cutaneous HPVs mechanistically contribute to skin carcinogenesis . Therefore , we hypothesized that MmuPV1 gene products must share biochemical properties and biological activities with those of cutaneous HPV gene products . The goal of this study was to identify host cell signal transduction pathways targeted by the MmuPV1 E6 protein , to compare and contrast them with those targeted by HPV8 E6 , to determine their effects on proliferation and survival of terminally differentiated keratinocytes , and to assess their role in wart formation in mouse infection models .
MmuPV1 infections cause skin warts [29–31] that can progress to cancers in conjunction with UV irradiation [32] . To determine whether MmuPV1 may be used to model pathogenesis and carcinogenesis of human cutaneous HPVs , we investigated whether it targets similar cellular signaling pathways as cutaneous HPV5 and 8 . We focused on the MmuPV1 E6 protein , because there are marked differences between the protein interactomes of mucosal versus cutaneous HPV E6 proteins [33 , 34] . Mucosal HPV16 E6 proteins interact with the LXXLL ( L , leucine; X any amino acid ) domain containing ubiquitin ligase UBE3A , the TP53 tumor suppressor , and cellular proteins containing a PDZ ( post synaptic density protein-PSD95 , Drosophila disc large tumor suppressor-Dlg1 , and zonula occludens-1 protein-zo-1 ) domain [33 , 35 , 36] . In contrast , cutaneous HPV5 and HPV8 E6 proteins interact with the LXXLL domain protein , MAML1 , as well as SMAD3 , which lacks an LXXLL motif , and they do not bind to PDZ domain proteins because these E6 proteins lack the appropriate C-terminal binding site [19 , 20 , 23 , 35 , 37] . To determine whether MmuPV1 E6 interacts with cutaneous HPV5 and HPV8 specific cellular interactors , we infected normal human oral keratinocytes ( NOKs ) with lentiviral vectors expressing MmuPV1 FLAG/HA-E6 , HPV8 FLAG/HA-E6 as a positive control , or GFP as a negative control . Lysates from cell populations with stable expression of the corresponding epitope tagged E6 proteins or GFP were then subject to HA immunoprecipitation followed by immunoblot . These experiments show that similar what we previously observed with HPV8 in immortalized foreskin keratinocytes , HPV8 and the MmuPV1 E6 protein interact with MAML1 as well as with intracellular cleaved NOTCH1 ( ICN1 ) ( Fig 1A ) . Like HPV8 E6 , MmuPV1 E6 binds SMAD2 and SMAD3 . MmuPV1 E6 preferentially interacts with SMAD2 whereas HPV8 E6 preferentially interacts with SMAD3 . Unlike what had been observed with HPV5 E6[23] , we did not observe any differences in steady state levels of SMAD2 or SMAD3 in HPV8 E6 or MmuPV1 E6 expressing cells . MmuPV1 E6 , unlike HPV8 E6 , does not detectably interact with EP300/CREBBP . These results suggest that MmuPV1 E6 does not modulate EP300 activities as has been reported for HPV8 E6 [17 , 38 , 39] but that MmuPV1 and HPV8 E6 share the capacity to associate with components of the NOTCH and TGF-β tumor suppressor pathways . It was previously shown that HPV8 can inhibit NOTCH signaling [18–20 , 35] and the highly-related HPV5 E6 protein was shown to inhibit TGF-β signaling [23] . When active , both of these pathways are known tumor suppressors in the skin [22 , 25] . Hence , we determined whether MmuPV1 E6 inhibited these two tumor suppressor pathways . To assess the effect of HPV8 and MmuPV1 E6 on TGF-β signaling , we performed luciferase assays in U2OS cells using a SMAD responsive luciferase reporter that can monitor transcriptional activity of SMAD2 and SMAD3 after TGF-β stimulation . HPV8 E6 and MmuPV1 E6 expression vectors or empty vector were cotransfected with a SMAD3 expression plasmid . Signaling was activated by adding exogenous TGF-β1 , co-transfection of a vector expressing a constitutively active mutant of the TGF-βreceptor 1 ( TGFBR1-T204D ) , or both . Both TGF-β1 treatment and expression of TGFBR1-T204D led to greater than 20-fold increases ( 23 . 6±2 . 7 , 20 . 6±1 . 9 , respectively ) in reporter activity as compared to control transfected cells ( Fig 1B ) . Co-transfection of HPV8 or MmuPV1 E6 expression plasmids significantly inhibited the ability to activate this response . To rule out any effects of epitope tags , N-terminally-tagged and untagged constructs of HPV8 E6 and MmuPV1 E6 were assessed side by side and no difference was observed in their ability to inhibit NOTCH and TGF-β reporter induction ( S1 Fig ) . Additionally , we repeated these reporter experiments in TERT immortalized human foreskin keratinocytes ( iHFKs ) and we obtained similar results as previous obtained with U2OS cells ( S2A Fig ) . These results indicate that although we do not observe destabilization of SMAD2 or SMAD3 ( Fig 1A ) , we do observe inhibition of TGF-β activity by HPV8 E6 and show that cutaneous MmuPV1 E6 shares this ability . To investigate inhibition of NOTCH signaling we co-transfected a NOTCH responsive luciferase reporter with an expression plasmid encoding the active , cleaved NOTCH fragment ( ICN1 ) and HPV8 E6 , MmuPV1 E6 expression vectors , or empty vector into U2OS cells . ICN1 transfection in combination with empty vector yielded a 69 . 4 ( ±2 . 3 ) -fold increase in NOTCH reporter activity compared to cells transfected with the reporter alone ( Fig 1C ) . Co-transfection of HPV8 E6 inhibited ICN induced reporter activity ( 10 . 1±0 . 4 fold ) . Co-transfection of MmuPV1 E6 similarly inhibited ICN induced reporter activation ( 10 . 15±0 . 3 fold ) . Similar to the TGF-beta reporter studies , we tested the effect of HPV8 E6 and MmuPV1 E6 on NOTCH inhibition in iHFKs . Consistent with what we observed with U2OS cells , ICN1 transfection alone resulted in an increase in reporter activity ( 46 . 7±2 . 7 fold ) , which was inhibited upon HPV8 E6 or MmuPV1 E6 cotransfection ( 3 . 6±0 . 1 and 5 . 0±0 . 1 respectively ) ( S2B Fig ) . To confirm the ability of HPV8 E6 and MmuPV1 E6 to block expression of direct NOTCH transcriptional targets we assayed the mRNA levels of HES1 a canonical NOTCH regulated target . Similar to the gamma secretase inhibitor compound E ( GSI ) treated control cells , HPV8 E6 , or MmuPV1 E6 expressing cells showed reduced HES1 mRNA compared to untreated control cells . This demonstrates the ability of HPV8 E6 and MmuPV1 E6 to inhibit expression of endogenous NOTCH target genes ( Fig 1D ) . Since ICN1 transfection bypasses regulatory steps at the membrane such as ligand binding and NOTCH cleavage , we conclude that , similar to what we have shown for HPV8 E6 , MmuPV1 E6 inhibits NOTCH signaling downstream of early receptor proximal events , presumably through association with MAML1 in the nucleus . These results demonstrate that MmuPV1 E6 can inhibit NOTCH and TGF-β signaling and that this may a conserved function of skin cancer-associated PVs including HPV5 and HPV8 [19 , 20 , 23] . We do not observe SMAD destabilization but show evidence that inhibition of these pathways is downstream of ligand binding or other receptor proximal events . TGF-β induces cell-cycle arrest in keratinocytes [40] . Since HPV8 and MmuPV1 E6 can inhibit TGF-β signaling in a reporter assay ( Fig 1B ) , we predicted that HPV8 and MmuPV1 E6 could modify the TGF-β induced growth arrest response in human keratinocytes . We treated iHFKs stably expressing either HPV8 E6 or MmuPV1 E6 or GFP with TGF-β1 ( 10 ng/ml ) . Cell proliferation/viability was measured every 24 hours for 5 days using reduction of resazurin , a redox-sensitive dye that interrogates redox fitness of cells , as a proliferation/viability indicator . As expected , control iHFKs treated with TGF-β1 showed a significant decrease in proliferation/viability compared with untreated cells ( p-value = 0 . 0313 ) ( Fig 2A , top left ) . Concurrent treatment with the TGF-β receptor 1 ( TGFBR1 ) inhibitor SB-431542 ( TGFI ) abrogates TGF-β growth inhibition ( p-value = 0 . 1563 ) . As expected , treatment with compound E ( GSI ) , a NOTCH inhibitor , did not rescue TGF-β-induced growth arrest ( Fig 2A , top right ) . In contrast , iHFKs expressing either HPV8 E6 ( Fig 2A bottom left ) or MmuPV1 E6 ( Fig 2A , bottom right ) are largely resistant to TGF-β1 growth inhibition and proliferate similarly to untreated control iHFKs ( p-values = 0 . 8438 and 0 . 4375 , respectively ) . These results indicate that , consistent with their capacity to interact with R-SMADs , HPV8 and MmuPV1 E6 render keratinocytes insensitive to TGF-β induced growth arrest ( Fig 1A ) . Since HPV8 and MmuPV1 E6 expression inhibits reporter assay activity , we hypothesized that expression of these proteins would inhibit endogenous TGF-β responsive transcriptional targets . To test this , we treated GFP expressing iHFKs with TGF-β1 and monitored phosphorylation of SMAD2 and SMAD3 over a time course of 72 hours by immunoblot . Increase of phosphorylation as well as expression of CDKN1A , a TGF-beta target gene , was detected within minutes of treatment and increased to maximal signal by two hours ( Fig 2B ) . Consequently , we analyzed TGF-β transcriptional responses at two hours after TGF-β1 treatment for the remainder of our studies . One important transcriptional target of TGF-β is CDKN2B ( p15INK4B ) , a CDK4/CDK6 inhibitor , that blocks G1 progression by inhibiting cyclin D binding . We first verified that CDKN2B expression is dynamically regulated in iHFKs . Cells were treated with TGF-β1 , and RNA was harvested two hours after treatment . Expression of CDKN2B was assessed by RT-qPCR . TGF-β1 treatment increased abundance of CDKN2B mRNA in GFP control IHFKs , which was blocked by the ( Fig 2C ) TGF-β inhibitor SB-431542 ( TGFI ) . Similar to inhibitor treated cells , HPV8 and MmuPV1 E6 iHFKs also failed to induce CDKN2B expression after TGF-β1 treatment . As expected based on our previous reporter assays , HPV8 and MmuPV1 E6 can inhibit the expression of critical TGF-β targets genes following stimulation by TGF-β1 . We sought to better understand the step at which E6 inhibits TGF-β signaling . Normally , receptor-mediated phosphorylation of SMAD2 and SMAD3 leads to their translocation to the nucleus . In the nucleus SMAD2 and SMAD3 complex with SMAD4 , a TGF-β coactivator . Contrary to previous studies with HPV5 E6 [23] , we did not observe consistent changes to SMAD2 or SMAD3 steady state levels in HPV8 E6 or MmuPV1 E6 expressing iHFKs ( Fig 1A ) . We hypothesized that E6 may block phosphorylation or nuclear translocation of SMADs to inhibit TGF-β signaling . We then examined phosphorylation of SMAD2/3 during TGF-β1 treatment and its subsequent nuclear translocation . To do so , we treated iHFKs with TGF-β1 and harvested the cells 2 hours post treatment , prepared nuclear and cytosolic fractions and performed immunoblot analyses . We observed similar levels of phosphorylated SMAD2 and SMAD3 in the cytosolic and nuclear fractions of HPV8 and MmuPV1 expressing cells as in control iHFKs ( Fig 3A ) . As expected , treatment with the TGF-β inhibitor SB-431542 abrogated phosphorylation and nuclear translocation of SMAD2 and SMAD3 . These results indicate that E6 inhibition of TGF-β is downstream of nuclear translocation of phosphorylated SMADs . Since E6 does not prevent phosphorylated SMADs from entering the nucleus , we predicted it may prevent transcriptional complex formation . After phosphorylation and transport into the nucleus , SMAD2 and SMAD3 proteins form complexes with SMAD4 and assemble at regulatory sites to induce transcription of target genes . The CDKN2B promoter contains a well-defined SMAD binding element ( SBE ) [41] . We determined whether HPV8 or MmuPV1 E6 disrupt SMAD association with DNA , thereby blocking transcriptional activity . We treated iHFKs with TGF-β1 , performed chromatin immunoprecipitations ( ChIPs ) and measured occupancy of SMAD2 and SMAD4 at the SBE of the CDKN2B promoter . As expected , there was an increase in occupancy of both SMAD2 and SMAD4 after TGF-β1 treatment of control iHFKs ( Fig 3B ) . In contrast , HPV8 E6 and MmuPV1 E6 expressing iHFKs showed reduced SMAD2 and SMAD4 occupancy at the CDKN2B promoter even after TGF-β1 treatment . Surprisingly , HPV8 and MmuPV1 E6 were both detected at the SBE of the CDKN2B promoter . Given that HPV8 and MmuPV1 E6 proteins bind to SMAD2/3 and are not known to directly bind DNA , it is conceivable that E6 binding may interfere with binding of the SMAD2 antibody used for the ChIP assays and occlude the SMAD4 binding site . Another possibility is that E6 may prevent stable SMAD complexes from forming leading to decreased signal of both SMAD2 and SMAD4 . Since we had not been able to observe interaction of either HPV8 E6 or MmuPV1 E6 with SMAD4 by immunoprecipitation ( S3 Fig ) , we hypothesized that one way that E6 may prevent stable SMAD complexes from forming would be that E6 may prevent SMAD4 from interacting with phosphorylated SMAD2/3 . To test whether E6 binding to SMAD2/3 precludes SMAD4 binding , we transfected U2OS cells with plasmids encoding a FLAG-tagged SMAD2 and plasmids encoding untagged versions of either HPV8 E6 or MmuPV1 E6 . Two days after transfection we treated these cells with TGF-β1 and prepared lysates at two hours after treatment . After immunoprecipitating SMAD2 using FLAG antibodies we analyzed co-precipitated SMAD4 by immunoblot analysis . We observed that SMAD4 association with SMAD2 following TGF-β1 treatment was decreased upon HPV8 or MmuPV1 E6 co-transfection ( Fig 4A ) . Similarly , formation of the SMAD3/SMAD4 complex was also inhibited upon HPV8 or MmuPV1 E6 expression ( Fig 4B ) . These data support a model whereby HPV8 and MmuPV1 E6 inhibit TGF-β signaling by disrupting transcription factor complex formation at regulatory sites by excluding SMAD4 association with the receptor SMADs 2 and 3 . Our results demonstrate that HPV8 E6 and MmuPV1 E6 can inhibit TGF-β signaling , including the induction of keratinocyte growth arrest and supports a model wherein E6 blocks the formation of DNA bound SMAD complexes by preventing SMAD4 from interacting with SMAD2/3 . The ability of HPV8 E6 to bind MAML1 has been suggested to be required for NOTCH inhibition and we predict that this interaction would also be required for MmuPV1 E6 inhibition of NOTCH signaling . We sought to directly test this hypothesis . HPV8 E6 interacts with an LXXLL motif in MAML1 . Structural studies have identified the amino acid residues that make direct contact between BPV1 E6 and an LXXLL containing peptide derived from Paxillin ( PXN ) [42] . Based on these insights we set out to identify amino acid residues in HPV8 and MmuPV1 E6 that are necessary for MAML1 binding . In addition to PXN , BPV1 E6 can also interact with MAML1 . There are key differences in the amino acid sequences surrounding the core LXXLL motifs of the HPV8 or MmuPV1 E6 binding PXN and MAML1 , and UBE3A ( E6AP ) that binds to HPV16 E6 [42] but not to HPV8 or MmuPV1 E6 ( Fig 5A ) . Therefore , we hypothesized that HPV8 and MmuPV1 E6 bind MAML1 using similar amino acid contacts as those required in BPV1 E6 for PXN binding . Hence we mutated putative LXXLL contact residues in HPV8 E6 that are conserved with BPV1 E6 and tested them for MAML1 binding . The following residues were targeted by mutation: leucine ( L ) 59 , lysine ( K ) 64 , arginine ( R ) 138 , and K142 . To test whether any of these mutations led to dramatic changes in E6 conformation , we measured binding to EP300 , which does not contain an LXXLL motif . We used previously published EP300 binding defective mutants HPV8 E6 Δ132–136 and K136N mutants [37 , 43] as controls and expected these to retain MAML1 binding . U2OS cells were transfected with plasmids expressing either wild type HPV8 E6 or HPV8 E6 mutants and analyzed E6 binding to endogenous EP300 and MAML1 by immunoblot . The HPV8 E6 L59D , K64A , and K142A as well as the EP300 binding deficient Δ132–136 and K136N mutants were all defective for MAML1 binding ( Fig 5B ) . However , the L59D mutant was also defective for EP300 binding , similar to the previously described EP300 binding deficient Δ132–136 mutant; hence , these mutations likely result in global structural alterations . Contrary to what was previously published , the K136N mutant retained EP300 binding [37 , 43] . The R138S mutant retained EP300 and MAML1 binding , whereas the K64A and K142A mutants were defective for MAML1 binding while retaining EP300 association . Alignment of the HPV16 , BPV1 , HPV8 , and MmuPV1 E6 protein sequences ( Fig 5C ) , revealed that the positively charged K64 residue in HPV8 E6 corresponded to the positively charged R52 in MmuPV1 E6 , and the positively charged K62 in HPV16 E6 . In contrast , the positively charged K142 of HPV8 E6 corresponded to the positively charged R130 in MmuPV1 , but corresponded to an uncharged T140 residue in HPV16 . We mutated these residues in MmuPV1 E6 to create MmuPV1E6-R52A and MmuPV1E6-R130A and tested them for their ability to bind MAML1 and SMAD2/3 by immunoprecipitation followed by immunoblot ( Fig 5D ) . Similar to the HPV8 E6 K64A and K142A mutants the MmuPV1 E6 R52A and R130A mutants showed diminished binding for MAML1 and retained the ability to bind to SMAD2/3 . Given that the HPV8 E6 K64A and the MmuPV1 R52A mutants retained higher MAML1 binding than the HPV8 K142A and the MmuPV1 E6 R130A ( Fig 5B and 5D ) we chose the HPV8 K142A and MmuPV1 E6 R130A mutants for further analysis . The previously described NOTCH reporter assay was used to assess the ability of HPV8E6-K142A and the corresponding MmuPV1E6 R130A mutant to inhibit NOTCH signaling ( Fig 5E ) . Consistent with decreased MAML1 association , these mutants were unable to fully inhibit NOTCH signaling . Lastly , to further confirm that NOTCH inhibition by MmuPV1 E6 was due to MAML1 binding , we mutated the two aspartate residues of the LDDLL motif in MAML1 to histidines ( LHHLL ) . Using the NOTCH reporter assay we verified that the MAML1 LHHLL mutant was not dominant negative and that cotransfection of the MAML1 LHHLL mutant was able to partially block E6 mediated inhibition of NOTCH signaling ( Fig 5F ) . Thus , either mutation of papillomavirus E6 residues that disrupt LXXLL binding or mutations in the MAML1 LXXLL motif interfere with E6 inhibition of NOTCH signaling . This shows that the ability of the HPV8 and MmuPV1 E6 proteins to inhibit NOTCH requires the interaction with MAML1 and does not occur through an indirect mechanism . After determining that MmuPV1 E6 and HPV8 E6 share the capacity to inhibit NOTCH and TGF-β signaling , we predicted that , similar to HPV8 E6 [18] , MmuPV1 E6 can inhibit keratinocyte differentiation . NOTCH signaling is a critical driver of keratinocyte differentiation and negatively regulates proliferation [44] . Similarly , TGF-β has been implicated in maintaining epithelial stemness and controlling proliferation competency in differentiating keratinocytes [45] . We have previously shown that HPV8 E6 can inhibit differentiation of keratinocytes , a process at least partially dependent on NOTCH inhibition [18] . HPV8 and MmuPV1 E6 expressing telomerase immortalized oral keratinocytes ( NOKs ) were grown to confluency in low calcium containing , serum free medium and then switched to calcium containing DMEM supplemented with 10% FBS to induce differentiation for up to 6 days . RNA was isolated at days 0 , 2 , and 6 , and expression of involucrin and filaggrin were measured by RT-qPCR . GFP expressing cells showed a marked increase in the levels of involucrin , a marker of intermediate stage keratinocyte differentiation , by day 6 ( Fig 6A ) which was absent from HPV8 E6 or MmuPV1 E6 expressing cells . Treatment of control keratinocytes with the gamma secretase inhibitor compound E ( GSI ) , which blocks NOTCH cleavage , or the TGF-beta receptor 1 inhibitor SB-431542 ( TGFI ) either alone or in combination , similarly blocked the differentiation induced increase of involucrin mRNA . Next , we measured the expression of filaggrin , a marker of later stages of keratinocyte differentiation ( Fig 6B ) . GFP expressing cells showed increased filaggrin expression after 6 days of calcium induced differentiation , but there was no similar increase in HPV8 E6 or in MmuPV1 E6 expressing cells . Treatment with TGFBI but not GSI blocked filaggrin expression . Filaggrin transcript levels were even higher during GSI treatment as compared to control cells , an observation that has also been seen in differentiated keratinocytes harboring heterozygous NOTCH1 deletions [46] . These authors suggested that NOTCH regulates intermediate differentiation and in the absence of signaling , differentiating keratinocytes prematurely initiate the late differentiation program including filaggrin expression . Additionally , we analyzed involucrin protein levels at days 0 , 2 , and 6 post-calcium induction by immunoblot . As observed in Meyers et al [18] , control cells show robust induction of involucrin expression after 2 and 6 days of calcium treatment but this increase is not observed in HPV8 E6 and in MmuPV1 E6 expressing cells ( Fig 6C ) . Calcium treatment of keratinocytes causes a decrease in proliferation and eventually death of terminally differentiated cells . We hypothesized that HPV8 and MmuPV1 E6 expressing cells would be resistant to these effects of terminal differentiation . GFP-control , HPV8 E6 or MmuPV1 E6 expressing NOKs were grown to confluency and switched to calcium-containing media . The medium was changed regularly , and the cells were observed for 32 days . Pictures were taken every two days until day 16 ( S4 Fig ) and at day 32 ( Fig 7A ) . Before calcium addition , all cell populations had similar morphologies ( S4 Fig ) . Most of the calcium treated control cells had expired and detached from the plate by day 32 . In contrast , the HPV8 and MmuPV1 E6 expressing keratinocyte populations remained attached to the plate . In parallel we directly measured proliferation/viability of these cells using resazurin . Consistent with the observed changes in morphology , proliferation/viability of control cells started to decline after 4 days of calcium treatment and continued to decrease over the entire time period observed ( Fig 7B ) . In contrast , HPV8 and MmupV1 E6 expressing keratinocytes compared to control cells remained metabolically active and survived throughout the 32 days of differentiation ( p-values 0 . 0012 and 0 . 0028 respectively ) . Interestingly , however , chemical inhibition of NOTCH and/or TGF-β signaling in control keratinocytes or HPV16 E6 expressing keratinocytes did not significantly differ from control cells ( p-values GSI 0 . 8203 , TGFI 0 . 4363 , dual 0 . 2973 , HPV16 E6 0 . 2136 ) . This indicates that HPV8 or MmuPV1 E6 expression prolongs the survival of differentiating keratinocytes , but that TGF-β and/or NOTCH inhibition does not account for these effects . MmuPV1 provides the ability to assess the biological importance of individual viral gene products and their biochemical activities to viral pathogenesis in vivo . To assess a role for E6 in virally induced pathogenesis , tails of 8–10 week old FoxN1nu/nu mice were infected MmuPV1 quasivirions encapsulating wild type , E6-null ( E6STOP ) or E6R130A mutant MmuPV1 genomes following topical scarification of the epidermis at the designated doses of virus ( Table 1 ) . Previous experiments indicated that infections with these doses of wild type viruses are sufficient to induce papillomas at 100% of sites infected [32] . Consistent with these findings , we found that infections with quasiviruses carrying wild-type MmuPV1 genomes induced papillomas at 100% efficiency . Unencapsidated , wild type MmuPV1 DNA is also infectious [31] . While 10 μg of wildtype MMuPV1 DNA induced warts at 100% of sites , the same amount of MmuPV1 E6STOP or E6R130A mutant DNA did not cause any papillomas ( Table 2 ) . To confirm that quasiviruses carrying E6STOP or E6R130A MmuPV1 mutant genomes were indeed infectious , we performed in vitro infections of mouse keratinocytes and tested for the transcription of E1^E4 spliced products by RT-PCR ( Fig 8A ) . We found that E1^E4 spliced products could be detected post-infection with wild type as well as the mutant quasivirions , suggesting that mutant quasivirions are infectious but are defective for papilloma formation in immunodeficient mice . Our previous in vitro experiments suggested that the ability of MmuPV1 E6 to inhibit NOTCH and TGF-β signaling would impair differentiation and increase cellular proliferation in MmuPV1-induced papillomas . Immunohistochemical analysis of differentiation layer markers cytokeratin 14 ( K14 ) and cytokeratin 10 ( K10 ) ( Fig 8B ) was used to assess the differentiation state of papillomas in comparison with normal uninfected skin . The papillomas showed expansion of basal-like epithelial cells as evidenced by immunohistochemical ( IHC ) analysis of the basal epithelial marker K14 ( Fig 8B left panel ) . K10 expression is found in suprabasal compartment of normal , uninfected murine skin . MmuPV1-induced papillomas showed a delayed staining pattern of K10 in the papilloma ( Fig 8B right panel ) To assess cellular proliferation in papillomas we evaluated incorporation of bromodeoxyuridine ( BrdU ) following intraperitoneal injection of the drug one hour prior to harvesting tissue . IHC was performed to identify BrdU-positive cells in normal epithelium or papillomas and the percentage of BrdU-positive cells was calculated . Approximately 9% of cells in normal uninfected tail skin of BALB/c-Foxn1nu were BrdU-positive ( Fig 8C ) , and positive cells were restricted to the basal layer of the epithelia . Due to the mildly hyperproliferative nature of nude mouse epithelia [47] the percentage of BrdU positivity is somewhat higher than in the skin of normal immunocompetent mice . Nevertheless , there was a significantly higher percentage of BrdU-positive cells ( 24% , p = 0 . 036 ) in the papillomas and BrdU positivity was not confined to basal cells ( Fig 8C ) . This demonstrates that there is both an increase in cellular proliferation and licensing of DNA synthesis in suprabasal cells within MmuPV1-induced papillomas similar to what we observed in MmuPV1 E6 expressing iHFKs ( Fig 2 ) .
Cutaneous papillomaviruses such as HPV5 and HPV8 have long been associated with SCCs in EV patients and long-term immunosuppressed individuals [2 , 7 , 48] . Unlike cancers associated with mucosal HPV infections , cutaneous HPV associated SCC arise in sun exposed areas , and hence these viruses are thought to cooperate with UV to induce SCCs [49] . HPV8 E2 , E6 and E7 each have oncogenic activities when expressed in basal epithelial cells of transgenic mice , but the molecular mechanisms by which these HPVs contribute to cancer development have remained enigmatic . Moreover , HPV sequences are not maintained in every cancer cell , and hence cutaneous HPVs are not necessary for the maintenance of the transformed state [10] . Given the well documented interaction with UV exposure , most molecular studies with cutaneous HPVs have focused on modulation of cell cycle arrest , apoptosis and DNA repair after UV irradiation [50] . HPV8 E6 has been reported to interact with the pro-apoptotic BCL2 family member BAK1 thereby inhibiting apoptosis in response to UV [51] . In addition , cutaneous HPV E6 proteins have been reported to inhibit ATM and ATR activation through an EP300 dependent mechanism , thereby inhibiting DNA repair in response to UV exposure [16 , 38] . Interactions with BAK and EP300 have also been reported for mucosal HPV E6 proteins [14 , 52–54] . Several studies , however , have identified cellular proteins that specifically interact with cutaneous HPV5 and HPV8 E6 but not mucosal HPV E6 proteins . These include members of receptor regulated SMADs ( R-SMADs ) 2 and 3 , which are key to TGF-β signaling [23 , 33 , 35] . In addition , HPV5 and 8 E6 bind to MAML1 , an essential co-activator of NOTCH signaling [19 , 20 , 35] . HPV8 and MmuPV1 E6 proteins inhibit these pathways through stoichiometric interactions and they do not appear to destabilize these proteins by co-opting the cellular ubiquitin conjugation machinery . While mucosal HPV E6 proteins do not detectably interact with MAML1 or SMAD2 and SMAD3 , they may target these pathways through other mechanisms . High-risk HPV E6 proteins may inhibit some aspects of NOTCH signaling indirectly by targeting the TP53 tumor suppressor for degradation [55] and high-risk HPV16 E7 can inhibit TGF-β mediated growth inhibition [56] and interact with SMAD3 [57 , 58] . Even though TGF-β and NOTCH are important tumor suppressor pathways in keratinocytes , it is unclear whether and how inhibition of these pathways by cutaneous HPV E6 proteins contribute to induction of lesions and cancer . The lack of an animal system where the biological relevance of specific viral host interactions can be investigated with respect to the viral life cycle and pathogenesis has greatly hindered papillomavirus research . While early studies with bovine papillomavirus 1 ( BPV1 ) and cottontail rabbit papillomavirus 1 ( CRPV1; more recently referred to as Sylvilagus floridanus Papillomavirus 1—SfPV1 ) enabled infections of autologous hosts , the respective host animals are not genetically tractable . The isolation of MmuPV1 from cutaneous warts of immunodeficient nude mice and the fact that it can be used to experimentally infect laboratory mice have been important steps towards enabling viral pathogenesis studies . Moreover , the recent discovery that MmuPV1-induced warts can undergo malignant progression when subjected to UV [32] suggests that experimental MmuPV1 infections may allow modeling some aspects of SCC formation by cutaneous HPVs . As a first step towards determining whether MmuPV1 may be a useful pathogenesis model of cutaneous HPV infections , we investigated whether MmuPV1 E6 shared cellular interactors with HPV5 and HPV8 E6 proteins . We initially focused on MAML1 , the R-SMADs SMAD2 and SMAD3 that are required for TGF-β signaling and EP300 . We found that similar to what has been reported for HPV5 and 8 E6 [23 , 33 , 35] , MmuPV1 interacts with MAML1 and TGF-β R-SMADs ( Fig 1A ) , thereby inhibiting these two important tumor suppressor pathways ( Fig 1B and 1C ) . Our results suggest that similar to what we previously reported for HPV8 E6 , MmuPV1 E6 inhibits NOTCH by interacting with a nuclear transcription factor complex that contains MAML1 and cleaved , active intracellular NOTCH ( ICN ) [18] . HPV8 and MmuPV1 E6 also share the ability to associate with TGF-β R-SMADs , but HPV8 E6 appears to preferentially associate with SMAD3 , whereas MmuPV1 E6 associates preferentially with SMAD2 ( Fig 1A ) . This was somewhat surprising given the high degree of sequence identity ( 83 . 9% ) between the two proteins . However , in both cases , E6/R-SMAD associations inhibit transcriptional responses to TGF-β ( Fig 1B ) . Our results do not support earlier studies that reported SMAD3 destabilization in HPV5 E6 expressing cells [23] ( Fig 1A and Fig 3A ) . Moreover , HPV8 and MmuPV1 E6 do not appear to markedly affect R-SMAD phosphorylation and nuclear translocation ( Fig 3A ) . Our results ( Figs 3B and 4 ) suggest a model whereby E6 R-SMAD binding inhibits SMAD4 binding and thus formation of an active transcriptional complex . Cancer-associated SMAD2 mutations are also defective for SMAD2/SMAD4 complex formation [59] , and cutaneous papillomavirus E6 proteins seem to functionally mimic SMAD2 mutations . Since MmuPV1 and HPV8 E6 can be detected at the SRE of the CDKN2B promoter ( Fig 3B ) , we propose that similar to what has been observed in their inhibition of NOTCH signaling , these E6 proteins interfere with the transcriptional activity of a DNA bound SMAD2/SMAD3 containing transcription factor complex . Additional experiments are required , however , to carefully test this model . SMAD2 and SMAD3 do not contain recognizable LXXLL motifs and hence they are expected to bind different E6 sequences than the LXXLL containing MAML protein . Consistent with that notion , we found that the MAML binding defective HPV8 and MmuPV1 E6 mutants retained SMAD2/3 binding ( Fig 5D ) . In contrast to HPV8 E6 , MmuPV1 E6 proteins does not detectably bind EP300 ( Fig 1A and Fig 5D ) . We also tested MmuPV1 E6 EP300 binding in murine cells and did not detect an association . Similar to SMAD2 and SMAD3 , EP300 does not have an LXXLL motif . EP300 is an important co-activator for many different transcriptional programs including NOTCH signaling . EP300 associates with a C-terminal sequence of MAML1 that is referred to as Transcriptional Activation Domain ( TAD ) 1 [60] , whereas MmuPV1 and HPV8 E8 associate with a separate LXXLL motif containing domain referred to as TAD2 and hence do not directly compete for EP300 binding to TAD1 . Based on our finding that MmuPV1 does not co-precipitate EP300 and that MAML1 defective HPV8 E6 mutants retain EP300 association we conclude that EP300 binding to HPV8 E6 is not mediated through MAML1 and moreover , that E6 binding to TAD2 may prevent EP300 binding to TAD1 . This may provide a molecular mechanism for inhibition of NOTCH transcription by cutaneous papillomavirus E6 proteins . In addition , given that MmuPV1 associated warts can progress to SCCs , it would appear that EP300 binding is not strictly required to cooperate with UV for SCC formation . Many studies that have implicated EP300 as a major cellular effector of cutaneous HPV E6 activities have been based upon the use of the HPV8 E6 Δ132–136 and K136N mutants [37 , 43] . Our experiments unexpectedly revealed that the Δ132–136 mutant is also defective for MAML1 binding , whereas the K136N mutant did not exhibit any overt defects for EP300 binding ( Fig 5B ) . Caution should be taken when interpreting data obtained with these mutants . The NOTCH and TGF-β tumor suppressors are critical determinants of differentiation and cell fate in keratinocytes and act by coordinating cell-cycle withdrawal and driving keratinocytes toward terminal differentiation and ultimately , cell death [61 , 62] . Our results show that similar to MmuPV1 induced cutaneous warts , HPV8 and MmuPV1 E6 expressing human keratinocytes are differentiation resistant and remain proliferatively active ( Figs 6 and 7 ) . The differentiation process is largely TGF-β and NOTCH dependent as TGF-β and/or NOTCH inhibitor treatment of normal keratinocytes mimics the effects of E6 expression , but extended survival of differentiated keratinocyte was independent of these two pathways ( Fig 7B ) . In addition , we also observed that HPV8 and MmuPV1 E6 expressing keratinocytes remain viable for extended periods of time under conditions that induce differentiation ( Fig 7 ) . Interestingly , TGF-β and/or NOTCH inhibition in normal keratinocytes was not sufficient for this phenotype ( Fig 7B ) . Taken together , our results suggest HPV8 and MmuPV1 E6 allow infected cells to remain proliferatively active and not only resist differentiation cues but also remain viable over extended periods of time . This would be manifested by an expansion of basal-like , proliferatively active as is seen in MmuPV1 induced skin lesions ( Fig 8 ) . The number of cutaneous HPVs that have been isolated and characterized has dramatically increased over the last few years . In addition to HPV5 and HPV8 , a large number of beta genus HPVs and more recently also gamma genus HPVs have been detected in cutaneous lesions and SCCs [10 , 63] . Proteomic studies of E6 associated cellular proteins have started to shed some light on similarities and differences of cellular pathways that may be targeted these by the various cutaneous HPVs [33] . The next important challenge will be to determine how subversion of these various pathways contributes to the pathogenesis and oncogenicity of these viruses . Are there low-risk and high-risk cutaneous HPVs ? If so , is the oncogenic potential dependent on inhibition of specific cellular pathways as has been shown for mucosal HPVs ? Our results provide evidence that MmuPV1 will be an important , biologically relevant model to address some of these issues , particularly as they relate to NOTCH and TGF-β inhibition by E6 . Our experiments show that MmuPV1 E6 expression is necessary for wart formation and that a MmuPV1 quasivirus carrying a genome encoding a MAML1 binding defective E6 mutant does not cause wart formation ( Tables a and b ) . Using recently published structures of papillomavirus E6 proteins bound to cellular targets we may be able to generate MmuPV1 mutants that are defective for binding to R-SMADs or other associated cellular target proteins and test these both in vitro and in vivo . Even without such an E6 mutant we can perform additional experiments with small molecule inhibitors and/or by infecting mouse strains , even immune competent mice [32] , that carry mutations in specific signaling pathways to conclusively assess the importance of E6 mediated NOTCH and TGF-β inhibition for MmuPV1 replication and pathogenesis in vivo .
U2OS human osteosarcoma and HCT116 human colon carcinoma cells were obtained from ATCC and grown in Dulbecco’s Modified Eagle Medium ( DMEM ) with high glucose ( Gibco ) supplemented with penicillin-streptomycin and 10% fetal bovine serum ( FBS ) . Tert-immortalized human foreskin keratinocytes Cl398 [64] ( iHFKs ) ( obtained from Al Klingelhutz , University of Iowa ) or normal oral keratinocytes ( NOK ) [65] were maintained in keratinocyte serum free media ( KSFM ) ( Gibco ) supplemented with 0 . 2 ng/ml EGF , 25mg/ml bovine pituitary extract , and penicillin-streptomycin . Cells were differentiated by switching KSFM to DMEM/10% FBS . Recombinant human TGF-β1 ( Millipore ) was used at a final concentration of 10 ng/ml in all experiments . Compound E ( Millipore ) and SB-431542 ( Sigma ) were used at 2 μM and 10 μM respectively . Plasmids used in transient transfections were pCMV BamNeo vectors with Flag-hemagglutinin epitopes fused to the amino termini of HPV E6 proteins: pNCMV ( vector ) HPV16 E6 , HPV8 E6 , MmuPV1 E6 . Lentiviral plasmids used were generated through cloning of pLenti6 . 3 /V5 TOPO Gateway compatible vector ( Invitrogen ) : GFP ( control ) , HPV8 E6 , HPV16 E6 , and MmuPV1 E6 . Notch reporter construct , HES1-luc[66] , HA-tagged ICN1[67] , and MAML1 full-length[67] expression plasmids ( obtained from Jon Aster , Harvard Medical School ) were used as previously described [68] . TGF-β reporter construct , ( CAGA ) 9-MLP-Luc ( obtained from Jennifer Pietenpol , Vanderbilt University School of Medicine ) was used as previously described [69] . SMAD2 and SMAD3 expression plasmids were obtained from Michael Hoffman ( University of Wisconsin ) . All mutations were created using QuikChange II site-directed mutagenesis kit ( Agilent ) . Transient transfections of U2OS cells was performed using Polyethylenimine ( PEI ) ( Polysciences ) as described [70] and analysis of transfected cells was performed at 48 hours post-transfection . Transient iHFK transfections were performed using Fugene 6 ( Promega ) and were analyzed at 48 hours post transfection . Preparation of and infection with recombinant lentiviruses was as previously described [71] . Selection of infected cells using Blasticidin ( 10 μg/ml ) began two days post infection and was maintained for seven days . Cells were than maintained as described in KSFM . Reporter assays were performed using Dual-Luciferase Reporter Assays System ( Promega ) . Lysates of cells transfected with the appropriate plasmids ( 200 ng reporter , 200 ng vector or E6 , 10 ng renilla , and 200 ng ICN1 or 200 ng MAML1 where appropriate ) were prepared in 100 μl of passive lysis buffer at 48 hours after transfection and 20 μl of lysate was used for each reading . Readings were done in triplicate using a LMax II plate reader ( Molecular Devices ) and values normalized for transfection efficiency using the co-transfected renilla luciferase expression plasmid . RNA was isolated using Quick-RNA MiniPrep ( Zymo Research ) . cDNA was synthesized using Quantitect Reverse Transcription Kit ( Qiagen ) . Quantitative PCR ( qPCR ) was performed in triplicate on a StepOne Plus ( Applied Biosystems ) thermocycler using SYBR Green PCR Master Mix ( Applied Biosystems ) reagents . PCR primers used are listed in S1 Table . Data shown was calculated using ΔΔCT method and normalized to expression of the RPLP0 as the housekeeping gene . Cells were lysed in 1% NP40 buffer ( 1% Nonidet P-40 ( NP40 ) , 120mM NaCl and 50mM TrisHCl ( pH 8 . 0 ) . Immunoprecipitations of HA epitope tagged proteins were performed using HA antibodies coupled to agarose beads ( Sigma ) . Samples were run on NuPAGE 4–12% Bis-Tris Gels ( Invitrogen ) according to manufacturer’s instructions . Proteins were electrotransferred to Polyvinylidene fluoride ( PVDF ) membranes ( Immobilon-P; Millipore ) . The membranes were blocked in 5% nonfat dry milk in TBST ( 137 mM NaCl , 2 . 7 mM KCl , 25 mM Tris [pH 7 . 4] , 0 . 1% Tween 20 ) and probed with the appropriate antibody . Primary antibodies ( 1:1000 dilution ) used for immunoblots are listed in S2 Table . Secondary anti-mouse and anti-rabbit antibodies conjugated to horseradish peroxidase ( Amersham ) were used at dilutions of 1:10 , 000 . Subcellular fractionation was done using the REAP method [72] . In brief , cell pellets were washed with phosphate buffered saline ( PBS ) and repelleted . The cytoplasmic fraction was isolated by tituration of the pellet in PBS containing 0 . 1% NP40 . The nuclear pellet was washed and resuspended in 1% NP40 lysis buffer followed by sonication and treatment with Pierce Universal Nuclease ( Thermo Fisher Scientific ) . Resazurin was used at 25 μg/ml in PBS to assess redox fitness [73] . Cells were incubated with dye for one hour and then sample fluorescence was read in triplicate using 560 nm excitation and 590 nm emission filters on a Synergy H1 microplate reader ( BioTek ) . Chromatin was prepared using SimpleChIP Enzymatic Chromatin IP Kit ( Cell Signaling Technology ) according to manufacturer’s instructions and approximately 106 cells were used for each assay . Antibodies used are listed in S2 Table . Co-precipitated DNA was isolated according to manufacturer’s protocol and quantitative PCR was performed as described previously [18] . Data is expressed as compared to percent input after subtraction of isotype matched IgG signal . Immunodeficient euthymic BALB/c FoxN1nu/nu ( Harlan ) were used in this study . All infected mice were housed in aseptic conditions in micro-isolator cages . Animals were handled only by designated personnel and personal protection gear was changed between cages to prevent any cross contamination from virus . All animal experiments were performed in full compliance with standards outlined in the "Guide for the Care and Use of Laboratory Animals” by the Institute of Laboratory Animal Resources ( ILARC ) of the Commission on Life Sciences ( CLS ) , National Research Council ( NRC ) as specified by the Animal Welfare Act ( AWA ) , associated Animal Welfare Regulations ( AWRs ) , Public Health Service ( PHS ) Policy and Office of Laboratory Animal Welfare ( OLAW ) and approved by the Governing Board of the National Research Council ( NRC ) , whose members are drawn from the councils of the National Academy of Sciences ( NAS ) , National Academy of Engineering ( NAE ) , and Institute of Medicine ( IM ) . Mice were housed at McArdle Laboratory Animal Care Unit in strict accordance with guidelines approved by the Association for Assessment of Laboratory Animal Care ( AALAC ) , at the University of Wisconsin Medical School . All protocols for animal work were approved by the University of Wisconsin Medical School Institutional Animal Care and Use Committee ( IACUC , Protocol number: M02478 ) . Infections were performed using quasivirions containing MmuPV1 wild type or mutant genomes as described previously [74 , 75] . Briefly , 293FT cells ( ATCC ) were cotransfected with a MmuPV1 capsid protein expression plasmid ( pMusSheLL- a gift from Chris Buck , National Cancer Institute ) [30 , 76] and MmuPV1 wild type or mutant DNA for encapsidation . After 48 h at 37°C , cells were harvested and virions were purified using Optiprep gradient centrifugation . The generated quasivirions were quantified and used to infect of FoxN1nu/nu mice ( Harlan ) as described [32] . Briefly , in vivo infections with purified MmuPV1 quasivirions were performed on scarified skin of the animals’ tails . Animals were anesthetized and four spots on tails were scarified using a 27-gauge syringe needle to scrape the epithelia ( not sufficient to cause bleeding ) followed by pipette delivery of virus solution using a siliconized pipette tip . In vivo infection with wild type or mutant MmuPV1 genomes was as previously published reports [30 , 31 , 76 , 77] with some modifications . The viral genomes were recovered by excision from the plasmid backbone using the restriction enzyme XbaI , followed by intramolecular religation using T4 DNA ligase , as detailed on the website of the Laboratory of Cellular Oncology ( http://home . ccr . cancer . gov/Lco ) . Animals were scarified as described above and four days post-scarification inoculated with 10 μg recircularized viral DNA ( in a 10 μl volume ) by injecting with a 30-gauge needle into the scab . Mouse keratinocytes JB6-clone 41 ( gift from Dr . Nancy H . Colburn , NCI ) were maintained in modified Eagle’s Medium MEM containing 5% FBS and infected with MmuPV1 mutant or wild-type quasivirions at a multiplicity of infection of 10 . Forty-eight hours post-infection , total RNA was isolated using the RNeasy kit ( Qiagen ) and reverse-transcribed into cDNA using SuperScript III ( LifeTechnologies ) as per manufacturers’ instructions . E1^E4 transcripts were analyzed by PCR . GAPDH was used as a positive control . Primer sequences for the detection of MmuPV1 E1^E4 spliced transcripts and GAPDH have been published previously [76] and are listed in S1 Table . PCR products were resolved by Agarose gel electrophoresis . Skin was harvested , fixed in 4% paraformaldehyde , and embedded in paraffin . Serial sections ( 5 μm thick ) were analyzed for Keratin markers and BrdU . For immunohistochemistry , sections were deparaffinized and rehydrated with xylenes and graded ethanol , respectively . Endogenous peroxidase activity was quenched with 3% H2O2 in methanol and followed with heat-induced antigen retrieval in 10 mM citrate , pH 6 . 0 . Antigen antibody complexes were detected with biotinylated horse anti-mouse/rabbit IgG ( Vector Laboratories ) and were visualized with 3 , 3′-diaminobenzidine ( Vector Laboratories ) . Tissues were counterstained with hematoxylin . All images were taken with a Zeiss AxioImager M2 microscope using the AxioVision software version 4 . 8 . 2 . To assess cellular proliferation , we evaluated incorporation of bromodeoxyuridine ( BrdU ) ( 203806 , Calbiochem ) at one hour after intraperitoneal injection . Tissue was harvested and processed for immunohistochemistry using a BrdU antibody as described above . For each experimental group ( normal skin and papillomas ) , three slides , each derived from an individual animal , were analyzed by microscopy . Ten random fields of normal skin or the papilloma were selected on each slide and the total number of epithelial cells and the number of BrdU-positive cells were manually counted . The percentage of BrdU-positive cells was calculated . A two-sided Wilcoxon rank-sum test was used to compare the average percentage of BrdU-positive cells between the two groups . | The lack of a genetically tractable small animal model to study viral infection and pathogenesis has significantly hindered papillomavirus research . The recent discovery of Mus musculus papillomavirus 1 ( MmuPV1 ) , which can replicate and form skin warts and cancers in experimentally infected laboratory mouse strains , has the potential to be transformative to the field . However , it is important to determine whether MmuPV1 targets some of the same cellular signaling pathways as the skin cancer-associated human papillomaviruses ( HPVs ) . We show that MmuPV1 E6 shares with the skin cancer-associated HPV8 E6 protein the capacity to inhibit NOTCH and TGF-β signaling . Importantly , MmuPV1 E6 expression and specifically the ability of E6 to inhibit NOTCH signaling are necessary for wart formation in mice . Hence , MmuPV1 will be an excellent animal model to study key aspects of the life cycle and pathogenesis of skin cancer-associated HPVs . | [
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"analysis"... | 2017 | Cutaneous HPV8 and MmuPV1 E6 Proteins Target the NOTCH and TGF-β Tumor Suppressors to Inhibit Differentiation and Sustain Keratinocyte Proliferation |
Since 2009 , Fundación Mundo Sano has implemented an Aedes aegypti Surveillance and Control Program in Tartagal city ( Salta Province , Argentina ) . The purpose of this study was to analyze temporal dynamics of Ae . aegypti breeding sites spatial distribution , during five years of samplings , and the effect of control actions over vector population dynamics . Seasonal entomological ( larval ) samplings were conducted in 17 , 815 fixed sites in Tartagal urban area between 2009 and 2014 . Based on information of breeding sites abundance , from satellite remote sensing data ( RS ) , and by the use of Geographic Information Systems ( GIS ) , spatial analysis ( hotspots and cluster analysis ) and predictive model ( MaxEnt ) were performed . Spatial analysis showed a distribution pattern with the highest breeding densities registered in city outskirts . The model indicated that 75% of Ae . aegypti distribution is explained by 3 variables: bare soil coverage percentage ( 44 . 9% ) , urbanization coverage percentage ( 13 . 5% ) and water distribution ( 11 . 6% ) . This results have called attention to the way entomological field data and information from geospatial origin ( RS/GIS ) are used to infer scenarios which could then be applied in epidemiological surveillance programs and in the determination of dengue control strategies . Predictive maps development constructed with Ae . aegypti systematic spatiotemporal data , in Tartagal city , would allow public health workers to identify and target high-risk areas with appropriate and timely control measures . These tools could help decision-makers to improve health system responses and preventive measures related to vector control .
Mosquitoes of Aedes genus are the principal vectors of Dengue , Yellow Fever , Chinkungunya and Zika viruses in the Americas [1 , 2] . Aedes aegypti ( Diptera: Culicidae ) transmits Dengue virus in the tropical and subtropical South America regions , and its transmission is influenced by various factors , including vector mosquito density , circulating virus serotypes , and human populations susceptibility [3] . In Argentina , Ae . aegypti is the most relevant mosquito from epidemiologic point of view . This specie is characterized by its adaptation to the urban environment , its capacity and preference of breeding in artificial containers [4] , the resistance of its eggs to desiccation and the feeding behavior of the female which bites in multiple occasions during each gonadotrophic cycle [5] . These characteristics , together with this vector wide distribution in Northern Argentina , constitute fundamental factors that influence circulation and transmission of Dengue and other related viruses in the region [6] . After a successful vector eradication campaign , at national level , in the 70´s [7] , the first outbreak of dengue in Argentina was documented in 1998 . Since then , intermittent outbreaks of the disease , with variable incidence rates , were registered in an almost continuous manner in the center and northern regions of the country [8] . A major dengue outbreak reached subtropical regions of Argentina in 2009 , affecting more than 25 , 900 people including localities such as Buenos Aires and Córdoba [9 , 10]; although the largest percentage ( over 90% ) corresponded to case reports from Chaco , Catamarca and Salta provinces [8] . In this last province , in Tartagal city , around 665 dengue cases were confirmed including the first fatal case of this disease to be ever registered in Argentina [8] . From 2010 to 2014 , a total of 338 suspected cases were registered in this city , from which 56 cases were confirmed ( Hospital Provincial J . D . Perón , Tartagal , personal communication ) . Taking into account 2009 epidemiological situation , in October of that year , Mundo Sano initiated an Ae . aegypti surveillance and control program with the objective of reducing the risk of dengue transmission in the city of Tartagal , Since then , a permanent surveillance system of breeding sites and key infestation determinant factors involved in was implemented to generate a systematic information record of high epidemiological value . Considering Dengue native transmission , cases introduction from Paraguay , Bolivia and Brazil , and the absence of an effective vaccine [11] , the north region of Argentine needs continuous vector control programs applications . Traditional Ae . aegypti control measures include elimination ( breeding sources reduction ) or larval habitats chemical treatment to prevent adults production , and space spray insecticides application to reduce adult population densities [12] . In this sense , current control methods require a clear identification of the areas and the periods of mayor entomologic risk , as well as the identification of the viral propagation flow in a community [13] . Multiple environmental factors , including biophysical and social ones , constitute a complex web that determines the spread of vector-borne diseases [14] . In this sense , Ostfeld and collaborators [15] indicated that despite the complexity , an analysis of the variables linked to vectors distribution and the identification of dengue cases can be a useful tool to generate future spatial and temporal scenarios for dengue . Spatial analysis of health events contribute to early detect situations involving diseases transmission [15] , while the detection of disease clusters allows the identification of nonrandom events and the possibility of inferring their epidemiological determinants [16] . Surveillance tools , such as incidence maps , have been used to enhance public health preparedness for dengue outbreaks by providing a visual aid for decision-making [17 , 18] . On the other hand , the use of satellite images in epidemiological analyses allows the identification of key environmental factors ( temperature , rainfall and humidity ) that influence the dynamic of the vectors , as well as their interactions [19 , 20] . Since the beginning of remote sensing ( RS ) technology , studies on vector-borne diseases have focused on identifying and mapping vector habitats [21] , assessing environmental factors related to vector biology [22–24] and studying disease epidemiology [25 , 26] . Recent studies investigated the application of RS and spatial analysis techniques to identify and map landscape elements , that collectively define vector and human population dynamics related to disease transmission risk [27 , 28] . In addition , the development of increasingly sophisticated Geographic Information Systems ( GIS ) and RS has provided a new set of tools for public health professionals to monitor and respond to health challenges [29 , 30] . In this frame , Louis and collaborators [12] have detected a great diversity of both predictors and modeling approaches employed to create dengue risk maps through a systematic review and determined that the field of predictive dengue risk mapping is young and still evolving . In addition , different studies propose measures of prevention and control of Ae . aegypti for the elaboration of maps based on the results obtained from a bounded availability ( space-time ) of recorded data from both , field data and satellite imagery [31–33] . In this sense , an increase in the quality ( amount and accuracy ) of the field data used for the development of predictive maps will allow public health workers to identify areas of high risk for adequate control of the disease [19 , 34 , 35] . Despite the knowledge of Ae . aegypti biology and the use of monitoring tools , the precise detection of high density spots of vector breeding sites , as places of occurrence of the disease , remains poorly understood . Therefore , the purpose of this study was to analyze 5 years space-time dynamics of Ae . aegypti breeding sites and control actions effect on its populations , in Tartagal City ( Prov . of Salta , Argentina ) . We discuss the predictive capacity of Ae . aegypti spatial distribution models , generated through environmental variables and minimal field data . This models constructed for dengue surveillance based on entomological risk maps , are considered a step in the generation for vector control strategies .
Tartagal city is located at the base of the Argentinean sub-Andean hills ( 22°32’ S , 63°49’ W; 450 m above sea level ) in Salta Province ( Fig 1 ) . As the third largest urban center of the province , with 79 , 900 inhabitants , it includes several ethnic groups such as native Amerindians . The city is located 100 km northern of Capricorn Tropic and to 55 km Southern of Argentinean-Bolivian border ( Fig 1 ) . The city is surrounded by subtropical native forests and crops such as beans , cotton , soybean , maize , grapefruit and tomato . The climate is subtropical , with an average annual temperature about 23°C; and an average maximum of 39°C ( in summer ) and average minimum of 9°C a ( in winter ) respectively . Annual cumulative precipitation is about 1 , 100 mm , with a dry season from June to October with a monthly average rainfall of 30 mm , that sharply contrasts with the wet season from November to May with a monthly average rainfall reaching 160 mm . Tartagal is characterized by a cultural diversity based on the presence of several autochthonous ethnic groups and emigrant population and continued migration from the bordering country of Bolivia . This feature produces an important effect on the cultural , social and economic profile of this community . The urban area of Tartagal city covers approximately 15 km2 , and is composed by 1 , 027 blocks and 17 , 911 housing units . Each of the housing units was georeferenced by the use of GPS receiver . In Fig 1 , sectors ( a ) and ( b ) refer to new neighborhoods that were incorporated in data collection and entomological control actions performed in 2011 and 2013 , respectively . Presence and abundance data of Ae . aegypti larval stages breeding sites were registered from 2009 to 2014 in Tartagal city , using a methodology called Focal Cycle ( FC ) . This method consists in the entomological surveillance and chemical treatment of 100% of the housing units in the study area . A total of 10 FCs were performed; the first 8 were performed in a continuous manner between the years 2009 and 2012 ( Table 1 ) . The analysis of the data showed that during each year winter and spring , the presence of breeding sites and larval stages remained low . In order to optimize resources without losing any information , since 2012 , during the winter-spring periods , random larval samplings were performed in 20% of the blocks of the city , alternating with FCs in the summer-autumn periods ( Table 1 ) . A total of 5 random larval samplings ( denominated M1 to M5 ) were performed during the study period . During winter-spring period , chemical treatment was substituted by physical management and/or removal of containers that could serve as breeding sites . For each period , entomological field records consisted in the complete inspection of the housing units , within each block , registering information on the type and number of containers , grouped by the following categories: tires , tanks , drums , barrels , vases , pots , building materials , auto parts , bottles , cans , plastic , wells , cisterns , natural receptacles , and others ( washing machines , refrigerators , toilets , etc . ) . Total number of containers was counted , such as the number of containers with water , and with larval stages . Larval stages were collected in individual tubes by container , labeled and transported to Mundo Sano entomological laboratory , in Tartagal city , for taxonomic determination using a specific morphological key [36] . Housing units were considered positive when they presented at least one container , with one or more larvae or pupae of Ae . aegypti . Additionally , a series of places were inspected and identified as critical breeding sites , since they presented an elevated number of containers in comparison to those registered in the housing units . The cemetery , municipal garbage dump , tire repair shops , small garbage accumulation sites and other similar places were included in this category . In each FC , the entomological indexes at the housing unit level were calculated using the House Index ( HI ) = number of positive homes/number of houses inspected ) x 100 and Breteau Index ( BI ) = total number of breeding sites/number of houses inspected ) x 100 [37 , 38] . These indexes are generally accepted for operational use [39 , 40] . After the entomological data collection , focal control actions were performed in each housing unit which entailed , for each FC round , mechanical treatment ( modification , elimination or destruction ) together with the application of the larvicide in a 1 mg/L dose , following the guidelines elaborated by TDR/WHO and the Argentinian Ministry of Health [41 , 42] . These actions were accompanied by a communication campaign through the use of printed pamphlets destined to inform the general population about the disease and its risks . Moreover , with the objective of reducing the environmental burden of active and potential breeding sites generally accumulated in the peridomicile , neighborhood rallies were organized in collaboration with the local municipality and the participation of local public and private entities , to get rid of containers that favor the accumulation of water during the weeks prior to the start of the rainy season and during the summer months . In order to analyze the spatial and temporal distribution of the positive breeding sites , GIS vector layers were created including FCs data that were performed during summer and autumn of each year ( Table 1 ) . In this sense , and in order to comply with what was previously detailed , FC1 and 2 were combined since the interval of time between these FCs is equivalent to the time registered for the other FCs analyzed: FC5 , FC8 , FC9 and FC10 ( Table 1 ) . In order to avoid confusion , the unit of time of years will be used during the analysis to reference the FCs that correspond to the summer and autumn season of each year . Vector layers were generated using the free-access software Quantum GIS Desktop v2 . 6 . 1 . Brighton ( QGIS ) . Density breeding site maps were elaborated using discreet information ( sites of individual sampling ) through QGIS tool “heatmap” , in order to analyze the manner in which Ae . aegypti breeding sites were distributed in the city . Annual density breeding site maps ( summer-autumn ) were generated using the Kern density algorithm that calculates the density of positive points ( grouping of the points ) for a determined area . Using this methodology , the heat map allows for a visual identification of the hot spots for a particular time and place . The methodology of statistical spatial analysis exploration , developed by Kulldorf [43] was used to identify spatial clusters with Ae . aegypti larval stages presence , with greater density than those expected by a random distribution . The analysis would then indicate some areas with a greater presence of breeding sites than others . Sites with the presence of larval stages were indicated as positive cases ( 1 ) and those without any presence were indicated as negative controls ( 0 ) . The analysis consisted of a spatial scan through the superposition of exploratory circles , over sites with a record of larval presence . Each circle is a possible cluster and , taking into account the number of events inside and outside an expected number of events , each probability is calculated . The circle that presents the maximum probability , and an excess in the number of events observed versus expected , is defined as the most probable cluster [43] . In this case , the maximum size of the cluster was assigned as 30% of the total population under study . In our analysis , for each place and window size ( circle ) , the null hypothesis assumes that sites with the presence of larval stages are randomly distributed , while the alternative hypothesis indicates that there is a greater risk inside the window in comparison to the outside . A maximum of 999 Monte Carlo replications were performed in other to search for statistically significant composites . Only the composites that achieved statistical significance ( p<0 . 05 ) under Bernoulli´s distribution were reported . The purely spatial exploration model was used for each year within 20102014 period in Tartagal . The statistical analysis was performed with SaTScan v9 . 3 . 1[44] software , while cartographic representations were done in QGIS software . SPOT images ( Satellite Pour l’Observation de la Terre ) were used to characterize the types of environmental coverage in Tartagal . This is a commercial high-resolution optical imaging Earth observation satellite system , operating from space . In this case , we used the SPOT 5 J product , of 10 meter resolution in multispectral mode , with four bands on short wave infrared: green ( 0 . 50–0 . 59 μm ) –red ( 0 . 61–0 . 68 μm ) –nearest infrared ( 0 . 78–0 . 89 μm ) and middle infrared ( 1 . 58–1 . 75 μm ) . SPOT image ( 16-11-2013 ) data was used to generate land cover classifications and macro-environmental products of the study area ( Fig 2 ) . All the images used were supplied by the Comisión Nacional de Actividades Espaciales ( CONAE ) . Unsupervised classification ( k-means ) classifiers have been used to classify the image of the study area as described by Rotela and collaborators [45] . Seven land cover classes were identified: bare soil , low vegetation ( grass ) , high vegetation ( trees ) , urban buildings , superficial water , shadows , and pasture and crops ( Fig 2 ) . A set of ground truth points ( about 35/40 points for each class ) were generated using Google Earth in order to validate classification accuracy . The confusion matrix , when using control points , showed an overall accuracy of 79 . 4% and a Kappa coefficient of 0 . 74 . The classes of ( low and high ) vegetation , and bare soil and pasture reached values above 70% accuracy , and the urban class presented lower registers . QGIS and ENVI 5 . 1[46] software were used to create the vectors and assess the accuracy of the classification . Based on the land cover classes previously created , two different types of macro-environmental variables were generated for each class , expressed as i ) “distance maps or buffer image” ( Fig 3 ) and ii ) "percentage" of each land cover class ( Fig 4 ) , according to Rotela and collaborators [45] . In our study , the window size for the maps generated was 31x31 pixels , attributing to the central pixel the average value of the central window . A flight range of 150 m for Ae . aegypti [47 , 48] was used to generate the new land cover classes ( distance and percentage ) , which could describe the environment that represents the average habitat of the species . All these analyses were performed using ENVI 5 . 1 . Tartagal information provided by the Instituto Nacional de Estadística y Censos ( INDEC ) was used to generate 2 layers that included demographic information related to the availability of drinking water ( public network ) ( Fig 4h ) and the distance to critical points ( cemetery and garbage dump ) ( Fig 3g ) . First INDEC layer reflects the lack of this service , as an indicator of the use of containers for outdoor water storage , as potential generator of artificial Ae . aegypti breeding sites . INDEC information was transformed to a vector layer that included percentage values of the service per Census radio units by the use of QGIS software . In order to assess the contribution of each of the selected variables to the prediction model , MaxEnt software [49] was used to predict suitable sites for the development of Ae . aegypti breeding sites in Tartagal , based on the environmental requirements of the species [19 , 50–52] . Ecological modelling calculates the probability of vector breeding sites presence using environmental and demographic variables , and the actual vector breeding sites presence as training sites . All the positive sites for Ae . aegypti larval stages from the sampling performed in Tartagal during 2014 were used . In order to analyze the possible relationship of a product that compiled all variables , values are generated . Thus , each pixel of the study area presents a landscape value ( indicated by the set of variables , see Table 2 ) and may have an associated value indicating the probability . This analysis is based on two basic premises , i ) the first one relates to the presence of sites where the species successfully grow , and the second ii ) refers to the selected environmental variables that adequately represent the ecological requirements of the species . Each presence site is indicated by a pair of geographic coordinates ( WGS84 Datum ) , and represents a place where Ae . aegypti breeding sites were found during the sampling period . The Maximum Entropy approach ( MAXENT ) was used to model and predict the ecological niche distribution of the vector . In general , this algorithm detects non-random relationships between two data sets: i ) georeferenced records of the presence of the species , and ii ) a set of land cover type "raster" , digital data representing the environmental and demographic variables relevant to determine the distribution of the species in a particular scale of analysis [49] . The environmental data set consists of 19 variables in raster format , of 10 m pixel size ( see Table 2 for data access to Tartagal ) . For the generation of vector presence probability maps , we applied the Maximum Entropy method based on the MaxEnt 3 . 3 . 3a software [49] , available online at http://www . cs . princeton . edu/~schapire/maxent/ , reserving 25% of Aedes aegypti presence points for validation , and with a 1000 repetitions run .
The temporal variation of Ae . aegypti spatial distribution of positive breeding sites presented a wide distribution all around Tartagal city ( Figs 5 and 6 ) , with the highest densities spatially concentrated in city outskirts , in comparison to the central areas ( Fig 5 ) . The temporal representation of positive sites in the city registered variations in distribution and number over the years , observing a similar spatial pattern as previously described ( Fig 6 ) . Fig 7 shows that house indexes ( HI and BI ) values decreased between 2010 and 2014 . Throughout this period , both indexes registered their highest levels during the summer and autumn seasons , which coincide with FC1 , FC2 , FC5 , FC8 , FC9 and FC10 ( Table 1 ) , while the lower values were associated with winter and spring ( FC3 , FC4 , FC6 , FC7 , M1 , M2 , M3 , M4 and M5 ) ( Table 1 and Fig 8 ) . Sectors with presence of Ae . aegypti breeding sites were distributed throughout the entire study area , especially at the beginning of the program during 2010 and 2011 ( Fig 8 ) . Through the analysis of hotspots , three important aspects were detected: 1 ) the gradual reduction in the density of breeding sites detected each year , 2 ) the presence of sectors with a density of breeding sites that persist throughout the study period analyzed , located in the northeast , north , east and southeast regions of Tartagal , and 3 ) the highest density of breeding sites were associated with peripheral sectors while the lowest ones were registered in the central areas of the city ( Fig 8 ) . In 2010 , the northeast and southeast Tartagal sectors reached the highest density of breeding sites , with up to 20 positive breeding sites per housing unit . These two sectors remained positive throughout the study period although with variations in the density values ( Fig 8 ) . During 2011 and 2012 , the north and east sectors of the city were identified as areas with high density of breeding sites ( Fig 8 ) . The year 2013 showed a spatial configuration that was similar to the previous years but with a marked reduction in breeding site density ( Fig 8 ) , while in 2014 , the areas that registered the highest density of breeding sites were sectors located in the east and southeast ( Fig 8 ) . Statistically significant ( p < 0 . 05 ) differences were observed in spatial clusters between 2010 and 2014 ( Fig 9 ) . In general , the clusters with the largest dimensions were located in the northeast , north , east and southeast sectors of the city , with clusters that varied in size throughout the study period ( Fig 9 ) . In 2011 and 2014 , the east sector registered the highest clusters with radius greater than 1 . 5 km ( Fig 9 ) . On the other hand , in 2010 , 2012 and 2014 the southeast sector presents clusters with radius greater than 0 . 5 km . In the northeast sector , clusters with radius greater than 0 . 5 km were registered only during 2010 and 2013 , while in the north sector these size clusters were only registered in 2013 ( Fig 9 ) . Another aspect observed using cluster analysis was the concentration of clusters in one or two sectors of the city for the year 2010 and 2014 . For the rest of the years , numerous clusters of radius size between 100 and 300 m were registered throughout different sectors ( Fig 9 ) . The predictive map obtained by the model ( Fig 10 ) was assessed with measurement accuracy ( MaxEnt software ) , therefore the area under the curve ( AUC ) in receiver operating characteristic ( ROC ) analysis was scored at 0 . 918 . Its predictive ability for the 2014 data set is classified as acceptable according to Parolo and collaborators [53] . The model predicted that the environmental variables that best explain 75% of the distribution of Ae . aegypti breeding sites were: the percentage of bare soil ( 44 , 9% ) , percentage of urbanization ( 13 , 5% ) , and water distribution ( 11 , 6% ) ( Table 3 ) .
Population dynamics of Ae . aegypti observed in our study , during 5 years of continuous work , allowed us to evaluate and fine tune our control strategy in the local context . Therefore , in the future , we would have to consider certain variables not currently contemplated: 1 ) determine the periodicity of control actions in accordance to the operational capacity of the work groups , 2 ) provide a solution for closed or uncooperative housing units which escape control activities and constitute sources of re-infestation , and finally , 3 ) determine the volume of data necessary for the elaboration of a highly predictive model for dengue transmission . | As reported in Porcasi et al . , in Argentina we are working on an integrated risk stratification system based in geospatial technologies that have moderately consolidated national scale , but need more understanding of its urban scale mechanisms . In this work , relevant results are shown on how Ae . aegypti breeding sites are distributed in dynamic spatial patterns in a small city on northern Argentina . 5 years of entomologic data were obtained by Mundo Sano Foundation , which is implementing an Aedes aegypti Surveillance and Control Program in Tartagal City ( Salta Province , Argentina ) . The focus of this contribution is based on the difference that can found between one year data typical analysis and long term temporal evolution of spatial patterns . Although environmental sanitation activities and chemical control of breeding sites with larvicide were performed after each entomological surveillance all around Tartagal , sectors with higher densities of breeding sites remained present throughout study period . Nonetheless , the distribution of breeding sites showed a spatial dynamic with high density clusters in city outskirts . | [
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... | 2016 | Temporal Dynamics and Spatial Patterns of Aedes aegypti Breeding Sites, in the Context of a Dengue Control Program in Tartagal (Salta Province, Argentina) |
Trichinellosis is a typical food-borne zoonotic disease which is epidemic worldwide and the nematode Trichinella spiralis is the main pathogen . The life cycle of T . spiralis contains three developmental stages , i . e . adult worms , new borne larva ( new borne L1 larva ) and muscular larva ( infective L1 larva ) . Stage-specific gene expression in the parasites has been investigated with various immunological and cDNA cloning approaches , whereas the genome-wide transcriptome and expression features of the parasite have been largely unknown . The availability of the genome sequence information of T . spiralis has made it possible to deeply dissect parasite biology in association with global gene expression and pathogenesis . In this study , we analyzed the global gene expression patterns in the three developmental stages of T . spiralis using digital gene expression ( DGE ) analysis . Almost 15 million sequence tags were generated with the Illumina RNA-seq technology , producing expression data for more than 9 , 000 genes , covering 65% of the genome . The transcriptome analysis revealed thousands of differentially expressed genes within the genome , and importantly , a panel of genes encoding functional proteins associated with parasite invasion and immuno-modulation were identified . More than 45% of the genes were found to be transcribed from both strands , indicating the importance of RNA-mediated gene regulation in the development of the parasite . Further , based on gene ontological analysis , over 3000 genes were functionally categorized and biological pathways in the three life cycle stage were elucidated . The global transcriptome of T . spiralis in three developmental stages has been profiled , and most gene activity in the genome was found to be developmentally regulated . Many metabolic and biological pathways have been revealed . The findings of the differential expression of several protein families facilitate understanding of the molecular mechanisms of parasite biology and the pathological aspects of trichinellosis .
Thichinella spiralis is often referred as one of the largest intracellular parasite that cause trichinellosis with an estimation of more than 10 million people infected world-wide [1] , [2] . Like many other food-borne zoonotic parasites , T . spiralis exists in several life-cycle stages . The complex life cycle of T . spiralis is completed in two niches , the intra-multicellular niche in intestinal epithelium ( adults , Ad ) and the intracellular niche in the skeletal muscle fibers ( muscle larvae , ML ) . After being ingested with infected muscle tissue , the ML are released and revived in the small intestine , which invade the epithelial layer where they mature , mate and produce the newborn larvae ( NBL ) . The NBL migrate through the lymphatic and blood vessels , invade striated muscle cells and develop into the ML , which is infective to the next host [3] . Thus , unlike other nematodes , T . spiralis are ovoviviparous [4] , [5] , which makes them evolutionary divergent from other nematodes previously analyzed by genomic approaches [6] , especially the well-characterized free-living worm Caenorhabditis elegans . C . elegans is the first multicellular organism , of which the genome has been sequenced and phylogenetically classified in Clade V [7] . T . spiralis is a member of clade I that diverged early in the evolution of the Nematoda , with remarkably different biological and molecular characteristics from other nematodes [6] . Previous analysis of gene expression of T . spiralis at different developmental stages has mainly been carried out by sequencing of expression sequence tag ( EST ) [6] . However , the fragmentary data of ESTs are insufficient for a full understanding of the parasite biology . Recent study indicated that T . spiralis has a much smaller genome with an estimation of 64 Mb in nucleic DNA , coding for around 15 , 808 proteins [8] . The availability of the genomic information has made it possible for a deep dissection of the parasite's basic biology . In recent years , next-generation sequencing ( NGS ) techniques have dramatically improved the efficiency and the speed of gene discovery [9] , [10] . NGS technology generates millions of short sequence reads from a single instrumental run which can be effectively assembled and employed for gene discovery and comparison of gene expression patterns . Further , NGS allows for a detection of genes with very low expression levels . It has been frequently used to characterize specific gene families or genetic pathways . In this study , we compared gene expression variations among the three developmental stages of T . spiralis using next generation sequencing technology . Thousands of genes , especially the genes involving in host/parasite interactions and parasite development were identified . The data will facilitate discovery of potential vaccine and drug targets of T . spiralis .
Muscle larvae ( ML ) of T . spiralis ( strain ISS534 ) were obtained from rats at 35 days post infection by digestion of minced skeletal muscle according to the previously described method [11] , [12] . The adult worms and newborn larvae were collected as described previously [12] , [13] . The study of using laboratory animals was reviewed and approved by the Ethical Committee of Jilin University affiliated to the Provincial Animal Health Committee , Jilin Province , China ( Ethical Clearance number IZ-2009-008 ) . All animal work was conducted according to the guidelines of the Chinese Law of Animal Protection ( Section 6 ) . Total RNA of T . spiralis ( Ad , NBL and ML ) was purified using Trizol reagent ( Invitrogen , CA , USA ) according to the manufacturer's instructions . RNAs were dissolved in Diethylpyrocarbonate ( DEPC ) -treated water and treated with DNase I ( Invitrogen , CA , USA ) . Total RNA was quantified by measuring the absorbance at 260 nm with a Nanodrop 1000 machine ( Thermo Scientific CA , USA ) . The DGE libraries were prepared by using the Illumina gene expression sample preparation kit [14] . Briefly , 6 µg of total RNA from each preparation was treated with Oligo- ( dT ) conjugated magnetic beads to purify mRNA . Double-strand cDNA was synthesized guided by the Oligo- ( dT ) as a primer and digested with the endonuclease NlaIII that recognizes the CATG sites . The Illumina adaptor 1 , containing a MmeI restriction site , was added to the cDNAs attached to the magnetic beads , which was further digested with MmeI . Following MmeI digestion and dephosphorylation , cDNA fragments were purified and the Illumina adaptor 2 was ligated to the 3′ends of the tags to create tag library with different adapters at both ends . After15 cycles of linear PCR amplification , the 95 bp fragments were purified from 6% TBE PAGE gels and attached to the Illumina sequencing chip for sequencing . After removal of low quality and adaptor sequences , the clean 21 bp tag sequences containing CATG were mapped onto the reference genome sequences , allowing for no more than one nucleotide mismatch . To compare the differences in gene expression in three DGE libraries , the tag frequency was statistically analyzed according to the method FDR ( False Discovery Rate ) , a method used to determine the threshold of P-value in multiple tests [15] . A FDR<0 . 001 and an absolute value of the log2Radio>1 was used as the threshold to judge significant differences in gene expression . KEGG Ontology ( KO ) of the transcripts was identified trough blasting the KEGG database . Gene Ontology ( GO ) analysis of all differentially expressed genes was performed by searching in the GO database . The enriched p-values of KO and GO were calculated according to the hypergeometric test [16]:In this equation , N means the number of genes with GO/KO annotation , n means the number of differentially expressed genes in N , M means the number of genes in each GO/KO term , and m represents the number of differentially expressed gene in each GO/KO term . For GO enrichment analysis , all P-values were treated with Bonferroni correction . We selected a corrected p-value , 0 . 05 as a threshold to determine significant enrichment of the gene sets . In contrast , for KO enrichment analysis , we used a FDR , 0 . 05 as a threshold to determine significant enrichment of the gene sets . WEGO was employed to make a GO classification [17] . Phylogenetic tree was built for DNase II protein families using PHYLIP ( version 3 . 69; [18] ) after aligning the family members with CLUSTAL X ( version 2 . 1 ) . And a neighbor joining tree was generated using PHYLIP-NEIGHBOR . Then , the phylogenetic tree was visualized and edited using the Tree Figure Drawing Tool - FigTree ( version 1 . 3 . 1 ) . Total RNA of T . spiralis ( muscle larvae , adult worms and newborn larvae ) was extracted using Trizol reagent ( Invitrogen , CA , USA ) and treated with Dnase I ( Invitrogen , CA , USA ) . The RNAs were dissolved in Diethylpyrocarbonate ( DEPC ) -treated water and reverse transcribed with 200 U SuperScript™ III Reverse Transcriptase ( Invitrogen ) according to the manufacturer's instructions . The specific primers were listed in File S1 as forward and reverse primers based on the stage-specifically expressed genes . The GAPDH gene was used as an endogenous reference . The qPCR was performed using the SYBR Kit ( Applied Biosystems , Foster City , CA , USA ) according to the manufacturer's protocol using an Applied Biosystems 7500 detection system . The relative expression was analyzed using the SDS1 . 4 software ( Applied Biosystems , Foster City , USA ) .
To generate digital gene expression signatures of T . spiralis at different developmental stages , DGE libraries were generated from the three developmental stages of the parasite , and sequenced using Solexa ( Illumina ) high through-put technology . A total of 5 , 289 , 863 tags from muscular larvae ( ML ) , 5 , 214 , 135 tags from adult worms ( Ad ) , 5 , 055 , 659 tags from newborn larvae ( NBL ) were obtained ( Table 1 ) . After filtering the low quality tags and adaptor sequences , the total number of clean tags in ML , Ad and NBL were 5 , 077 , 645 ( 96 . 0% of total tags ) , 5 , 003 , 105 ( 96 . 0% of total tags ) , 4 , 849 , 883 ( 96 . 0% of total tags ) , respectively ( Table 1 ) . The sequence data has been submitted to the GEO website ( ftp://ftp-private . ncbi . nlm . nih . gov/fasp/ ) with an accession number of GSE39151 . Heterogeneity and redundancy are two significant characteristics of gene expression in the three developmental stages of T . spiralis , which have previously been observed in other metazoans [19] . The distribution of clean tags in the three libraries shows a consistent pattern , with most of the tags coming from highly expressed genes . The percentage of distinct tags with high counts dropped dramatically and the distinct tags with more than 100 copies accounted less than 10% . However , more than 75% of total clean tags have an account above 100 ( File S2 ) . The clean tags were then mapped onto the draft genome of T . spiralis ( ftp://ftp . ncbi . nlm . nih . gov/genbank/wgs/wgs . ABIR . 1 . gbff . gz ) [8] and the numbers of tags that could be mapped onto genes with no more than one base pair mismatch in Ad , ML , NBL were 2 , 670 , 150 , 2 , 187 , 559 and 2 , 254 , 426 , respectively ( Table 1 ) . In total , around 10 , 000 genes were identified from the three libraries , accounted for approximately 65% of genes in the annotated genome [8] ( File S3 ) . To identify genes that differentially expressed in the three developmental stages , gene expression variations were analyzed by pair-wise ( Ad versus ML , NBL versus Ad , and NBL versus ML ) comparison of the sequence tags . A number of genes were found differentially expressed between the developmental stages ( Figure 1 and File S4 ) . The number of differentially expressed genes between NBL and Ad was more than that between Ad and ML . And the number of up-regulated genes expressed in NBL was more than that in the other two stages . Apart from the differentially expressed genes , a number of stage-specific genes were identified and the number of stage-specific genes expressed in NBL was twice as much as that of Ad or ML ( Figure 2 ) . Among the differentially expressed genes , genes coding for families of proteases such as astacin protease , serine protease , and DNase II ( Table 2 , and File S4 and S5 ) were more prominent . Serine protease and DNase II constitute the two excretory-secretory ( E-S ) protein families involved in host-parasite interactions in trichinellosis [6] , [20] . The two gene families showed obviously stage-specific variations in expression in three developmental stages . 47 differentially expressed DNase II family genes were identified and most of these genes were up-regulated in NBL compared to the other stages ( Figure 3 , Table 2 and File S6 ) . In contrast , only 6 DNase II genes were up-regulate in ML compared to the other stages ( Table 2 and File S6 ) . Further , most of these genes have homology with 27 previously identified DNase II homologues ( File S6 ) [20] . Tsp_11476 and Tsp_12138 showed very high expression in NBL , while Tsp_06568 was detected in rather high abundance in Ad; and Tsp_00874 and Tsp_00875 were mainly expressed in ML ( Figure 4A ) . Another interesting gene family is that encode serine protease . Contrast to DNase II family , most of serine protease genes were up-regulated in Ad compared to the other stages . In the three developmental stages of the parasite , a large number of serine protease family genes showed stage-specific expression , especially Tsp_00436 , Tsp_15812 , Tsp_07356 , Tsp_07750 and Tsp_14046 ( Figure 4B and Table 2 ) . Apart from the DNase II and serine protease families , genes encoding zinc metalloprotease , serine protease inhibitor ( serpin ) , the heat shock protein ( HSP ) , macrophage migration inhibitory factor ( MIF ) and antioxidant enzymes were also identified . Zinc metalloprotease is high homologous with nematode astacin protease . Genes coding for serpin and zinc metalloprotease showed a similar stage-specific expression pattern with up-regulation in NBL rather than the other stages , especially the genes like tsp_00173 , tsp_01570 , tsp_06688 , tsp_09479 , tsp_04481 , tsp_01304 , tsp_00804 and tsp_03942 . Superoxide dismutase ( SOD ) and glutathione perxidase are two important antioxidant enzymes which protect the parasite from reactive oxygen species . The genes encoding these enzymes also showed stage-specific expression . The genes tsp_01933 ( SOD ) and tsp_06126 ( SOD ) showed very high expression in NBL , while tsp_11103 ( SOD ) and tsp_02268 ( glutathione perxidase ) were detected in rather high abundance in ML . Tsp_06335 encoding MIF and tsp_11249 encoding cystatin were mainly expressed in Ad . The gene encoding HSP 70 ( Tsp_06317 ) was found up-regulated in ML ( Table 2 and File S7 ) . The enzymes involved in metabolisms showed obviously stage-specific in transcription pattern . Phosphofructokinase ( tsp_05639 ) , enolase ( tsp_09466 ) and Pyruvate Kinase ( tsp_08030 ) were up-regulated in NBL . Whereas , the expression of Tsp_08363 encoding the major hexokinase isoenzyme showed no significant differences . Tsp_05267 encoded the minor hexokinase isoenzyme and was mainly expressed in Ad and ML . Lactate dehydrogenase ( tsp_08060 ) and phosphoenolpyruvate carboxykinase ( tsp_007989 ) , the key enzymes in anaerobic metabolism , were up-regulated in NBL rather than the other stages . Whereas Two genes ( tsp_03114 , tsp_08643 ) encoding subunits of pyruvate dehydrogenase , which associated with glycolysis in the citric acid cycle via conversion of pyruvate to acetyl-CoA were mainly expressed in Ad and ML . Citrate synthase ( tsp_01728 ) and isocitrate dehydrogenase ( tsp_05617 ) were up-regulated in Ad . Another isocitrate dehydrogenase ( tsp_06181 ) was up-regulated in ML ( Table 2 and File S7 ) . In order to verify the genes that were actually differentially expressed in the three developmental stages , the expression of 16 genes respectively coding for DNase II ( Tsp_11476 , Tsp_12138 , Tsp_06568 , Tsp_00874 and Tsp_00875 ) , serine protease ( Tsp_00436 , Tsp_15812 , Tsp_07356 , Tsp_07750 and Tsp_14046 ) , and 6 genes respectively encoding heat shock protein A ( Tsp_06317 ) , macrophage migration inhibitory factor ( MIF ) ( Tsp_06335 ) , cystatin ( Tsp_11249 ) , systeine-glycine ( Tsp_01806 ) and two genes with unknown functions ( Tsp_05189 and Tsp_11467 ) were analyzed by quantitative real-time PCR . The q-PCR results confirmed the data obtained in the sequencing analysis ( Figure 4 A , B and C ) . Among the transcripts of the 9 , 969 genes identified , 35% ( 3 , 496 ) could be assigned into one or more GO categories which were consistent with previous studies [8] . The remaining uncharacterized genes are likely to fulfill novel functions . In each of the three main categories ( molecular function , cellular component and biological process ) of the GO classification , ‘binding and catalytic activity’ , ‘cell and cell part’ and ‘cellular process and metabolic process’ are dominant ( Figure 5 and File S8 ) . In KO classification , 156 differentially expressed genes were significantly enriched in Ad versus ML , 408 genes in NBL versus ML and 395 genes in NBL versus Ad . Most of these genes encode proteins participating in ‘metabolism’ , ‘environmental information processing’ , ‘cellular processes’ and ‘organism systems’ , i . e . glycolysis/gluconeogenesis , oocyst meiosis , calcium signaling pathway , vascular smooth muscle , insulin signaling pathway and lysosome ( File S9 ) . The comparison between Ad and the other two stages revealed that most of the expression of genes related to oocyst meiosis was up-regulated in Ad , while most of the genes correlated to glycolysis/gluconeogenesis , calcium signaling pathway and vascular smooth muscle contraction were up-regulated in NBL instead of Ad and ML . One of the advantages of DGE technique is that it could reveal transcripts with strand specificity [14] . We thus analyzed the sense and antisense transcripts of the transcriptome obtained . Of the 9 , 969 genes identified in this study , approximately 70% showed evidence of transcription in both orientations . The antisense transcripts obtained in Ad , ML and NBL were 6 , 303 , 6 , 690 , 7 , 033 , respectively ( File S10 ) . While the transcripts from both strands obtained in Ad , ML and NBL were 5 , 269 , 5 , 657 and 6 , 058 , respectively . Among these genes , 2 , 682 , 2 , 818 and 3 , 105 genes had tags corresponding to sense strands more than that from antisense strands in Ad , ML and NBL . Further , 2 , 476 , 2 , 662 and 2 , 867 genes had more tags from antisense strands than that from sense strands in Ad ML and NBL stages ( Table 3 and File S10 ) .
The availability of the draft genomic sequence of T . spiralis has made it possible to deeply investigate the global gene expression and gene regulation mechanisms in the development of the parasite . By comparing the DGE libraries obtained from three developmental stages of T . spiralis , we have identified a large number of functionally important genes with differential expression features . First , like in other organisms with multiple developmental stages , the genome of T . spiralis is developmentally regulated . More than 70% of the genes in the genome were found activated in all three developmental stages , of which between 5–11% of genes are preferentially expressed at different stages . In general , more transcripts were identified in the new borne larvae , suggesting that genes at this stage are more active ( Figure 1 , 2 , Table 1–2 and File S2 ) . Unlike adult worms , the new-borne larvae will need to invade intestine epithelium and establish new niches in the tissue , thus the parasite need a different biological arsenal from the adult stage for adaptation and development in the host . Secondly , functional analysis of the transcripts revealed a large number of genes encoding excretory-secretory proteins , especially families of parasite-derived DNase II and proteases . Previous studies have indicated that families of serine proteases and DNase II of T . spiralis were important parasitic components in host/parasite interactions and modulation of host immune responses [20]–[24] . Serine proteases have been proved to be critical for invasion of the mammalian host cells by trypanosoma cruzi and steinernema carpocapsae [25]–[28] . Here , we found 30 genes coding for homologous serine proteases were differentially expressed in the development of the parasite ( Table 2 and Files S4 and S5 ) . With the identification of the stage-specific expression of these proteases , it is now possible to further explore their importance in parasitization and as target for intervention such as vaccine development . DNase II belongs to the acidic endonuclease family . They are essential nucleases nucleases that exist in non-metazoan , fulfilling a variety of functions from digesting DNA of apoptotic cell corpses and dietary DNA to modulating host immune responses [24] . They possess a histidine-rich domain which is believed to be the functional core of the protein family . Though there is more than one copy of DNase II genes in many organisms , the number of DNase II genes in T . spiralis is an exception with 47 copies identified in the strain studied . Phylogenetic analysis showed that these DNases exist in different subgroups ( Figure 3 ) . In light of the findings of their expression variation in different developmental stages , it is postulated that different variants fulfill different functionality . Recent studies suggested that DNase II in T . spiralis may function as self-protective molecules through modulation of host innate immune responses by cleaving DNA from apoptotic host cells [20] , [29] . Deep investigation of functional specialty of the DNase II family members will be able to reveal mechanisms of host-parasite interaction and pathogenesis of trichinellosis . Apart from the above protease and DNase II families , genes encoding heat shock protein 70 ( HSP ) , macrophage migration inhibitory factor ( MIF ) , systeine-glycine protein and cystatin were also found to be expressed in stage-specific manner . These molecules have been previously reported to be important in host/parasite interactions [30]–[35] . It has been proved that the parasites produce HSPs upon heat or oxidative stress that are related to resistance to harsh environment changes and are therefore beneficial to their survival [30] . MIF is a cytokine ubiquitous in mammals . However , many pathogens have genes encoding MIF homologues and the function of pathogen-derived MIF is believed to modulate host immune responses [35] . Thus it is not a surprise that T . spiralis also possesses this gene . Cystatins is a kind of cysteine protease inhibitor and has been reported as an important immuno-modulatory factor that contributes to the immune evasion strategies of the parasites [31] . Zinc-dependent metalloprotease of T . spiralis showed significant homology to the astacin metalloprotease family of Caenorhabditis elegan . Astacin metalloprotease have diverse functions including hydrolysis of extracellular matrix components such as type I collagen [36] . Since the parasites need to degrade the fibrinogen and plasminogen when they invade the epithelial and muscul cells ( Ad and ML ) or migrate to the small mesenteric veins ( NBL ) , zinc-dependent metalloproteinases might be one of the effecter molecules in the process of tissue invasion . Serpins are a large protein family and can inactivate proteinases by forming complexes with serine proteinase . It has been reported that serpins could inhibit the host immune response and protects many parasites to evade the host immune defense [37]–[39] . Among the developmental stages , NBL is most fragile but is more exposed to the host immune system . That may explain why the genes encoding the zinc-dependent metalloprotease and serpin were up-regulated in NBL . Theses enzymes can help NBL for a more efficient penetration of host defensive barriers and , in the meantime , avoid the immune attack . SOD and glutathione perxidase are two main important antioxidants . SOD can catalyze the conversion of superoxide anion into hydrogen peroxide and molecular oxygen , while glutathione perxidases catalyze the reduction of hydrogen peroxide along with oxidation of glutathione to glutathione disulfide . Secretion of antioxidant enzymes is believed to protect the parasite from reactive oxygen species which arise from host phagocytes [40] . Like DNase II and serine protease families , a number of genes encoding SOD and glutathione perxidases were identified and some of them showed different expression pattern in various developmental stages of T . spiralis . It is suggested that the DNase II , serine protease , SOD and glutathione perxidase were all contributed to self-protection of the parasite during invasion . Thirdly , functional annotation of differentially expressed genes has partially revealed biological processes in different developmental stages of the parasite ( Figure 5 and File S9 ) . For example , oocyst meiosis is directly involved in the reproduction of the parasites . Therefore , most of differentially expressed genes related to oocyst meiosis were up-regulated in Ad compared to the other two stages , which was likely due to the preparation for the production of eggs . On the other hand , glycolysis/gluconeogenesis , calcium signaling pathway and vascular smooth muscle contraction pathways are mainly involved in energy metabolism , development and structure of tissue regeneration . Thus genes associated with these pathways were found up-regulated in NBL instead of Ad and ML due to the rapid growth . Previous study indicated that there is a shift from anaerobic to aerobic metabolism in the developmental stage from ML to Ad [41]–[43] , whereas no related information about the metabolism of NBL is available . In this study , we analyzed the differential expression pattern of the genes encoding the key enzymes involved in energy metabolisms . Phosphofructokinase , enolase and Pyruvate Kinase are essential enzymes in glycolysis . Genes coding for the three enzymes were up-regulated in NBL which indicated that energy metabolism were more activated in the NBL stage . Lactate dehydrogenase is a critical enzyme in anaerobic metabolism which can catalyze the conversion of pyruvate into lactate . Another key enzyme in anaerobic metabolism is phosphoenolpyruvate carboxykinase which can catalyze the conversion of phosphoenolpyruvate ( PEP ) into oxaloacetate and the reverse citric acid cycle . The genes encoding the two enzymes were found mainly expressed in NBL . Thus it is likely that T . spiralis adapted mainly an anaerobic metabolism in NBL stage . On the other hand , the expression of genes encoding pyruvate dehydrogenase , citrate synthase and isocitrate dehydrogenase , critical enzymes involved in citric acid cycle in aerobic metabolism , were up-regulated in Ad . No obvious significant differences in the expression of genes involved in the citric acid cycle and anaerobic metabolism between ML and Ad was observed . Lastly , recent studies have revealed widespread expression of complementary sense-antisense transcript pairs by transcriptome sequencing [14] , [44]–[47] and antisense-mediated gene regulation in developmental processes of different organisms have been proposed [48]–[50] . More than 7000 of the protein-coding genes were found bi-directionally transcribed in T . spiralis , accounted for approximately 70% of the identified genes in this study and 45% of all estimated genes in the genome , which was similar to that observed in S . japonicum , mice and human [14] , [44] , [45] , [51] . To our knowledge , this is the first study to observe such abundant antisense transcripts in T . spiralis . Since the antisense transcripts were also polyadenylated , antisense RNAs can encode proteins [45] , [51] , [52] . However , most antisense transcriptions in the mammalian genome were found to be non-protein-coding RNAs [45] , [49] , [51] . Though the function of antisense transcripts has not been well understood , functional validation studies indicated that antisense transcripts were a heteromorphous group with common features and may modulate the expression level of the sense transcripts or influence the sense mRNA processing [53] . The mechanism of such regulation remains unknown; however , a mechanism of gene regulation by natural antisense transcripts ( NAT ) derived endogenous siRNAs ( endo-siRNAs ) has recently emerged . Endogenous siRNAs derived from transposable elements , NAT and long hairpin RNAs have been identified in Drosophila and mouse and S . japonicum . Endo-siRNAs can silence homologous transcripts by RNA interference ( RNAi ) . Therefore , it is likely that natural antisense transcripts as sources of endo-siRNAs possess similar regulatory function in T . spiralis as in other organisms [13] . In summary , approximately 65% of genes in T . spiralis genome were identified and a large number of functionally interesting genes were discovered and analyzed in the three developmental stages of the parasite through high through-put RNA sequencing techniques . More than 45% of the protein-coding genes showed evidence of transcription from both sense and antisense strands . The data from this study has paved a way for deep investigation of the parasite biology and host parasite interaction . | Trichinellosis of human and other mammals was caused through the ingestion of the parasite Trichinella sparilis in contaminated meat . It is a typical zoonotic disease that affects more than 10 million people world-wide . Parasites of the genus Trichinella are unique intracellular pathogens . Adult Trichinella parasites directly release newborn larvae which invade striated muscle cells and causes diseases . In this study , we profiled the global transcriptome in the three developmental stages of T . spiralis . The transcriptomic analysis revealed the global gene expression patterns from newborn larval stage through muscle larval stage to adults . Thousands of genes with stage-specific transcriptional patterns were described and novel genes involving host-parasite interaction were identified . More than 45% of the protein-coding genes showed evidence of transcription from both sense and antisense strands which suggests the importance of RNA-mediated gene regulation in the parasite . This study presents a first deep analysis of the transcriptome of T . spiralis , providing insight information of the parasite biology . | [
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] | 2012 | Global Gene Expression Analysis of the Zoonotic Parasite Trichinella spiralis Revealed Novel Genes in Host Parasite Interaction |
Scrub typhus is a vector-borne zoonotic disease that can be life-threatening . There are no licensed vaccines , or vector control efforts in place . Despite increasing awareness in endemic regions , the public health burden and global distribution of scrub typhus remains poorly known . We systematically reviewed all literature from public health records , fever studies and reports available on the Ovid MEDLINE , Embase Classic + Embase and EconLit databases , to estimate the burden of scrub typhus since the year 2000 . In prospective fever studies from Asia , scrub typhus is a leading cause of treatable non-malarial febrile illness . Sero-epidemiological data also suggest that Orientia tsutsugamushi infection is common across Asia , with seroprevalence ranging from 9 . 3%–27 . 9% ( median 22 . 2% IQR 18 . 6–25 . 7 ) . A substantial apparent rise in minimum disease incidence ( median 4 . 6/100 , 000/10 years , highest in China with 11 . 2/100 , 000/10 years ) was reported through passive national surveillance systems in South Korea , Japan , China , and Thailand . Case fatality risks from areas of reduced drug-susceptibility are reported at 12 . 2% and 13 . 6% for South India and northern Thailand , respectively . Mortality reports vary widely around a median mortality of 6 . 0% for untreated and 1 . 4% for treated scrub typhus . Limited evidence suggests high mortality in complicated scrub typhus with CNS involvement ( 13 . 6% mortality ) , multi-organ dysfunction ( 24 . 1% ) and high pregnancy miscarriage rates with poor neonatal outcomes . Scrub typhus appears to be a truly neglected tropical disease mainly affecting rural populations , but increasingly also metropolitan areas . Rising minimum incidence rates have been reported over the past 8–10 years from countries with an established surveillance system . A wider distribution of scrub typhus beyond Asia is likely , based on reports from South America and Africa . Unfortunately , the quality and quantity of the available data on scrub typhus epidemiology is currently too limited for any economical , mathematical modeling or mapping approaches .
Scrub typhus is an infectious disease caused by Orientia tsutsugamushi , an obligate intracellular bacteria , transmitted by the bites of chigger mites [1] . In Southeast Asia , scrub typhus is a leading cause of treatable non-malarial febrile illness [2] . The first accounts linking febrile illness with the appearance of “harmful” mites ( Japanese: “tsutsuga” mushi ) range back to 313 AD in China [3] . Scrub typhus was originally associated with the Asian-Pacific “Tsutsugamushi triangle , ” until recent evidence from the Arabian Peninsula , Chile and possibly Kenya suggested a wider global distribution in tropical and subtropical regions [4–7] . The use of improved diagnostic methods , increased medical investigations and awareness have recently contributed to greater recognition of scrub typhus in some countries , such as in Laos , India , southern China , South Korea , and Japan [8] . There is also evidence suggesting that a combination of climate change and expansion of humans into previously uninhabited areas may play a role in both re-emergence and apparent rising incidence of scrub typhus [9–11] . There are no licensed vaccines for scrub typhus , and no systematic vector control efforts in place . Despite increasing awareness in endemic regions , the public health burden and global distribution of scrub typhus remains poorly known . Although scrub typhus received much attention before and during the Second World War and to a lesser degree during the Vietnam/American war , basic epidemiology is poorly understood with limited data on incidence and burden of disease for patients , their families , societies and the economy . This ignorance is probably due to a combination of factors; clinical presentation is very similar to other causes of fever , diagnostic difficulties contribute to mis-diagnosis and under recognition , and appropriate diagnostic tests are not widely available . Following the discovery of chloramphenicol in the 1940s , the scientific interest dropped rapidly and scrub typhus has since received little global attention [12] . The data quoted by the World Health Organization ( WHO ) stating that over a billion people are at risk and one million cases are estimated per year is referenced to a paper published 20 years ago in 1997 [13 , 14] . Extrapolation based on geographical mite distributions and densities are not helpful due to patchy data , limited by the dynamics of infected mite populations and insufficient characterization of transmitting vectors . With new data and improvements in approaches to estimating the burden of febrile illnesses , it is important to reevaluate the burden of scrub typhus . Rationale for this study: Scrub typhus is among the leading causes of undifferentiated treatable fever in Asia . The mortality rates appear low at first glance , but considering the numbers of those exposed and/or infected a significant disease burden is expected globally . The following research questions were addressed: What is the estimated global burden of disease for scrub typhus ? What data on seroprevalence and minimum incidence for scrub typhus are available by geographical regions ? What data on DALYs , YLLs and YLDs are available , and what is the mortality rate of treated scrub typhus ? In this study we summarized the literature relating to the disease burden and economic impact of scrub typhus since the year 2000 in order to estimate the global incidence and burden of this disease .
A literature search of three databases: Ovid MEDLINE ( 2000-present ) , Embase Classic + Embase ( 2000-present ) and EconLit ( 2000-present ) was conducted on 11th April 2016 using three search strategies . First search terms: Scrub typhus , Orientia tsutsugamushi , Rickettsia tsutsugamushi , chigger borne rickettsiosis , chigger borne typhus , Orientia tsutsugamushi infection , Rickettsia tsutsugamushi infection , tsutsugamushi disease , tsutsugamushi fever ( keyword ) AND prevalence , incidence , epidemiology . A second search included the above search for scrub typhus and all variations AND cost , cost analysis , cost of illness , drug costs , economics , health care cost , hospital costs , cost benefit analysis , cost effectiveness analysis , quality adjusted life year . A third search included scrub typhus AND mortality or death on the 1st Oct 2016 , for which all currently available data was included ( no date restrictions ) . Data on untreated mortality have been reported [15] , and therefore only papers with treated infection were included in estimating mortality . All titles and abstracts were reviewed by 2 authors for inclusion and any disagreements were discussed and inclusion based on the senior author’s opinion . Only English language publications were included . A total of 190 publications were selected for full article review ( Fig 1A and 1B , S1 File ) . The final number of articles included for full data extraction was 87 . The data extraction form was trialed on the first 5 papers and required minor alterations . Due to the limited nature of data available no summary measures were applied . Studies were examined for selection bias and graded as follows: Papers were also graded on diagnostic tests used:
Five countries report a passive national surveillance system for scrub typhus . In South Korea scrub typhus was designated a group III notifiable disease ( requiring mandatory reporting and routine monitoring ) in 1994 . Cases are confirmed by the Korean Centre for Disease Control and Prevention ( KCDC ) and must show one of the following: an increase in the IFA IgM to O . tsutsugamushi of ≥ 1:16; an increase in the anti-O . tsutsugamushi IFA IgG titre to ≥1:256; a ≥ 4 fold increase in IFA titre . Data from KCDC suggest that the annual minimum incidence increased from 5 . 7 to 17 . 7/100 , 000 people from 2001 to 2012 ( >3-fold ) ( Table 1 ) [16–18] . Interestingly , the number of patients recorded in urban areas has also increased dramatically , for example , the annual minimum incidence in Ulsan Metropolitan City increased from 2 . 8/100 , 000 in 2003 to 59 . 7/100 , 000 in 2013 ( >21 fold ) . In Seoul there is evidence of urban scrub typhus , further demonstrating the changing geographical scope and habitat of infected chigger mites [16] . In Japan scrub typhus is a notifiable disease and must be reported to the National Epidemiological Surveillance of Infectious Diseases ( NESID ) within 7 days of diagnosis by a physician . Confirmed cases are based on: isolation or identification of the organism in the blood; PCR positivity; detection of serum IgM; a ≥ 4 fold increase in IFA titre . Data from NESID show an increase of annual minimum incidence from 0 . 6/100 , 000 in 2000 to 3 . 6/100 , 000 in 2008 ( 6-fold ) [19 , 20] . In Thailand , scrub typhus patients have been reported to the Bureau of Epidemiology for the last 30 years . Data can be viewed online on the homepage available under URL: http://www . boe . moph . go . th/boedb/surdata/disease . php ? dcontent=situation&ds=44 Cases are defined based on one or more of the following: isolation or identification of the organism in the blood or tissue sample; PCR positivity; a ≥ 4 fold increase in IFA titre ( IgG and/or IgM ) ; ≥ 1:400 IFA in acute serum ( IgG and/or IgM ) ; IgM ELISA positivity . Data from the Bureau of Epidemiology noted an increase of annual minimum incidence from 6 . 0/100 , 000 in 2003 to 17 . 1/100 , 000 in 2013 ( 2 . 9 fold ) [21] . In China , scrub typhus is a notifiable disease that must be reported to the China Center for Disease Control and Prevention . Cases are defined as those with clinically compatible infection and one or more of the following; isolation or identification of the organism in a blood or tissue sample; PCR positivity; a ≥ 1:160 Weil-Felix test; a ≥ 4 fold increase in IFA titre ( IgG and/or IgM ) . The reported countrywide minimum incidence increased from 0 . 1/100 , 000 to 1 . 1/100 , 000 people/year from 2006 to 2014 ( >11-fold ) [22] . The reported incidence rates vary widely by region with the southern provinces more affected . Guangdong Province saw an increase in reported annual minimum incidence from 0 . 4/100 , 000 to 3 . 6/100 , 000 people from 2006 to 2013 ( >8-fold ) , whereas in 2012 the provinces of Laiwu and Guangzhou City had annual incidences of 5 . 5/100 , 000 and 9 . 9/100 , 000 people , respectively [10 , 23–25] . There are seroprevalence data available from Bangladesh , Indonesia , Laos , Malaysia , Papua New Guinea and Sri Lanka ( Table 1 ) . Seropositivity ranged from 9 . 3%–27 . 9% suggesting high background exposure levels to O . tsutsugamushi in these countries [26–31] There are several case series describing the frequency of scrub typhus among patients presenting with fever . In India , scrub typhus was the causative agent in 16 . 1–96 . 9% of febrile patients presenting to hospitals ( Table 2 ) . However , these studies all suffer from selection bias , as other causes of febrile illness had already been excluded . Studies from Cambodia , Laos , Nepal , and Kenya were subject to less bias as they included complete prospective series of patients presenting with fever to healthcare facilities and demonstrated rates from 1 . 8–22 . 3% ( Table 2 ) . Data from specific sub-populations are presented in Table 3 . Two studies describe the importance of scrub typhus in women during pregnancy from Laos and the Thai-Myanmar border—with scrub typhus occurring in 3 . 6–5 . 4% of febrile patients [32–34] . Maternal infection with scrub typhus during pregnancy was associated with poor maternal and fetal outcomes; 2/9 ( 22 . 2% ) of cases in Laos and 4/11 ( 36 . 4% ) in Thailand/Myanmar suffered either abortion or stillbirth . Among Lao patients with meningitis/encephalitis , 16 . 0% of those with a diagnosed bacterial cause for their infection had evidence for scrub typhus [35] . However , only 54 . 8% of these patients received treatment with appropriate antimicrobials during admission and the mortality rate associated with CNS complications was 13 . 6% . There are no data on morbidity or long-term sequelae available . National surveillance data from patients in China , Japan , Korea and Taiwan suggest that the age group of 60–69 years was at highest risk of scrub typhus [18 , 20 , 22 , 36] . In Thailand those aged 45–54 years were most commonly infected . In Japan and Thailand males were more at risk of scrub typhus but in all other countries with reports , females are more at risk . In South Korea , China , Taiwan and Thailand farmers were most at risk ( 38 , 183/54 , 558–70% of infections in China from 2006–2014 ) ; unfortunately such data are lacking from Japan . Age stratification in untreated mortality revealed increasing risk with increasing age , with the age classes 51–60 and >60 years old associated with a 45 . 6% and 59 . 8% mortality rate respectively [15] . The long-term impact of infection with scrub typhus has barely been examined . In Taiwan the hazard ratio of developing acute coronary syndrome was 1 . 4 ( 95% CI 1 . 1–1 . 8 ) in those with previous infection with scrub typhus compared to the general population without [37] . A recent case series from India that included patients with unexplained fever and/or multi-system involvement , found 24 . 4% to have scrub typhus , and 53 . 1% of patients with scrub typhus had acute kidney injury [38] . A retrospective cohort of severe scrub typhus cases admitted to an ICU in South India , found that respiratory complications requiring mechanical ventilation occurred in 87 . 9% , and that dysfunction of 3 or more organ systems occurred in 85 . 2% [39] . Case fatality ratios vary widely between countries , with those countries with easily accessible and established health systems showing lower mortality rates compared to countries with limited facilities ( Fig 2 and Table 4 ) . In a previous review , untreated scrub typhus infection was associated with an estimated mortality of 6 . 0% ( median , range 0–70 . 0% ) [15] . This review of treated scrub typhus , which included 39 studies and 91 , 692 patients found a median mortality of 1 . 4% ( range 0–33 . 3% ) . The burden of disease data for scrub typhus is highly limited . Only one study , from Laiwu Province in China , has calculated the DALYs associated with scrub typhus [24] . This study estimated that 13 DALYs were lost due to scrub typhus across the province ( 6 in males , 7 in females at a rate of 1 . 06/100 , 000 ) . However , in this province no deaths were reported and therefore these data cannot be extrapolated to countries such as India or Laos with evidence of scrub typhus associated mortality . A South Korean study evaluating the net benefit of a scrub typhus prevention program , estimated the cost of scrub typhus ( medication and hospital costs and loss of earnings ) at $6 . 6 million per year in 2008 [56] . However , scrub typhus mortality in South Korea was only 0 . 14% and 75% of patients with a diagnosis were hospitalized . Therefore , these figures cannot be applied to other economically poorer countries where health practice is very different [17] .
Only 5 countries have established scrub typhus surveillance systems . All of these have shown an increasing minimum incidence of scrub typhus over recent years , with increasing evidence of shift towards urbanized areas . However , the apparent increase in minimum incidence is confounded by local enhanced knowledge of the disease and it remains uncertain whether these data reflect true de novo emerging disease or emerging awareness of a pre-existing disease . Surveillance systems also use diverse diagnostic tests and therefore inter-country comparisons are not always possible . There are no data on whether these surveillance systems have been evaluated to determine an estimate of missed cases , however it is likely that the numbers are conservative estimates . Regardless of these flaws , surveillance systems are an essential part of disease control strategies . Improved febrile disease surveillance providing national data should be initiated in more afflicted countries , as this would result in morbidity and mortality data that could be used to direct healthcare resources , future vaccine demand and delivery and assessment of effectiveness of any control programs . Clearly , striving towards improved surveillance should be key , with a focus on providing reliable numerators ( using diagnostic assays with suitable sensitivities and specificities ) , and representative denominators ( well-defined target populations ) . Additionally , no ‘multiplier data’ or ‘multiplier studies’ are available—these are considered to improve estimation of incidence by using healthcare utilization surveys and to correct for under-ascertainment in healthcare facility studies [58] . Seroprevalence data was available from 5 countries only–indicating high background exposure levels , and therefore a high probability that larger numbers of unidentified and/or asymptomatic infections occur . Disease seroprevalence data must be interpreted with caution due to unknown antibody dynamics over time and uncertainty as to whether those seropositive became sick or were asymptomatic . In scrub typhus , both humoral and cell-mediated protective immune responses wane over time , but detailed understanding of this remains elusive [61] . Moreover , the population-wide frequencies of patients with reversion to seronegativity and potential disease susceptibility remain unknown , and therefore the actual exposure in these studies is likely to be substantially higher [62] . Scrub typhus is a leading cause of treatable non-malarial febrile illness in prospective fever etiology studies ( n = 14 ) . An increasing number of studies have unraveled the major contribution of scrub typhus to the febrile illness burden . However , the large variation of scrub typhus rates in prospective fever studies ( median 23 . 4% IQR 5 . 2–39 . 7 ranging from 1–96 . 9% depending on country and patient selection ) , reflect a lack of standardization and comparability among study designs and diagnostic modalities used . None of these studies have used modeling or extrapolation to take into account data from healthcare utilization surveys , which may give a more accurate idea of numbers of people with scrub typhus . In addition , recent studies have raised concern on the persistence of O . tsutsugamushi after treatment , especially using bacteriostatic drugs such as tetracyclines and macrolides [63 , 64] . Based on very limited data , scrub typhus is likely to have considerable impact on vulnerable populations–the median untreated mortality of scrub typhus in the elderly was ~29%—approximately 5-fold higher compared to the overall population mortality of 6% [15] . In women with scrub typhus during pregnancy , miscarriages occurred in 17% and poor neonatal outcomes in 42% of cases , which is more severe than the consequences of malaria in pregnancy [65] . Further , the mortality in patients requiring a lumbar puncture for scrub typhus CNS complications in Laos was 14% [59] . Scrub typhus is usually an easily treatable disease and the majority of these complications could be prevented by early recognition/diagnosis and increased usage of empirical doxycycline [66] . It is difficult to draw any definitive conclusions from the case-fatality data due to the heterogeneity in studies . They range from national surveillance data to case series of those admitted to ICU . National surveillance data from China , Japan and Korea provide case fatality ratios of 0 . 068–0 . 26% . However , the health facilities in these countries are significantly more advanced than other endemic countries . The fever studies from South India provide estimates of case fatality risk , but they vary from 0–33 . 3%—importantly , these data included patients who presented to hospital and therefore will miss those that do not have severe disease . DALY data are lacking in all countries except from one area of China , where a rate of 1 . 06/100 , 000 people was found , with a zero mortality rate . Case series and studies from Taiwan and India examining long-term complications , imply that the mortality and morbidity from scrub typhus is under-recognized and that possible long term consequences may occur many years later , and may be important contributors to the overall DALY burden [37 , 67] . Despite scrub typhus being the foremost cause of treatable febrile illness in Asia it is not evaluated by the Global Burden of Disease studies [68] . This study involved an extensive search of the literature and includes up-to-date and relevant studies . However , there are several limitations; as English is not the native language in the majority of countries where scrub typhus is endemic , there is a potential bulk of relevant literature that is not indexed in the databases used . The risks of publication bias and the heterogeneity of methods and reporting in the articles limit the conclusions . Specific difficulties relating to the diagnosis of scrub typhus suggest that studies reporting data from national surveillance systems are likely to suffer from missing data due to those that do not seek medical attention are misdiagnosed or not reported . The majority of fever studies suffers from selection bias and often relies on suboptimal diagnostic tools . Reports from Africa , the Middle East and most recently South America , suggest that scrub typhus is more widespread than previously appreciated . The molecular detection of Orientia spp . in rodents from Southern France and Senegal suggest that rodent-mite cycles could maintain the pathogen in nature but whether these Orientia spp . represent human pathogens is unknown [69] . The countries most affected by scrub typhus are currently experiencing profound demographic , economic and ecological changes [70] . Deforestation , growing cities and climate change may lead to migration of rodents carrying infected mites and expand to more urban and non-endemic areas [8 , 11 , 16] . Recently the impact of an earthquake on exposing the population to the possibly perturbed soil dwelling vectors causing scrub typhus was highlighted in Nepal [71] . Ancestor et al . mapped non-malarial causes of fever , including scrub typhus , in the Mekong region [2] . Kelly et al . developed a vector map of scrub typhus based on literature review to include probable and confirmed cases that included geo-referenced locations [72] . These are useful resources that can be built upon to estimate incidence in areas where data is limited . In scrub typhus the extracted information of studies from the 1940s requires careful consideration to identify what data are clinically relevant today . Derne et al . summarized and mapped the distribution of rickettsia and their vectors in Oceania , confirming the widespread presence and providing a scaffold to build upon [73] . Ideally , concerted efforts in providing well maintained up-to-date mapping of human cases and vector ( chigger mite ) distribution would contribute substantially to understanding the burden of disease . Burden of disease studies often use syndromic ‘envelopes’ for certain conditions ( for example “diarrhea” or “fever” ) . Developing a fever ‘envelope’ approach for estimating its burden of disease , in conjunction with detailed fever etiology studies would provide improved , standardized and globally comparable incidence data [74 , 75] . The resulting data could be stratified further and would inform on the actual burden of disease , as well as provide valuable baseline data to support economic evaluations and mathematical modeling of future interventions [76] . For example , an incentive for identifying endemic areas of scrub typhus may result in increasing cost-effectiveness of rapid diagnostic test ( RDT ) use . Testing for frequent bacterial pathogens is likely to be economical , reducing hospitalization rates , and informs not only treatment requirements , but also appropriate antibiotic usage [77] . In the case of dengue , the quality of data available has improved substantially and in 2010 there were an estimated 96 million apparent and 294 million unapparent dengue infections globally [78] . Although dengue and scrub typhus both top the list of fever etiologies in multiple studies in Asia , the more easily-treatable disease is neglected–it is time for more integrated expert collaborative research to provide these urgently needed objective data [57 , 78 , 79] . These data–despite their limitations–make a case for scrub typhus as an important neglected tropical disease of mainly rural populations , with an increasing urban proportion . In countries with established surveillance systems , the reported incidence is increasing and robust documentation of scrub typhus in Chile suggests a much wider global presence than previously understood . The lack of data on global incidence and disease burden highlights the need for this treatable infection to receive increased attention and research to inform health policy . | Scrub typhus is a mite-transmitted infectious disease that can be life-threatening . Diagnosing this disease is difficult , requiring special techniques that are often not readily available . As the actual impact of scrub typhus on the population and its geographical distribution remains unknown , we searched systematically for available information in medical databases . Scrub typhus is common: more than every fifth person in areas where scrub typhus occurs carry antibodies as a sign of previous contact . All countries with an established surveillance system have recorded an increase in scrub typhus cases over the past 8–10 years , while reports from South America and Africa suggest a wider distribution beyond Asia . Scrub typhus is a serious disease: approximately 6% of cases die if untreated , and 1 . 5% if treated , but mortality can reach 13% in areas where the usual treatment does not always work well . Death rates of complications are higher , reaching 14% in brain infections , 24% with multiple organ failure , and pregnancies with scrub typhus can have poor outcomes , with high miscarriage rates . Despite many limitations on the amount and quality of available reports , we found that scrub typhus is a severely underappreciated tropical disease , affecting mainly rural populations , but increasingly urban areas as well . | [
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Natural sounds convey perceptually relevant information over multiple timescales , and the necessary extraction of multi-timescale information requires the auditory system to work over distinct ranges . The simplest hypothesis suggests that temporal modulations are encoded in an equivalent manner within a reasonable intermediate range . We show that the human auditory system selectively and preferentially tracks acoustic dynamics concurrently at 2 timescales corresponding to the neurophysiological theta band ( 4–7 Hz ) and gamma band ranges ( 31–45 Hz ) but , contrary to expectation , not at the timescale corresponding to alpha ( 8–12 Hz ) , which has also been found to be related to auditory perception . Listeners heard synthetic acoustic stimuli with temporally modulated structures at 3 timescales ( approximately 190- , approximately 100- , and approximately 30-ms modulation periods ) and identified the stimuli while undergoing magnetoencephalography recording . There was strong intertrial phase coherence in the theta band for stimuli of all modulation rates and in the gamma band for stimuli with corresponding modulation rates . The alpha band did not respond in a similar manner . Classification analyses also revealed that oscillatory phase reliably tracked temporal dynamics but not equivalently across rates . Finally , mutual information analyses quantifying the relation between phase and cochlear-scaled correlations also showed preferential processing in 2 distinct regimes , with the alpha range again yielding different patterns . The results support the hypothesis that the human auditory system employs ( at least ) a 2-timescale processing mode , in which lower and higher perceptual sampling scales are segregated by an intermediate temporal regime in the alpha band that likely reflects different underlying computations .
Speech , music , and many natural sounds have a rich temporal structure over multiple timescales [1–5]; such sounds contain perceptually critical information that is encoded over short periods ( e . g . , the identity and exact sequence of phonemes in a spoken word ) and , concurrently , information encoded over longer periods ( e . g . , the intonation change over a word that signals intent or affect ) . Successful perceptual analysis of these signals requires the auditory system to extract acoustic information at multiple scales . This presents a specific problem: how does the auditory system process different , co-occurring rates of information across multiple timescales ? And , by extension , how can the requirements on temporal and spectral resolution simultaneously be met ? To derive the appropriate perceptual representations , the auditory system must extract rapidly varying information on a scale of milliseconds ( approximately 10–50 ms ) , operating with high temporal resolution , and concurrently analyze more slowly varying signal attributes on a scale of hundreds of milliseconds ( about 150–300 ms ) , enabling sufficient spectral resolution [6] . A strictly hierarchical model , starting , say , with short/small integration windows at more peripheral processing regions , which are then concatenated to build longer windows , cannot by itself meet the perceptual demands [7–9] . Behavioral research suggests that the human auditory system may optimize processing by operating within separate temporal ranges instead of in a unitary way across a continuum of temporal variation [10–15] . Timescales on the order of tens of milliseconds are argued to be optimized for rapid temporal integration , such as in modulation detection [16] , gap detection [17] , and nonsimultaneous masking [18] . On the other hand , previous models of temporal integration typically assuming leaky integration demonstrate timescales above 150 ms , such as loudness summation [19 , 20] , signal detection in noise [21 , 22] , and temporal integration at threshold [14 , 23–26] . Here , we investigate whether these very clear behavioral results arguing for distinct timescales could be illuminated , especially in terms of their neural implementation , by considering cortical oscillations . Cortical oscillations reflect rhythmic activity of neural populations at different scales [27 , 28] , and the frequencies of cortical oscillations are thought to reveal the corresponding temporal characteristics of sensory processing [29–32] . Therefore , by hypothesis , neural activity in the human auditory system may show a multiscale oscillatory pattern , which could reflect temporal scales of auditory processing . A short processing timescale , which guarantees high temporal resolution and may reflect the extraction or decoding of fine-grained acoustic information , could be reflected in the gamma band of oscillations that tracks fast acoustic dynamics , 30–50 Hz . A longer processing timescale , which integrates acoustic information on a timescale of 150–300 ms , could be reflected in the theta band oscillation , 4–7 Hz . Of course , sounds contain information in ranges lying between these well-separated temporal regimes . Is there really such a compelling segregation of function in the time domain ? Apart from the 2 timescales mentioned above , cortical oscillations within the alpha band ( 8–12 Hz ) have been found in many studies to critically correlate with auditory attention [33–35] , auditory working memory [36 , 37] , and listening effort [34 , 38] and , furthermore , to predict speech intelligibility under challenging environments [39] , which demonstrates a significant role for the alpha band in auditory processing . It is reasonable to hypothesize that the alpha band is fundamental to auditory perception , as demonstrated in a range of studies , but the timing ( i . e . , its intermediate position between lower theta and higher gamma activity ) also invites the conjecture that alpha range activity reflects aspects of auditory processing on a timescale of approximately 100 ms . Here , we investigate directly the most straightforward hypothesis: does alpha band activity reflect the processing of acoustic information in a manner parallel to lower and higher neural frequencies ? Studies using amplitude-modulated sounds or click trains have shown a low-pass modulation transfer function ( MTF ) with a rebound above 30 Hz [40–45] . Although these findings demonstrate dominant auditory responses in the low frequency range ( delta-theta and alpha ) and the gamma band , the cortical oscillations entrained by stimuli of corresponding frequencies of the regular modulation are not sufficient to demonstrate the importance of theta , alpha , and gamma bands in auditory processing , because the entrainment could simply reflect a modulation-frequency following response which may have little to do with the auditory system actively processing acoustic information in the theta and gamma bands . In studies using speech stimuli with irregular and complex modulations , it has been found that theta band activity entrains to the envelope of speech ( and other signals ) , and that phase locking in the theta band is enhanced by increased speech intelligibility [46–54] . Different evidence suggests that the gamma band also plays an important role in phonemic and syllabic processing and comprehension of speech [55–60] . The results from speech processing suggest that theta band oscillations , instead of being simply entrained by modulations of the corresponding frequency , may actively “chunk” complex acoustic signals at a timescale corresponding to periods of the theta band [61 , 62] , while the gamma band is involved in processing detailed information , because the comprehension of speech and syllable processing requires access to acoustic information at the phonemic scale . Here , we aimed to elucidate what kind of mechanism might form the basis of such multiscale hearing by using irregular temporal modulations of auditory signals to ask whether neural oscillations are entrained equally to auditory stimuli of different irregular modulation rates or , rather , only specific , restricted bands; moreover , we asked whether different stimulus modulation rates can be decoded from specific neural frequency bands . We focus on cortical oscillations in the theta and gamma bands as well as the alpha band , in which strong effects related to auditory processing have been reported [63 , 64] . Building on experiments by Boemio et al . ( 2005 ) [65] and Luo and Poeppel ( 2012 ) [66] , we generated acoustic stimuli with modulation rates that were centered at the typical periods of the neural theta ( 4–7 Hz ) , alpha-beta ( 8–15 Hz ) , and gamma ( 31–45 Hz ) frequency bands ( See Fig 1a for illustration of the stimuli ) . We measured using magnetoencephalography ( MEG ) the robustness of neural responses evoked by the stimuli and examined the correlations between the neural responses and the acoustic structure of the stimuli . We manipulated the signal-to-noise ratio ( SNR ) of the stimuli and evaluated the behavioral relevance of oscillations at the different rates . Next , classification analyses of the MEG data were performed to test which components of the neutrally elicited MEG signal were informative about the auditory signals . We show , contrary to expectation , that different temporal rates of the neurophysiological signal are differentially related to the behavioral , classification , and neural results . We further support the findings of processing at distinct timescales by performing mutual information analyses . Our combined neurophysiological and psychophysical results point to oscillatory neural mechanisms that underlie the segregated and discontinuous multiscale auditory processing of signals which , subjectively , feel seamless and continuous . In particular , we argue that the neural computations reflected in theta and gamma band activity differ in a principled way from those reflected in the alpha band and that alpha “splits” auditory processing into paired low and high processing scales .
We first examine how well listeners can recognize stimuli with varying frequency modulation rates , including especially rates that correspond to theta , alpha , and gamma band periods . The behavioral sensitivity to different modulation rates provides a first indication whether specific rates are preferred by the human auditory system . We refer to the stimulus type with a modulation rate in theta band range ( 4–7 Hz ) as a theta ( θ ) sound , the stimulus type with modulation rate in the alpha band range ( 8–12 Hz ) as an alpha ( α ) sound , and the stimulus type with modulation rate in the gamma band range ( 30–45 Hz ) as a gamma ( γ ) sound . The cochleograms of 3 sounds are shown in Fig 1a . In the behavioral test , participants were asked to categorize the θ , α , and γ sounds by button press . Sounds were presented either as clean stimuli or presented in noise at various SNRs . The results ( Fig 1b ) demonstrate that participants identified all clean stimuli well ( all d′ > 2 ) and that performance deteriorated in all conditions as the SNR level decreased . Particularly noteworthy is the reduced performance in correctly identifying the α sounds across all levels compared to both γ and θ sounds . The behavioral performance in identifying the 3 clean stimuli was analyzed using a 1-way repeated measures ANOVA ( rmANOVA ) with Stimulus-type as the main factor . There is a main effect of Stimulus-type ( F ( 2 , 28 ) = 9 . 78 , p = 0 . 001 , ηp2 = 0 . 411 ) . Planned comparisons using paired t tests reveal that the identification of α sounds was significantly worse than the identification of the other 2 stimuli ( comparison to θ sound , t ( 14 ) = −3 . 16 , p = 0 . 021 , d = −0 . 82; comparison to γ sound , t ( 14 ) = −4 . 95 , p = 0 . 001 , d = −1 . 28; Bonferroni corrected ) . There was no difference in identification performance between the θ and γ sounds ( t ( 14 ) = 0 . 65 , p > 0 . 05 , d = 0 . 17 ) . For the masked stimuli , a Stimulus-type × SNR 2-way rmANOVA reveals main effects of Stimulus-type ( F ( 2 , 28 ) = 17 . 60 , p < 0 . 001 , ηp2 = 0 . 557 ) and SNR ( F ( 4 , 56 ) = 144 . 86 , p < 0 . 001 , ηp2 = 0 . 912 ) as well as a Stimulus-type × SNR interaction ( F ( 8 , 112 ) = 2 . 47 , p = 0 . 017 , ηp2 = 0 . 150 ) . A quadratic trend analysis indicates that identification of stimuli decreased as SNR decreases ( F ( 1 , 14 ) = 510 . 35 , p < 0 . 001 , ηp2 = 0 . 973 ) . We computed the intertrial phase coherence ( ITC ) across all frequencies from 2 Hz to 50 Hz and in a time range from 300 ms to 1 , 800 ms after the onset of the stimuli to measure how oscillatory cortical activity in each frequency band responds to different modulation rates . ITC measures the robustness of neural responses to stimuli across trials . If cortical oscillations at a certain frequency band reliably respond to a specific stimulus ( and therefore , by hypothesis , probably encode information about this stimulus ) , we would obtain high ITC values at this frequency band . We created a distribution of ITC values by randomizing the onset time of each trial to normalize ITC in each subject across all frequencies and we converted the original ITC to z-scores of ITC ( zITC ) . ( See Materials and methods for details . ) The zITC values for the clean stimuli across all frequencies are plotted in Fig 2a and are shown as topographies separated by each band in Fig 2b . ITC , as a general measure of entrainment , shows that the neural theta band ( 4–7 Hz ) is strongly coherent to θ sounds and that the neural gamma band ( 31–45 Hz ) is strongly coherent to γ sounds . Unexpectedly , the alpha band ( 8–12 Hz ) reveals no selective response to the temporally corresponding stimuli . However , the α and γ sounds also evoked robust phase coherence at a frequency range below 8 Hz , in which zITC of the group means for α and γ sounds are above 1 . 64—the critical z-score equivalent to an alpha level of 0 . 05 ( 1-tailed , Bonferroni corrected ) . Crucially , no robust phase coherence was found in the alpha and beta bands . This nonuniform phase-response pattern across temporal regimes does not support the simplest hypothesis of comparable neural tracking for all stimulus modulation rates . Consistent results were obtained in the spatial analysis . The topographies of zITC , as shown in Fig 2b , reveal clear auditory response patterns both for θ sounds in the theta band and for γ sounds in the gamma band . Such auditory response patterns , though weak , were also observed in the theta band for α and γ sounds . To measure the effects of different stimuli on zITC , a Stimulus-type × Hemisphere × Frequency band 3-way rmANOVA was performed . This revealed main effects of Stimulus-type ( F ( 2 , 28 ) = 3 . 88 , p = 0 . 033 , ηp2 = 0 . 217 ) and Frequency band ( F ( 3 , 42 ) = 30 . 50 , p < 0 . 001 , ηp2 = 0 . 685 ) as well as an interaction between Stimulus-type and Frequency band ( F ( 6 , 84 ) = 3 . 67 , p = 0 . 003 , ηp2 = 0 . 208 ) . The main effect of Hemisphere was not significant ( F ( 1 , 14 ) = 0 . 22 , p = 0 . 648 , ηp2 = 0 . 015 ) . Planned post hoc comparisons using paired t tests with Bonferroni correction on the main effect of Stimulus-type show that zITC of θ sounds is larger than zITC of α sounds ( t ( 14 ) = 3 . 36 , p = 0 . 014 , d = 0 . 87 ) . Post hoc analysis of the main effect of Frequency band with Bonferroni correction shows that zITC at the theta band is larger than the alpha band ( t ( 14 ) = 7 . 39 , p < 0 . 001 , d = 1 . 91 ) , beta band ( t ( 14 ) = 9 . 47 , p < 0 . 001 , d = 2 . 45 ) , and gamma band ( t ( 14 ) = 7 . 43 , p < 0 . 001 , d = 1 . 92 ) and that zITC at the alpha band is larger than the beta band ( t ( 14 ) = 3 . 52 , p = 0 . 021 , d = 0 . 91 ) . Post hoc analyses on Stimulus-type × Frequency band interactions using adjusted false discovery rate ( FDR ) correction [67 , 68] show that , in the theta band , zITC of θ sounds is larger than both zITC of α sounds ( t ( 14 ) = 4 . 00 , p = 0 . 007 , d = 1 . 14 ) and γ sounds ( t ( 14 ) = 3 . 86 , p = 0 . 010 , d = 1 . 00 ) ; in the gamma band , zITC of γ sounds is larger than zITC of α sounds ( t ( 14 ) = 2 . 93 , p = 0 . 044 , d = 0 . 76 ) . Before adjusted FDR correction , zITC of γ sounds is significantly larger than θ sounds at the gamma band ( t ( 14 ) = 2 . 20 , p = 0 . 022 , d = 0 . 57 ) . There are no significant differences of zITC across different sounds found in the alpha and beta bands after adjusted FDR correction . To investigate whether θ , α , and γ sounds evoke robust phase coherence in the frequency bands other than their corresponding frequency bands , a 1-sample t test of zITC in comparison with a baseline of 1 . 64 was conducted in each frequency band for each sound . zITC above 1 . 64 means that the phase coherence observed is above the 95th percentile of ITC distribution over trials of randomized onset time . After adjusted FDR correction , we found robust phase coherence in the theta band for θ sounds ( t ( 14 ) = 38 . 06 , p < 0 . 001 , d = 9 . 82 ) and for γ sounds ( t ( 14 ) = 3 . 01 , p = 0 . 028 , d = 0 . 78 ) but not for α sounds ( t ( 14 ) = 1 . 66 , p = 0 . 240 , d = 0 . 43 ) . There are no significant results in alpha , beta , and gamma bands . zITC of γ sounds in the gamma band is not significant ( t ( 14 ) = −0 . 15 , p = 1 . 00 , d = −0 . 03 ) , likely because robust phase coherence peaks within a narrow frequency range centered around 37 Hz , as shown in the spectrum of zITC in Fig 1a . Averaging zITC of γ sound from 30 to 45 Hz decreases the mean of zITC in the gamma band . To summarize this first set of analyses , we showed that cortical oscillations in the theta and gamma bands , but not in the alpha band , robustly entrained to sounds with modulation rates in the corresponding frequency ranges . The further analysis of zITC indicates that theta band oscillations reliably respond to γ sounds whose modulation rate is in the gamma band range . zITC for α sound in the theta band also shows phase coherence , although it is not significantly above threshold . This preferential 2-scale ( theta and gamma ) response pattern of the cortical auditory system shown by phase coherence results aligns with the findings in neurophysiology related to speech perception and production [69] ( although nonspeech stimuli were used here ) . These consistent results across different stimulus types motivate the hypothesis that 2 discrete timescales , 150–300 ms ( theta band ) and approximately 30 ms ( gamma band ) , play an important role in general auditory processing . The robust phase coherence observed in the theta band for all 3 sounds further suggests that the phase coherence reflects more than passive entrainment to modulated sounds . Although the alpha band has been found in many auditory tasks to correlate with auditory perception , we did not find robust and distinct entrainment in the alpha band here . It is possible that the stimulus-evoked activity in the alpha band does not manifest in phase but in power , or that alpha does not show preference to any sounds and can be equally entrained by all 3 sounds . Next , we therefore analyzed power responses to 3 sounds . In the classification analysis shown subsequently , we investigated whether phase patterns in each frequency band provide critical information for processing θ , α , and γ sounds . Having first quantified phase coherence patterns , we next tested whether neural response power reveals patterns that support the observed segregation across bands , as the power response may be differentially modulated by different sounds in specific frequency bands , which could reflect power coding for temporal information . We analyzed evoked power and induced power separately , because evoked power is conceived as a stimulus-locked response while induced power is often argued to be generated by nonstimulus-locked processes [70 , 71] . The spectrograms of evoked power for each clean stimulus are plotted in Fig 3 . The θ and γ sounds show corresponding responses in the MEG signal , but the α sound does not: the power evoked by θ sounds largely distributes in the theta band; the power evoked by γ sounds distributes in the gamma band . In contrast , the power evoked by α sounds is not well observed in the alpha or any other frequency band . A Stimulus-type × Hemisphere × Frequency band 3-way rmANOVA was performed and revealed main effects of Stimulus type ( F ( 2 , 28 ) = 4 . 29 , p = 0 . 024 , ηp2 = 0 . 234 ) and Frequency band ( F ( 3 , 42 ) = 28 . 84 , p < 0 . 001 , ηp2 = 0 . 673 ) as well as an interaction between Stimulus-type and Frequency band ( F ( 6 , 84 ) = 2 . 88 , p = 0 . 013 , ηp2 = 0 . 171 ) . The main effect of Hemisphere was not significant ( F ( 1 , 14 ) = 0 . 33 , p = 0 . 575 , ηp2 = 0 . 023 ) . Post hoc paired t tests with Bonferroni correction on the main effect of Stimulus-type show that the evoked power of θ sounds was larger than that of α sounds ( t ( 14 ) = 3 . 58 , p = 0 . 009 , d = 0 . 92 ) . Post hoc analysis of the main effect of Frequency band showed that the evoked power in the theta band is larger than that in the alpha band ( t ( 14 ) = 3 . 88 , p = 0 . 010 , d = 1 . 00 ) , in the beta band ( t ( 14 ) = 8 . 84 , p < 0 . 001 , d = 2 . 28 ) , and in the gamma band ( t ( 14 ) = 5 . 14 , p < 0 . 001 , d = 1 . 33 ) . Evoked power in the alpha band is larger than the beta band ( t ( 14 ) = 5 . 23 , p < 0 . 001 , d = 1 . 35 ) , and evoked power in the gamma band is larger than beta band ( t ( 14 ) = 6 . 32 , p < . 001 , d = 1 . 64 ) . A post hoc analysis of the Stimulus-type × Frequency band interaction using adjusted FDR correction shows that , in the theta band , the evoked power of θ sounds is larger than the evoked power of α sounds ( t ( 14 ) = 4 . 77 , p < 0 . 001 , d = 1 . 23 ) and γ sounds ( t ( 14 ) = 3 . 32 , p = 0 . 020 , d = 0 . 86 ) . In the gamma band , the evoked power of γ sounds is larger than the evoked power of α sounds ( t ( 14 ) = 3 . 94 , p = 0 . 006 , d = 1 . 02 ) . There is no significant difference between evoked power across different sounds found in alpha and beta bands after adjusted FDR correction . Power responses induced by the different stimuli were explored by a Stimulus-type × Hemisphere × Frequency band 3-way rmANOVA . The main effect of Frequency band was significant ( F ( 3 , 42 ) = 15 . 36 , p < 0 . 001 , ηp2 = 0 . 523 ) . A post hoc analysis with Bonferroni correction showed that the power at the beta band was less than the theta band ( t ( 14 ) = 6 . 88 , p < 0 . 001 , d = 1 . 78 ) , alpha band ( t ( 14 ) = 5 . 26 , p = 0 . 001 , d = 1 . 36 ) , and gamma band ( t ( 14 ) = 5 . 77 , p < 0 . 001 , d = 1 . 49 ) . There is no difference in power between theta , alpha , and gamma bands . The main effect of Hemisphere was marginally significant ( F ( 1 , 14 ) = 4 . 51 , p = 0 . 052 , ηp2 = 0 . 244 ) , with the power in the right hemisphere larger than in the left hemisphere . The selective phase coherence observed in the theta and gamma bands that we show in Fig 2 may be a result of reliable auditory responses to any sounds , but not necessarily caused by the specific temporal structure of the stimuli . We therefore asked next whether phase patterns of cortical oscillations actually correlate with the temporal structure of the stimuli . We first used a measure , cochlear-scaled correlation , inspired by the concept of cochlear-scaled entropy , to extract salient acoustic changes that may reset the phase of cortical oscillations and therefore lead to robust phase coherence across trials [51] . The cochlear-scaled correlation was calculated using a moving temporal window and represents acoustic changes along time ( see Materials and methods for details ) . Next , we computed mutual information between the phase series of cortical oscillations at each frequency and the cochlear-scaled correlations . Mutual information can quantify how much information in the temporal structure of the stimuli can be explained by the phase patterns of cortical oscillations and indicate whether the robust phase coherence across trials observed in the neural frequency bands is evoked by the temporal structure of a specific sound . To compute the cochlear-scaled correlation , we decomposed the stimuli using a Gammatone filterbank with 64 bands , then averaged the amplitude of the envelope in each cochlear band using a moving temporal window of 10 ms , generating 64 total values ( 1 per band ) . A Pearson’s correlation was then calculated for these values between each adjacent time point . The cochlear-scaled correlation for each sound is shown in Fig 4a . We computed mutual information between the cochlear-scaled correlation of each sound and the phase series of the neural oscillation to all 3 sounds . For example , we computed mutual information between the cochlear-scaled correlation of the θ sounds and the 3 phase series evoked by θ , α , and γ sounds . We used the phase series evoked by α and γ sounds as controls to examine whether mutual information between the cochlear-scaled correlation of θ sounds and the phase series evoked by θ sounds is significant . The results of mutual information analysis are shown in Fig 4b . We ran a 1-way rmANOVA on mutual information with the factor of phase responses evoked by sound type ( phase sound type ) . After adjusted FDR correction across frequencies , we found significant main effects of the phase sound type between 4 and 6 Hz using the cochlear-scaled correlation of θ sounds ( p < 0 . 05 ) and significant main effects of the phase sound type between 33 and 39 Hz using the cochlear-scaled correlation of γ sounds . Importantly , no significant main effects of the phase sound type were found for mutual information computed using the cochlear-scaled correlation of α sounds . In the post hoc comparison with Bonferroni correction , we averaged mutual information within the frequency ranges in which significant main effects were observed and found that , when the cochlear-scaled correlation of θ sounds was used , the mutual information computed using phase series of θ sound is significantly larger than that using phase series of α sound ( t ( 14 ) = 5 . 29 , p < 0 . 001 , d = 1 . 37 ) and that using phase series of γ sound ( t ( 14 ) = 3 . 97 , p = 0 . 004 , d = 1 . 03 ) . When the cochlear-scaled correlation of γ sounds was used , the mutual information computed using phase series of γ sound is significantly larger than that using phase series of θ sounds ( t ( 14 ) = 5 . 05 , p < 0 . 001 , d = 1 . 30 ) and that using phase series of α sounds ( t ( 14 ) = 5 . 71 , p < 0 . 001 , d = 1 . 47 ) . The results of the mutual information analyses demonstrate that phase patterns in the theta and gamma bands track the temporal structure of the stimuli , as quantified by the cochlear-scaled correlation . The robust phase coherence observed in the theta band for θ sounds and that in the gamma band for γ sounds is indeed caused by tracking specific acoustic structures , rather than simply being evoked by generic acoustic stimuli . The fact that no significant results were found for α sounds suggests , again , that the alpha band may play a different role in processing sounds , and especially their temporal structure . In contrast , the theta band and the gamma band may be central to auditory processing and the construction of neural representations underlying perceptual analysis . We did not find specialized tracking for γ sounds in the theta band , although γ sounds evoked robust phase coherence in the theta band ( Fig 2a ) . This could be because the theta band , instead of faithfully coding the temporal structure of γ sounds , chunks acoustic information and forms a perceptual unit on a timescale of approximately 200 ms ( the theta band range ) . We further explored the contribution of the theta band to coding temporal information of γ sounds in the following classification analyses . We next performed classification analysis to investigate whether information in each frequency band of cortical oscillations can be used to classify different stimulus types . If a frequency band , for example , the theta band , reflects sufficient information to classify θ , α , and γ sounds , it implies that the theta band plays an important role in processing θ , α , and γ sounds . On the other hand , if a frequency band does not contribute to classifying any sounds , it may indicate that this frequency band is not a key component to processing the sounds . We first use phase and power responses in a frequency range of 4–45 Hz and a time range of 300–1 , 900 ms ( i . e . , after the onset of stimuli ) for classification to test whether phase or power provide information related to the temporal structure of each sound . Second , we measure the contribution of each frequency band in classification to determine which frequency band is by hypothesis critical to auditory processing . Finally , we use a new method to classify stimulus type at each time point to investigate temporal progression of the classification performance . Classification performance was first computed for each stimulus type using the phase and power response profile of all frequency bands ( 4–45 Hz ) . Confusion matrices of phase classification and power classification are plotted in Fig 5a . d-prime values computed based on the confusion matrices , indicated by D′ ( to differentiate it from d′ in the behavioral results ) , are shown in Fig 5b . A Stimulus-type × Classification source ( phase classification or power classification ) 2-way rmANOVA reveals the main effect of Classification source ( F ( 1 , 14 ) = 30 . 44 , p < 0 . 001 , ηp2 = 0 . 685 ) , with the D′ of phase-based classification significantly larger than the D′ of power-based classification . To determine whether classification performance is better than chance ( D′ = 0 ) , a 1-sample t test with Bonferroni correction was applied on each stimulus type and each classification source . For phase classification , the performance of all stimulus types was better than chance ( for θ sounds , t ( 14 ) = 6 . 19 , p < 0 . 001 , d = 1 . 60; for α sounds , t ( 14 ) = 6 . 40 , p < 0 . 001 , d = 1 . 65; for γ sounds , t ( 14 ) = 6 . 44 , p < 0 . 001 , d = 1 . 66 ) . For power classification , classification performance of α sounds was significant ( t ( 14 ) = 4 . 73 , p = 0 . 002 , d = 1 . 22 ) as well as θ sounds ( t ( 14 ) = 3 . 10 , p = 0 . 046 , d = 0 . 80 ) , but performance of power classification was only slightly above chance . These results demonstrate that phase patterns of cortical oscillations reliably encode the temporal dynamics of stimuli . Next , to explore how each frequency band might contribute to phase-based classification of different stimulus types , classification was conducted using different combinations of frequency bands ( e . g . , theta band plus gamma band , etc . ) . We then compared the classification performance that was obtained with or without a particular frequency band included in the analysis . For example , to quantify the theta band contribution , we obtained 2 values by either averaging D′ values across frequency band combinations that included the theta frequency band , or without the theta frequency band . The contributions of each frequency band to different stimuli are plotted in Fig 5c . A Stimulus-type × Frequency band × Inclusion ( with or without a given frequency band ) 3-way rmANOVA shows main effects for Frequency band ( F ( 3 , 42 ) = 6 . 28 , p = 0 . 001 , ηp2 = 0 . 310 ) and Inclusion ( F ( 1 , 14 ) = 16 . 34 , p = 0 . 001 , ηp2 = 0 . 539 ) . The 2-way interactions between Frequency band and Inclusion ( F ( 3 , 42 ) = 6 . 28 , p = 0 . 001 , ηp2 = 0 . 310 ) and between Stimulus-type and Frequency band ( F ( 6 , 84 ) = 5 . 42 , p < 0 . 001 , ηp2 = 0 . 279 ) were significant as well as the 3-way Stimulus type × Frequency band × Inclusion interaction ( F ( 6 , 84 ) = 5 . 42 , p < 0 . 001 , ηp2 = 0 . 279 ) . To further examine how each frequency band contributes to classification for each stimulus type , paired t tests with adjusted FDR correction were performed on each frequency band and each stimulus type . The theta band contributes to the decoding of all stimulus types ( for θ sounds , t ( 14 ) = 8 . 16 , p < 0 . 001 , d = 2 . 11; for α sounds , t ( 14 ) = 3 . 68 , p = 0 . 010 , d = 0 . 95; for γ sounds , t ( 14 ) = 3 . 16 , p = 0 . 021 , d = 0 . 82 ) . The beta band deteriorated decoding of γ sounds ( t ( 14 ) = −4 . 66 , p = 0 . 002 , d = 1 . 20 ) . Crucially , the alpha band did not contribute significantly to decoding any stimuli . Before adjusted FDR correction , the gamma band shows a contribution to decoding γ sounds ( t ( 14 ) = 2 . 07 , p = 0 . 057 , d = 0 . 53 ) . After removing 1 subject who showed abnormal decoding performance , we found that the gamma band significantly contributes to decoding γ sounds after FDR correction ( t ( 13 ) = 3 . 35 , p = 0 . 017 , d = 0 . 90 ) . Finally , because we found that the theta and gamma bands provided the main contributions to classification , we examined how classification performance in the theta band and gamma band progress temporally by using each time point of a phase series to classify a stimulus type . Classification was conducted in the theta band and the gamma band separately by combining the MEG channels selected from 500 ms before the onset of the stimuli to 2 , 000 ms after . We used a cluster-based permutation test to quantify significance of classification performance ( see Materials and methods for details ) . The results are shown in Fig 6a . We then averaged classification performance for each sound on each band from 300 to 1 , 900 ms after the onset of stimuli ( Fig 6b ) and found that in the theta band , classification performance for θ sounds is significantly larger than that for α sounds ( t ( 14 ) = 3 . 06 , p = 0 . 027 , d = 0 . 79 ) and γ sounds ( t ( 14 ) = 4 . 04 , p = 0 . 006 , d = 1 . 04 ) after Bonferroni correction . In the gamma band , classification performance for γ sounds is significantly larger than for θ sounds ( t ( 14 ) = 2 . 74 , p = 0 . 048 , d = 0 . 71 ) and α sounds ( t ( 14 ) = 4 . 79 , p < 0 . 001 , d = 1 . 24 ) after Bonferroni correction . The significant classification performance in the theta band after the onset of stimuli for all stimulus types demonstrates that the theta band not only entrains to sounds with corresponding modulation rates but also provides critical information for classifying stimuli of all modulation rates . The gamma band showed significant classification performance for γ sounds; by averaging time points from 300 to 1 , 900 ms , we see significantly higher classification performance in the gamma band for γ sounds than for θ and α sounds . This argues for a higher degree of specificity for gamma tracking . To summarize the classification results: phase series in the theta band temporally track acoustic dynamics across all modulation rates ( used in our study ) , which suggests that the theta band is not only entrained by modulation rates with corresponding frequency range but also chunks sounds with modulation rates outside of the theta band range into acoustic segments at a timescale corresponding to the theta period range . The gamma band specifically locks to modulation rates with a timescale corresponding to gamma band , approximately 30 ms . The contribution of the alpha band must be seen as functionally separate from the other bands . If , broadly speaking , the ( paired ) activity of the theta and gamma bands is associated with the construction of perceptual objects in audition , contrary to the alpha band , it stands to reason that the localization of the theta and gamma neural activity should be associated . The supporting information ( see S1 Text ) provides additional new data to verify that the neural sources are overlapping by localizing the MEG-recorded activity in source space based on individual participants’ structural MRIs . Connecting back to the behavioral data ( Fig 1b ) , we tested how the degraded behavioral performance induced by noise is correlated with the neural markers ( ITC and power response ) to indicate which neural marker may account for the behavioral results . The positive correlation between d′ and ITC was significant in the theta band for 3 stimuli ( for θ sounds , r = 0 . 815 , t ( 14 ) = 8 . 50 , p < 0 . 001 , d = 2 . 19; for α sounds , r = 0 . 378 , t ( 14 ) = 3 . 50 , p = 0 . 014 , d = 0 . 90; for γ sounds , r = 0 . 417 , t ( 14 ) = 3 . 64 , p = 0 . 014 , d = 0 . 94 ) . The positive correlation between d′ and evoked power was significant in the theta band for θ sounds ( r = 0 . 622 , t ( 14 ) = 8 . 20 , p < 0 . 001 , d = 2 . 12 ) . Analysis of the correlation between d′ and induced power response showed a negative correlation . A significant negative correlation was found in the alpha band for α sounds ( r = −0 . 370 , t ( 14 ) = −3 . 01 , p = 0 . 049 , d = −0 . 78 ) and in the gamma band for γ sounds ( r = −0 . 347 , t ( 14 ) = −3 . 22 , p = 0 . 048 , d = 0 . 83 ) . Adjusted FDR correction was applied to all tests . The ITC in the theta band showed significant correlation with behavioral performance on recognizing all 3 sounds . These results echo the classification results and demonstrate that the phase series in the theta band provides critical information for auditory processing .
In this MEG-based neurophysiological experiment , we investigate temporal coding at different scales by exploring the entrainment of auditory cortical oscillations to sounds with different modulation rates . Because healthy listeners ( appear to ) perceive sounds that contain modulation rates over various temporal scales in a manner that reflects a continuous MTF with a low-pass filter shape [16 , 72] , the most straightforward hypothesis suggests that different auditory stimulus rates are tracked in a comparable manner across modulation rates . Contrary to this hypothesis , we find that oscillations in the ( slower ) theta and ( faster ) gamma bands reliably track acoustic dynamics—but not in the ( intermediate ) alpha and beta bands . Subsequent analyses showed that the information carried in the neural theta band contributes to the decoding of all modulation rates used in this study , whereas the gamma band mainly contributes to decoding only gamma-modulated sounds . Moreover , intertrial coherence in the theta band correlates with identification performance across all stimuli , underscoring that there is a clear perceptual consequence of the entrained oscillatory activity . Our results are consistent with previous work showing entrainment of auditory cortical activity to low acoustic modulation rates [31 , 32 , 45–47 , 50 , 51 , 53 , 54 , 57 , 73–81] . The present results lend support from a new perspective to those studies using modulated sounds with amplitude modulation rates less than 10 Hz that have found strong entrainment in the delta and theta bands . Importantly , the failure to observe entrainment in the alpha and beta bands in the present study also aligns with previous electrophysiological data of monkey primary auditory cortex: cortical oscillations could entrain to modulation rates in the delta-theta bands but not at 12 Hz [73] . Preliminary MEG data have also revealed such a response pattern [66] . The neural gamma band , in addition to the delta and theta bands , also reliably codes temporal information . The finding that the neural gamma band is entrained by sounds with a corresponding temporal modulation rate—and that this alignment contributes to the classification of γ sounds—indicates that the auditory system can track acoustic dynamics over short timescales , approximately 30 ms . This observation is consistent with studies using amplitude modulation created by binaural beats; in that work , strong entrainment both in the theta and gamma bands is found—but , again , not in the alpha or beta bands [82] . Recordings in the primary auditory cortex of monkeys also show a phase-locked response using amplitude modulation at 30 Hz [83 , 84] . The data we show also confirm the contribution of gamma band entrainment to speech separation found in the multiple talker environments [57] . These studies , complemented by the data shown here , support the emerging view that the auditory system extracts precise temporal information mainly on 2 discrete , segregated timescales . The classification results we report show that the phase information of theta band oscillations contributes to decoding sounds with modulation rates not only at the theta band timescale but also at the alpha and gamma scales . One possible explanation is that theta band oscillations ( i ) track acoustic dynamics at that specific temporal scale and ( ii ) at the same time actively chunk ( at a scale of the mean theta band period ) sounds with faster modulation rates , so that acoustic properties over larger scales can be further extracted [85] . The gamma band exclusively contributes to the classification of sounds with corresponding temporal dynamics . This may indicate specific processing at a fine-grained scale for acoustic temporal details . Overall , theta band oscillations may be necessary , although not sufficient , for processing sounds with temporal variations across different scales , and gamma band oscillations may be needed for fine-grained processing . The classification results also reveal data patterns that have not been shown in previous findings on the MTF . As the MTF shows decreased neural responses with increasing modulation rate , it is plausible to conjecture that temporal coding for acoustic dynamics would also show such a pattern . However , as observed in the classification analysis , the temporal coding capability does not correspond to the magnitude of the MTF—large ITC values for the alpha band do not indicate high temporal coding capability . Some previous findings , though , do show a tendency for a rebound of neural activity in the gamma band [40 , 41 , 44] , although the magnitude of gamma band activity is small compared with lower frequency bands [45] . The most relevant finding is from Wang et al . [45] , in which a robust response to amplitude-modulated sounds of 31 . 5 Hz was indicated by the percentage of subjects that showed robust auditory steady-state response ( aSSR ) . These results were suggestive , although the study did not test temporal coding of different frequency bands and concluded “the MTF of the low-frequency aSSR generally has a low-pass pattern and only weakly depends on the carrier bandwidth . ” Therefore , although we built our current study on assumptions about the MTF , the previous findings could not resolve the question of auditory temporal coding on different timescales or demonstrate that the power of alpha band is preserved ( and “reserved” ) not for temporal coding but for other auditory cognitive process . The absence or marked reduction of tracking acoustic dynamics in the alpha band suggests that neural activity reflected in the alpha band may play a different role in audition . In the auditory system , the neural computations reflected in the alpha band signal may be more explicitly involved in auditory attention , working memory , listening effort , or functional inhibition [34 , 36–39 , 86] , and the alpha band may as such be more related to aspects of auditory perception that differ from constructing the elementary perceptual representations . The alpha band is associated with suppressing activity of cortical areas that are irrelevant for ongoing sensory processing according to tasks [87] , which suggests ( for the current context ) that alpha band oscillations co-occur with and segregate 2 temporal coding regimes ( theta and gamma bands ) and modulate auditory processing as a top-down process [88] . The alpha band is involved in processing in other sensory systems and has been well established in visual and somatosensory perceptual analysis [89–92] . It will be relevant to further investigate in the auditory domain how the alpha band interacts with theta and gamma domains to comprehend auditory analysis more fully . The finding that , like alpha band activity , the beta band also does not track acoustic dynamics may reflect that neural oscillations on that scale are reflective of different operations as well . Beta band oscillations have been argued to play a role in predictive coding [93 , 94]; the task of the present study does not require active prediction . The random phase in the beta band adds more noise to the classification process , so removing the phase information of the beta band actually results in better classification performance . Our finding of entrainment , and specifically concurrent parallel processing at different scales of the theta and gamma bands , converges with the 2 perceptual time constants often found in behavioral studies [13] . Experiments on temporal integration frequently report a time constant in the ( few ) hundreds of milliseconds [14 , 21–26] , while studies examining the high temporal resolution of the auditory system show a time constant less than 30 ms [16–18] . The behavioral results we show also point to higher perceptual sensitivity for theta and gamma sounds compared with alpha sounds . However , the behavioral method used in the present study cannot circumvent a concern with a selection bias to different sounds , because participants may identify θ sounds and γ sounds more easily simply because the modulation rates of these 2 sounds are located at the perceptual extremes in this experimental design . We acknowledge that this behavioral task is suboptimal—and primarily employed to ensure attention during neurophysiological recording—so we scrupulously refrain from overinterpreting these data , beyond pointing out that the pattern is consistent with our hypothesis . Notwithstanding this potential concern , results from a recent psychophysical study dovetail with the view that the auditory system works concurrently on a short timescale ( about 30 ms ) to extract fine-grained acoustic temporal detail while processing more global acoustic patterns on a longer timescale ( >200 ms ) [95] . Importantly , our results , based on nonspeech stimuli , suggest that such dual-scale entrainment is not speech specific but may rather reflect an intrinsic auditory processing property . The auditory cortex tunes to both theta frequency and gamma frequency acoustic dynamics [65] . The alpha band reflects different operations . This segregated , dual tuning of the auditory system at different scales may facilitate the extraction of information of different types in speech , such as featural , segmental , or phonemic information versus syllabic scale information [1] . We suggest that the measured oscillatory patterns at different timescales encode acoustic information over multiple scales , which leads to a temporal multiplexing of sensory information [59 , 96] . Mounting evidence shows that human perceptual systems employ a discrete process in which continuous signal information is broken up into segments [29 , 30 , 97–99] . As natural sounds contain information at multiple scales , the auditory system may chunk continuous sounds using temporal windows of different sizes to sample information at different timescales , instead of processing acoustic information on a unitary scale . This multiplexing strategy solves the requirement in auditory processing that both fine resolution and integration over time are needed for perceiving sounds with regularities both at large and small scales . One model proposes that , although a very high resolution is represented in subcortical areas , in the auditory cortex , there are 2 main temporal windows used for processing acoustic information: one centered around 200 ms and the other around 30 ms [6 , 100] . On this view , acoustic information is analyzed and integrated using 2 temporal windows at these scales so that perceptual information at such “global” and “local” scales , whether in speech or nonspeech , can be abstracted concurrently to form a unitary percept that forms the basis for perceptual decision-making , lexical access , memory encoding , and other cognitive operations building on elementary perceptual representations . This design , however , builds in a hole in processing , a segregation of function between low and high processing rates—perhaps optimized for sensory sampling—by an intermediate rate , perhaps optimized for allocating attentional and memory resources and functionally inhibiting task- or stimulus-irrelevant actions . Whereas we typically address segregation of function in the spatial domain , i . e . , different regions are specialized for different operations , here , we provide a compelling example of cortical segregation of function in the time domain .
The study was approved by the New York University Institutional Review Board ( IRB# 10–7277 ) and conducted in conformity with the 45 Code of Federal Regulations ( CFR ) part 46 and the principles of the Belmont Report . Sixteen right-handed volunteers ( 9 females; mean age: 24 . 8; standard deviation: 3 . 2 ) participated in this experiment . All participants provided informed written consent and received monetary compensation for their participation . Handedness was determined using the Edinburgh Handedness Inventory [101] . All participants had normal hearing and no neurological deficits . We excluded the data from 1 participant because of noise issues during neurophysiological recording . Therefore , the analysis included the data from 15 participants ( 8 females; mean age: 25 . 2; standard deviation: 3 . 0 ) . We created 3 stimulus types following the methods used in Boemio et al . ( 2005 ) and Luo and Poeppel ( 2012 ) . Each stimulus was 2 s long and generated by concatenating narrow-band frequency-modulated segments . The mean starting frequency of each segment was randomly drawn from 2 frequencies , 1 , 000 Hz and 1 , 500 Hz . If the mean starting frequency is 1 , 000 Hz , the frequency-modulated segment could sweep up to 1 , 500 Hz . If the mean starting frequency is 1 , 500 Hz , the frequency-modulated segment could sweep down to 1 , 000 Hz . The bandwidth of segments was 100 Hz ( within a critical band at the center frequencies used ) . We generated each segment by adding up 100 frequency-modulated sinusoids with randomized amplitude and phase . To create a segment that sweeps down , the starting frequency of 100 sinusoid is randomly distributed between 1 , 450 Hz and 1 , 550 Hz and the end frequency is distributed between 950 Hz and 1 , 050 Hz . To create a segment that sweeps up , the starting frequency of 100 sinusoid is randomly distributed between 950 Hz and 1 , 050 Hz and the end frequency is distributed between 1 , 450 Hz and 1 , 550 Hz . The duration of the segments for each of the 3 stimulus types was drawn from a Gaussian distribution with means of 190 ms , 100 ms , and 27 ms , with standard deviations of 30 ms , 15 ms , and 3 ms , respectively . The distribution of the segment durations of the stimuli aligned with the range of periods typical of theta ( 4–7 Hz ) , alpha/low beta ( 8–15 Hz ) , and low gamma ( 30–45 Hz ) band neural oscillations . We refer to the stimulus type with mean segment duration of 190 ms as a theta ( θ ) sound , the stimulus type with mean segment duration of 100 ms as an alpha ( α ) sound , and the stimulus type with mean segment duration of 27 ms as a gamma ( γ ) sound . The cochleograms of the 3 stimuli were created using a Gammatone filterbank with 64 banks to decompose the stimuli from 50 to 22 , 050 Hz [102 , 103] and are shown in Fig 1a , with the corresponding prior distributions of segment duration for each stimulus type . We generated white noise segments of 4 s using the random number generator , the function “randn , ” in Matlab R2014a ( The MathWorks , Natick , MA ) . Then , we embedded the 3 types of clean stimuli into white noise to create noise-masked stimuli at 5 levels of SNR: −9 , −13 , −17 , −21 , and −25 dB . The onset of white noise preceded the onset of the embedded clean stimulus for a random interval uniformly distributed from 1 s to 1 . 5 s . Thirty stimuli for each SNR level and each stimulus type were created using individually generated noise . As only 1 sample of each stimulus type was generated , the θ , α , and γ sounds were the same across all conditions . Therefore , there were 18 total conditions that included 3 clean stimuli and 15 ( 3 types × 5 SNR levels ) noise-masked stimuli . In total , 540 trials ( 18 conditions × 30 trials per condition ) were presented . The order of all stimuli was pseudorandomized for each participant . After each stimulus was presented , participants were required to push 1 of 3 buttons to indicate the type of stimulus . Between 1 and 2 s after participants responded , the next stimulus was presented , so that all stimuli were presented at random onset points . Participants were required to keep their eyes open and to focus on a white fixation cross in the center of a black screen . All stimuli were normalized to about 65 dB SPL and delivered through plastic air tubes connected to foam ear pieces ( E-A-R Tone Gold 3A Insert earphones , Aearo Technologies Auditory Systems ) . MEG signals were measured with participants in a supine position and in a magnetically shielded room using a 157-channel whole-head axial gradiometer system ( KIT , Kanazawa Institute of Technology , Japan ) . A sampling rate of 1 , 000 Hz was used with an online 1–200 Hz analog band-pass filter and a notch filter centered around 60 Hz . After the main experiment , participants were presented with 1-kHz tone beeps of 50 ms duration as a localizer to determine their M100 evoked responses , which is a canonical auditory response [104] . Ten channels in each hemisphere , selected based on the peak of M100 response between 60 ms and 120 ms , were used as auditory channels for each participant individually . A layout of channels that are selected based on the peak of M100 response across 15 subjects is shown in Fig 7 . Behavioral data analysis was conducted in MATLAB using the Palamedes toolbox 1 . 50 [105] . For each SNR level as well as the 3 clean stimuli , a 3-by-3 confusion matrix was created and then was collapsed into three 2-by-2 tables by treating 1 stimulus as “target” and pooling the observations on the other 2 stimuli as “noise . ” Correct identification of the target stimulus was counted as a “hit” while misidentification of the other 2 stimuli as the target stimulus was counted as “false alarm;” d-prime values were computed based on hit rates and false alarm rates of each table . A half artificial incorrect trial was added to the table with all correct trials [106] . MEG data analysis was conducted in MATLAB using the Fieldtrip toolbox 20140619 [107] and wavelet toolbox . Raw MEG data were noise reduced offline using the time-shifted principle component analysis [108] and sensor noise suppression [109] . Trials were visually inspected , and those with artifacts such as signal jumps and large fluctuations were discarded . An independent component analysis was used to correct for eye blink , eye movement , heartbeat-related and system-related artifacts . Twenty-five trials were included in the analysis for each condition . Each trial was divided into 5-s epochs ( 1-s prestimulus period and 4-s stimulus period ) . Baseline was corrected for each trial by subtracting out the mean of the whole trial before further analysis . To extract time-frequency information , single-trial data for each condition in each MEG channel were transformed using functions of the Morlet wavelets embedded in the Fieldtrip toolbox , with a frequency ranging from 1 to 50 Hz in steps of 1 Hz . To balance spectral and temporal resolution of the time-frequency transformation , from 1 to 20 Hz , the window length increased linearly from 1 . 5 circles to 7 circles and was kept constant at 7 circles above 20 Hz . Phase and power responses ( squared absolute value ) were extracted from the wavelet transform output at each time-frequency point . The ITC , a measure of consistency of phase-locked neural activity entrained by stimuli across trials , was calculated for each time-frequency point ( details as in [110] ) . ITC in different frequency bands reflects phase tracking of cortical oscillations to temporally modulated stimuli . As the baselines of phase-locking may be different across frequency bands , which may be affected by power distributions with a 1/f characteristic , a shuffling method of onset time was used to avoid this confound . Within each condition , the onset time of the stimulus was randomly chosen on each trial and a new dataset for each condition was created . The same analysis of ITC was applied on this new dataset . To create a distribution of shuffled ITCs , this shuffling procedure was repeated 1 , 000 times . The z-score of ITC was computed using the percentile of the original ITC in the distribution . Induced power was normalized by dividing the mean power value in the baseline range ( −0 . 6 to −0 . 1 s ) and taking logarithms with base 10 and then was converted into values with the unit of decibel by multiplying by 10 . The evoked power response was computed by applying the time-frequency transform on averaged temporal responses across all trials . The baseline correction was the same as that used in computing the induced power . The ITC and power data were averaged from 0 . 3 s to 1 . 8 s poststimulus onset to minimize the effects of stimulus-evoked onsets and offsets and within 4 frequency bands: theta ( 4–7 Hz ) , alpha ( 8–12 Hz ) , beta ( 13–30 Hz ) , and gamma ( 31–45 Hz ) . To examine whether phase and power in different frequency bands can explain behavioral performance , correlations between behavioral performance and neural measurements were tested by calculating correlation coefficients between d′ and either ITC or power across the 5 SNRs , and then a 1-sample t test was performed for each frequency band and each stimulus type to determine whether the correlation is significant . As ITC is not normally distributed , the rationalized arcsine transform was applied before calculation of correlation coefficients [111] . All calculations were first conducted in each MEG channel and then averaged across selected auditory channels . Statistical analyses of ITC and power were conducted separately for the 3 clean stimuli and the 15 masked stimuli using rmANOVA . When multiple comparisons were performed , to control familywise error rate and , at the same time , not to cause a high rate of false negatives , the Bonferroni correction was used when there were less than 10 comparisons performed , and an adjusted FDR was used when there were more than 10 comparisons performed [67 , 68] . The stimuli in this study were created by concatenating frequency sweep segments of different durations , which may create sharp acoustic edges at the boundary between 2 adjacent segments . These acoustic edges indicate boundaries of frequency sweep segments and represent ( one aspect of ) the temporal structure of the stimuli , which correlates with modulation rates . By computing mutual information between temporal patterns of these acoustic edges and phase series of MEG signals , we can investigate in which frequency band the neural phase pattern best correlates with the temporal structure of stimuli . The results could tell us whether a frequency band is more or less involved in processing certain stimuli . To quantify the acoustic edges and extract the temporal structure of the stimuli , inspired by the concept of cochlear-scaled entropy [112] , we created an index , cochlear-scaled correlation . We first used a Gammatone filterbank of 64 banks , ranging from 50 Hz to 22 , 050 Hz , to decompose the sounds and extracted the envelope of each cochlear band . Then , a moving average window of 10 ms was applied on the envelope of each cochlear band to create a vector of length 64 at each time point . We computed the Pearson’s correlation between 2 vectors of adjacent time points and the correlation results were then down-sampled to a sampling rate of 100 Hz , which corresponds to the sampling rate of the phase series of the MEG signals . The cochlear-scaled correlation for each sound is shown in Fig 4a . The visualization shows that cochlear-scaled correlation can confer the temporal structure of stimuli . Within a frequency sweep segment , the cochlear-scaled correlation is high—close to 1—and at the boundaries of frequency sweep segments , a sudden drop of correlation coefficient is evident . The temporal structure of these sudden drops of correlation efficiency correlates with modulation rates , with the θ sound having the fewest drops and the γ sound having the most drops . To quantify shared information between the cochlear-scaled correlation and phase series of MEG signals , we used the framework of mutual information [96 , 113] . Mutual information ( MI ) was calculated with the Information Breakdown Toolbox in MATLAB [114 , 115] . We computed the MI between phase series of each frequency ( 2–50 Hz ) extracted from the time-frequency analysis described above and the cochlear-scaled correlation of the θ , α , and γ sounds [50 , 59 , 80 , 116] . For example , when we computed MI between the cochlear-scaled correlation of the θ sounds and the 3 phase series evoked , respectively , by θ , α , and γ sounds , we used the phase series evoked by α and γ sounds as control conditions and examined whether MI between the cochlear-scaled correlation of θ sounds and the phase series evoked by θ sounds is significant . The mutual information value of each frequency was calculated for each subject and for each channel across trials before averaging . The cochlear-scaled correlation we compute is simply the values at each time point corresponding to the time point of phase . For each frequency of the MEG response , the phase distribution was composed of 6 equally spaced bins: 0 to pi/3 , pi/3 to pi * 2/3 , pi * 2/3 to pi , pi to pi * 4/3 , pi * 4/3 to pi * 5/3 , and pi * 5/3 to pi * 2 . By choosing 6 bins for phase information , we ensured that there was enough temporal resolution to capture acoustic dynamics , because ( at least ) greater than 2 times the temporal resolution than the frequency focused on is needed to quantify information at the frequency . The cochlear-scaled correlation was grouped using 8 bins equally spaced from the minimum value to the maximum value . Eight bins were chosen because we wanted to have enough discrete precision to capture changes in acoustic properties while making sure that each bin has sufficient counts for mutual information analysis , because the greater number of bins would lead to zero counts in certain bins . The estimation of mutual information is subject to bias caused by finite sampling of the probability distributions because limited data were supplied in the present study ( a finite number of trials ) . Therefore , a quadratic extrapolation embedded in the Information Breakdown Toolbox was applied to correct bias . MI is computed on various subsets of the trials of the dataset of each condition . A quadratic function is then fit to these data points , and the actual mutual information is taken to be the zero-crossing value . This new value reflects the estimated mutual information for an infinite number of trials and greatly reduces the finite sampling bias [117 , 118] . A single-trial classification analysis of stimulus type was carried out on the clean stimulus condition to examine how the auditory system encodes information at different timescales . The procedure was described in detail in Ng et al . ( 2013 ) , and similar methods were also used in Luo and Poeppel ( 2012 ) , Herrmann et al . ( 2013 ) , and Cogan et al . ( 2011 ) . For each stimulus type , 1 trial was left out , and then a template was created by averaging across the remaining trials for this type of stimulus ( the circular mean is used for phase average ) . Three templates were created , and the distance between each template and the left-out trial from 1 of the 3 stimulus types was computed . The circular distance was applied for phase classification by taking the circular mean over time and frequency; the l2 norm of the linear distance was used for power classification . A trial was given 1 template’s label if the distance between this trial and the template was the smallest among 3 templates . A confusion matrix of classification was constructed by carrying out classification for each trial of each stimulus type on each auditory channel . Then , classification performance was measured using the same method used in the behavioral data analysis: correctly labeling the target stimulus was counted as a “hit” while labeling the other 2 stimuli as the target stimulus was counted as “false alarm;” d′ was calculated based on hit rates and false alarm rates and averaged across all auditory channels . Instead of d′ , D′ was used to differentiate d′ computed in the classification analysis from d′ in behavioral results . An index of classification efficiency using phase and power response of difference frequency band was indicated by the mean of D′ over 3 stimulus types , which was compared to the total d′ of the identification task , which indicates participants’ sensitivity in the behavioral study [106] . We carried out classification analysis using only the theta and the gamma bands on each time point to examine how classification performance progresses temporally in 2 frequency bands . We assumed that , on each time point , phase angles across trials can be summarized using a von Mises distribution—the circular analogue of the normal distribution—with its mean approximated by the group mean of phase angles across trials and its kappa value , an index for variance of von Mises distribution , estimated by computing variances of phase angles across trials [119] . For each sound , we calculated the mean and kappa value from 24 out of 25 trials on each time point for 1 sound and left 1 trial out as for classification . The means and kappa values for 3 sounds were estimated and then were used to estimate likelihoods of the left-out trial from each sound belonging to 3 distributions . We computed the likelihoods for each channel and each frequency and summarized the likelihoods across all channels selected and frequencies with each frequency band by adding up log likelihoods of each channel and each frequency . The summarized log likelihood was used to classify the left-out trial . For example , if a left-out trial from a θ sound has a high log likelihood in the distribution estimated by 24 trials from α sound but lower log likelihoods in the other 2 distributions , we classified this left-out trial from θ sound as from an α sound . A confusion matrix of classification was constructed by carrying out classification on each time point in each frequency band for each trial of each stimulus type . Then , classification performance was converted to D′ using the procedure described above in Single-trial classification . A cluster-based permutation test was conducted on the classification results [120] . For each frequency band , after we assigned the classified labels to the trials from all 3 sounds , we randomly shuffled the classified labels across 75 trials from 3 sounds and created a new dataset of classification results . We then converted the classification results to D′ and conducted 1-tailed 1-sample t tests to determine whether D′ on each time point is larger than the baseline line , D′ = 0 . We set the threshold of significance as 0 . 05 and computed cluster-level t values of each cluster comprising time points above the threshold . The cluster with the largest cluster-level t value was picked for creating a distribution . This procedure was repeated 1 , 000 times and a distribution over cluster-level t values was formed . We set the 95th percentile of the distribution over cluster-level t values as the threshold . Then , on the classification results from the original data , we conducted a 1-tailed 1-sample t test in each frequency band for each sound and set the threshold as 0 . 05 . The cluster-level t values of clusters comprising time points with significant classification performance were computed and the clusters with cluster-level t values larger than the threshold determined from the distribution created by permutation were considered as significant clusters . This cluster-based permutation test was conducted in each frequency band for each sound . Data are deposited in the Dryad repository: http://dx . doi . org/10 . 5061/dryad . f357r [121] . | Correctly perceiving behaviorally significant sounds—speech , music , and the acoustic environment—requires integrating acoustic information over time to extract relevant regularities . A fundamental question about this process is: How does the auditory brain integrate information of continuously varying sounds , typical of many natural auditory signals ? How does the brain “sample” the input ? To investigate this question , we measured how cortical activity is entrained by sound using the noninvasive technique magnetoencephalography . We presented sounds with temporal structure at different timescales and examined how the brain encodes such signals . We found , unexpectedly , that the human auditory system does not treat all rates equally but predominantly uses 2 nonoverlapping timescales , the slower ( theta ) and faster ( gamma ) bands , to track acoustic dynamics , while the timescale corresponding to an intermediate ( alpha ) timescale is likely reserved for other cortical operations , perhaps relating to attention and functional inhibition . The data support the hypothesis that the human auditory system employs ( at least ) a 2-timescale processing mode and that the perception of natural sounds , which feels seamless and continuous , is underpinned by segregated and discontinuous neural processing . | [
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... | 2017 | Concurrent temporal channels for auditory processing: Oscillatory neural entrainment reveals segregation of function at different scales |
Elongator is a conserved protein complex comprising six different polypeptides that has been ascribed a wide range of functions , but which is now known to be required for modification of uridine residues in the wobble position of a subset of tRNAs in yeast , plants , worms and mammals . In previous work , we showed that Elongator's largest subunit ( Elp1; also known as Iki3 ) was phosphorylated and implicated the yeast casein kinase I Hrr25 in Elongator function . Here we report identification of nine in vivo phosphorylation sites within Elp1 and show that four of these , clustered close to the Elp1 C-terminus and adjacent to a region that binds tRNA , are important for Elongator's tRNA modification function . Hrr25 protein kinase directly modifies Elp1 on two sites ( Ser-1198 and Ser-1202 ) and through analyzing non-phosphorylatable ( alanine ) and acidic , phosphomimic substitutions at Ser-1198 , Ser-1202 and Ser-1209 , we provide evidence that phosphorylation plays a positive role in the tRNA modification function of Elongator and may regulate the interaction of Elongator both with its accessory protein Kti12 and with Hrr25 kinase .
Elongator is a conserved , multi-subunit protein complex containing six different polypeptides ( Elp1-Elp6 ) , first discovered in yeast in association with the elongating form of RNA polymerase II and initially proposed to play a role in transcriptional elongation [1] , [2] . Although Elongator is non-essential in yeast , knockout of the mouse IKBKAP gene encoding Elongator's largest subunit leads to embryonic lethality and the protein is crucial for vascular and neural development [3] . The hereditary neuropathy Familial Dysautonomia results from human IKBKAP mutations , while mutations in other Elongator subunits have been associated with Amyotrophic Lateral Sclerosis [4] and Rolandic Epilepsy [5] . Elongator in Caenorhabditis elegans is also involved in neuronal function and development [6] , [7] , while in plants it plays a role in proliferation during organ growth [8] . While it is therefore clear that Elongator is important for neural function in higher organisms [9] , [10] , it has been proposed to have a bewildering range of seemingly unrelated functions . The Elp3 subunit of Elongator has a histone acetyltransferase ( HAT ) domain that can acetylate histones in vitro [11] and yeast Elongator mutants show changes in histone acetylation in vivo [12] . Elongator has also been proposed to acetylate α-tubulin and the neuronal protein Bruchpilot [7] , [13] , [14] and has been implicated in paternal DNA demethylation in mouse zygotes [15] . In yeast , Elongator mutants adapt slowly to changing growth conditions , show sensitivity to high temperature , rapamycin , caffeine , hydroxyurea and various other chemical stressors , and are resistant to zymocin [16] , [17] , a protein toxin secreted by the yeast Kluyveromyces lactis that kills other yeasts including Saccharomyces cerevisiae [18] . Yeast Elongator has also been implicated in transcriptional silencing , replication-coupled nucleosome assembly [17] and polarized secretion [19] . However , while these apparently diverse roles could imply that Elongator is multifunctional , work in yeast [20] , C . elegans [6] , plants [21] and mammals [22] has demonstrated that many if not all of these proposed functions reflect a primary role for Elongator in tRNA modification , specifically in the addition of mcm5 ( 5-methoxycarbonylmethyl ) and ncm5 ( 5-carbamoylmethyl ) groups to uridine when present at the ‘wobble’ position ( U34 ) in tRNA anticodons . Eleven out of 13 such tRNAs in yeast contain either mcm5U , ncm5U or 5-methoxycarbonymethyl-2-thiouridine ( mcm5s2U ) in the wobble position and addition of the mcm5 and ncm5 moieties requires Elongator [20] . This role in tRNA modification explains the zymocin-resistant phenotype of Elongator mutants: zymocin is a tRNA anticodon nuclease that inactivates tRNAGlu ( UUC ) by cleaving the anticodon on the 3′ side of U34 , but the mcm5 modification is necessary for tRNA recognition and cleavage [23] , [24] . Wobble uridine-containing tRNAs read codons ending with a purine , and the mcm5/ncm5 modifications are needed to confer full decoding competence on these tRNAs [6] , [20] , [25]-[27] . Wobble uridine modification is most likely the primary role of Elongator , at least in yeast , because elevated expression of just two Elongator-dependent tRNAs , tRNALys ( UUU ) and tRNAGln ( UUG ) , can suppress all the phenotypes associated with loss of Elongator function apart from zymocin resistance , which remains unaffected because elevated levels of the two tRNAs do not restore the tRNAGlu ( UUC ) modification required for cleavage by zymocin [28] , [29] . Suppression of Elongator mutant phenotypes by elevated tRNA levels without restoration of wobble uridine modification strongly suggests that these phenotypes are caused by translational defects resulting from hypomodified tRNAs , a notion supported by findings that U34 modification promotes binding of tRNALys ( UUU ) , tRNAGln ( UUG ) and tRNAGlu ( UUC ) to the ribosomal A-site [30] . Recent structural work indicates that Elp4 , Elp5 and Elp6 are RecA-fold proteins that form a heterohexamer containing two copies of each polypeptide , which interacts with two copies of an Elp1-Elp2-Elp3 sub-complex [31] . The recombinant heterohexamer binds and hydrolyses NTPs and shows tRNA binding that is reduced when the NTP can be hydrolyzed [31] , while a separate tRNA-binding motif in the C-terminal domain of Elp1 may also mediate tRNA interaction with the Elp1-Elp2-Elp3 sub-complex [32] . Thus while the existence of additional substrates cannot be excluded , it is now clear that Elongator plays a conserved role in wobble uridine modification [6] , [21] , [22] and that this role , through effects at the level of translation , is likely to underpin the majority of phenotypes resulting from Elongator deficiency , at least in yeast . Within Elongator , Elp3 is highly likely to catalyze the tRNA modification as it contains a ‘radical SAM’ domain [33] that in other proteins can mediate RNA modification reactions [34] , and both its radical SAM and histone acetyltransferase ( HAT ) domains are required for wobble uridine modification [20] , [29] . This is supported by the recent finding that recombinant archaeal Elp3 can catalyze modification of tRNA wobble uridines in an in vitro reaction containing SAM , acetyl-CoA , tRNA and Na2S2O4 [35] . Previously , we reported that the largest subunit of Elongator ( Elp1 ) is a phosphoprotein and identified mutations in either HRR25 ( encoding a casein kinase I ) or SIT4 ( encoding a protein phosphatase ) that conferred zymocin resistance [36]-[38] . Elp1 was present as a hypophosphorylated isoform in hrr25 mutants and as a hyperphosphorylated isoform in sit4 mutants , whereas wild-type cells contained similar amounts of both isoforms [37] , [39] . We therefore sought to investigate the potential functional significance of Elp1 phosphorylation by locating the phosphorylation sites on Elp1 , identifying several such sites that are critical for Elongator-dependent tRNA modification . Our findings therefore raise the possibility that Elongator activity ( and hence tRNA modification ) could be regulated , potentially constituting a novel mechanism for translational control .
To identify sites of phosphorylation in Elongator we affinity isolated the complex from yeast cells expressing a TAP-tagged version of Elp1 . Tryptic digests of the affinity-purified material were subjected to phosphopeptide enrichment involving a two-step procedure using Hypersep SCX and TiO2 , followed by tandem mass spectrometry to locate sites of phosphorylation . To maximize the chance of detecting phosphopeptides , in addition to isolating Elongator from wild-type yeast cells we also prepared and analyzed the complex from a sit4Δ strain , in which lack of Sit4 phosphatase leads to Elp1 hyperphosphorylation [37] , [39] . We also analyzed Elongator prepared from a kti12Δ mutant in which Elongator is hypophosphorylated [37] , [39] in case additional phosphorylation sites could also be detected under these circumstances . In this way we identified eight phosphorylation sites in Elp1 , one each in Elp2 and Elp4 and two in Elp5 ( Table 1 ) . With the exception of Ser-222 in Elp4 [40] , all of these sites are novel and were not identified in any of the recent proteome-wide phosphoproteomics studies . Since we previously showed that Elp1 phosphorylation state changes are associated with altered Elongator function [37] , [39] , we focused on the phosphorylation sites we identified in Elp1 . S1 Fig . shows representative MS/MS spectra providing evidence for the eight sites identified in Elp1 , which with one exception were identified within monophosphorylated peptides . Apart from Ser-1205/Thr-1206 , where phosphorylation of the two sites cannot be unambiguously distinguished from the mass spectra , all phosphorylation sites can be identified with high confidence . All Elp1 sites identified in Elongator isolated from either the sit4Δ mutant or the kti12Δ mutant were also found in Elp1 from the wild-type strain ( Table 1 ) . Since analysis of our mass spectrometry data also provided additional weak evidence for phosphorylation on Elp1 Ser-1209 , we raised a phosphospecific antibody against a synthetic peptide carrying phosphate on this residue to examine whether it was a genuine phosphorylation site . As shown in Fig . 1B , when used to probe Elp1 by Western blotting , this phosphospecific antibody gave a strong signal that was lost when Ser-1209 was mutated to alanine , thereby demonstrating its phosphospecificity . Thus Elp1 Ser-1209 represents a ninth Elp1 phosphorylation site ( Table 1 ) . Fig . 1A shows that five of the nine sites are located centrally within Elp1 . Four of these five sites map to the Elp1 amino-terminal domain , which is strongly predicted to form a β-propeller structure that may mediate interactions with other Elongator subunits or accessory proteins . The four remaining phosphorylation sites are tightly clustered close to the carboxy-terminus of Elp1 , in a region that is located adjacent to its tRNA binding region [32] and predicted to be disordered ( Fig . 1A ) . Phosphorylation sites are generally found to be enriched in disordered regions [41] , in particular those sites that show dynamic variation in phosphorylation state [42] . Beyond Ser-1209 , it is striking that every fourth residue between Thr-1212 and Thr-1230 is either threonine or serine ( Fig . 1E ) . However , despite this intriguing pattern of phosphorylatable residues we were unable to detect phosphate groups in this region . To determine whether any of the Elp1 phosphorylation sites we had identified were likely to be functionally significant , we mutated each in turn to non-phosphorylatable alanine . These mutants were assayed for zymocin sensitivity by eclipse assay , in which growth inhibition of an S . cerevisiae strain around a colony of zymocin-producing K . lactis indicates loss of Elongator-dependent mcm5 modification of tRNAGlu ( UUC ) [16] , [24] . Fig . 1C shows that with the exception of the S1209A mutant , no alanine substitution at a single phosphorylation site conferred detectable resistance to zymocin and hence loss of Elongator function . In contrast , the S1209A substitution conferred complete zymocin resistance in this assay . Combinations of alanine substitutions at multiple sites were therefore also generated and tested ( Fig . 1C and S1 Table ) . This analysis indicated that any mutants in which both Ser-1198 and Ser-1202 had been replaced by alanine were also zymocin-resistant . Thus Ser-1198 , Ser-1202 and Ser-1209 define three phosphorylated residues in Elp1 that are required for Elongator function , but with Ser-1198 and Ser-1202 apparently showing some redundancy . Furthermore , these residues are highly conserved in Elp1 in both lower and higher eukaryotes ( Fig . 1D ) . Since Elp1 becomes hyperphosphorylated in cells lacking Sit4 phosphatase that are defective for Elongator function [37] , [39] , we also examined whether any alanine substitutions could reverse the zymocin resistance shown by a sit4Δ mutant . However , none of the mutations tested altered the zymocin-resistance phenotype of the sit4Δ strain ( S1 Table ) . Similarly , we also tried mimicking constitutive phosphorylation at many of the sites by making glutamate or aspartate substitutions alone or in combination ( S1 Table ) , but failed to find any substitution ( s ) that conferred a significant loss of function phenotype . To investigate the Elp1 C-terminal phosphorylated region , additional combinations of alanine , aspartate or glutamate substitutions at the phosphorylation sites were generated . In addition to using the eclipse assay , we monitored tolerance to intracellular expression of the zymocin tRNase ( γ ) subunit from the galactose-inducible GAL1 promoter as a more quantitative measure of zymocin resistance [43] . Since zymocin sensitivity provides a readout largely for the mcm5 modification state of just tRNAGlu ( UUC ) , we also monitored Elongator function via efficiency of ochre suppression mediated by SUP4 , a tRNATyr ( UUA ) that requires Elongator-dependent wobble uridine modification for efficient ochre ( UAA ) codon readthrough [20] , [24] . This involved single copy integration of a plasmid that carried both SUP4 and a ura3 ochre allele [32] , such that suppression ( and hence Elongator's tRNA modification function ) could be monitored by growth in the absence of uracil . Fig . 2 shows that the S1209A substitution conferred complete resistance to intracellular expression of the zymocin γ subunit and greatly reduced SUP4-dependent ura3oc suppression , in each case conferring a phenotype comparable to that observed upon complete loss of Elongator function ( elp1Δ ) . In contrast , an S1209D substitution showed considerable restoration of function , while the equivalent glutamate substitution ( potentially a poorer mimic of phosphoserine due to its longer side chain ) was less effective . The regain of functionality caused by the phosphomimic aspartate substitution therefore provides evidence that phosphorylation of Ser-1209 acts positively for Elongator function . Although the double S1198A S1202A substitution mutant conferred strong zymocin resistance it showed considerable residual Elongator function in the SUP4 assay , allowing for growth in the absence of uracil similar to that shown by the ELP1 wild-type control . However , concurrent glutamate substitutions at both positions largely restored zymocin sensitivity , supporting the notion that phosphorylation of these two sites acts positively for Elongator function . S1198A S1202A in combination with T1204A S1205A T1206A , a triple alanine substitution that on its own had essentially no effect on Elongator function in any of the assays , conferred stronger zymocin resistance than S1198A S1202A alone and dramatically reduced SUP4-dependent suppression efficiency , indicative of an additive Elongator defect in the quintuple mutant . The triple T1204A S1205A T1206A mutation was used here because phosphorylation at Ser-1205/Thr-1206 could not be unambiguously distinguished ( see above ) . Mutants where acidic residues substituted combinations of these five positions , either alone or in combination with alanine substitutions , indicated that Elongator functionality was not greatly affected by acidic substitutions , although the T1204E S1205D T1206E triple substitution improved Elongator function when combined with the S1198A S1202A double mutant ( seen most clearly by the eclipse assay ) . While the difference in the severity of phenotype observed between the zymocin- and SUP4-based assays for some elp1 mutants might reflect differential effects on tRNAGlu ( UUC ) and tRNATyr ( UUA ) , we consider it more likely to reflect different loss of modification thresholds required to score positive in these assays; while as little as ∼40% reduction in modification may generate sufficient uncleavable tRNAGlu ( UUC ) to confer zymocin resistance [44] , a much larger reduction in modification may be required before there is insufficient functional tRNATyr ( UUA ) to support effective ochre suppression . Our results therefore indicate that blocking phosphorylation at all four sites identified in this region of Elp1 leads to reduced Elongator function , with the S1209A mutant showing the greatest defect followed by S1198 S1202A and T1204A S1205A T1206A in decreasing order of severity , but with additivity between S1198 S1202A and T1204A S1205A T1206A leading to a defect as severe as that of S1209A . That acidic substitutions mimicking phosphorylation at each of the four sites conferred considerable Elongator function is consistent with the notion that phosphorylation at these sites functions positively for Elongator . To look directly at the tRNA wobble uridine modifications , we prepared tRNA from selected elp1 mutant strains and used LC-MS analysis to quantitate the levels of modified U34 nucleosides . Fig . 3 shows that in the elp1Δ control strain , mcm5U and ncm5U were absent from tRNA as expected . The S1209A and quintuple S1198 S1202A T1204A S1205A T1206A strains showed almost no mcm5U or ncm5U , consistent with the major defect in Elongator-dependent wobble uridine modification indicated by the phenotypic assays ( Fig . 2 ) . The S1198A S1202A double mutant showed reduced levels of mcm5U and ncm5U consistent with a less severe Elongator defect , while strains with the S1198E S1202E double or S1209D single phosphomimic alleles , or with the triple T1204A S1205A T1206A allele , had levels of mcm5U and ncm5U intermediate between those of the S1198A S1202A mutant and ELP1 wild-type . Taken together , the profiles for the ncm5 and mcm5 modification nucleosides are consistent with our phenotypic analyses ( Fig . 2 ) and our data therefore support the notion that phosphorylation of Ser-1209 plays a major , positive role in Elongator-dependent tRNA modification and that phosphorylation at Ser-1198 , Ser-1202 and Ser-1205/Thr-1206 makes a similar but partly redundant contribution to Elongator activity . Phosphorylation site mutations that alter Elongator functionality could in principle do so by affecting assembly of the holo-Elongator complex , either because they interfere with assembly or because phosphorylation of these sites could regulate assembly of the Elongator complex . When myc-tagged Elp2 was used to immunoprecipitate Elongator from cell extracts , elp1 phosphorylation site mutations at positions 1198 , 1202 , 1205 , 1206 and 1209 , alone or in combination , did not affect the recovery of Elp1 , Elp3 and Elp5 in the Elp2 immunoprecipitates in comparison with strains where Elp1 was wild-type ( Fig . 4 ) . In particular , there was no evidence for any changes in the assembly of the complex when Elp1 carried the S1209A substitution , which is essentially null for Elongator function as discussed above . Thus similar co-immune precipitation of all four proteins was observed , regardless of whether these mutations affected Elongator functionality . Although we have not tested every mutant elp1 allele constructed in this study , assembly of the Elp1-Elp2-Elp3 subcomplex and its association with the Elp4-Elp5-Elp6 subcomplex appear essentially normal , irrespective of the consequences of the mutations for Elongator functionality . Since Elongator interacts with both its accessory factor Kti12 and with Hrr25 kinase and because Elongator function requires both Kti12 and Hrr25 kinase activity [16] , [37] , [45] , [46] , we next used co-immunoprecipitation to examine the effect of selected phosphorylation site mutations on Elongator's association with Kti12 and Hrr25 . Elongator complex in which Elp1 carries the double S1198A S1202A mutation showed reduced interaction with Hrr25 that was not seen with the corresponding phosphomimic allele ( S1198E S1202E ) , but combining S1198A S1202A with T1204A S1205A T1206A did not further reduce interaction with Hrr25 ( Fig . 5 ) despite the greater loss of Elongator function in the quintuple mutant . Conversely , mutation of Elp1 Ser-1209 to alanine led to enhanced interaction between Elongator and Hrr25 ( Fig . 5 ) . In all ELP1 mutants tested , Elongator retained its ability to interact with Kti12 , but the elp1-S1209A allele also led to enhanced Kti12 interaction ( Fig . 6A ) . We examined the effect of the Elp1 S1209A substitution on Kti12 association in more detail by tagging the genomic copy of ELP1 with GFP and carefully quantitating the recovery of HA-tagged Kti12 in strains expressing either the wild-type or mutant Elp1-GFP fusions . This confirmed that the S1209A mutation leads to enhanced Kti12 association: in comparison with wild-type Elp1 , Elp1-S1209A was reproducibly associated with approximately twice as much Kti12 ( Fig . 6B , C ) . Thus the S1198A S1202A and S1209A mutations have opposite effects on association with Hrr25 and the 1209A mutation enhances association with Kti12 , suggesting that phosphorylation at these sites affects the interaction between Elongator and key proteins required for its functionality . We next wished to identify the protein kinase ( s ) that mediate modification of the phosphorylation sites in the Elp1 carboxy-terminal domain that are important for Elongator function . To take an unbiased approach , we screened a library of 119 GST-protein kinase fusions [47] for their ability to phosphorylate Elp1 in vitro using TAP-purified Elongator as a substrate . Purified Elongator showed significant background phosphorylation specifically on Elp1 when incubated with [γ-32P]ATP in the absence of added kinase . However , five protein kinases were identified that could clearly phosphorylate Elp1 in vitro ( Hrr25 , Yck1 , Yck2 , Yck3 and Hal5: S2A Fig . ) . Given that Hrr25 interacts with Elongator [37] , [48] , [49] as seen in Fig . 5 and because Elp1 is already known to become hypophosphorylated in a hrr25 mutant [37] , it is an excellent candidate for an in vivo Elp1 kinase . Hrr25 , Yck1 , Yck2 and Yck3 all belong to the casein kinase I ( CKI ) family of protein kinases [50] , but while hrr25 mutants show clear defects in Elongator function [37] , [38] , Yck1 , Yck2 and Yck3 are membrane associated via lipid modification [51] , [52] and do not confer detectable zymocin resistance when the corresponding genes are deleted [38] . Thus we consider it unlikely that Yck1-3 mediate functionally important phosphorylation events on Elp1 in vivo and that they were identified in our in vitro screen due to shared substrate specificity with the related Hrr25 CKI isozyme . Preliminary analysis also failed to generate data supporting a role for Hal5 in Elongator function ( S2B-S2C Fig . ) . In further support of a role for Hrr25 as a direct Elp1 kinase , we next showed that the Elp1 kinase activity present in affinity-isolated Elongator preparations was due to Hrr25 . This made use of a yeast strain dependent on an ‘analog-sensitive’ HRR25 allele ( hrr25-I82G ) in which the mutant Hrr25 kinase has acquired the capacity to be inhibited specifically by addition of the ATP analogs 1NM-PP1 or 3MB-PP1 [53] . When Elongator was isolated from the hrr25-I82G strain , the Elp1 phosphorylation observed upon incubation of the isolated complex with [γ-32P]ATP was blocked by addition of either 1NM-PP1 or 3MB-PP1 ( Fig . 7A ) . This was in contrast to Elongator isolated from a control strain expressing wild-type HRR25 , phosphorylation of which was refractory to these inhibitors . In fact the hrr25-I82G strain became zymocin resistant when grown in the presence of 1NM-PP1 , emphasizing the positive role of Hrr25 kinase in Elongator's tRNA modification in vivo ( Fig . 7B ) and consistent with Elongator-minus phenotypes of hrr25 mutants [37] , [38] . To determine whether Hrr25 phosphorylation of Elp1 occurred at any of the functionally relevant sites that we had identified , Elongator containing wild-type or mutant forms of Elp1 was purified and tested for incorporation of phosphate from [γ-32P]ATP both in the absence and presence of recombinant Hrr25 . Fig . 7C shows that even when reactions were supplemented with Hrr25 , Elp1 in which the C-terminal region had been deleted was not phosphorylated . Elongator in which both Elp1 Ser-1198 and Ser-1202 had been replaced by either alanine or glutamate showed negligible incorporation of radiolabel in comparison with the Elp1 in wild-type Elongator . In contrast , Elongator in which Elp1 Ser-1209 was substituted with either alanine or aspartate showed high levels of 32P incorporation into Elp1 in the presence of recombinant Hrr25 . Since HRR25 is an essential gene we were unable to examine Ser-1209 phosphorylation with the anti-phophoserine-1209 antibody in the complete absence of Hrr25 . However , in the kinase-dead hrr25-3 mutant , which shows loss of Elongator function and absence of the phosphorylated isoform detected in wild-type cells by gel shift assay [37] , a similar level of Ser-1209 phosphorylation to that seen in a wild-type strain was observed ( S3 Fig . ) . Although more complex interpretations are possible , these observations are therefore most simply explained if Ser-1198 and Ser-1202 are major sites of Hrr25 phosphorylation and Ser-1209 is not a major site for modification by the kinase . In the absence of added Hrr25 , Elongator complex containing Elp1-S1209A still showed high levels of Elp1 phosphorylation that were not seen with the corresponding aspartate substitution or when Elp1 was wild-type , although after addition of recombinant Hrr25 the level of Elp1 phosphorylation of the Elp1-S1209A and Elp1-S1209D complexes was similar as noted above ( Fig . 7C ) . These data are consistent with the enhanced interaction of Elp1-S1209A-containing Elongator with Hrr25 that was seen by co-immune precipitation ( Fig . 5 ) . To complement these experiments , the ability of recombinant Hrr25 to phosphorylate synthetic peptides corresponding to the C-terminal phosphorylated region of Elp1 was tested , using both mass spectrometry to identify phosphorylated residues in reactions with unlabeled ATP and by monitoring incorporation of 32P-phosphate in reactions containing [γ-32P]ATP . When a peptide containing Elp1 residues 1193-1213 was phosphorylated by recombinant Hrr25 in vitro and analyzed by mass spectrometry , three types of phosphopeptide were detected: monophosphorylated peptide modified on either Ser-1198 ( S4A Fig . ) or Ser-1202 ( S4B Fig . ) , and diphosphorylated peptide modified on both these residues ( S4C Fig . ) . These data therefore confirm the identity of Hrr25 as a protein kinase that can directly phosphorylate Elp1 on serine residues that are important for Elongator function , and are consistent with a model in which phosphorylation at one of these two sites may then favor phosphorylation of the second site . When all phosphorylatable residues apart from Ser-1198 and Ser-1202 were changed to alanines , dual phosphorylation on Ser-1198 and Ser-1202 was still seen , indicating that phosphorylation of these two positions was not dependent on phosphorylation of Ser-1205 or any other downstream residues . When the 1193-1213 peptide was incubated with Hrr25 together with [γ-32P]ATP , it readily incorporated radiolabelled phosphate ( Fig . 8B and C; peptide 89 ) . Although the related peptide in which all serine and threonine residues apart from Ser-1198 and Ser-1202 had been substituted by alanines was still an excellent Hrr25 substrate ( Fig . 8C; peptide 92 ) , incorporation of 32P at later times was reduced in comparison ( Fig . 8B ) , consistent with the possibility of additional phosphorylation to the right of Ser-1202 . Additional single alanine substitutions at either Ser-1198 or Ser-1202 in peptide 92 greatly reduced but did not completely prevent phosphorylation ( Fig . 8C , peptides 1001 , 1002 ) . However , replacement of both Ser-1198 and Ser-1202 by either alanines or glutamates completely blocked phosphorylation of the peptide despite the presence of the five serine and threonine residues downstream ( peptides 90 and 91 ) . Another peptide encompassing Elp1 residues 1207-1229 was not phosphorylated at all following incubation with Hrr25 ( Fig . 8D , peptide 95 ) , supporting the notion that neither Ser-1209 nor any of the repeating threonine and serine shown in Fig . 1E can be directly phosphorylated by Hrr25 kinase . These data are therefore consistent with interdependence of Ser-1198 and Ser-1202 phosphorylation by Hrr25 and absence of Hrr25 phosphorylation on the downstream serines and threonines . Since phosphorylation of specific Ser or Thr residues by CKIs such as Hrr25 is often primed by phosphorylation at another Ser or Thr residue 2-4 positions upstream [54] , it was possible that Hrr25 might phosphorylate Ser-1205 once Ser-1202 had been modified , and that phosphorylation at Ser-1205 might then promote modification of Ser-1209 . However , phosphomimic glutamate substitutions at 1198 and 1202 , which supported essentially normal Elongator functionality in vivo ( Fig . 2 ) , also prevented phosphorylation of the 1193-1213 peptide on any other downstream site . Furthermore , phosphorylation of a related peptide ( Elp1 1192-1212; Fig . 8E ) , which was also a good in vitro substrate for Hrr25 , was also completely blocked for Hrr25 phosphorylation when synthesized with phosphoserine at the two positions corresponding to Elp1 Ser-1198 and Ser-1202 . This again supports the model that Ser-1198 and Ser-1202 are the only major sites of Hrr25 phosphorylation and also indicates that it is unlikely that efficient priming of phosphorylation by Hrr25 on downstream residues , such as Ser-1205 and Ser-1209 , occurs as a result of the upstream phosphorylation events at Ser-1198 and Ser-1202 . Thus although these experiments do not as yet fully explain all the intricacies of Hrr25 phosphorylation in this region , taken together they nonetheless demonstrate that Hrr25 phosphorylates Elp1 directly on Ser-1198 and Ser-1202 , two serine residues that we identified as functionally relevant in vivo phosphorylation sites .
We have identified nine phosphorylation sites on the Elp1 subunit of yeast Elongator complex and based on the phenotypes of non-phosphorylatable and phosphomimic mutations , provide evidence that phosphorylation on four sites near the Elp1 C-terminus ( Ser-1198 , Ser-1202 , Ser-1205/Thr-1206 and Ser-1209 ) plays a positive role in Elongator function . Previously , we showed that a sit4 phosphatase mutant trapped Elp1 in a slower-migrating , hyperphosphorylated form whereas hrr25 kinase mutations led to presence of just a faster-migrating , hypophosphorylated Elp1 isoform [37] , [39] . Both types of mutation cause loss of Elongator function [37] , [39] , suggesting that dynamic phosphorylation and dephosphorylation of Elp1 is needed in functional Elongator and predicting that mimicking constitutive phosphorylation on at least some of the sites we have identified should be inhibitory . It is therefore surprising that all of the phosphomimic alleles we tested conferred significant Elongator function . Possibly the acidic substitutions do not fully mimic constitutive phosphorylation and thereby allow for substantial residual function , even though constitutive phosphorylation might inhibit Elongator . Thus while our alanine substitution mutants lend support to the idea that phosphorylation at the mapped sites functions positively for Elongator activity , we cannot rule out a requirement for dynamic phosphorylation/dephosphorylation at the sites we have identified . It is also possible that additional , inhibitory phosphorylation sites exist that were not found in our study . For example , although we could not detect phosphorylation of Thr-1212 in vivo or in vitro , both alanine and glutamate substitutions at this site caused severe loss of Elongator function ( S5 Fig . ) , consistent with the notion that dynamic phosphorylation and dephosphorylation of this site could be important if it is phosphorylated in vivo . Despite conducting an unbiased , ‘kinome-wide’ screen to identify kinases responsible for Elp1 C-terminal domain phosphorylation , Hrr25 , the yeast CKI that associates with Elongator and has already been implicated in Elongator function [37] , [48] , [49] was the sole convincing candidate to be identified . Through several lines of evidence , we have now established that Hrr25 directly phosphorylates Elp1 on two in vivo phosphorylation sites that we have mapped and shown to be relevant for Elongator function: Ser-1198 and Ser-1202 . Thus purified Elongator shows Hrr25-dependent phosphorylation that requires the presence of these two residues , while peptides derived from the Elp1 C-terminal region show direct , interdependent phosphorylation by recombinant Hrr25 on Ser-1198 and Ser-1202 . Conversely , we found no evidence for direct phosphorylation of Ser-1209 by Hrr25 using three different synthetic peptides containing Ser-1209 , or using purified Elongator complex in which Ser-1198 and Ser-1202 were mutated but Ser-1209 was intact . Two types of consensus phosphorylation site have been proposed for CKIs: pS [X]1-3 [ST] and [DE]2-4 [X]0-2 [ST] , where pS indicates an upstream phosphoserine residue that is needed to prime phosphorylation at the downstream Ser or Thr ( shown in bold and underlined ) but which can be substituted by an acidic patch in the second class of motif [54] . Ser-1198 conforms to the latter type of motif and is closely related to other mapped Hrr25 phosphorylation sites [55]-[57] in yeast proteins ( S6 Fig . ) . In contrast , Ser-1202 matches the former motif , suggesting that priming-independent phosphorylation at Ser-1198 by Hrr25 might then prime phosphorylation by Hrr25 at Ser-1202 and predicting that Ser-1198 should still be efficiently phosphorylated when Ser-1202 is replaced by alanine . However , the latter mutation largely blocked phosphorylation at Ser-1198 in our in vitro peptide kinase assays and instead we observed interdependent phosphorylation at these two residues . Furthermore , replacement of one of the two upstream aspartate residues with alanine did not obviously interfere with phosphorylation of Ser-1198 and Ser-1202 . Thus although the sites are direct targets of Hrr25 and match the accepted consensus for CKI phosphorylation , dependency of Ser-1202 phosphorylation on prior Ser-1198 modification remains to be demonstrated . Similarly , Ser-1205 and Ser-1209 both fit the pS [X]1-3 [ST] consensus and might be modified once Ser-1202 phosphorylation has occurred , and yet we could not demonstrate Hrr25-dependent incorporation of phosphate at these sites even using a peptide where the upstream sites were already phosphorylated . Given the known substrate specificity of CKIs , it is therefore surprising that we can find no evidence for such priming of phosphorylation at Ser-1205 and Ser-1209 following modification of Ser1202 . Furthermore , since the repeating pattern of phosphorylatable residues shown in Fig . 1E also fits the pS [X]1-3 [ST] motif we are even more surprised that we failed to detect phosphorylation of these sites either in vivo or in vitro . In spite of the strong evidence we have provided that Hrr25 is a direct Elp1 kinase , inability to detect direct phosphorylation of either the key residue Ser-1209 or of Ser-1205/Thr-1206 by Hrr25 implies that at least one additional Elp1 kinase is involved in Elp1 phosphorylation . While these sites might become better substrates for Hrr25 in the context of fully assembled Elongator complex rather than within synthetic peptides , the fact that we detect normal levels of Ser-1209 phosphorylation in a hrr25 mutant that is defective for Elongator function strongly supports the involvement of a different kinase at Ser-1209 . Although all phosphorylation site mutants examined showed essentially normal assembly of the Elongator complex and retained the ability to interact with the accessory protein Kti12 , the S1209A mutation enhanced association of Kti12 with Elongator in comparison with cells expressing either the wild-type protein or the S1198A S1202A mutant . Kti12 stoichiometry is important – either too much or too little interferes with Elongator function [39] , [43] , [45] – and thus Ser-1209 phosphorylation may regulate Elongator's interaction with its accessory protein . Furthermore , the S1209A mutation caused increased Hrr25 association with Elongator whereas the double S1198A 1202A mutation led to reduced Hrr25 association . Thus Hrr25 not only directly phosphorylates Elp1 but may also regulate its own interaction with Elongator through doing so . Although the S1209A single and S1198A S1202A T1204A S1205S T1206A quintuple mutants both conferred strong loss of Elongator function , their differing effects on Hrr25 association implies that they are defective for different reasons – phosphorylation at S1198A S1202A may stabilize Hrr25 binding whereas phosphorylation at Ser-1209 may be required to displace the kinase . Since the interaction between Hrr25 and Elongator is dependent on Kti12 [37] it is also possible that the enhanced interaction of both proteins with Elongator seen when Elp1 cannot be phosphorylated on Ser-1209 are directly related . Intriguingly , enhanced interaction of both Kti12 and Hrr25 with the Elongator complex is also seen in hrr25 mutants that cause Elp1 phosphorylation defects and zymocin resistance [37 and S7 Fig . ] . However , these properties of hrr25 mutants are more similar to those of the elp1-S1209A mutant , which is mutated at a site apparently not directly phosphorylated by Hrr25 , rather than mirroring the properties of the elp1-S1198A , S1202A mutant that removes the only sites in Elp1 that we have shown to be direct Hrr25 targets . This suggests an as yet undiscovered connection between phosphorylation at Ser-1209 and Hrr25 kinase . Regardless of this , our data nonetheless indicate that Elp1 phosphorylation by Hrr25 and other kinases could modulate the interaction between Elongator and Kti12 . Although there is clearly more to learn about the role of phosphorylation in Elongator function , two types of model can be proposed . On the one hand , phosphorylation might regulate Elongator , turning its wobble uridine modification activity up or down in response to growth conditions and the demand for protein synthesis , or perhaps in response to cellular stresses . Given that the translation of some mRNAs is particularly dependent on wobble base modification and Elongator function [58]-[60] and that tRNA modifications ( including Elongator-dependent ones ) oscillate during the cell cycle and in response to stress signals [61] , [62] , this raises the interesting possibility that Elongator may be part of a translational control mechanism functioning through tRNA modification . Our proposal that Elp1 phosphorylation operates in a largely positive sense for Elongator , based on the properties of the phosphorylation site mutants that we have examined , is consistent with such a regulatory role . The Elp1 kinase Hrr25 is needed for a wide range of cellular functions [49] , [56] , [57] , [63]-[65] that do not appear to provide a clear insight into the signals that might regulate Elp1 phosphorylation . However , the Hrr25 requirement for full functionality of two different components of the translation machinery - wobble uridine-containing tRNAs [37 , this work] and ribosomes [49] , [57] - might reflect a role in regulation of the cell's capacity for protein synthesis . In addition , both hrr25 mutants [66] and Elongator-deficient yeast [17] are sensitive to hydroxyurea and methyl methanesulfonate . Since efficient translation of the ribonucleotide reductase gene RNR1 requires mcm5-modified tRNAs [62] , [67] , Elp1 phosphorylation may also be linked to the known role of Hrr25 in expression of genes such as RNR2 and RNR3 in response to DNA damage [66] . Alternatively , Elp1 phosphorylation might be dynamic , with sequential phosphorylation and dephosphorylation of specific residues driving the biochemical mechanism through which Elongator carries out the tRNA modification reactions . Such a dynamic role might operate through modulation of Elongator's interaction with factors such as Kti12 or with tRNA . It is intriguing in this context that the C-terminal phosphorylated region in Elp1 is immediately adjacent to a basic region that binds specifically to tRNA and that mutation of the tRNA binding domain leads to reduced interaction with Kti12 [Fig . 6C and ref . 32] . Thus phosphorylation of Elp1 could potentially influence how Elp1 interacts with tRNA , perhaps through interaction between the acidic phosphate groups and the basic residues present in the tRNA binding domain . In conclusion , while there is still much to learn about Elp1 phosphorylation and its involvement in Elongator function , our work clearly demonstrates the importance of at least four in vivo phosphorylation sites in the C-terminal domain of Elp1 for Elongator-dependent tRNA wobble uridine modification , shows that Hrr25 kinase directly modifies two of these sites and provides evidence that phosphorylation regulates the association between Elongator and both its accessory protein Kti12 and its kinase Hrr25 .
Basic yeast methods , growth media , and routine recombinant DNA methodology were performed as previously described [68] , [69] . All plasmids used in this study are listed in Table S2 and yeast strains are listed in Table S3 . To generate yeast strains dependent on wild-type or mutant forms of ELP1 , ELP1 was first deleted from BY4741 and WAY008 ( ELP3-TAP ) using pFA6a-KanMX6 and pCORE-UH deletion cassettes to obtain the elp1Δ knockout mutant strains WAP034 and WAY037 , respectively . Wild-type or mutant ELP1 variants carried on the low-copy plasmid YCplac111-ELP1-6HA ( Table S2 ) were then introduced into these elp1Δ strains as the sole source of Elp1 . The mutant versions were made by either site directed mutagenesis ( Qiagen QuikChange ) using YCplac111-ELP1-6HA as template , or by replacing the relevant region using standard cloning procedures and synthetic DNA carrying the desired mutations . The majority of plasmids made using the latter approach were produced by DNA2 . 0 , Inc ( Menlo Park , CA , USA ) . All elp1 mutants made by site-directed mutagenesis were verified by DNA sequencing of the entire ELP1 ORF plus approximately 200 base pairs upstream and downstream . When mutations were introduced by cloning , the region that had been replaced was sequenced to exclude the possibility of gene synthesis errors . The wild-type and mutant versions of YCplac111-ELP1-6HA were transformed into WAY034 , WAY037 , or elp1Δ::KanMX6 ELP2-myc3 strains carrying additional epitope-tagged components ( Table S3 ) for phenotypic screening , Elongator-complex purification or Western blot analysis , respectively . pFA6a-CTAP4-HIS3MX6 was made by ligating the 1322 bp BglII-PmeI fragment from pFA6a-3HA-HIS3MX6 to the 3603 bp BglII-PmeI fragment from pFA6a-CTAP4-NatMX6 [70] such that the NatMX marker was replaced by HIS3MX . Using this template , a C-terminal TAP tag was added to ELP1 in RL-343-F0 and RL-343-F1 ( Table S3 ) using standard one-step tagging methodology [71] . sit4Δ::LEU2 and kti12Δ::LEU2 knockouts were made as previously described [39] , [43] , as was introduction of elp1Δ::KanMX6 in strains used for co-immune precipitation with myc-tagged Elp2 [16] . Construction of ELP1-HA6::KlTRP1 utilized the pYM3 tagging plasmid and S2/S3 primer set described by Knop et al . [72] . For phosphorylation site mapping , Elongator complexes were purified from WAY009 , WAY010 , WAY011 , WAY-H-P1T and WAY-Has-P1T ( Table S3 ) by two-step tandem affinity purification of Elp1-TAP from 2-12 liter cultures in YPAD medium grown to OD600 1 . 0-1 . 5 , as described previously [73] . All purifications were done in the presence of PhosSTOP and Complete protease inhibitor cocktail ( Roche ) . Elongator complexes in which Elp1 contained phosphorylation site mutations were similarly purified from WAY037 ( elp1Δ::pCORE-UH ELP3-TAP::HIS3MX: Table S3 ) harboring YCplac111-ELP1-6HA or its mutant derivatives ( Table S2 ) , growing cells as above but in SCD-Leu to select for retention of the plasmid . Elongator protein preparations were digested in solution with Trypsin ( Trypsin Gold , mass spectrometry grade; Promega V5280 ) . The resulting peptides were cleaned over Hypersep C18 columns ( Thermo Scientific ) to remove buffer contaminants , eluting the peptides in 70% acetonitrile , 0 . 1% trifluoroacetic acid ( TFA ) . Phosphopeptide enrichment was done in a two-step procedure using a Hypersep SCX column ( Thermo Scientific ) followed by TiO2 enrichment of mono-phosphorylated peptides from the SCX flow-through . SCX binding and washing buffer contained 10 mM KH2PO4 , 25% acetonitrile ( pH 3 . 0 ) and the elution buffer contained 10 mM KH2PO4 , 25% acetonitrile , 350 mM KCl ( pH 3 . 0 ) . The flow-through was reduced in volume to ∼100 µl , supplemented with 100 µl 80% acetonitrile/2% TFA containing 200 mg/ml 2 , 5-dihydroxybenzoic acid ( DHB ) , pH 2 . 0 , then the sample applied to 5 micron Titansphere TiO2 beads ( GL Sciences ) and rotated gently at room temperature for 1 h . The beads were washed twice with 80% acetonitrile/2% TFA , 200 mg/ml DHB ( pH 2 . 0 ) , then 3 times with 80% acetonitrile/2%TFA ( pH 2 . 0 ) , before eluting with 60 µl 400 mM NH4OH ( pH 11 . 0 ) and then supplementing with 2 µl of 100% formic acid . All phosphopeptide fractions were cleaned over C18 and submitted for mass spectrometry in 0 . 1% TFA . Phosphopeptide fractions were analyzed by LC-MS/MS using a Dionex U300 system ( Dionex California ) with a PepMap C18 column coupled to either an Orbitrap XL or Orbitrap Velos ( Thermo Fisher Scientific ) . Peptides were eluted using a 45 min 5%-90% acetonitrile gradient , sequencing the top 5 most intense ions with the following settings: CID , FTMS 335-1800 Da , 60 , 000 resolution , MS/MS charge state 1+ rejected , >2+ accepted . Peak picking , recalibration and peptide mass fingerprinting was done using MaxQuant software [74] , [75] , searching masses against the Saccharomyces Genome Database orf_trans_all database ( January 5th 2010 release ) with 10 ppm MS error , ≤ 2 missed cleavages , Trypsin/P enzyme , variable modifications set to Acetyl ( protein N-term ) , Oxidation ( M ) , Phospho ( ST ) and Phospho ( Y ) and a fixed modification of Carbomidomethyl ( C ) . The MS/MS tolerance was set to 0 . 5 Da and the false discovery rate for site , protein and peptide identification was set to 0 . 01 . All phosphorylation sites identified in this way were verified by hand annotating the spectra . The analysis was done on four biological replicates of the finally optimized SCX-TiO2 protocol . An anti-Elp1 phosphoSer-1209 antibody was raised in a rabbit by BioGenes GmbH , using a peptide antigen ( H2N-CTSTQE-pS-FFTRY-CO . NH2 , where pS indicates phosphoserine ) and their standard procedures ( see http://www . biogenes-antibodies . com ) . The phosphospecific fraction of the final bleed was purified by serum depletion using immobilized non-phosphorylated peptide ( H2N-CTSTQESFFTRY-CO . NH2 ) , then phosphospecific antibodies were isolated from the depleted serum using immobilized phosphopeptide . The purified phosphospecific antibody fraction was tested for phosphospecificity by Western blotting , showing loss of signal when protein extracts were prepared from an elp1-1209A mutant and following competition with the phosphopeptide . The antibody was stored at -20°C in 50% ( v/v ) glycerol and used at a 1 in 3000 dilution . A small excess of the non-phosphorylated peptide was added to the primary dilution before use to prevent any cross-reaction with the non-phosphorylated epitope . To test the effect of extracellular zymocin on wild-type and mutant strains , killer-eclipse assays were performed as described previously [76] using the K . lactis zymocin producer strain NCYC1368 and YPD plates prepared using Kobe I agar ( Roth 5210 ) . To test the effect of intracellular expression of zymocin's γ subunit on growth , wild-type and mutant elp1 strains were transformed with pAE1 , which expresses the γ subunit from the galactose-inducible GAL1 promoter [43] . The response to zymocin γ-toxin induction was monitored on galactose plates after 3-4 days at 30°C . SUP4 suppression efficiency was measured following integration of pSB3 in single copy at the his3Δ0 locus and then monitoring growth on SCD-Leu-Ura after 3 days at 30°C as described previously [32] . To test the effect of Hrr25 inhibition on Elongator function , strains dependent on w . t . or analog-sensitive Hrr25 were grown overnight in YPAD medium , diluted to 1 . 0 A600/ml and then 10-fold dilutions were spotted onto YPAD agar with or without 1% ( v/v ) zymocin and containing 10 µM 1NM-PP1 or an equivalent volume of DMSO as drug vehicle control . Growth was documented after 2 days growth at 30°C . Zymocin was prepared using cell-free culture medium from K . lactis NCYC1368 which had been grown at 30°C for 2 days in YPAD medium , by 50-fold concentration using Amicon Ultra-15 Centrifugal Filter Units ( Millipore ) followed by sterilization by filtration . elp1Δ yeast strains containing YCplac111-ELP1-HA6 or its mutant derivatives were grown in SCD-Leu to select for the plasmid and then total tRNA was prepared by RNA extraction and LiCl precipitation as described previously [32] . Prior to LC-MS/MS analysis , 5 µg of each tRNA sample were digested into nucleosides by incubation at 37°C for 2 h in the presence of 1/10 volume of 10× nuclease P1 buffer ( 0 . 2 M NH4OAc pH 5 . 0 , ZnCl2 0 . 2 mM ) , 0 . 3 U nuclease P1 ( Sigma Aldrich , Munich , Germany ) and 0 . 1 U snake venom phosphodiesterase ( Worthington , Lakewood , USA ) . Next , 1/10 volume of 10× fast alkaline phosphatase buffer and 1 U fast alkaline phosphatase ( Fermentas , St . Leon-Roth , Germany ) were added , and samples were incubated for additional 60 min at 37°C . The digested tRNA samples were analyzed on an Agilent 1260 HPLC series equipped with a diode array detector ( DAD ) and a triple quadrupol mass spectrometer ( Agilent 6460 ) . A Synergy Fusion RP column ( 4 µm particle size , 80 Å pore size , 250 mm length , 2 mm inner diameter ) from Phenomenex ( Aschaffenburg , Germany ) was used at 35°C column temperature . The solvents consisted of 5 mM ammonium acetate buffer adjusted to pH 5 . 3 using acetic acid ( solvent A ) and pure acetonitrile ( solvent B ) . The elution was performed at a flow rate of 0 . 35 ml/min using a linear gradient from 0% to 8% solvent B at 10 min , 40% solvent B at 20 min and 0% solvent B at 23 min . For additional 7 min , the column was rinsed with 100% solvent A to restore the initial conditions . Prior to entering the mass spectrometer , the effluent from the column was measured photometrically at 254 nm by the DAD for detection of the 4 canonical nucleosides . The triple quadruple mass spectrometer , equipped with an electrospray ion source ( Agilent Jet Stream ) , was run at the following ESI parameters: gas ( N2 ) temperature 350°C , gas ( N2 ) flow 8 L/min , nebulizer pressure 50 psi , sheath gas ( N2 ) temperature 350°C , sheath gas ( N2 ) flow 12 L/min and capillary voltage 3000 V . The MS was operated in positive ion mode using Agilent MassHunter software and modified nucleosides were monitored by multiple reaction monitoring ( dynamic MRM mode ) . Identification of ncm5U and mcm5U peaks was performed as described previously [77] . Peak areas were determined employing Agilent MassHunter Qualitative Analysis Software . In the case of the major nucleosides , peak areas were extracted from the recorded UV chromatograms in order to avoid saturation of the mass signals . For inter-sample comparability of the detected modifications , the peak areas of the modified nucleosides were normalized to the UV peak area of uridine . Detection of tagged proteins used anti-TAP ( Thermo Scientific , CAB1001 ) , anti-myc and anti-HA antibodies ( Roche ) and was performed as previously described [16] , [45] . Protein concentrations were determined using Quick StartTM Bradford Protein Assay ( BioRAD ) [78] and checked with anti-Pfk1 antibodies recognizing yeast Pfk1 ( 1:50 , 000 , kindly provided by Dr . J . Heinisch ) or anti-Cdc19 serum ( 1:10 , 000 , kindly provided by Dr . J . Thorner ) so as to ensure equivalent loadings . For detection of the Hrr25 kinase in total yeast extracts and in immune precipitates , a generic anti-Hrr25 antibody [64] was used ( 1:10 , 000 dilution ) . Antibody cross-linking to Dynabeads M-270 Epoxy ( Invitogen ) , preparation of protein extracts and immune precipitation were performed according to the manufacturer's instruction and as described previously [16] , [79] . In general , 1 µg of antibody coupled to Dynabeads was used per 1 mg total cell extract in B60 buffer . All 119 GST-protein kinases constructs in the library described by Zhu et al . [47] were expressed in yeast and purified as described previously [80] . Yeast Elongator complex was isolated as described in the main paper and used as a substrate for the GST-kinases , which were initially assayed in 23 pools of 5 kinases under similar conditions to those described below . Reactions were separated by SDS-PAGE and radiolabelled Elp1 detected by autoradiography . Eight out of the 23 pools showed phosphorylation occurred above the background level observed due to co-purification of Hrr25 with Elongator ( see Results ) . From these eight pools , 40 individual kinases were individually screened for their ability to phosphorylate Elp1 . Reactions in which Elongator was omitted controlled for any radiolabeled bands that co-migrated with Elp1 and were therefore due to kinase autophosphorylation or co-purified kinase substrates . E . coli BL21 ( DE3 ) pLysS ( Novagen ) was transformed with pTrcHis-HRR25 for overexpression and purification of His6-Hrr25 using HisPur cobalt resin ( Thermo Scientific ) according to the manufacturer's instructions . To analyze Elp1 phosphorylation in vitro , purified wild-type or mutant Elongator complex ( 1 µg ) was incubated for 30 min at 30°C in 20 µl P-buffer ( 50 mM HEPES-KOH ( pH 8 . 0 ) , 5 mM MgCl2 , 50 mM KCl ) containing 100 µM ATP and 0 . 5 µCi of radiolabelled [γ-32P]ATP ( 3000 Ci/mmol ) , in the absence or presence of purified recombinant His6-Hrr25 ( 1 µg ) . Phosphorylation reactions were stopped by the addition of NuPAGE 4× LDS Sample Buffer ( Life Technologies ) and heated for 5 min at 95°C . Samples were run on a NuPAGE ( 4-12% ) polyacrylamide/Bis-Tris SDS gel ( Life Technologies ) at 200 V for 1 h . Gels were stained with Bio-Safe Coomassie ( BioRad ) , dried and subjected to autoradiography . To examine the effect of chemical inactivation of yeast Hrr25 on Elp1 phosphorylation in purified Elongator complex , Elongator was purified as above from strains WAY-H-P1T ( wild-type HRR25 ) and WAY-Has-P1T ( allele-sensitive hrr25 I82G ) and assayed as described above in the presence or absence of 10 µM 3-MB-PP1 or 1-NM-PP1 . Time course phosphorylation reactions of synthetic peptides were carried out in 150 µl P-buffer containing 500 pmol synthetic peptide ( Biomatik ) , 400 pmol recombinant His6-Hrr25 , 1 mM ATP and 10 µCi [γ-32P]ATP ( 6000 Ci/mmol ) and incubated at 30°C . Samples ( 5 µl ) were collected at time intervals , spotted on Whatman Grade P81 Ion Exchange Cellulose Chromatography Paper , washed 3 times in 1% phosphoric acid , dried and quantitated by liquid scintillation counting [81] . To visualize phosphorylated peptides , 10 µl reactions were assembled at the above described stoichiometry , incubated for 30 min at 30°C and then terminated by addition of 4× SDS loading dye and heating at 95°C for 5 min . After separation by SDS-PAGE at 200 V using a NuPAGE 12% polyacrylamide/Bis-Tris gel ( Life Technologies ) with MES running buffer ( 50 mM MES , 50 mM Tris-base , 0 . 1% SDS , 1 mM EDTA , pH 7 . 3 ) , phosphorylated peptides were visualized by autoradiography . For mass spectrometric analysis of phosphorylated peptides , 150 µl reactions were conducted as above but omitting the radiolabelled ATP . | tRNA molecules function as adapters in protein synthesis , bringing amino acids to the ribosome and reading the genetic code through codon-anticodon base pairing . When the tRNA contains a uridine residue in the “wobble position” of its anticodon , which base-pairs with purine residues in the third position of a cognate codon , it is almost always chemically modified and modification is required for efficient decoding . In eukaryotic cells , these wobble uridine modifications require a conserved protein complex called Elongator . Our work shows that Elp1 , Elongator's largest subunit , is phosphorylated on several sites . By blocking phosphorylation at these positions using mutations , we identified four phosphorylation sites that are important for Elongator's role in tRNA modification . We have also shown that Hrr25 protein kinase , a member of the casein kinase I ( CKI ) family , is responsible for modification of two of the sites that are important for Elongator function . Phosphorylation appears to affect interaction of the Elongator complex both with its kinase ( Hrr25 ) and with Kti12 , an accessory protein previously implicated in Elongator function . Our studies imply that Elp1 phosphorylation plays a positive role in Elongator-mediated tRNA modification and raise the possibility that wobble uridine modification may be regulated , representing a potential translational control mechanism . | [
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"uridi... | 2015 | Phosphorylation of Elp1 by Hrr25 Is Required for Elongator-Dependent tRNA Modification in Yeast |
Plasmodium vivax malaria has a wide geographic distribution and poses challenges to malaria elimination that are likely to be greater than those of P . falciparum . Diagnostic tools for P . vivax infection in non-reference laboratory settings are limited to microscopy and rapid diagnostic tests but these are unreliable at low parasitemia . The development and validation of a high-throughput and sensitive assay for P . vivax is a priority . A high-throughput LAMP assay targeting a P . vivax mitochondrial gene and deploying colorimetric detection in a 96-well plate format was developed and evaluated in the laboratory . Diagnostic accuracy was compared against microscopy , antigen detection tests and PCR and validated in samples from malaria patients and community controls in a district hospital setting in Sabah , Malaysia . The high throughput LAMP-P . vivax assay ( HtLAMP-Pv ) performed with an estimated limit of detection of 1 . 4 parasites/ μL . Assay primers demonstrated cross-reactivity with P . knowlesi but not with other Plasmodium spp . Field testing of HtLAMP-Pv was conducted using 149 samples from symptomatic malaria patients ( 64 P . vivax , 17 P . falciparum , 56 P . knowlesi , 7 P . malariae , 1 mixed P . knowlesi/P . vivax , with 4 excluded ) . When compared against multiplex PCR , HtLAMP-Pv demonstrated a sensitivity for P . vivax of 95% ( 95% CI 87–99% ) ; 61/64 ) , and specificity of 100% ( 95% CI 86–100% ) ; 25/25 ) when P . knowlesi samples were excluded . HtLAMP-Pv testing of 112 samples from asymptomatic community controls , 7 of which had submicroscopic P . vivax infections by PCR , showed a sensitivity of 71% ( 95% CI 29–96%; 5/7 ) and specificity of 93% ( 95% CI87-97%; 98/105 ) . This novel HtLAMP-P . vivax assay has the potential to be a useful field applicable molecular diagnostic test for P . vivax infection in elimination settings .
Plasmodium vivax is the most geographically widespread of the Plasmodium species that infect humans [1] and can cause severe and fatal disease [2] . In the 2014 World Malaria Report it was estimated that there were 15 . 8 million cases of P . vivax in 2013 , accounting for 47% of malaria cases outside the African region [3] . Asymptomatic sub-microscopic P . vivax infection is commonly reported in endemic countries [4–6] , accounting for on average 69 . 5% of P . vivax infection relative to those with patent parasitaemias from community surveys [7] , compared with 50 . 8% for P . falciparum [8] . The parasite reservoir of P . vivax is also aided by the dormant liver stage which can cause relapsing infection , with fast gametocyte production allowing transmission earlier in the course of the disease , and the development of multidrug resistance [9] posing difficulties for both clinical management and malaria elimination goals . In non-referral settings in elimination areas , the diagnostic tools currently available for detection of P . vivax infections for case management and surveillance are microscopy and immunochromatographic lateral-flow antigen detection in the form of “rapid diagnostic tests” ( RDTs ) . Reference laboratories may also offer expert microscopy and PCR . The reliability of RDTs for diagnosing P . vivax infections , particularly at low level parasitemia , remains less than that for P . falciparum [4 , 10] . While the most recent WHO RDT testing report found the highest performing parasite lactate-dehyrogenase ( pLDH ) based P . vivax RDTs were equivalent to HRP2-based P . falciparum RDTs at parasitemias of 200/μL [11] , they remain inadequately sensitive for the detection of lower level P . vivax and P . falciparum parasitemia in sub-patent asymptomatic individuals . While microscopy and RDT provide adequate diagnostic accuracy for case management of symptomatic patients in clinical settings , they have been shown to be inadequate in detecting a large proportion of low density P . falciparum infections in active community surveillance [12] , and for mass screening and treatment programs [13] . Studies using RDTs specifically for detection of P . vivax in this context are yet to be performed , although similar results could be expected given the inherently lower parasitemias associated with P . vivax infection . Although molecular based assays such as conventional PCR are capable of detecting very low density infections , they are not suitable for large scale community surveillance due to complex procedures that do not allow provision of test results on the day of sample , expensive reagents and the requirement for specialised equipment . Loop mediated isothermal amplification ( LAMP ) is a molecular diagnostic technology that has the potential to be a readily-applicable tool in settings such as malaria elimination . LAMP is an isothermal process that relies on the Bacillus stearothermophilus ( Bst ) enzyme and does not require cyclical temperature changes [14] . As such , unlike PCR , it offers an opportunity for field adaptation because of its low technology requirement . The output of a LAMP reaction can be visualised as a magnesium pyrophosphate precipitate detectable by turbidimetry [14] , metal ion detectors such as calcein [15] , hydroxynaphthol blue [16] and pico-green [17] . In addition to melting curve analysis [18] , LAMP end products have also been visualised using a bioluminescent output in real time ( BART ) [19] , a lateral flow dipstick [20] , or a portable device with a fluorescence detecting unit ( realAmp ) [21] . LAMP has also recently been performed on non-instrumented nucleic acid amplification ( NINA ) platforms [22] improving its potential for field application . LAMP has been shown to detect all Plasmodium species [23] including P . knowlesi [24 , 25] , to be amenable to use with crudely extracted DNA from whole blood [26] , and to have a limit of detection of 5 parasites/ μL for identifying Plasmodium genus and P . falciparum [27] . Commercially available Loopamp MALARIA detection kits ( Eiken chemical co ) using Plasmodium genus and P . falciparum have been found to perform well in regional health facilities , but were not capable of specifically detecting P . vivax [28 , 29] . Furthermore , for specific detection of P . vivax , the three LAMP assays published to date have had low analytic sensitivity , with reported detection limits of 100 plasmid copies/ μL for 18s rRNA target [23] ( which was equivalent to 500 parasites/ μL when tested by Patel et . al [30] ) , 125 parasites/μL for Pvr64 target ( highly conserved repeat region in P . vivax genome ) [30] and 100 copies/ μL for alpha-tubulin target [31] . Here we describe the development and validation of a novel P . vivax specific LAMP assay targeting mitochondrial DNA in a high-throughput , colourimetric platform . Its performance was compared to PCR and microscopy in a district hospital , non reference laboratory setting .
Primers were tested against parasite DNA from well characterised parasite lines and clinical samples of P . falciparum ( 3D7 ) , P . vivax , P . malariae , P . ovale spp . and P . knowlesi for cross-reactivity and LOD . In order to determine the analytical sensitivity of the HtLAMP-Pv , a two-fold dilution series of a P . vivax DNA sample beginning at a starting parasitemia of 90 , 000 parasites/ μL ( as determined by quantitative PCR ) was evaluated in duplicate . HtLAMP-Pv performance was compared with that of previously published P . vivax primers [23 , 30] in the HtLAMP platform . In addition , a sample of starting parasitemia of 2000 parasites/ μL ( as determined by expert microscopy ) was serially diluted in 50% haematocrit uninfected blood . DNA from each dilution was extracted using Qiagen blood kit and tested in duplicate using the HtLAMP-Pv assay . The limit of detection of the HtLAMP-Pv assay was compared with the SD Bioline Pf/Pan RDT ( Alere Standard Diagnostics ) . This RDT detects P . falciparum histidine-rich protein II ( HRP-II ) and Plasmodium lactate dehydrogenase ( pLDH ) with a reported sensitivity of 100% at 200 parasites/μL [11] , microscopy and quantitative PCR on a blood sample obtained from a P . vivax blood stage clinical trial ( ACTRN12614000930684 ) participant . Informed consent was obtained as per the approval of the QIMR Berghofer HREC . Briefly , a wild type P . vivax bank was produced using blood collected from a patient , who had returned to Australia from a malaria endemic country with PCR-proven P . vivax malaria infection , prior to treatment with artemether-lumefantrine . The clinical trial was performed as described [32] and a 2 ml EDTA-blood sample was collected from the clinical trial participant at peak parasitemia prior to commencement of antimalarial treatment . This sample was serially diluted in 50% haematocrit blood and each dilution was subjected to an LDH ELISA assay , thick film blood smear for expert microscopy , an SD Bioline Pf/Pan RDT ( Alere Standard Diagnostics ) and 4 x 5 μL filter paper ( whatman ) blood spots . The filter paper blood spots and the remaining whole blood , which had been stored at -20°C , was extracted in 10 μL and 50 μL volumes using modified chelex-saponin based DNA extraction protocols as described below . The extracted DNA was stored at -20°C until performance of the HtLAMP-Pv assay . The sensitivity and specificity of the P . vivax HtLAMP was tested retrospectively on clinical samples from patients enrolled in a randomised controlled trial and case-control study performed in Sabah , Malaysia commencing in December 2012 as outlined by Grigg et al . [33] . Briefly , samples were collected from microscopy positive , symptomatic patients presenting as outpatients to Kota Marudu District Hospital and asymptomatic , microscopy negative , community controls as a result of reactive active case detection from within the village of a case patient from Kota Marudu district , Sabah . These were stored as 20 μL filter paper ( Whatman ) blood spots . A subset of 149 microscopy positive samples and 112 microscopy negative samples were used to compare the performance of the P . vivax HtLAMP ( HtLAMP-Pv ) , with microscopy and PCR . DNA extraction of the filter paper samples from symptomatic patients and HtLAMP-Pv were performed in the Kota Marudu district hospital laboratory , with no standing molecular diagnostic capability , using a plastic bucket adapted into a water bath , a centrifuge and a portable spectrophotometer . Two local staff members were trained in the process of performing and interpreting the assay as part of its evaluation . The P . vivax DNA used for the analysis of sensitivity was extracted from whole blood samples as per Qiagen manufacturing protocol ( QIAamp DNA mini kit ) with some modifications . Briefly , 500 μL of packed red cell blood sample was mixed with 500 μL of PBS . To an aliquot of 500 μL of this mix , 400 μL of Qiagen AL Lysis buffer and 40 μL of Qiagen proteinase K were added . After incubation at 56°C for 10 minutes , 400 μL of 100% ethanol was added , mixed then loaded into a spin column for centrifugation at 8 , 000 rpm for 1 min . The spin column was then washed once with 650 μL of AW1 and then AW2 spinning each wash at 8 , 000 rpm for 1 min followed by a dry spin after the AW2 wash at 13 , 000 rpm for 1 minute . Nucleic acid extract were eluted in 100 μL of elution buffer and stored at -20°C until LAMP reactions were performed . P . vivax DNA from the Kota Marudu clinical filter spot samples from symptomatic patients was extracted using an established chelex protocol [34] with incubations shortened to improve turnaround time . Briefly , 6 mm filter paper punch samples were incubated in PBS containing 0 . 5% saponin for 2 hours at 37°C , before being centrifuged , washed in PBS , heated at 98°C for 30 minutes in 150 μL 6% chelex and centrifuged at 4 , 000 rpm for 3 minutes . The resultant 100 μL DNA supernatant was then stored at -20°C until analysis by PCR and LAMP . Red cell pellet samples from asymptomatic individuals were extracted using a different chelex protocol . Briefly , 1 ml of non-ionised water was incubated with 10 μL of whole blood for 15–30 minutes at room temperature , followed by centrifugation at 10 , 000–15 , 000 x g for 3 minutes . After discarding supernatant , 200 μL of 5% chelex was added , vortexed for 5–10 seconds , incubated at 55°C for 30–90 minutes and vortexed again for 5–10 seconds . The sample was then heated for 10 minutes at 100°C , vortexed for 5–10 seconds and centrifuged for 3 minutes at 10 , 000–15 , 000 x g . The DNA supernatant was placed in a sterile microfuge tube for storage at -20°C . Whole blood samples , for the comparative study with RDTs , were extracted using a chelex-based DNA extraction methodology [35] modified by the addition of saponin . Briefly , 10 μL of whole blood was mixed with either 200 μL of 0 . 5% saponin and incubated at 37°C for 30 minutes . Samples were then centrifuged , supernatant discarded and pellet heated at 98°C in 150 μL of 6% chelex for 30 minutes . The resultant supernatant was stored at -20°C . The process was also performed on 50 μL of whole blood . In order to establish specificity of the P . vivax LAMP primers , a plasmid containing P . vivax cox1 gene was constructed . The target region of the gene was amplified using COX1 specific PCR primers . Reactions were performed in 20 μL total volume containing 1X NH4 buffer , 2 mM MgCl2 , 200 μM dNTPs , 200 μM primer mix and 0 . 5 U Taq polymerase ( Bioline ) . The ~500 bp PCR product was visualised following agarose gel electrophoresis . The PCR product was purified using a commercial kit ( Roche ) and TA cloned using pGEM-T easy as per manufacturer instructions . Recombinant E . coli were identified by blue-white colour selection . Presence of the P . vivax cox1 gene within the plasmid was confirmed by PCR and Sanger sequencing . Copy number of the pvcox1 gene in the P . vivax genome was estimated by quantitative real time PCR SYBR green PCR assay using the Light Cycler 96 ( Roche ) . Two single copy genes coding for P . vivax mdr1 ( GenBank Acc No . AY618622 . 1 ) and P . vivax aldolase ( GenBank Acc No . AF247063 ) were used as reference genes to estimate the pvcox1 copy number . PCR reactions were set up in triplicates using the Roche Fast Start Essential DNA Green Master Mix ( Cat . No . 06 402 712 001 ) , 10 μM of each primer ( Table 1 ) and 1 μl of each DNA ( cox1 plasmid , mdr/aldolase plasmid , Pv gDNA ) in a 12 μl reaction volume . Cycling conditions were: 950 C for 3 mins; 45 cycles of 950 C 30 secs and 600 C for 1 min . This was followed by melt curve analysis to confirm correct products were synthesised . To assess PCR amplification efficiencies , standard curves containing five ten-fold dilutions of two plasmids containing either pvcox 1 , or pvmdr1 and pvaldolase ( 1:1 ) Suwanarusk , 2007 [36] were prepared , starting from the same initial concentration of 7 . 2 x10-4 ng/μl . Differences in CT value between pvcox1 and pvmdr1/pvaldolase at each plasmid concentration was calculated and averaged to derive ΔCTcal . After confirming similar amplification efficiencies , genomic DNA extracted from 5 well characterised P . vivax isolates with varying DNA concentrations were used in the copy number assay . These samples were from patients enrolled in a clinical trial and surveillance study , with ethics approval granted by the Malaysian Medical Research Ethics Committee and Menzies School of Health Research , Australia , conducted in Kota Marudu district , Sabah , Malaysia[37 , 38] . Mean Ct values were calculated from triplicate and analysed using Graph Pad Prism ( version 6 ) . The pvcox1 copy number in each sample was calculated as N = 2 ΔΔ Ct +/- SD i . e . N = 2 ( CTpvmdr1-CTpvcox1 ) - ( CTpvmdr1cal-CTpvcox1cal ) as reported in Suwanarusk , 2007 [36] . Nested PCR for P . vivax was performed as previously published [39] . Reactions were performed in 20 μL total volume containing 1X buffer , 2 mM MgCl2 , 200 μM dNTPs , 200 μM primer mix ( rPLU5new/rPLU6 for nest 1 and rVIV1/rVIV2 for nest 2 ) and 0 . 5 U Taq polymerase ( Bioline ) . PCR products were visualised on a 2% agarose gel . Multiplex PCR [40] for the detection of P . falciparum , P . vivax , P . malariae and P . ovale was performed on clinical samples from the 149 symptomatic case samples , with P . knowlesi confirmed using PCR as described by Imwong et al . [41] . Nested PCR , as described by Singh et al . [42] , was performed on the 112 community control samples . Quantitative PCR on the P . vivax blood stage clinical trial sample was performed as previously described [32] . High throughput ( HtLAMP ) was performed on a 96-well standard u-bottom microtitre plate ( Sterihealth ) as previously described [43] . Briefly , reactions were performed in 25 μL total volume containing 5 μL DNA , 1X buffer ( 20 mM Tris HCL pH 8 . 8 , 10 mM KCl , 8 mM MgSO4 , 10 mM ( NH4 ) SO4 ) , 1 . 25 mM each dNTP , 1 . 78 μM each FIP/ BIP , 0 . 8 μM each LF/ LB , 0 . 2 μM each F3/ B3 ) , 120 μM hydroxynaphthol-blue ( Fluka , CAS number 63451-35-4 ) and 8 units Bst polymerase ( New England Biolabs , Ipswich , MA ) . The microtitre plate was incubated in a waterbath at 65°C for 30 minutes before the colour change and precipitate in each well was recorded . A blue colour change with a visible precipitate was scored as a positive result , and purple colour without a precipitate was a negative result ( Fig 1 ) . The microtitre plate was then read in an ELISA plate reader at 600 nm wavelength to obtain an optical density ( OD ) reading of each well . The threshold value for a positive reaction was calculated using the mean plus two standard deviations of the no template control ( NTC ) wells . A positive or negative OD reading for each sample was then calculated using the threshold value and correlated with the visually detected colour change . Samples that were discordant in terms of colour change and OD threshold were deemed negative . PCR , nested or multiplex , for the detection of P . vivax was used as the gold standard by which the sensitivity and specificity of HtLAMP-Pv was calculated . PCR is the best established molecular diagnostic tool available for the detection of Plasmodium parasites and therefore an appropriate choice for comparison of a new molecular diagnostic modality . Sensitivity was estimated as the number of LAMP positives that were also PCR positive , divided by the number of PCR positives . Specificity was estimated as the number of LAMP negatives that were also PCR negative divided by the total number of PCR negatives .
Two sets of LAMP primers ( VIV1 and VIV2 ) targeting P . vivax mitochondrial sequences were designed manually . Each set of primers was tested for its ability to amplify P . vivax-specific DNA . However , only one set of primers ( VIV2 ) targeting the P . vivax mitochondrial cox1 gene ( Table 2 and Fig 2 ) , was subject to further validation as the other set failed to amplify P . vivax DNA . The specificity of VIV2 primers was investigated by searching for nucleotide similarity using the BLAST algorithm at the NCBI nucleotide database ( www . ncbi . nlm . nih . gov/Blast . cgi ) and found to have limited sequence identity only to other Plasmodium species . Given the conserved nature of the mitochondrial genome and the sequence similarity across different P . vivax strains from around the world , no evaluation of VIV2 primers on different P . vivax strains was performed . The VIV2 primer set was tested in duplicate on DNA extracts from one PCR-confirmed sample of each of the following species: P . vivax , P . falciparum , P . knowlesi , P . malariae , P . ovale wallikeri and P . ovale curtisi . There was amplification of P . vivax and P . knowlesi DNA but no amplification product was detected for P . falciparum , P . malariae , P . ovale wallikeri or P . ovale curtisi . Using the single-copy P . vivax aldolase gene as reference , the estimated copy number of pvcox1 in five selected P . vivax samples ranged from 9 . 2–16 . 47 with a mean of 12 . 43 ( ± 1 . 233 ) . Using the pvmdr1 gene as reference , the estimated copy number of pvcox1 ranged from 7 . 32–14 . 05 with a mean of 10 . 28 ( ± 1 . 182 ) ( Fig 3 ) . Using a two-fold DNA dilution series of a clinical P . vivax sample , with a starting parasitemia of 90 , 000 parasites/ μL as determined by quantitative PCR , the LOD was 1 . 4 parasites/ μL ( Table 3 ) . Using the same dilutions , the LOD of P . vivax LAMP primers published by Han [23] and Patel [30] , in the HtLAMP platform was determined to be706 parasites/ μL and 176 parasites/ μL respectively . HtLAMP-Pv performed on the microscopy-determined P . vivax dilution series in whole blood had an LOD of 2 parasites/ μL ( Table 4 ) . Quantitative PCR analysis of the P . vivax blood stage clinical trial sample confirmed a peak parasitemia of 12 parasites/ μL prior to commencement of antimalarial therapy . HtLAMP-Pv was positive and the RDT was negative at this level of parasitemia . The limit of detection ( LOD ) of the HtLAMP-Pv assay varied depending on the type and volume of sample from which DNA was extracted . The LOD for filter paper extracted using saponin and chelex was more than 12 parasites/ μL whereas the LOD for 10 μL and 50 μL of whole blood extracted using saponin and chelex was 3 parasites/ μL and 1 . 5 parasites/ μL respectively ( Table 5 ) . Of the 149 patients with microscopy-confirmed malaria from the district of Kota Marudu , Sabah , Malaysia , 145 were confirmed by PCR: 4 samples were excluded due to lack of microscopy and PCR data , 56 were identified as P . knowlesi ( median parasitemia 2005 parasites/ μL , range 26–143 , 790 ) , 64 as P . vivax ( median parasitemia 4676 parasites/ μL , range 53–89 , 640 ) , 7 as P . malariae , 17 as P . falciparum ( median parasitemia 18 , 725 parasites/ μL , range 837–693 , 922 ) and 1 as a mixed P knowlesi/P . vivax infection . HtLAMP-Pv was compared with multiplex PCR in these clinical samples . The sensitivity of HtLAMP-Pv for P . vivax was 95% ( 62/65 , 95% CI 87–99 ) and specificity was 55% ( 44/80 , 95% CI 43–66 ) respectively compared with multiplex PCR and 94% ( 59/62 , 95% CI 85–98 ) and 53% ( 44/83 , 95% CI 42–64 ) respectively compared with expert microscopy ( Table 2 ) . The low specificity of the assay can be attributed to cross-reactivity of the VIV2 primers with P . knowlesi , with 97% sequence homology at the cox1 gene between P . vivax and P . knowlesi . When P . knowlesi samples were excluded from the analysis , the specificity was 100% compared with both multiplex PCR and microscopy ( Table 6 ) . HtLAMP-Pv was compared with nested PCR for red cell pellet samples from asymptomatic , microscopy negative community controls from the malaria endemic district of Kota Marudu , Sabah , Malaysia . The sensitivity of HtLAMP-Pv was 71% ( 95% CI 29–96; 5/7 ) and specificity was 93% ( 95% CI 87–97; 98/105 ) compared with PCR . The HtLAMP assay turnaround time was 1 hour after DNA extraction , 4 hours when combined with whole blood chelex saponin protocol and 6 hours when combined with filter paper rapid DNA extraction protocol . HtLAMP-Pv testing of a total of 149 filter paper samples was performed successfully in a regional hospital laboratory in Kota Marudu district , Sabah , Malaysia . Good workflow set up ensured that there was no contamination despite the lack of formal molecular diagnostic infrastructure . Locally trained staff was able to perform and interpret results of the HtLAMP-Pv assay using only a centrifuge , pipettes , water bath and a portable spectrophotometer .
Field-applicable diagnostic tools for the detection of Plasmodium vivax are essential components for the malaria eradication agenda [44] . Given the widespread distribution and unique challenges P . vivax poses compared with P . falciparum , there is a pressing need for the development of species- specific molecular diagnostic tools . LAMP is a molecular diagnostic tool which holds much promise in terms of its ability to be deployed in non-referral laboratory settings , given its simplicity and rapid assay turnaround time , ability to be performed on crudely extracted DNA from both whole blood and filter paper and lack of expensive equipment . The colourimetric , 96 well microtitre plate-based platform for performing LAMP ( HtLAMP ) for the detection of Plasmodium parasites , as previously described [43] , increases the throughput of the LAMP using minimal equipment . The objective of this paper was to develop and validate a P . vivax specific HtLAMP assay on this platform with good diagnostic accuracy . The 6-kb mitochondrial genome of the genus Plasmodium encodes three mitochondrial proteins- cytochrome B ( cytb ) and subunit 1 and 3 of cytochrome c oxidase ( cox1 and cox3 ) , and is estimated to be present in relatively high copy number . The complete mitochondrial genome of P . vivax ( Genbank AY598035 ) has been shown to be closely related to P . knowlesi [45] . Previously published standard PCR primers for P . vivax targeting cox1 have shown 100% sensitivity and specificity [46] , but were not evaluated against P . knowlesi . LAMP primers targeting mitochondrial sequences for the detection of P . genus and P . falciparum demonstrated an analytical sensitivity of 5 parasites/ μL [27] suggesting that mitochondrial DNA offers an attractive target , presumably due to increased copy number of mitochondrial targets within cells . Recent estimates of genomic sequence coverage have shown that the P . falciparum genome contains ~20 copies/cell of the mitochondrial genome [47] . This HtLAMP-Pv assay targeting the conserved cox1 gene demonstrated excellent analytic sensitivity , being able to detect 1 . 4 parasites/ μL . This is the lowest LOD so far achieved for a published P . vivax–specific LAMP assay . The estimated copy number for cox1 in P . vivax is approximately 11 copies/ cell . Therefore , it is likely that the sensitivity of the HtLAMP-Pv assay is a reflection of the increased number of mitochondrial targets per cell . Previously published P . vivax LAMP primers , which targeted non-mitochondrial genes , when used in the HtLAMP platform had limits of detection of 706 parasites/ μL and 176 parasites/ μL which correlated well with published limits of detection of 125–500 parasites/ μL for these P . vivax primers sets [30] . The pkcox1 gene of P . knowlesi exhibits 97% sequence identity with pvcox1 at the nucleotide level , and thus the cross-reactivity of the VIV2 primers between these two species was expected . However , there was no cross-reactivity with P . falciparum ( 87% sequence identity ) , P . ovale wallikeri ( 92% ) , P . ovale curtisi ( 92% ) or P . malariae ( 93% ) . Validation of the HtLAMP-Pv in clinical samples of symptomatic patients with vivax , falciparum and knowlesi malaria demonstrated sensitivity for P . vivax of 94–95% and a specificity of 53–55% compared with microscopy and multiplex PCR respectively . The poor specificity however was a reflection of the cross-reactivity with P . knowlesi . When P . knowlesi samples were excluded from the analysis , the specificity of the HtLAMP-Pv assay improved to 100% , compared with both multiplex PCR and microscopy . While this cross-reactivity appears to be a limitation of this HtLAMP-Pv assay , P . knowlesi malaria is uncommon or absent in most areas of P . vivax endemicity , so this would be an important consideration only in Malaysia , where P . knowlesi predominates [48] and in the other countries in south-east Asia where P . knowlesi human infection has been documented [49] . In terms of treatment , both P . vivax and P . knowlesi respond to artemisinin-based combination therapy ( ACT ) [50] . However , in elimination programmes utilising primaquine for radical cure of P . vivax malaria , there is a potential risk of inappropriate use of this potentially haemolytic drug in people with P . knowlesi infections . This is a problem localised to Southeast Asia , and would not pose a problem for LAMP-based detection and radical treatment of P . vivax for malaria elimination elsewhere . The HtLAMP-Pv assay was also evaluated in a limited sample set of asymptomatic , microscopy negative , community control patients enrolled from the same village as a case patient , as a result of reactive active case detection . Although the LOD of HtLAMP-Pv appears to be 1 . 4 parasites/ μL , in this sample set its sensitivity was only 71% . This may be due to the very low parasitemias in these 7 PCR positive samples or variability due to stochastic effects at such low parasitemias . Therefore further validation in a larger sample set is required to confirm the sensitivity of HtLAMP-Pv in this population in order to evaluate the potential role for HtLAMP-Pv as a diagnostic tool in malaria elimination settings . HtLAMP-Pv showed significantly better sensitivity than the SD Bioline Pf/Pan RDT at low parasitemia . RDTs were negative in the serially diluted samples at 12 parasites/ μL . HtLAMP-Pv was positive at this level . The analytical sensitivity of the assay varied depending on whether filter paper samples or whole blood was used irrespective of the chelex-saponin DNA extraction protocol used . This may have important implications for choosing the type of sample collected in addition to choosing the appropriate diagnostic tool for surveillance or screening policy and protocols for malaria elimination programs . In this study we also demonstrated that HtLAMP-Pv performed well in the 96-well microtitre plate platform for increasing the throughput of the assay in a non-referral laboratory in a district hospital in Sabah , Malaysia . DNA extraction was performed in the non-referral laboratory using a chelex protocol on filter paper blood spots and the HtLAMP-Pv assay was able to process these samples in a simple water-bath within one hour from time of DNA extraction . Positive and negative results were readily identified by two locally trained staff by visual inspection of the colour change . Optical densitometry readings at 600 nm in portable photospectrometer were used to provide objective confirmation of the visually detected results . As such the validation of this HtLAMP-Pv assay in a rural district laboratory setting confirms the potential it has as a field-applicable molecular diagnostic tool . Furthermore , the process of assay validation using the combination of visual and optical densitometry values has previously shown that the visually detectable colour change was reliable for determining both positive and negative results [43] . Therefore while the photospectrometer offers objective confirmation , it is not an essential component of the assay . Some of the limitations to this platform pertain to DNA extraction . Firstly , in order to maintain cost effectiveness of the assay , modified chelex-based protocols were used for whole blood and filter paper extractions . Although these multi-step DNA extraction processes , which relied on a centrifuge , were performed adequately in a resource limited setting , further simplification of DNA extraction would enhance the feasibility of the HtLAMP-Pv assay . It would also allow a greater number of samples to be processed , as might be required for mass surveillance for malaria elimination , thereby making full use of the high throughput aspect of the HtLAMP platform . Secondly , while equivalent small volumes of blood on filter paper and whole blood have shown whole blood to produce better analytical sensitivity in the HtLAMP platform [43] , the limit of detection of HtLAMP-Pv using larger volumes of blood on filter paper is yet to be established . In conclusion , this study outlines the development and validation of a novel P . vivax-specific LAMP assay which combines a low limit of detection with a high throughput , colourimetric , field applicable molecular diagnostic assay . As such , this HtLAMP assay holds much promise as a diagnostic tool to support malaria elimination efforts in resource-limited P . vivax endemic settings . | Plasmodium vivax has a worldwide distribution and is the second most common causative agent of human malaria . The dormant liver stage of P . vivax allows the infection to recur unless diagnosed and treated appropriately , which poses a significant challenge to the goals of malaria elimination and eradication as outlined by the WHO . Although highly sensitive molecular diagnostic tools are available in reference laboratory settings , the currently available diagnostic tools outside referral settings for the detection of P . vivax are limited to microscopy and rapid diagnostic tests , which are insufficiently sensitive for the detection of low level parasitemia particularly in asymptomatic individuals . Based on a DNA amplification technology called loop-mediated isothermal amplification ( LAMP ) , this study describes the development and validation of a colourimetric , high throughput assay ( HtLAMP ) suitable for the detection of P . vivax infection in non-referral settings . The sensitivity of the assay combined with its field applicability offers the potential for it to play an important role as a diagnostic tool for the purpose of malaria elimination . | [
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"organi... | 2016 | Sensitive Detection of Plasmodium vivax Using a High-Throughput, Colourimetric Loop Mediated Isothermal Amplification (HtLAMP) Platform: A Potential Novel Tool for Malaria Elimination |
ChIP-based genome-wide assays of transcription factor ( TF ) occupancy have emerged as a powerful , high-throughput method to understand transcriptional regulation , especially on a global scale . This has led to great interest in the underlying biochemical mechanisms that direct TF-DNA binding , with the ultimate goal of computationally predicting a TF's occupancy profile in any cellular condition . In this study , we examined the influence of various potential determinants of TF-DNA binding on a much larger scale than previously undertaken . We used a thermodynamics-based model of TF-DNA binding , called “STAP , ” to analyze 45 TF-ChIP data sets from Drosophila embryonic development . We built a cross-validation framework that compares a baseline model , based on the ChIP'ed ( “primary” ) TF's motif , to more complex models where binding by secondary TFs is hypothesized to influence the primary TF's occupancy . Candidates interacting TFs were chosen based on RNA-SEQ expression data from the time point of the ChIP experiment . We found widespread evidence of both cooperative and antagonistic effects by secondary TFs , and explicitly quantified these effects . We were able to identify multiple classes of interactions , including ( 1 ) long-range interactions between primary and secondary motifs ( separated by ≤150 bp ) , suggestive of indirect effects such as chromatin remodeling , ( 2 ) short-range interactions with specific inter-site spacing biases , suggestive of direct physical interactions , and ( 3 ) overlapping binding sites suggesting competitive binding . Furthermore , by factoring out the previously reported strong correlation between TF occupancy and DNA accessibility , we were able to categorize the effects into those that are likely to be mediated by the secondary TF's effect on local accessibility and those that utilize accessibility-independent mechanisms . Finally , we conducted in vitro pull-down assays to test model-based predictions of short-range cooperative interactions , and found that seven of the eight TF pairs tested physically interact and that some of these interactions mediate cooperative binding to DNA .
A major challenge in the analysis of genomic sequences is the annotation of cis-regulatory elements . Significant progress has been made towards this goal through high throughput methods such as ChIP-chip and ChIP-SEQ that describe the locations where specific transcription factors ( TFs ) bind to the genome in vivo [1]–[3] . ChIP-based characterization of TF binding profiles can help elucidate specific regulatory interactions between TFs and genes [4] . A number of genome-wide ChIP data sets , corresponding to diverse TFs and cellular conditions , have been generated through the efforts of various laboratories and consortia [1] , [3] . Such data sets also offer the opportunity to apply computational and statistical methods to understand the determinants of TF-DNA binding at a quantitative level [5]–[7] . Given the central role of the TF-DNA binding process in the regulatory activity of a TF , such an understanding can provide a holistic view of transcriptional regulation and also set the stage for future computational methods for predicting cell type-specific TF-binding profiles . The most extensively studied determinant of TF occupancy is the DNA binding specificity of the TF . Various experimental approaches [8]–[11] have been successful in obtaining motifs representing the diversity and relative affinities of DNA sequences bound by an individual TF . An initial expectation is that a TF's motif will allow prediction of its binding levels genome-wide . On the other hand , it is clear that interactions with other TFs can significantly influence binding to regulatory sequences . For example , interaction of Hox proteins with a cofactor results in greater DNA binding specificity [12] and the tramtrack ( TTK ) protein can regulate transcription independent of its own DNA binding domain via its interaction with the Trithorax-like ( TRL , also known as GAGA binding factor ) [13] . Furthermore , TF occupancy at a genomic location in a given cell type also depends on the concentration of that TF , as well as the motifs and concentrations of other TFs that might facilitate or inhibit DNA-binding at the location [14] . A number of recent studies have used genome-wide datasets to characterize parameters that correlate with TF occupancy . In several studies , genome-wide measurements of in vivo DNA accessibility were tested for the ability to help describe TF ChIP data . These studies clearly demonstrate that TF occupancy has a close relationship with in vivo DNA accessibility [6] , [7] , with both factors likely influencing each other [6] , [15]–[19] . While these studies reveal that experimental analysis of accessibility can improve modeling of ChIP data , they do not reveal the underlying genomic sequence features that contribute to accessibility . In another study [5] , sequence motifs experimentally and computationally identified in Drosophila were shown to contribute to context-specific TF occupancy . Application of discriminative motif analysis to a TF assayed across multiple conditions can successfully identify predictive motifs associated with context-specific binding . However , whether TFs bound to these discriminative motifs contribute to occupancy by direct interaction with the primary TF , accessibility or other mechanisms is not assessed . In this work , we test the influence of various potential sequence determinants of in vivo TF-DNA binding – the TF's binding motif , as well as the positive or negative influence of other TFs binding in the vicinity – on each of 45 TF-ChIP data sets in Drosophila . For this analysis , we took advantage of over 300 distinct DNA binding specificity motifs determined for individual TFs [20] , which encompasses approximately 40% of all predicted Drosophila TFs , and relied upon stage-specific whole-genome RNA-SEQ data [21] to determine which secondary TFs are expressed at the time of the ChIP experiment . We follow the general framework proposed by Kaplan et al . [6] , which involves: ( 1 ) building computational models that predict TF binding at a location , and ( 2 ) assessing how well a baseline model that only uses the “primary motif” ( i . e . , binding motif of the “ChIP'ed” TF ) fits the ChIP data , as compared with more complex models that incorporate additional determinants such as motifs for additional secondary TFs ( i . e . , TFs other than the ChIP'ed TF ) . We use the biophysical model STAP [22] to perform these tests . Improvements in the goodness-of-fit measure are evaluated statistically , and a cross-validation framework is adopted to account for differing model complexity in the comparisons . We evaluate each potential determinant separately in order to limit the number of free parameters in the models . For each identified secondary TF , we performed statistical tests to categorize the mechanistic basis of its contribution . In particular , we asked if a secondary motif's influence is likely to be ( a ) through long-range ( ≤150 bp ) versus short-range ( ≤30 bp ) interactions with the primary motif , ( b ) through synergistic or antagonistic interactions , and ( c ) through modulation of local DNA accessibility or direct interactions between TFs . We find widespread evidence of the effect of secondary TFs on the primary ( ChIP'ed ) TF's binding levels , including both enhanced occupancy ( “cooperativity” ) and reduced occupancy ( “antagonism” ) . Cooperative and antagonistic influences of secondary motifs can act through: 1 ) long-range interactions between primary and secondary motifs , suggesting indirect effects such as chromatin remodeling , 2 ) short-range interactions with specific inter-site spacing biases , suggesting a direct association , or 3 ) through overlapping binding sites , suggesting competition for site occupancy . Two types of experimental evidence support our computational assignments of secondary TFs that influence occupancy via local chromatin architecture or cooperative DNA binding . Extending previous observations [6] , [7] , we find that DNA accessibility is the primary genomic feature correlated with TF occupancy across the majority of the 45 data sets examined here . We then use accessibility data to re-examine secondary TF motifs that improved prediction of ChIP data in our accessibility-agnostic analysis . We identify several secondary motifs whose contribution is reduced or lost when accessibility information is part of the model , suggesting that the secondary TF influences binding mainly by modulating accessibility patterns . The TFs vielfaltig ( VFL , also known as Zelda ) and TRL ( also known as GAGA factor ) appear to synergistically influence the binding of several primary TFs in early and mid-stage embryonic development respectively . Interestingly , the influence of VFL is sometimes imposed through accessibility , while in other cases it is independent of accessibility . In contrast , the influence of TRL is imposed exclusively through accessibility . The TF motifs for extradenticle ( EXD ) , retained ( RETN ) , jing interacting gene regulatory 1 ( JIGR1 ) and homothorax ( HTH ) commonly antagonize TF occupancy through accessibility-mediated and accessibility-independent mechanisms . We find many cases where the secondary motif's influence remains significant upon accounting for accessibility , thus suggesting alternative mechanisms such as cooperative or antagonistic DNA-binding by the primary and secondary TFs . We identify eight examples where the arrangement of primary and secondary motifs implies cooperative binding via physical interaction , and demonstrate that for all but one of these cases the TFs do , in fact , directly interact in vitro and that several bind cooperatively in vitro to sequences that are occupied in vivo . Overall , our analysis demonstrates that a biophysical model for the combinatorial action of primary and secondary TFs used with an extensive collection of binding motifs for known TFs can describe the mechanistic basis for in vivo patterns of TF occupancy .
We began our analysis with fifty-five TF-ChIP data sets obtained from diverse sources ( see Methods ) . Each TF-ChIP data set was represented by 1000 peaks and 1000 random non-coding sequence windows , all of length 500 bp . This representation was selected with the goal of identifying TF motifs that improve the ability to properly rank the occupancy within the peak group and/or improve the ability to discriminate between peaks and random sequences . The average ChIP score of each window was treated as the TF occupancy level in that window ( Methods ) , and is henceforth called the “ChIP score” . For each data set a Position Weight Matrix ( PWM or motif ) representing the DNA binding specificity of the ChIP'ed TF was identified ( Methods ) and designated as the “primary motif” . We used the STAP program [22] ( Figure 1A ) to predict a binding level , henceforth called “STAP score” , for each sequence window in a data set , using the primary motif from that data set . We then computed the Pearson Correlation Coefficient ( CC ) between ChIP scores and STAP scores across the 2000 windows in each data set , and call this the “baseline CC” for the data set . This value captures the ability of the primary TF's binding motif to determine that TF's relative occupancy levels both within the most highly bound regions and in peak versus non-peak regions . Since STAP has one free parameter for which it requires training data ( sequences and their binding levels ) , we performed 4-fold cross-validation to obtain STAP scores for all 2000 windows , with 500 test windows in each fold . Out of the 55 data sets , seven did not show a sufficiently high correlation ( CC ≥0 . 15 and p-value<1E-11 ) here or in any other test that we report in the following sections , and three data sets presented technical problems in the training phase , e . g . , inconsistent parameter values learned over different folds of cross-validation . These 10 data sets ( Supplementary Table S1 ) are excluded from the rest of our report . In all of these examples , the TF motif is broadly confirmed by similarity to motifs for the same TF obtained by other methods or to motifs for homologous TFs . Thus , the low correlation may reflect a high degree of recruitment to DNA by other proteins , technical problems with this group of ChIP datasets , or with the model as applied to these datasets . The results of this first exercise are shown in Table 1 and Figure 2A . We noted the baseline CC in this test to be ≥0 . 15 ( p-value<1E-11 ) for 39 data sets , with the highest CC reported for the data set “TRL_Cchip_s5_14” , i . e . , ChIP-chip data for the TF TRL in stage 5–14 embryo , obtained from the ( C ) avalli laboratory ( see Table 1 legend for data set nomenclature scheme ) . We repeated this exercise , for all 55 data sets , using a second program , TRAP , also based on a biophysical model of DNA binding [23] with default parameter settings , and noted that CC values from STAP were generally better ( Figure 2B ) , although there were several data sets where the two methods gave almost identical CC values . We also observed from Figure 2B that the ten data sets that we exclude from most of the analyses in this work ( red symbols ) received poor CC values from both STAP and TRAP . The purpose of this exercise was not to identify a superior method for occupancy prediction; such an attempt would have been biased since we have more experience with STAP than TRAP , and our TRAP analysis was run without training of free parameters . Our goal was to provide evidence that STAP-based predictions provide a reasonable baseline for more advanced models that will be examined below . Figure 2C provides a scatter-plot visualization of the STAP results on the data set “TRL_Cchip_s5_14” , which has the best baseline CC value ( CC = 0 . 765 ) . Figure 2D provides an alternative visualization of the same results , as an ROC curve showing how the false positive rate of calling a ChIP peak based on STAP scores varies as we vary the STAP score threshold . We see that 89 . 7% of the 1000 ChIP peaks can be detected using STAP scores while making 10 . 3% false positive predictions; the Area Under Curve ( AUC ) is 0 . 96 . Next to TRL , the TF with the highest CC is biniou ( BIN ) , with the data set “BIN_Fchip_s14” exhibiting a CC of 0 . 654 and an AUC of 0 . 895 ( Supplementary Figure S1 ) . We note that this data set had been previously observed , in [24] , to have a high enrichment of the TF motif in ChIP peaks . The ROC for a data set with a more typical value of CC is shown in Supplementary Figure S2 ( CC = 0 . 305 , AUC = 0 . 679 ) . Figure 2E provides a different visualization of the accuracy of STAP predictions , as genome browser tracks of ChIP and STAP scores for the TF BCD on a single gene locus . The CC values reported above can arise from differences in STAP scores of peaks and non-peaks in a data set , and from correctly modeling the ChIP scores within peaks and/or non-peaks . To examine the contribution of these two types of agreement between data and model , we separately calculated CC values among the peaks and non-peaks ( Supplementary Table S10 , Supplementary Figures S5A , B ) . We found several data sets where a significant overall CC was accompanied by a lower but significant CC within peaks , e . g . , BCD_Bseq_s5 , where the overall CC is 0 . 560 , and the CC within peaks is 0 . 466 ( Supplementary Figure S5A , C ) . These are examples where the goodness-of-fit arises from discrimination of peaks and non-peaks as well as from quantitative modeling of binding levels . In a few data sets , the signal appears to arise mainly from separation of peaks and non-peaks , e . g . , BIN_Fchip_s14 , where the overall CC is 0 . 654 but the CC within peaks and non-peaks is 0 . 233 and 0 . 185 respectively . By and large , the CC values within peaks were higher than those within non-peaks , as expected ( Supplementary Figure S5B ) . Interestingly , for a few data sets the correlation within non-peaks was much greater than within peaks . These include UBX_Mchip_s5_14 , EN_Mchip_s5_14 , DISCO_Mseq_s5_11 , EVE_Mseq_s14 , and MAD_Bchip_s5 , with peaks of the latter two exhibiting significant negative correlation between STAP predictions and ChIP scores ( Supplementary Figure S5D ) . For these TFs , the motif score may be uniformly high in the top peaks , but help discriminate between very low and intermediate occupancy levels in the randomly selected regions , leading to stronger correlation within non-peaks . We also repeated the evaluation of the above “baseline” model with a modified definition of data sets: now , the 1000 non-peaks of each data set was replaced with 1000 non-peaks randomly chosen from the ChIP peaks of other TFs . CC values analogous to those of Table 1 ( Column “CC ( M1 ) ” ) were computed and compared to those from Table 1 ( Supplementary Table S15 and Figure S9 ) . We observed that for a few data sets the new CC value is lower , suggesting that the primary TF motif in these cases may represent common features of TF bound regions . ( We visit these cases in a later section . ) We also noted several cases where the CC values were significantly higher when using other TFs' peaks as the non-peaks of a data set ( e . g . , Supplementary Figure S10 ) . We believe such examples better reveal the role of the primary TF motif in determining the TF-DNA binding strength within accessible regions , since all segments considered in the newly defined data sets are ChIP peaks of some TF . Overall , our analysis of primary TF motif scores in different sets of genomic regions supports the idea that the 2000 regions selected for further study can provide insight into diverse types of mechanisms contributing to in vivo TF binding . STAP uses a simple thermodynamic model to define TF-DNA occupancy for a genomic region based on binding site affinities , the equilibrium constant of the TF for its optimal site , and the TF's concentration level [22] . While the binding site affinity relative to that of the optimal site can be quantified using the PWM [25] , the latter two quantities are formally unknown . The formula used by STAP ( see Methods ) features these two quantities as a mutual product , which is treated as a free parameter in the model . This TF-specific free parameter , henceforth denoted by γ , may lead to less of a difference in the contributions of high and moderate affinity sites . That is , at higher γ values , as would result from a high effective TF concentration , both high and moderate affinity sites may be fully occupied ( saturated occupancy ) whereas a stronger bias for high affinity sites will be observed at lower values ( Figure 1A ) . As noted above , we use a cross-validation setting where the parameter is trained on three-quarters of the data and used to predict STAP scores in the left-out quarter , and the process is repeated four times . We examined the role of this parameter in the accuracy of the STAP model by varying it in the broad range 10−1 to 105 and recording the CC at each value of γ . As shown in Figures 3A–C , the optimal parameter value varies across data sets , between 100 to 104 , with a roughly equal split into low , medium and high regions of the allowed range . All experiments reported in the rest of this paper were constrained to use γ in the range 100–104 . We note that a value of γ = 100 indicates that the optimal site has a fractional occupancy of 0 . 5 at cellular levels of TF concentration , while a value of γ = 104 indicates a fractional occupancy of ∼1 . Figures 3A–C also reveal that for any given data set there is a substantial variation in the accuracy of STAP scores as we vary the TF-specific γ parameter . For instance , the CC value in the data set “TWI_Fchip_s9” ( TWI at stage 9 , source: Furlong lab ChIP-chip data ) is about 0 . 25–0 . 30 at the two extreme values of γ ( 10−1 and 105 respectively ) , but reaches a much higher value of 0 . 42 at γ = 102 . This dependence on the γ parameter , along with the variability of optimal γ across data sets underscores the importance of this parameter in the model . The parameter is analogous to the motif transition probability parameter in HMM-based models used in motif scanning , and our observation highlights the need for data set-specific training of this parameter in order to achieve the most accurate predictions . More generally , we conclude that simply adding the strengths of motif matches in a window is not necessarily the best way to predict TF occupancy in that window . For a given TF , the γ parameter is proportional to the TF's concentration level in the experimental conditions . Therefore , if we have ChIP data on the same TF from two different stages , the optimal γ values ought to reflect the relative concentration levels in those stages . The examined collection of data sets included eight such pairs of data sets comprising ChIP data for the same TF from two different developmental stages . We therefore plotted the ratio of the trained γ values in the two stages versus the ratio of the TF's expression levels in those stages . We noted ( Figure 3D ) that the ratios of γ values were roughly consistent with ratios of expression levels , in that if one ratio is >1 , the other ratio is also greater than or close to 1 , and not <1 . Expression levels were obtained from RNA-SEQ data from whole-embryos and may therefore be only a crude approximation of cell type-specific protein concentrations . This , and the fact that all ChIP experiments were performed on whole-embryo extracts , are expected to affect the sensitivity of this analysis , and may be the reason why we did not see a more quantitative agreement between the two ratios ( i . e . , points always close to the diagonal ) . Our tests so far examined how different aspects of the primary TF , such as its binding specificity and concentration , affect its DNA-binding profile . In the next set of tests , we sought to evaluate the role of TFs other than the primary TF in determining the latter's occupancy . To this end , we used STAP with two motifs – the primary motif and one secondary motif at a time – and allowed cooperative interaction between TF molecules bound at sites within a certain distance , called the “distance threshold” , of each other ( Figure 1B ) . There are now three free parameters: the two γ parameters corresponding to the primary and secondary motif , and a parameter representing the interaction energy between bound molecules of the primary and secondary TF . Evaluations performed under a cross-validation scheme ensured that CC values here are comparable to those in the baseline results from Table 1 . In the first set of tests of cooperative effects , we set the distance threshold to be 150 bp , therefore allowing long-range interaction that is similar to the length of DNA in one nucleosome . ( We use “long-range” here to contrast with “short-range” interactions inferred from site pairs with ≤30 bp spacing in the next subsection , but note that “long-range” has different connotations in other contexts , e . g . , to refer to interactions beyond enhancer boundaries [26] , [27] . ) For each data set , we tested a secondary motif for every TF among the most highly expressed genes in the appropriate developmental stage , based on RNA-SEQ data [21] . We compared the CC of a ( primary motif , secondary motif ) pair to that from the primary motif ( Table 1 ) , and examined all cases where the improvement in CC was ≥0 . 04 ( see Methods ) . The improvement , henceforth called ΔCC , was subjected to two different assessments of statistical significance . First , we recomputed the ΔCC with one hundred random variants of the secondary motif ( see Methods ) , and asked what fraction of these random ΔCC values were better than the original ΔCC , thus obtaining a “ΔCC p-value” . Second , we utilized the ΔCC values from every tested secondary motif to compute a Z-score ( see Methods ) . This mimics standard outlier detection procedures and designates a ΔCC value as significant if it appears to be an outlier compared to other observed ΔCC values for this data set . This is analogous to a multiple hypothesis correction , since we test over 50 candidate secondary motifs per data set . Additionally , we required that the cooperative interaction model has a greater CC than a model where the secondary motif alone is used by STAP . Thereby , we identified data sets where the combination of the primary and secondary motifs , through cooperative interactions , can describe the primary TF's occupancy better than either motif in isolation . This analysis revealed 25 cases of significant improvements ( ΔCC ≥0 . 04 , p-value≤0 . 05 and Z-score ≥3 ) , spread over 18 data sets ( Supplementary Table S2 ) . Table 2 tabulates the secondary motif with the most pronounced effect for each of these data sets . We noted that these effects arise mainly from an improved ability to discriminate peaks from non-peaks , and in only 4 ( respectively 2 ) of these 18 cases the cooperativity model improves the CC even within peaks ( respectively non-peaks ) ( Supplementary Table S11 , Supplementary Figure S6 ) . Remarkably , for 15 of these 18 data sets , the most influential secondary motif was either VFL ( 8 cases ) or TRL ( 7 cases ) . Figure 4A shows an example where the use of VFL as a secondary motif significantly improves the ability to discriminate ChIP peaks from non-peaks . Overall , the VFL motif significantly improves primary TF occupancy predictions for 10 data sets ( Supplementary Table S3 , Figure S4A ) , of which 9 were from an early developmental stage ( stage 5 ) , and the tenth was from a broader span of developmental stages including stage 5 . We noted that VFL is highly expressed in later stages as well and its motif was tested as a secondary motif in the corresponding data sets , but significant influences were not detected in those data sets . VFL has been proposed to play a “pioneer factor” role [28] in early development [29] , [30] , and its motif has been found to be highly over-represented in so-called “HOT” regions that represent the most accessible regions of the genome [31] , [32] . Yanez-Cuna et al . [5] recently showed the VFL motif to be required for DNA-binding by the TF TWI , as well as for regulatory activity of TWI-bound enhancers , and to be enriched in early binding sites of other TFs such as MEF2 . Our findings support these strong lines of evidence for an important facilitative role of VFL in determining TF-binding , and explicitly quantify this role for 10 different TF-ChIP data sets . We found the TRL motif to influence the binding levels of primary TFs in eight data sets overall , of which six are from the later developmental stages 9–14 ( Supplementary Table S4 ) . As discussed in [33]–[35] , TRL plays an important role in regulating the chromatin structure and packaging large segments of the chromosome into active ( euchromatic ) or inactive ( heterochromatic ) domains . The TRL motif was also prominent among sequence signatures of context-specific TF-DNA binding reported in [5] , although this previous study did not explicitly quantify its facilitative effect on various primary TFs . It is interesting to note that TRL is the TF with the highest baseline CC ( Table 1 ) , reflecting the possibility that TRL-DNA binding is largely dependent on the TF itself and does not require facilitative effects of secondary TFs . This is consistent with speculation that TRL is a “pioneer factor” [28] , [36] . In these initial tests , STAP was configured to allow interaction between primary and secondary motif as long as their bound sites were within 150 bps . We next asked if the promiscuous effects of VFL and TRL could be observed when reducing this distance threshold to 30 bps , which would suggest that short-range mechanisms of interaction might be involved . We found that in most cases the effects of these two motifs were not significant at the shorter distance range ( Supplementary Tables S17 , S18 ) , and in the four cases where significant effects were detected at this range , the magnitude of the effect was lower than that at 150 bp range . A possible interpretation of this finding , especially in light of available knowledge about these two proteins , is that they act as chromatin remodelers over relatively long scales ( 150 bp or greater ) and facilitate TF binding by making binding sites of the primary TF more accessible . ( We revisit this point in a later section , by directly examining accessibility data . ) Notably , the data sets for VFL and TRL themselves did not reveal any secondary motifs with significant effects , once again supporting a possible pioneer factor role for these two TFs . While VFL and TRL clearly show the most frequent effects on TF binding , a number of other influential secondary TF motifs were also revealed by our analysis; these are shown in Table 3 . For each of these cases we report the ΔCC values at both distance thresholds ( 30 bp and 150 bp ) . Of particular interest were the ( primary motif , secondary motif ) pairs where the ΔCC was significant only at the 30 bp threshold , since this may reflect direct interactions . ( These significant short-range interactions were reflected in a better ability to discriminate peaks from non-peaks rather than an improved ranking of the peaks; see Supplementary Table S12 and Supplementary Figure S7 . ) A case in point is the data set HB_Bchip_s9 , for the TF hunchback ( HB ) , where the secondary motif Adh transcription factor 1 ( ADF1 ) improves the baseline CC of 0 . 204 to 0 . 303 when modeling heterotypic cooperativity at distance threshold 30 bp . The ΔCC of 0 . 099 is highly significant ( empirical P-value = 0 , i . e . , no shuffled motif yielded better ΔCC ) , while that at the 150 bp threshold does not meet our significance criteria . A similar effect was observed for the ADF1 motif on HB ChIP data in stage 5 embryos . We hypothesized that this is evidence for direct physical interaction between HB and ADF1 resulting in modulation of HB binding levels . We searched for sequence signatures of such a hypothesized interaction in the relative spacing of HB and ADF1 binding sites . Examination of the 250 highest ChIP peaks in the data set showed a statistically significant bias ( P-value 3E-4 , see Methods ) for spacing in the range 18–23 bps ( Figure 4B ) . A similar test on 250 non-peaks from the data set showed no bias for this range or any other . This analysis suggests that proximally located binding sites of HB and ADF1 result in increased HB occupancy in ChIP peaks . We examined other data sets where the ΔCC was significant , and found similar evidence of biased inter-site spacing in ChIP peaks ( Figure 4B ) , supporting the hypothesis that direct cooperative interactions may be a key factor in determining TF binding profiles in these cases . In some cases , e . g . , the pair ( D , TTK ) and ( GT , TTK ) , we noticed more than one preferred spacing range , separated by 11 bp , as might be expected due to proper phasing requirements between physically interacting TFs [37] . We also tested for biased inter-site spacing between the TFs Distal-less ( DLL ) and Zif Zinc-finger protein ( ZIF , also called CG10267 ) ( Figure 4B ) , because the ΔCC was found to be significant for this pair ( empirical p-value 0 ) , although the z-score of this ΔCC was 2 . 435 , slightly below our chosen threshold of 3 . 0 . For each of the predicted heterotypic interactions shown in Figure 4B , we assayed for direct physical interactions between the TFs using a modification of the LUMIER method [38] , [39] . In these experiments , one partner is expressed as a fusion to Maltose Binding Protein ( MBP ) and the other partner as a fusion to luciferase ( luc ) . To avoid possible bridging interaction by other eukaryotic proteins , the proteins were expressed using a purified prokaryotic in vitro expression system and then combined for analysis . MBP-tagged proteins were isolated using amylose beads and the luciferase activity retained on the beads ( via primary TF-secondary TF interaction ) , relative to a negative control with unfused luc , was used to calculate a luminescence intensity ratio ( LIR , see Methods ) . A value of seven or greater was selected as a cutoff for positive interactions . This threshold is based on a set of positive and negative control interactions among bHLH protein dimers examined using this assay ( HNP and MHB , unpublished ) as well as additional negative controls using luc fused to the TF CLK or MBP without a fusion partner ( Supplementary Figure S12 ) . This threshold is more than twice as stringent than those used in previous studies examining protein interactions in cell culture [38] , [39] and consequently may exclude some weaker interactions , including some that may only be significant in the context of cooperative binding to DNA . Each predicted interaction pair was examined in both configurations ( e . g . , the primary TF was fused to MBP in one experiment and to luc in the second ) . In addition , since Mothers against dpp ( MAD ) and Medea ( MED ) are known to bind DNA as a heteromeric complex [40]–[43] , it is possible that any interaction computationally identified for one of these proteins is the result of an interaction with the other one . In our in vitro experiments , only direct physical interactions between two proteins are tested . Therefore , each of the predicted interactions for either MAD or MED was also tested with the other . For five of the eight tested pairs ( i . e . , those not involving MAD or MED ) , a clear in vitro interaction was observed in both configurations ( Figure 4C , Supplementary Table S9 ) . For the two predicted interactions involving MAD , one of the two configurations gave a signal while the other was just below our selected cutoff . In one additional case , no physical interaction was observed between ribbon ( RIB ) and MED , but RIB was observed to interact with the MED binding partner , MAD . None of our tested negative controls was near the threshold and the interaction signal for most of the tested pairs was similar to two , well-established positive control interactions for this set of proteins , a MAD-MED heterodimer and a homodimer of giant ( GT ) , which is a member of the bZIP family of TFs that bind DNA as homodimers [44] . Thus , all of the tested predictions are supported by a moderate to strong in vitro interaction , demonstrating that at least some of the short range cooperative interactions identified by our computational model reflect actual physical interactions that were previously unrecognized in large scale protein-protein interaction screens . The physical interaction of TFs suggests that they may use cooperativity to increase binding of the primary TF to DNA sites with properly spaced binding sites for both TFs [45] . We tested this prediction for three of the above TF pairs using a variation of a previously described microwell assay [46] ( Figure 5 ) . The primary TF fused to luciferase and a secondary TF fused to MBP are used in an in vitro pull down assay with biotinylated dsDNA oligos containing a sequence from the ChIP peaks that contains binding sites for both TFs . The TFs are mixed with the biotinylated target site and an excess of unlabeled wild type or mutant competitor DNA . The competitor sequences used to examine cooperative DNA binding of ZIF and DLL are shown in Figure 5A and all sequences are shown in Supplemental Table S16 . Streptavidin-mediated recovery of luc-TF/biotin-DNA complexes in the presence of excess wild type competitor ( wt ) indicates the background signal . In experiments with both TFs present ( Figure 5B ) , the recovery of the luciferase-tagged primary TF in the presence of a competitor with mutations in both TF binding sites ( e . g . ΔZIFΔDLL ) increased 8–18 fold over the background in the presence of wt competitor ( Figure 5B , upper panels ) . In contrast , little increase was observed when this experiment was repeated without the secondary TF ( Figure 5B , lower panels ) , indicating that the secondary TF facilitates binding of the primary TF to these sites . The specificity of this interaction was confirmed by testing mutant competitor DNAs that disrupt the individual TF binding sites ( e . g . , ΔZIF or ΔDLL ) or that increase the intersite distance by five base pairs ( e . g . , “+5” ) . Each of these alterations in the DNA sequence results in reduced competition by the mutant DNA competitor relative to wild type and increased recovery of the primary TF ( Figure 5B ) . Furthermore , reduced competition is observed even when adding two competitors with mutations in one or the other individual TF binding site and each present at the same concentration as the wild type control; thus , high affinity binding requires the two TF binding sites to be present on the same DNA molecule with the proper spacing . These results indicate that the physical interactions detected for each of these pairs mediate cooperative DNA binding to an endogenous sequence from one of the top ChIP peaks . In light of the possibility that the influence of short range cooperative interactions may be more pronounced when the interacting TFs are at relatively modest concentration levels , we extended the tests reported in Table 3 to include all candidate secondary TFs with expression in the top 50% . The results , shown in Supplementary Table S14 , reveal that for several data sets stronger influences are detectable when allowing lower expression levels of the secondary TF . On the one hand , this means that the list of interactions identified in Table 3 is likely incomplete . On the other hand , the list shown in Table S14 must be interpreted with caution since testing more candidate secondary TFs may lead to spurious interactions being reported due to similarity of motifs between two candidates . Cooperative interactions are not the only manner in which one TF's binding may influence another's . Two TFs competing for overlapping binding sites can modulate each other's binding levels at the location [47] . Our next set of tests searched for evidence of this phenomenon in ChIP data sets . We used a two motif STAP model with no interaction terms , and compared the cross-validation CC from this model to the baseline CC of Table 1 . The only way in which a secondary TF site can influence the binding prediction for the primary TF in the two-motif model is if their sites overlap ( Figure 1B ) . The results , shown in Table 4 , comprise 17 cases of significant ΔCC over the baseline model ( ΔCC ≥0 . 04 , P-value≤0 . 05 , Z-score ≥3 ) . In at least 10 of these cases , the secondary motif's presence is strongly anti-correlated with the primary TF's ChIP score , i . e . , the competing motif is more frequent in non-peaks or in lower ranking peaks than in strong peaks . This may imply that the strong peaks exhibit selection against sites of the secondary TF competing with the primary motif . Figure 6A shows three examples of the pattern of overlap between sites of a primary TF and a secondary TF , observed in sequences with high STAP scores and low ChIP scores . We noted that in all of these cases , the overlapping sites tended to be suboptimal matches to either motif . Two different data sets involving HB , one from stage 5 and the other from stage 9 , were influenced by overlapping sites of the RETN motif ( Table 4 ) . RETN is a well-known repressor that acts through competitive binding when inhibiting activation by the TF engrailed ( EN ) [48] . Two other secondary motifs that seem to influence multiple data sets are EXD and HTH . Both of these homeodomain proteins play prominent roles during development as cofactors in repressor complexes with both Hox and other homeodomain proteins . Interestingly , in all three cases where EXD influences binding , there is no correlation between EXD sites and the primary TF occupancy , while in all three cases where HTH exerts an influence , there is a strong negative correlation ( ∼−0 . 18 ) between HTH motif presence and primary TF binding ( see Discussion ) . The next set of tests was directed at detecting evidence of antagonistic binding at non-overlapping sites . A possible mechanism for such a phenomenon is that of the secondary TF upon binding rendering the local DNA inaccessible , e . g . , through recruitment of HDACs [49] , as is speculated to be the case with some short-range repressors in Drosophila [50] . We used a two-motif STAP model with a TF-TF interaction term that is fit on training data , and compared the resulting CC to that from the primary motif alone ( Table 1 ) . This interaction term was constrained to be <1 , corresponding to an unfavorable energy of interaction in the underlying thermodynamics model ( Figure 1B ) . Note that this model incorporates both competitive binding and antagonistic influence from non-overlapping sites . Comparing the CC achieved by this model at either the 30 bp or the 150 bp distance threshold to the baseline ( Table 5 , Supplementary Figure S4B ) , we found 35 cases of significant improvements ( ΔCC ≥0 . 04 , P-value<0 . 05 , Z-score ≥3 ) . These included 6 data sets influenced by the EXD motif , 4 data sets by the HTH and RETN motifs , and 3 data sets by the JIGR1 motif . We noted that these four motifs were also observed to influence binding through competitive binding to overlapping sites ( Table 4 ) above . However , in such cases where a secondary motif had significant effect on binding levels both in the competitive binding mode as well as the antagonistic binding mode , the magnitude of the effect was always stronger in the latter mode . The strongest case of antagonistic influence at the 30 bp distance threshold was estimated for the data set CAD_Bseq_s5 , for the TF caudal ( CAD ) , where the RETN motif improves the CC from 0 . 178 to 0 . 401 . On the other hand , the strongest influence at the 150 bp threshold was by the EXD motif , also on the CAD_Bseq_s5 data set , where the baseline CC of 0 . 178 improved to 0 . 412 , and this effect was exclusive to the 150 bp range . In fact , a large majority of the antagonistic binding influences were significant exclusively at either the short ( 30 bp ) or the long ( 150 bp ) range ( Table 5 ) . This may suggest that the underlying mechanisms of short and long-range antagonistic influences are different , although we did not observe any motif-specific preferences for one range versus the other . We searched for inter-site spacing biases that might provide additional insights into the significant antagonistic influences identified above . It was commonly the case that ChIP peaks had a significant bias towards specific spacing values while non-peaks tended to avoid that range ( Figure 6B , e . g . , D-EXD ) . Interestingly , though less commonly , such spacing biases were also observed in non-peaks ( Figure 6B , Supplementary Tables S5 , S19 ) . Even when examining antagonistic influences of the same secondary TF , e . g . , HTH , we found some data sets where the spacing bias was exclusive to ChIP peaks and others where the bias was present in non-peaks . Separate examination of peaks and non-peaks for effects of antagonistic influence revealed that such effects are manifested in a better discrimination of peaks versus non-peaks as well as a better modeling of ChIP scores with peaks alone or , more commonly , within non-peaks ( Supplementary Table S13 and Supplementary Figure S8 ) . Recent work [6] , [7] has shown that DNA accessibility data , which reflects nucleosome positioning and other chromatin-related effects , has a very strong correlation with TF occupancy , and when used in conjunction with the primary TF's motif can lead to highly accurate predictions of occupancy . This has been demonstrated in the context of five TFs in Drosophila ( data from whole embryo ) and six TFs in human ( data from two cell lines ) . These prior results motivated us to examine the same hypothesis for the much larger collection of TF-ChIP data sets studied here . In all of our tests in this section we used DNaseI hypersensitivity data from [19] . In the first tests , we used a high threshold ( 90th percentile ) on developmental stage-specific accessibility to designate “accessible regions” , predicted zero occupancy in inaccessible regions , and used STAP and the primary motif to predict occupancy in accessible regions . Accessibility-filtered STAP scores computed in this manner correlated very highly with ChIP data ( Supplementary Table S6 ) , and led to substantial improvements upon the baseline results of Table 1 , for 38 of the 45 data sets . This confirms that the observations made by Kaplan et al . and Pique-Regi et al . are manifest over a larger dataset . The test above showed that motif and accessibility information together provide highly accurate predictions of ChIP scores . A natural question that arises then is: how strong is the influence of the primary TF's motif in determining its occupancy , beyond the influence of accessibility ? To answer this question we computed the “semi-partial correlation coefficient” ( SPCC ) between ChIP and STAP scores , which subtracts or “partials out” the contribution of accessibility information . Technically , this amounts to first predicting ChIP scores using accessibility alone , and then correlating the residual ChIP scores with STAP scores ( see Methods ) . We found that for the majority of data sets the SPCC values ( Table 1 , column SPCC ( m1 ) ) were comparable to the baseline CC values , demonstrating that , as expected , the primary motif plays a major role in shaping TF binding profiles . For ten data sets , SPCC was better than baseline CC , most notably for the data set TIN_Fchip_s9 where the primary motif's correlation improves from 0 . 428 to 0 . 507 upon partialing out accessibility . In these cases , factoring out the accessibility effects better reveals the expected relationship between primary motif presence in the sequence and occupancy . In contrast , five data sets showed a dramatically lower SPCC than CC ( Table 1 ) ; these were related to the TFs VFL , TRL and MED . This is consistent with hypothesis emerging in this work ( also see next paragraph ) and in recent literature that VFL and TRL have direct influence on accessibility patterns , and partialing out the correlation with accessibility results in much reduced correlation between primary motif and TF occupancy . The third of the trio of TFs identified here , MED , is also believed to direct the co-factor CBP to the genome [51] and thus influence accessibility profiles . The SPCC was lower than CC also for TWI , D and SLP1 , though not as dramatically . Sandmann et al . [52] have previously found TWI to bind to a large number of mesodermal enhancers and speculated that its role may be to facilitate chromatin remodeling . D is a SOX domain protein and there has been suggestion that this family of TFs may function as chromatin remodelers [53] . Interestingly , independent evidence in support of the accessibility-mediated effect of VFL , TRL , TWI , SLP1 and MED emerged when we repeated the evaluation of the single motif STAP model ( baseline , Table 1 ) on data sets composed of the top 1000 ChIP peaks and 1000 random non-peaks selected from ChIP peaks of other TFs ( Supplementary Table S15 and Supplementary Figure S9 ) . We found the CC on these data sets to be conspicuously below that on the default data sets where the non-peaks were random genomic segments . This implies that the primary motif in these cases is better able to discriminate peaks of the primary TF from random non-peaks than from other accessible regions ( peaks of different TFs ) . This in turn suggests that the motifs of VFL , TRL , TWI , SLP1 and MED may be common features of many ChIP peaks that discriminate them from random non-coding sequences irrespective of the bound TFs . Our next tests examined the effect of cooperative binding with secondary TFs in the light of accessibility information . Recall that the VFL and TRL motifs had emerged as the most promiscuous influences in our tests above ( Table 2 ) , and that their influence was noted as being predominantly long-range ( Supplementary Tables S3 , S4 ) , leading us to speculate that they may be mediated through modulation of local accessibility . We therefore asked if the improvements in CC due to either of these motifs are observed after removing the effects of accessibility information . We computed SPCC values of the cooperative interaction model after partialing out accessibility ( Figure 7A ) , similar to that described in the previous paragraph . We found that the effects of TRL disappear in all 8 data sets where it had been significant before considering accessibility , adding evidence in favor of our hypothesis that TRL's influence is mediated by accessibility . In contrast , VFL was found to exhibit a more diverse behavior: in 7 of 10 data sets its effects vanished after considering accessibility , while in 2 data sets ( CAD_Bseq_s5 and HB_Bseq_s5 ) , a pronounced influence ( ΔSPCC ≥0 . 04 ) remained even after partialing out accessibility ( Supplementary Table S7 ) . These two data sets also showed evidence of an inter-site spacing bias between VFL and the primary motif ( Supplementary Figure S3 ) . These findings suggest that VFL's influence on TF binding may involve distinct mechanisms , including not only a general effect on local accessibility , but also more TF-specific mechanisms potentially involving direct interactions with the primary TF . We repeated the above analysis on data sets where secondary motifs other than VFL and TRL had led to significant improvements in CC through a cooperative binding model ( Table 3 ) . The results , shown in Table 6 and Figure 7B , reveal that in most cases the influence of the secondary motif is pronounced even after partialing out accessibility information . This suggests that most of these secondary TFs operate through primary TF-specific interactions rather than by only influencing accessibility . Similar results were obtained when examining the cases of antagonistic influence by secondary motifs ( Figure 7C ) .
We studied mechanistic determinants of TF-DNA binding by computationally modeling genomic occupancy from over 40 ChIP data sets obtained from four different stages of embryonic development , in conjunction with over 300 TF motifs and stage-specific DNA accessibility and RNA-SEQ data . Our ultimate goal is to use the insights revealed here , both general and data set-specific , to develop improved computational tools that can quantify functional TF-DNA interactions genome-wide . Such tools can potentially inform models of TF regulatory networks in the same way that ChIP data is beginning to be used today [1] , [4] . We note that characterizing hundreds of TFs by the whole-genome ChIP-SEQ in the vast number of different cellular conditions is not currently feasible . Computational tools therefore offer an attractive alternative , especially if they can be shown to predict cell type-specific occupancy . TF motifs are already being characterized through high throughput technologies such as Bacterial 1-Hybrid [9] , SELEX [11] , [54] , and Protein-Binding Microarrays [55] . Cell type-specific DNA accessibility profiles and TF expression levels only need to be characterized once for a given cell state , and can then be used to predict binding profiles for all TFs . Our work provides initial evidence for the feasibility of this vision . At the same time , we note that the CC values reported here should not be interpreted as correlation coefficients between genome-wide predictions and observed levels of TF binding . The manner in which we chose to evaluate various models , i . e . , by examining agreement with ChIP scores on 1000 bound regions and 1000 randomly selected non-peaks , was dictated primarily by the goal of detecting significant influences on primary TF occupancy . We also note that the CC values varied substantially across data sets , from 0 . 765 for TRL to 0 . 062 for Dorsal ( DL ) ( Table 1 ) . This variation in model performance may reflect weaknesses of certain data sets or PWMs , or a variable reliance of ChIP scores on the primary TF's binding . Despite a general appreciation of the potential role of various determinants of TF binding , there have been very few systematic studies of the extent of their influence across a large number of TFs . We review three such studies that set the stage for our own work and explain the main goals and contributions of our work in the backdrop of these important prior studies . Kaplan et al . [6] studied ChIP-SEQ data on five TFs in early Drosophila development , and concluded that the TF motif and DNA accessibility are the most informative correlates of TF-DNA binding , as determined by the agreement between measured and predicted occupancy profiles . They also used TF sequence signatures to examine the role of competitive and cooperative interactions with other TFs with similar developmental roles and concluded that these interactions do not play a significant role overall . Their negative finding regarding secondary motifs may be limited to the small number of data sets examined , or be a limitation of the specific methodology adopted in the study ( including the use of a more limited set of motifs that were available then ) . Here , we perform much more extensive tests of the role of the above-mentioned binding determinants of TF binding , by analyzing 45 TF-ChIP data sets spanning multiple stages of embryonic development in D . melanogaster . We primarily consider the influence of a large number of secondary TFs that are highly expressed in that developmental stage . In contrast to the earlier findings , we find many cases where the primary TF's binding levels are significantly influenced by the presence or absence of binding sites for other TFs . In a related study , Pique-Regi et al . [7] considered the problem of classifying primary motif matches within ChIP peaks versus those outside of ChIP peaks , in the context of six ChIP-SEQ data sets from two human cell lines . They found accessibility and specific histone modifications to be the most useful features in this classification task , but did not consider the influence of secondary TFs . However , there are fundamental differences in the goals of our study from that of Pique-Regi et al . Their objective was to build a computational tool for annotating TF-bound sites genome-wide , and therefore their algorithm integrates several variables that correlate with binding , including evolutionary conservation , transcription start site proximity , DNA accessibility and histone marks . On the other hand , our focus is on the influence of variables that are expected to be mechanistic determinants of binding , and whose influence can be reasonably understood within an intuitive biophysical framework . We therefore focus specifically on testing whether and how binding sites of secondary TFs shape the primary TF's binding profile . In this pursuit , we rely upon motif , sequence and TF expression data , treating these as the “predictor variables” with which to model ChIP data . We do not include other variables such as evolutionary conservation ( which is not a mechanistic determinant ) or start site proximity ( whose influence cannot be easily modeled biophysically ) as predictors in this statistical exercise . DNA accessibility data is used in our analysis , not to improve occupancy prediction per se , but to answer a specific mechanistic question about how secondary TFs influence binding . Also , there is a fundamental technical difference between the data types modeled in the two studies: the variable we propose to model is not tied to TF-DNA interaction at an individual binding site as in [7] , but to the aggregate effect of all binding events within a 500 bp window . For the simplicity , we ask whether a model can predict the actual ChIP score at a genomic position , rather than ask whether a model can predict whether a putative motif match falls within a significant ChIP peak or not . A recent study by Yanez-Cuna et al . [5] searched for motif signatures of context specific binding of TFs . In particular , they analyzed ChIP data sets for the same TF from two different cellular conditions and asked if peaks exclusive to either condition could be discriminated on the basis of motif presence . They showed that such motif signatures do exist for the seven TFs examined and that general-purpose machine learning methods such as support vector machines can accurately classify context-specific binding sites using tens of motifs . In the same vein , they showed that bound and non-bound regions of a TF can be discriminated using a combination of tens of motifs , for most of the 21 TF-ChIP data sets examined . Additionally , they performed a closer examination of the binding determinants of one particular TF , twist ( TWI ) , and demonstrated that binding sites for the secondary TFs VFL and TTK significantly affect the correct prediction of many context-specific TWI binding sites . While Yanez-Cuna et al . mostly focused on demonstrating that accessory motif signatures can distinguish TF-DNA binding regions in different developmental stages , our primary goal was to precisely identify the most influential secondary motifs for each of 45 different TF-ChIP data sets . To this end , we focused largely on quantifying the influence of secondary motifs and assessing their statistical significance rigorously . By performing our analysis over many data sets , we were able to gain more general insights about the widespread or TF-specific roles of particular secondary TFs . In particular , our statistical tests are geared towards explaining the mechanistic basis of such roles: short- versus long-range effects , synergistic versus antagonistic effects , chromatin mediated versus direct interactions , etc . The review by Biggin [56] uses findings from recent studies to argue that accessibility is more important than the role of secondary TFs in determining primary TF binding levels . However , we do not attempt here to characterize the effect of accessibility as being stronger or weaker than the effect of interacting TFs . Integrating perspectives from Biggin and others [15]–[17] , [57] , [58] , DNA accessibility in vivo can be considered the result of multiple factors playing out simultaneously , possibly including innate sequence preferences of nucleosome location , a conglomerate of chromatin remodeling activities and displacement of nucleosomes by competition with TF binding . Under this view , there are practical limitations in the approach of directly comparing the improvement in occupancy prediction due to accessibility information to that due to secondary motif information alone . Moreover , while it may be possible to make broad statements regarding the influence of accessibility or other chromatin-related information on TF binding , secondary TFs , due to the combinatorial nature of gene regulation , will be factor-specific in their effects and thus will only be detectable on a few data sets . Accordingly , our goal is to characterize as many of these determinants of TF occupancy , from each ChIP data set , rather than assign any one number to the overall influence of , say , interactions between the primary and secondary TFs , which will be factor dependent by definition . A related study that examined the effects of secondary TFs on ChIP data is that of Gordan et al . [59] who reported on TF-ChIP data sets in yeast where a secondary motif alone was a better correlate of peak location than the primary motif . In some cases , this may be due to a problem with the primary motif ( H . N . P . and M . H . B . unpublished results ) . In other cases , such a situation may reflect indirect binding of the primary TF to the peak , via physical interaction with the bound secondary TF . It suggests an alternative model of ChIP data , where binding is predicted to be a sum or linear combination of the occupancy values of the primary TF ( direct binding ) and a secondary TF ( indirect binding ) . We have not explored this model here , and believe that it is an important goal for future studies . Our approach to including accessibility data in the analysis was to use partial correlations to examine secondary TF influences before and after factoring out the effect of accessibility on ChIP scores . Alternative approaches may directly include accessibility data in the occupancy models , as was done by Kaplan et al [6] , who changed prior probabilities of binding in their probabilistic model based on accessibility , and Pique-Regi et al . [7] , who included DHS and histone modification data as features in their classifier . Future modifications of our approach will attempt to include accessibility within the biophysical framework of STAP , and may potentially reveal the role of accessibility even more accurately . An intriguing observation from our analyses was the influence of competitive binding by the secondary TF EXD despite there being no correlation between EXD sites and the ChIP scores of the primary TF . It is puzzling because it suggests that the frequency of EXD sites does not differ between peaks and non-peaks , yet these sites somehow make a significant difference to binding predictions . However , it is possible that the frequency of EXD sites overlapping with primary TF sites is different between peaks and non-peaks , and the advanced model uses the competition for overlapping sites to predict lower occupancy in certain sequences than that predicted by the baseline model , leading to improved agreement with ChIP scores ( Supplementary Figure S11 ) . Our work opens up several important directions of future research into TF-DNA interaction on a genomic scale . While the models we explored used at most one secondary motif in one interaction mode , a more realistic model will require integration of more than one underlying mechanisms influencing primary TF occupancy . Accessibility information will play a crucial role in the predictive ability of such models . In the longer term , an important goal will be to develop integrative models where sequence , TF gene expression and developmental history is sufficient to predict , at least to a good approximation , both accessibility patterns and TF-DNA binding profiles . With the future availability of large collections of TF motifs , such computational surrogates for cell type-specific ChIP data will enable global studies of gene regulatory networks and provide specific regulatory assignments that can be experimentally confirmed .
We used 55 TF-ChIP data sets on 37 TFs active in early stages of Drosophila embryonic development . These include five ChIP-seq data sets and 20 ChIP-chip data sets from BDTNP [60] , seven ChIP-chip data sets from the Furlong lab [24] , and 21 normalized ChIP-chip and ChIP-seq data sets from the ModEncode project [1] , [61] . ChIP data of VFL and TRL were obtained respectively from [29] and [62] . Stage-specific genome-wide DNaseI hypersensitivity ( chromatin accessibility ) data , which is mapped to genome release 4 coordinates , was downloaded from the first replicate in the BDTNP web site and converted to release 5 coordinates using the liftOver tool and chain files from the UCSC web site ( http://hgdownload . cse . ucsc . edu/downloads . html ) . We used 614 Drosophila transcription factor motifs , corresponding to 322 distinct TFs , from the FlyFactorSurvey database [20] . The motifs were ranked based on expression of the associated TF gene , using RNA-SEQ data [21] for the appropriate developmental stage . In cases where a TF-ChIP data set corresponded to a range of stages , expression values were stage-normalized and averaged before ranking . Motifs corresponding to heterodimeric complexes ( such as HLH TFs in complex with DNA ) were not considered . Motifs in the top 10% of the expression-based ranked list for the appropriate developmental stage were tested as candidate secondary motifs . The one exception to this are results in Table 3 where the top 25% of the ranked list was considered . We smoothed each TF-ChIP data set and each DNase I data set by assigning scores to each 500 bps window over the genome , with a 50 bps shift . First , raw “read scores” in a data set were mapped to the nearest genomic position that is a multiple of 50 . The score of a 500 bp window was then computed by averaging over all read scores mapped to positions in that window; we refer to this as the “ChIP score” of the window . After this transformation , we selected 1000 non-overlapping , highest scoring windows as “peaks” and randomly extracted 1000 non-exonic , non-overlapping windows without replacement from the remaining genome as “non-peaks” . This set of 2000 windows and their ChIP scores constitutes a TF- and stage-specific data set in our analyses . A “primary” motif was designated for the data set , based on the availability of motifs for the ChIP'ed TF . In cases where there were multiple motifs available for the ChIP'ed TF , the motif with the highest correlation between STAP scores and ChIP scores over all 2000 windows ( see below ) was selected . “Secondary” motifs tested for potential effects on the primary TF's binding were selected based on expression data , as mentioned above . We used the STAP program [22] to predict the ChIP score of a window , using the primary motif and optionally a secondary motif for that TF . STAP has one or more free parameters that require training data – a set of sequences and their ChIP scores . Hence , we used cross-validation to train and test various models of TF-DNA occupancy that are encoded by STAP . We randomly divided the 1000 peaks into 4 equal partitions and also the 1000 non-peaks into 4 equal partitions . In each fold of cross-validation , three partitions from the peaks and non-peaks were used as the training set and one partition ( i . e . , 250 peaks and 250 non-peaks ) was the test set . Predicted ChIP-scores on each of the test sets of windows were collected together , and the resulting set of 2000 real and predicted ChIP score pairs were subjected to evaluations . Evaluations on a data set were considered a failure if the STAP parameter values learned in the four folds were widely different; this happened for one data set . This was described in [22] . STAP considers each molecular configuration σ that specifies which sites in the given sequence are bound by their respective TFs . Following standard statistical physics , the “Boltzmann weight” of σ , denoted by W ( σ ) , represents the relative probability of the system being in configuration σ , and is calculated based on TF concentration and the estimated binding affinity of every bound site in σ . The Boltzmann weight is a product of terms contributed by each TF-bound site in the configuration . This corresponds to the assumption that each bound TF interacts independently with the DNA , with energy contributions that add up [63] . See Figure 1A for an example where the sequence has two sites ( ‘A’ and ‘B’ ) for TF ‘A’ , or Figure 1B where there is one site for each of two TFs ‘A’ and ‘C’ . A site's contribution , q ( S ) , depends on the TF concentration and the strength of site S , and is given by:where [TF] is the concentration of the TF ( in arbitrary units ) , LLR ( ⋅ ) is the log likelihood ratio score of a site , computed based on the known position weight matrix ( PWM ) of the TF [25] , Smax is the strongest binding site of the TF , and K ( Smax ) is the equilibrium constant of the TF binding to this site . The product K ( Smax ) [TF] is a TF-specific free parameter denoted by γTF . Let Nk ( σ ) denote the number of bound sites of TF k in configuration σ . The STAP model predicts the occupancy of TF k as:Note that while Nk ( σ ) counts the number of bound sites for TF k only , the Boltzmann weight W ( σ ) depends on bound sites for all TFs . The accuracy of STAP predictions was assessed by computing the Pearson correlation coefficient ( CC ) between real and predicted ChIP scores of 2000 windows in a data set . To assess the impact of a secondary motif M2 in modeling a data set whose primary motif is M1 , we tested STAP in a single motif mode ( “STAP ( M1 ) ” ) and in two-motif mode ( “STAP ( M1 , M2 ) ” ) and compared the difference in their accuracies: ΔCC = CC ( STAP ( M1 , M2 ) ) – CC ( STAP ( M1 ) ) . A secondary motif M2 was deemed as a significant influence on the data set if the following conditions were met: We also evaluated the best secondary motif effect for each data set by computing an “Area Under ROC” ( AUC ) value for the interaction model ( Supplementary Table S8 ) . For each significant case of cooperative or antagonistic influence by a secondary motif , we searched for biases in the inter-site spacing between the primary and secondary TFs . Let us assume a pair of motifs ( M1 , M2 ) represents the binding specificities of the primary and secondary TFs . To test for a specific spacing bias , say ‘d’ base pairs , between ( M1 , M2 ) in a given set of segments , we grouped all pairs of adjacent heterotypic binding sites ( located by FIMO program with threshold of e−7 [65] ) into those having or not having inter-site distance of d . We counted the number of site pairs in each group and compared these counts to the corresponding counts in a “background” data set using one-tailed Fisher's exact test . The “background” data set was constructed by shuffling the locations of predicted sites in each segment , thus preserving the number of binding sites in each segment , and pooling together 10 such randomized data sets ( Kazemian et al . , manuscript in review ) . Tests of spacing bias were conducted on a set of top 250 scoring ChIP peaks and separately on a set of bottom 250 non-peaks . Semi-partial correlation is a statistical technique generally employed to assess the association of one random variable X with the other random variable Y after eliminating the effect of a third random variable Z on Y [66] . In our tests , X represents predicted TF-DNA binding , Y the experimental TF-DNA occupancy from ChIP , and Z the accessibility . The semi-partial correlation score between X and Y , after “partialing out” Z from Y , is computed as , where is the correlation coefficient between A and B . Protein interactions were measured in a modification of the previously described LUMIER or LuMPIS methods [38] , [39] except that each protein was expressed in vitro rather than in cell culture . Open reading frame ( ORF ) clones for transcription factors were part of the Berkeley Drosophila Genome Project the collection of universal donor clones [67] . ORFs were transferred into two vectors , pHPT7-FlRluc-BD and pHPT7-MBP-BD ( HNP and MHB , unpublished ) , using Cre Recombinase ( New England Biolabs , M0298L ) . For one TF , Mad , the ORF was PCR amplified ligated into AscI and PmeI restriction sites in each vector . These vectors contain a T7 promoter for in vitro transcription , a loxP site for cloning and either maltose binding protein ( MBP ) or Renilla luciferase ( luc ) coding regions . Clone names and primer sequences are provided in the supplementary information ( Table S9 ) . Proteins were made by coupled in vitro transcription/translation using the PURExpress In Vitro Protein Synthesis Kit ( NEB , E6800S ) . All samples were analyzed by Western Blot to confirm that some full-length product was obtained . Luciferase input was measured using the Renilla Luciferase Assay System ( Promega , E2820 ) . The proteins were diluted with IP Buffer ( 150 mM NaCl , 50 mM Tris pH 7 . 4 ) such that roughly 106 luciferase counts were added to each sample and an equivalent amount of MBP protein were mixed . Proteins were incubated with gentle rocking for 4°C for 2 hours . Amylose Resin ( NEB , E8021S ) blocked with 5 percent BSA was added to proteins and incubated with rocking at 4°C for 2 hours . The samples were washed twice with IP buffer and transferred to 96-well plates ( Corning , 07-200-589 ) for luciferase measurements . The luminescence intensity ratio was measured using as follows: Each experiment was performed in duplicate , the experiments were averaged , and the standard deviation was calculated . Source cDNAs , amplification primers and luciferase data are compiled in Supplementary Table S9 . In vitro synthesis of tagged TFs and luciferase assays were performed as described above . Target sequences were identified from the top ChIP peak regions that contained strong matches to the primary and secondary motifs with a spacing and orientation that was most frequently observed . Other criteria used in selecting target sequences included whether the ChIP peak lies within a known enhancer , and whether its predicted occupancy under STAP's cooperativity model is higher ( in rank ) than that under the baseline model without cooperativity . Double stranded DNA oligonucleotides were synthesized that contained wild type or altered sequences . One oligonucleotide containing the wild type sequence is biotinylated on the first base . The genomic coordinates for the wild type sequences and all mutant sequences are shown in Supplementary Table S16 . Protein-DNA interactions were measured in a modification of a previously described microwell-based assay [46] . Tagged TFs were expressed in vitro rather than in cell culture and diluted with low-stringency binding buffer ( 140 mM KCl , 5 mM NaCl , 1 mM K2HPO4 , 2 mM MgSO4 , 20 mM HEPES ( pH 7 . 05 ) , 100 µM EDTA , 1 µM ZnSO4 ) +1% BSA . Oligonucleotides were annealed and diluted using annealing buffer ( 50 mM Tris-HCl , 0 . 2 mM MgSO4 , pH 7 . 0 ) . Annealed oligo mixes were prepared with 5 ul of 1 . 2 uM biotinylated oligos , 5 ul of 24 µM competitor oligo , 2 ul of 500 ng/ul Poly ( dI-dC ) *Poly ( dI-dC ) , and 8 ul of annealing buffer ( final volume 20 ul ) and incubated for 1 hour . 106 luciferase counts of the luc-tagged primary TF and ( if appropriate ) an equivalent amount of MBP-tagged secondary TF were mixed ( 30 ul volume ) . The diluted proteins were added to the DNAs and incubated with gentle rocking at 4°C for 2 hours . Streptavidin coated 96 well plates ( ThermoScientific # 15502 ) were blocked with 5% BSA and low stringency binding buffer . The protein/oligo mixture was added to the plates and incubated for 2 hours at 4°C . The samples were washed twice with low stringency binding buffer . Recovered luciferase activity was measured directly in the plates . All values were normalized by dividing by the luciferase counts recovered in the sample containing an excess of wild type competitor DNA . | Chromatin Immunoprecipitation ( ChIP ) -based genome-wide assays of transcription factor ( TF ) occupancy have emerged as a powerful , high throughput method to understand transcriptional regulation , especially on a global scale . Here , we utilize 45 ChIP-chip and ChIP-SEQ data sets from Drosophila to explore the underlying mechanisms of TF-DNA binding . For this , we employ a biophysically motivated computational model , in conjunction with over 300 TF motifs ( binding specificities ) as well as gene expression and DNA accessibility data from different developmental stages in Drosophila embryos . Our findings provide robust statistical evidence of the role played by TF-TF interactions in shaping genome-wide TF-DNA binding profiles , and thus in directing gene regulation . Our method allows us to go beyond simply recognizing the existence of such interactions , to quantifying their effects on TF occupancy . We are able to categorize the probable mechanisms of these effects as involving direct physical interactions versus accessibility-mediated indirect interactions , long-range versus short-range interactions , and cooperative versus antagonistic interactions . Our analysis reveals widespread evidence of combinatorial regulation present in recently generated ChIP data sets , and sets the stage for rich integrative models of the future that will predict cell type-specific TF occupancy values from sequence and expression data . | [
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] | 2013 | Computational Identification of Diverse Mechanisms Underlying Transcription Factor-DNA Occupancy |
Gene expression is generally regulated by recruitment of transcription factors and RNA polymerase II ( RNAP II ) to specific sequences in the gene promoter region . The Integrator complex mediates processing of small nuclear RNAs ( snRNAs ) as well as the initiation and release of paused RNAP II at specific genes in response to growth factors . Here we show that in C . elegans , disruption of the Integrator complex leads to transcription of genes located downstream of the snRNA loci via a non-conventional transcription mechanism based on the lack of processing of the snRNAs . RNAP II read-through generates long chimeric RNAs containing snRNA , the intergenic region and the mature mRNA of the downstream gene located in sense . These chimeric sn-mRNAs remain as untranslated long non-coding RNAs , in the case of U1- and U2-derived sn-mRNAs , but can be translated to proteins in the case of SL-derived sn-mRNAs . The transcriptional effect caused by disruption of the Integrator complex is not restricted to genes located downstream of the snRNA loci but also affects key regulators of signal transduction such as kinases and phosphatases . Our findings highlight that these transcriptional alterations may be behind the correlation between mutations in the Integrator complex and tumor transformation .
Transcription is the primary control point for gene expression . It determines cell identity and function and must be finely regulated in each of its steps: initiation , elongation and termination [1 , 2 , 3] . Different types of RNA polymerases , as well as other proteins , are involved in these processes . In eukaryotes , RNAP II transcribes protein-coding genes and multiple genes encoding long and small non-coding RNAs . This holoenzyme complex is composed of 12 subunits . The C-terminal domain ( CTD ) of its largest subunit , Rpb1 , plays an essential role in all the transcription regulation steps and couples transcription termination to the processing of nascent RNAs [4 , 5] . Although the molecular mechanisms of transcription termination are not fully understood , it is widely accepted that 3′-end processing plays a central role . Three different cleavage complexes at the 3’-end have been described , depending on the nascent RNAs: poly ( A ) mRNAs , replication-dependent histone mRNAs and snRNAs [6] . In the case of snRNAs , the Integrator complex , along with other factors , is responsible for the site-specific cleavage at a conserved sequence named the 3’ box [7 , 8] . The term “Integrator complex” stands because it integrates the CTD of RNAP II with the 3’-end processing of snRNAs . Initially , 12 subunits were identified and named according to their predicted molecular weight ( Integrator subunit , Ints1-12 ) . Proteomic analyses confirmed its composition and identified new putative subunits [9 , 10] . A genome-wide RNAi screen performed in Drosophila S2 cells found two additional subunits that were renamed Ints13 ( also known as Asunder ) and Ints14 [11] . The Integrator complex is evolutionarily conserved in metazoans . The catalytic subunits Ints11 and Ints9 are clearly homologous to the mammal cleavage and polyadenylation specificity factor subunits , CPSF73 and CPSF100 , respectively [12] , which are involved in the cleavage of pre-mRNAs and histone mRNAs [13] . Importantly , both belong to a large group of zinc-dependent nucleases called the β-CASP family [14] . Small nuclear RNAs are commonly referred to as “uridine-rich small nuclear RNAs” ( U snRNAs ) because of their high content in uridine . They are small non-coding RNAs ( 60–200 nucleotides ) that are ubiquitous , intron-less , non-polyadenylated and generally highly expressed . RNAP II transcribes most of the snRNAs ( U1 , U2 , U4 , U4atac , U5 , U7 , U11 and U12 ) , but not U6 , which is transcribed by RNAP III [15] . Once the snRNAs are cleaved at their 3’-end by the Integrator complex , they are exported to the cytoplasm for further 3’ trimming and assembled with proteins to form small nuclear ribonucleoproteins ( snRNPs ) [15 , 16] . Except for the U7 snRNP that is involved in the 3′-end processing of replication-dependent histone mRNAs [17] , snRNPs are components of the spliceosome that mediates pre-mRNA splicing [15] , which consists of the removal of introns and ligation of exons within an mRNA molecule [18] . Additionally , in lower eukaryotes such as C . elegans or Trypanosoma , there is another class of snRNAs called spliced leader ( SL ) snRNAs that are involved in another type of splicing named trans-splicing . In SL trans-splicing , a short exon is donated from the 5′ end of a SL RNA and connected at or near the 5′ end of an mRNA , thus becoming the first exon of that transcript [19] . Multiple studies implicate some members of the complex in snRNA processing or other biological functions . For instance , Ints3 and Ints6 are involved in the DNA damage response [20 , 21 , 22] , Ints4 and Ints11 are required for the homeostasis of Cajal bodies [23] and Ints13 is a critical regulator of dynein-mediated processes [24 , 25 , 26] . Also , the Ints4 , Ints5 , Ints6 and Ints7 subunits are essential for normal development in different species [27 , 28 , 29 , 30] . Importantly , recent findings have extended Integrator functions to a broader spectrum of the RNAP II transcription cycle in addition to 3’-end processing , including transcription initiation , promoter-proximal pausing , elongation , and termination [8 , 31 , 32 , 33 , 34] . Here , we characterize the C . elegans Integrator complex likely comprised of thirteen subunits ( INTS-1 , -2 , -3 , -4 , -5 , -6 , -7 , -8 , -9 , -10 , -11 , -12 and -13 ) . We show that the Integrator complex is responsible not only for 3’-end processing of snRNAs U1 , U2 , U4 and U5 as previously described [8 , 29] , but also for SL families and some small nucleolar RNAs ( snoRNAs ) . Strikingly , we observed that depletion of the Integrator complex , results in read-through by the RNAP II leading to transcription of the closest gene located in sense . The resulting chimeric RNAs , which we named “sn-mRNAs” , are mostly spliced and polyadenylated and can , in the case of SLs , be translated into proteins . Finally , the transcriptomic profile of Integrator complex depleted nematodes reveals major changes in the kinome and phosphatome as well as other specific genes , pointing to a dramatic alteration of the signaling state of the cells and the existence of other specific functions of the different Integrator subunits .
In a screen for embryonic lethal mutations in C . elegans , we noticed a striking set of developmental and transcriptional defects in worms and embryos homozygous for the t1903 mutation . t1903 was a thermosensitive allele of dic-1/INTS6 , hereafter referred to as ints-6 owing to its homology to the human subunit 6 of the Integrator complex INTS6 gene . ints-6 encodes a protein of 869 amino acids that is highly conserved throughout the animal kingdom . The human homolog has been characterized as a member of the Integrator complex involved in the 3’-end processing of nascent snRNAs [7] . At the molecular level , INTS-6 is predicted to have a von Willebrand factor type A ( vWA ) domain at the N-terminal part that might serve as a surface for interaction with other proteins or molecules with which it forms complexes [35] . At its C-terminal end it features a COIL domain , which is a structural motif that in many proteins plays a fundamental role in subcellular infrastructure as a molecular ruler , positioning catalytic activities at fixed distances [36] . The t1903 mutation was a C to T substitution at position 3944 in the F08B4 . 1 gene that resulted in a swap from Ser to Phe in aa 850 of the protein ( Fig 1A ) . At the permissive temperature ( 15°C ) , the worms were viable but exhibited some embryonic lethality ( 21 . 5% , n = 2153 ) and reduced offspring ( 215±23 . 5 descendats vs 262±20 . 2 of a WT , mean±sem , n = 10 ) , whereas a shift to the restrictive temperature ( 25°C ) resulted in full embryonic lethality ( 100% , n = 818 ) and reduced offspring ( 82±3 . 6 descendats vs 185±8 . 4 of a WT , mean±sem , n = 10 ) ( S1 Fig ) . A knockout deletion in ints-6 ( tm1615 ) ( Fig 1A ) [37] has a zygotic effect and homozygotes arrest at the L3 larval stage [38] . To determine the developmental consequences of ints-6 disruption , we performed four-dimensional ( 4D ) microscope studies on embryos [39] . Analyses of thermosensitive ints-6 ( t1903 ) mutant embryos recorded at the restrictive temperature showed morphogenesis defects leading to embryo death ( Fig 1B ) . To further address the function of INTS-6 , we generated transgenic animals expressing green fluorescent protein-tagged INTS-6 ( INTS-6::3xFLAG::eGFP ) under the control of its own ints-6 promoter and the eft-3 promoter ( S2 Fig ) . Transgenes were assayed both as multicopy extrachromosomal arrays and single copy mosSCI integrated lines . Additional ints-6::3xFLAG tagged strains were generated by CRISPR [40 , 41 , 42] . Consistent with the localization and function of its human homolog [7 , 43] , the protein appeared as a predominantly nuclear protein ( Figs 1C , S3 and S4 ) . Gene expression assessed by GFP detection and by anti-FLAG immunostaining was detected in all cells from embryos to adults . There was , however , a difference between soma and germline expression detected by immunofluorescence: whereas in somatic cells and oocytes INTS-6 was detected in the nucleus , in the embryonic germline it was detected both in the nucleus as well as in cytoplasmic granules ( Figs 1C and S4 ) . To ascertain the role of ints-6 in 3’-end processing of the snRNAs , as suggested by its homology to the human Integrator complex subunit 6 and its nuclear localization , we performed deep-sequencing of total RNA obtained from ints-6 ( t1903 ) mutant worms . Processing of the 3’ end of snRNAs was already defective at 15°C ( permissive temperature ) whereas mRNA termination appeared unaffected . Shifting of the ints-6 ( t1903 ) mutant to 25°C ( restrictive temperature ) for 12h resulted in an increase in the amount of long transcripts derived from unprocessed snRNAs ( Figs 1D and S5 ) . 3’-end misprocessing was not restricted to U1 , U2 , U4 and U5 snRNA [7 , 29] but also affected SL1 and SL2 snRNAs families and certain small nucleolar RNAs ( snoRNAs ) , but not other types of non-coding RNAs ( ncRNAs ) such as ribosomal RNAs ( rRNAs ) , transfer RNAs ( tRNAs ) or lncRNAs ( S6 Fig , S1 Table ) . These results , homology to human subunit 6 of the Integrator complex , nuclear localization and its role in processing snRNAs , strongly suggest that F08B4 . 1/ints-6 is indeed a part of the C . elegans Integrator complex and prompted us to study the function of this complex in transcription and RNA metabolism during in vivo development of a complex organism . To define the polypeptide composition of the C . elegans Integrator complex , we focused on three defining features: its homology to Integrator subunits in other metazoans , the physical association of its subunits in a complex and the lack of 3’-end processing of snRNAs after disruption of the Integrator complex coding genes . A search in the GeneBank database , using the BLAST algorithm , identified protein homologs for the various Integrator subunits in different species ( Homo sapiens , Mus musculus , Gallus gallus , Danio rerio , Drosophila melanogaster and C . elegans ) . Virtually all Integrator subunits except INTS-14 in C . elegans are conserved throughout evolution ( Fig 2A , S2 Table ) . Immunoprecipitation followed by mass spectrometry analyses ( IP/MS ) further confirmed that the proteins identified as C . elegans Integrator homologs are components of a multiprotein complex . To investigate the polypeptide composition of the C . elegans Integrator complex , we generated integrated transgenic worms expressing ints-6::3xFLAG::eGFP and purified INTS-6-associated proteins by anti-FLAG affinity purification ( Figs 2B and S7 ) . WT N2 animals were used as a control for nonspecific binding . The FLAG affinity eluate was separated on a polyacrylamide gel and stained with Coomassie blue dye . Bands of different molecular weight were excised from the gel and subjected to mass spectrometry analysis . This resulted in the identification of INTS-1 ( C06A5 . 1 ) , INTS-2 ( ZC376 . 6 ) , INTS-3 ( Y92H12A . 4 ) , INTS-4 ( W04A4 . 5 ) , INTS-5 ( Y51A2D . 7 ) , INTS-6 ( F08B4 . 1 ) , INTS-7 ( D1043 . 1 ) , INTS-8 ( Y48G10A . 4 ) , INTS-9 ( F19F10 . 12 ) , INTS-11 ( F10B5 . 8 ) and INTS-13 ( R02D3 . 4 ) as components of an INTS-6-associated complex . INTS-10 ( F47C12 . 3 ) and INTS-12 ( T23B12 . 1 ) subunits were not detected in the mass spectrometry analysis , probably because they are the smallest subunits with molecular weights of 38 . 7 kDa and 25 . 9 kDa respectively ( Fig 2 ) . To evaluate the functional contribution of the INTS proteins identified as subunits of the Integrator complex , we assessed 3’-end snRNA processing after RNA interference to knockdown each of them ( S8 Fig ) . 3’-end snRNA processing was determined by RNA deep-sequencing , northern blot and retrotranscription , followed by PCR ( RT-PCR ) of regions downstream of the snRNA loci ( Figs 3A , 4 and S9 ) . U1 , U2 , U4 , U5 , SL1 , SL2 snRNAs and certain snoRNAs revealed a significant accumulation of long transcripts beyond their 3’ end after depletion of the Integrator subunits INTS-1 , -2 , -4 , -5 , -6 , -7 , -8 , -9 , -11 , whereas other types of ncRNAs , such as rRNAs , tRNAs or lncRNAs remained properly processed at their 3’ end ( S1 Table ) . This indicates a lack of the 3’-end processing of the nascent snRNA transcripts . RNAi knockdown of the Integrator subunits phenocopied the snRNA processing defects observed in the ints-6 ( t1903 ) mutant ( Figs 1 and 3 ) confirming loss of activity of the complex . RNAP II read-through downstream of the snRNAs ranged between 1% and 6% of the total amount of U1 and U2 snRNAs . snRNA expression did not significantly change after depletion of the Integrator subunits and remained as high as in WT ( S10 Fig ) . As a result , read-through transcription downstream of the snRNA loci reached the expression level of regulatory genes such as lit-1/NLK or daf-16/FOXO of the wnt and insulin pathways respectively ( S11 Fig ) . Depletion of INTS-3 , -10 , -12 and -13 led only to a slight snRNA misprocessing ( Figs 3A and 4 ) . In all cases , mature snRNA transcripts remained at a high level after Integrator depletion ( Figs 3 and S9 ) . This is consistent with the reported long half-life of RNAP II-transcribed snRNAs [44] . In addition , stable Integrator complex could retain some activity after RNAi knockdown of single subunits . This explains why mRNA of coding genes is mostly properly spliced , with little intron retention , and polyadenylated after RNAi knockdown of any Integrator complex subunit , as assessed by RNA deep-sequencing and retrotranscription followed by PCR ( RT-PCR ) of intron-containing regions of coding mRNAs . Interestingly , splicing defects are prominent in the genes located directly downstream of the snRNA loci ( 15–18% of intron retention for ints-1 , -8 , -9 , -11 subunit knockdown ) , whereas genes not affected by RNAP II read-through are only slightly affected ( Figs 3A , 4 and S12 ) . Since the production of ectopic RNAs may have deleterious consequences , we extended the characterization of the phenotypic consequences of Integrator disruption to the level of the whole organism . RNAi of some Integrator subunits exhibited phenotypes ranging from severe larval arrest ( ints-2 , -4 , -5 , -9 , -11 ) to reaching adulthood but having reduced offspring and subsequent embryonic lethality ( ints-1 , -6 , -7 , -8 ) , whereas the subunits that resulted in only slight snRNA processing defects ( ints-3 , -10 , -12 , -13 ) , showed non-obvious phenotypes ( Fig 3B ) . These phenotypic differences correlate with the amount of long transcripts detected beyond the 3’ end of the snRNAs after knockdown of the Integrator complex subunits ( Fig 3A ) . In summary , these findings indicate that the C . elegans Integrator complex is likely comprised of at least 13 subunits , INTS-1 to INTS-13 , involved in the 3’-end processing of snRNAs . To understand the consequences of the lack of 3’-end processing of nascent snRNAs in the organism , we first studied the structure of the uncleaved snRNAs formed upon depletion of any member of the Integrator complex . The structure of the long unprocessed transcripts was assessed by deep-sequencing of total RNA obtained from ints-6 ( t1903 ) and Integrator complex depleted worms ( Figs 1D and 3A ) . This result was further confirmed by northern blot and RT-PCR using specific primers for snRNAs and their downstream genes ( Figs 4 and S9 ) . snRNA loci are present in multiple copies within the genome , either in intergenic regions or within coding genes and are oriented either in sense or antisense to the downstream gene ( S1 Table ) . For snRNA loci located in sense in the 5’ region upstream coding genes , the lack of snRNA processing resulted in the formation of long chimeric sn-mRNAs containing the snRNA sequence at the 5' end , followed by the sequence corresponding to the region between the snRNA and the gene downstream and the mature mRNA of the gene on the 3' end ( Figs 1D , 3A and 4 ) . In all these cases , intron retention was detected but most of these transcripts were processed by splicing and polyadenylated at the expected sites ( S12 Fig ) . Thus , depletion of the Integrator complex caused upregulation of genes located downstream of the snRNA loci . In contrast , for snRNA loci located downstream and opposite to coding genes , the lack of 3’-end processing resulted in the transcription of cis-antisense RNAs of the coding genes . Directional deep-sequencing of total RNA from WT and ints-6 ( t1903 ) mutant worms revealed the existence of both types of transcripts: mRNA ( in sense ) derived from the endogenous promoter activity and antisense RNAs on the opposite strand , derived from the lack of processing of snRNAs located in antisense downstream of the gene . This suggests that these antisense RNAs might not be efficient at performing RNA silencing ( S13 Fig ) . This is consistent with the fact that only double-stranded RNA has been shown to be substantially effective at producing RNA interference . Indeed , injection of purified single strand RNA has at most a modest effect on gene expression [45] . These results reveal the formation of antisense RNAs upon knockdown of the Integrator complex , although its putative function remains an open question . To establish whether long chimeric sn-mRNAs could be translated to proteins , we generated transgenic worms containing genes that had in sense snRNAs in their 5’ upstream region ( from 1186 to 213 bp ) , tagged with FLAG and/or eGFP at the 3’-end of the coding gene in its genomic sequence . The 5 transgenes assayed contained two U1 snRNA genes ( H27M09 . 8 and F08H9 . 10 ) in the 5’ upstream region of the tagged genes H27M09 . 5 and F08H9 . 3 respectively; two U2 snRNA genes ( W04G5 . 11 and F08G2 . 9 ) in the respective 5’ region of the tagged W04G5 . 8 gene and ins-37 , an insulin-like peptide; and finally , an SL-2 ( sls-2 . 8 ) in the 5’ region of the tagged Y75B8A . 23 gene . A single copy of these transgenes was integrated into the C . elegans genome by mosSCI [41] . Transgenic worms were treated with RNAi of each member of the Integrator complex or crossed with the ints-6 ( t1903 ) mutant . Transgene expression was detected by RT-PCR and protein formation was assessed by western blot ( Fig 4 ) . For the transgenes assayed , we concluded that the lack of 3’-end processing of sls-2 . 8 snRNA upon depletion of the Integrator complex resulted in transcription of a long chimeric RNA containing sls-2 , the intergenic region and the downstream gene Y75B8A . 23 mostly spliced and polyadenylated . This chimeric RNA was translated into a protein detected by western blot of the FLAG tag at its C-terminal end ( Fig 4 ) . Neither the sls-2 nor the intergenic region contained any ATGs . The first translation start codon that could be used in this long chimeric RNA was the initial ATG of the Y75B8A . 23 gene . Y75B8A . 23 is a hitherto uncharacterized nematode-specific gene that is highly expressed during spermatogenesis at late larval stages [46] . Lack of 3’-end processing of U1 and U2 snRNAs in the transgenes assayed , caused by RNAi of any Integrator subunit , led to transcription of long chimeric sn-mRNAs . However , these U1- and U2-derived chimeric RNAs were not translated into proteins ( Fig 4 ) . We observed that U1 and U2 snRNA genes contain several ATGs in their sequence ( S3 Table ) as well as a specific secondary structure [47] . To determine whether initial ATGs in these long chimeric sn-mRNAs could serve as start codons for translating peptides , we generated transgenes that contained HA and MYC tags in-frame with the 1st and 2nd ATG of the U1 snRNA gene ( F08H9 . 10 ) and the U2 snRNA gene ( W04G5 . 11 ) respectively . Depletion of the Integrator complex by RNAi did not result in peptide formation as determined by western blot using anti-HA and anti-MYC specific antibodies ( S14 Fig ) . These findings indicate that specific SL-derived , but not U1- or U2-derived , long chimeric sn-mRNAs generated upon Integrator complex downregulation may be translated into proteins . Downregulation of the Integrator complex has a direct effect on the transcription of genes located downstream of the snRNA loci . To decipher whether gene expression alteration is restricted to those genes or has a broad effect on the general expression profile , we analyzed the long-term transcriptomic profile of nematodes depleted for each member of the Integrator complex by RNAi . We examined the gene expression profile of WT N2 worms synchronized at the first larval stage ( L1 ) and grown on RNAi feeding plates for each member of the Integrator complex , for 6 days at 15°C . The gene expression data were normalized by a negative binomial distribution model using DESeq2 and EdgeR implementations and compared to a control grown under the same conditions using the empty L4440 vector as the RNAi clone . Three biological replicas of each analysis were performed . Raw sequence data generated in this study are available in the Gene Expression Omnibus ( GEO ) data repository ( Accession number GSE111083 ) . Quantitative analysis of differential expression was performed as described in materials and methods [48] . The Euclidean distance analysis of the global expression profile similarity of the three biological replicas of each Integrator subunit knock-down , plus the control , defined three groups ( S15 Fig ) . These transcriptional groups broadly matched the different phenotypes observed for the depletion of each of the Integrator subunits with punctual replica exceptions ( Figs 3 , 5A–5B and S15 ) . The transcriptomic profile of the RNAi-depleted worms for the catalytic unit homologs , ints-9 and ints-11 , grouped together with ints-4 and ints-5 . The highly uniform phenotypic and transcriptional response to the absence of any of these subunits suggests that they are functionally related and defines a category within the Integrator complex hereafter referred to as the Catalytic Class . Integrator subunits exhibiting this Catalytic Class transcriptional signature showed the strongest phenotype after depletion of any Integrator subunits . RNAi of ints-4 , ints-5 , ints-9 or ints-11 led to a strong lack of 3’-end snRNA processing and to larval arrest of the fed worms ( Fig 3 ) . Secondly , the transcriptomic profile of ints-1 , ints-2 , ints-6 , ints-7 , and ints-8 RNAi shared some common features with the Catalytic Class . However , their short Euclidean distance in the global expression profile similarity analysis grouped them together in a second transcriptional phenocluster that we hereafter refer to as the Holder Class . RNAi of any Holder Class subunits led to a clear lack of 3’-end snRNA processing . ints-2 RNAi fed worms arrested as larvae , whereas the rest of the Holder Class subunits ( ints-1 , ints-6 , ints-7 ) reached adulthood but produced dead embryos in the next generation ( Fig 3 ) . Finally , far from the Catalytic and Holder Classes , the transcriptomic profiles of ints-3 , ints-10- , ints-12- , ints-13-depleted worms did not show significant differences from the control and grouped with WT N2 worms fed the RNAi of the empty L4440 vector . This third transcriptional phenocluster , that we named the Auxiliary Class , makes only a mild contribution to 3’-end snRNA processing under the assayed conditions ( Fig 4 ) . In addition , Auxiliary Class subunits RNAi did not show an obvious phenotype ( Fig 3 ) . This indicates that under standard laboratory conditions it does not play a central role in snRNA processing and its function , under these specific conditions , is accessory . To gain insight into the transcriptional role of the three Integrator complex phenoclusters , rather than the biochemical organization of the complex , we identified the overlapping set of genes significantly up- and down-regulated after knockdown of the Integrator subunits within each class ( S4 Table ) . As expected , genes located in sense downstream of the snRNA loci that are directly affected by the lack of snRNA processing were upregulated both in the Catalytic and Holder Class RNAi groups . However , these genes constituted only a small fraction of the total altered genes . The rest of the upregulated genes do not have any snRNA in their upstream region as visualized in the RNAseq experiments . Therefore their upregulation is not caused by read-through of the RNAP II downstream of the snRNA . ( S1 , S4 Table ) . In the Catalytic Class , common genes upregulated by knockdown of any subunit constituted , by far , the largest group of upregulated genes ( 1508 genes ) . Upregulated genes specific for the knockdown of any single subunit of the Catalytic Class represented less than 10% of the common response of the class; except for ints-5 that represented near 50% ( Figs 5C and 6 ) and were enriched in hydrolase , lyase and chitinase activity suggesting a high catabolism rate ( S5 Table ) . This transcriptomic signature highlights a major common function of these subunits . Genes upregulated after depletion of the Integrator Catalytic Class do not randomly fall within different GO molecular function categories . Instead , they are highly enriched in kinases and phosphatases involved in biological processes such as regulation of cell shape , cell proliferation , morphogenesis or signaling pathways . Interestingly , we did not detect activation of stress response pathways ( S4 Table ) . The activation of these pathways depends on ATR kinase that senses blocked transcription elongation rather than DNA lesions directly . In fact , transcriptional and post-transcriptional activation of the stress response occurs when transcription elongation is blocked even in the absence of DNA damage [49 , 50] . This result strongly indicates that knockdown of the Integrator complex does not abrogate gene transcription . In global terms , upon depletion of the Integrator Catalytic Class , 23% of the total 438 C . elegans kinases are significantly upregulated ( Representation Factor RF = 2 . 7 p<6 . 612e-21 ) . Similarly , 38% of the total 206 C . elegans phosphatases are significantly upregulated ( RF = 4 . 4 p<1 . 99e-31 ) ( wormbook . org , nemates . org ) ( Fig 5C ) . These transcriptomic changes either cause or reflect the dramatic alteration of the signaling state of the cells upon lack of snRNA processing . In contrast to this gene upregulation response , gene downregulation does not show such a clear , common pattern within the Catalytic Class . The set of common genes downregulated by knockdown of any subunit of the Catalytic Class ( 397 genes ) is smaller than the number of genes specifically downregulated by knockdown of ints-5 ( 557 genes ) or ints-11 ( 487 genes ) . This common downregulation mainly affects peptidases involved in metabolism ( Figs 5D and 6 ) . Regarding the Holder Class , the common genes upregulated by knocking down any subunit ( 473 genes ) overlap in 90% with the Catalytic Class , indicating the involvement of this class in the major activity of the Integrator complex ( Fig 5C ) . However , there is no significant common downregulation response to knockdown of the Holder Class . Among the members of this class , ints-1 shows a specific effect on the transcription of a subset of genes enriched in extracellular protein coding genes ( Figs 5D and 6 ) . Finally , RNAi depletion of Integrator subunits , grouped together as the Auxiliary Class , do not show significant differences from WT under laboratory conditions ( S15 Fig ) . In addition to the major functions of the Integrator classes , and specific subunits such as ints-1 and ints-5 , certain pairs such as ints-1 and ints-6 share a fraction of up- and downregulated genes , suggesting a functional relationship between them ( Figs 5C–5D and 6 ) . Both sets are enriched in extracellular protein coding genes , indicating a specific function of these subunits in regulating extracellular matrix components . Moreover , although affecting different sets of genes , knockdown of Integrator subunits 1 or 11 causes downregulation of neuronal genes . Knockdown of Integrator subunits 6 and 9 causes downregulation of genes involved in mitochondrial activity . And finally , knockdown of Integrator subunits 7 and 8 causes downregulation of genes coding for gap junction structures ( S5 Table ) . These data are available online for comparison and easy visualization of any of the multiple datasets by loading them onto the web version of the Upset application ( http://caleydo . org/tools/upset/ ) ( See Materials and Methods ) ( Fig 6 ) . Together , these data indicate that the Integrator complex has a major positive role in processing snRNAs and a negative function in the expression of genes involved in the regulation of signaling pathways by protein phospho-modification . Similar to what occurs in Drosophila and mammals , this effect likely reflects the direct role of the C . elegans Integrator complex on gene expression by regulating the RNAP II gene transcription cycle [8 , 31 , 32 , 33 , 34] . Additionally , formation of long , unprocessed sn-derived RNAs upon Integrator complex knockdown might have a cascade effect on the expression of other genes .
The global gene expression profile reflects , among other elements , the activity of RNA polymerase complexes on specific genes . This activity is tightly regulated by interaction with transcription factors and other protein complexes such as the Integrator complex . In this work we display the composition of the C . elegans Integrator as an evolutionarily conserved complex likely composed of 13 subunits: INTS-1 to INTS-13 . The existence of similar phenotypes and transcriptomic readouts on some Integrator subunit knockdowns led us to define three different transcriptional clusters: the Catalytic Class ( INTS-4 , INTS-5 , INTS-9 , INTS-11 ) , the Holder Class ( INTS-1 , INTS-2 , INTS-6 , INTS-7 , INTS-8 ) and the Auxiliary Class ( INTS-3 , INTS-10 , INTS-12 , INTS-13 ) . The transcriptomic profiles of the Integrator subunits are highly homogeneous within the different classes as well as the different replicas . Deviations such as a replica of ints-2 and ints-7 ( Fig 5A ) may reflect the biological and technical variability inherent to experiments with biological samples . Both factors may affect bioinformatic categorizations . In our assay , RNA interference of the Integrator subunits was efficient as indicated by western blot analyses , detection of the RNA-dependent RNA polymerase ( RdRp ) mediated amplification of the gene transcripts subjected to RNAi silencing and the phenocopy of the snRNA processing defects observed in the ints-6 ( t1903 ) mutant ( Figs 1 , 3 and S8 ) . Therefore , the resulting bioinformatic categorization justly reflects the similar transcriptional signatures of subunits grouped within the same phenocluster and the closer functional relationship between different intra-cluster subunits ( such as INTS-1 and INTS-6 ) and inter-cluster subunits ( such as the Catalytic and Holder Classes ) under these experimental conditions . This classification provides a reliable framework in which to classify the different transcriptional outputs . The existence of a common transcriptional signature reflects a functional relationship rather than belonging to a biochemical sub-complex that has not been assessed . The Integrator complex mediates 3’-end processing of snRNAs U1 , U2 , U4 , U5 , SLs and certain snoRNAs ( indicating the existence of different mechanisms of 3’-end processing for snoRNAs ) and has global effects on the transcriptomic profile . However , no effect on the processing of other ncRNAs was observed . Although , so far , no comprehensive analyses have been performed on the phenotypical comparison of the different Integrator subunits , a similar organization could be present in other species . Indeed , human and Drosophila INTS4 , INTS9 and INTS11 subunits biochemically associate in a module responsible for the catalytic activity of the complex . This module is critical for snRNA 3’-end processing and homeostasis of Cajal bodies [23 , 51] . Knockdowns of human catalytic subunits INTS9 and INTS11 show similar phenotypes [52] . Mutations in human Ints1 and Ints8 , grouped here within the Holder class , cause similar rare recessive human neurodevelopmental syndromes [53] . Finally , in addition to their function in the complex , human INTS10 , INTS13 ( grouped here within the Auxiliary class ) , and INTS14 also form a separate module that may be recruited to specific genomic sites to regulate gene expression during monocytic differentiation [54] . In contrast , in humans , INTS3 and INTS6 , which in our study show a different transcriptional output , mediate the DNA damage response and form a stable complex even in the absence of DNA damage [22] . Further studies are required to unravel the function of C . elegans INTS-3 and INTS-6 in the DNA damage response . Certain evolutionary divergence may exist within the Integrator complex , but altogether the data suggest that , bridging the gap between different species , a similar functional output of the different Integrator transcriptional classes might be generally conserved throughout evolution . Knockdown of the Integrator complex leads to transcription of genes located downstream of the snRNA loci by abrogating the 3’-end processing of the nascent snRNAs . Two scenarios are possible: the gene located downstream of the snRNAs can be orientated either in sense or antisense . In the first case , the lack of 3’-end processing of snRNAs leads to generation of chimeric sn-mRNAs that are capped , contain the snRNA at the 5’ end of the sequence , continue with the intergenic region and have the mRNA sequence of the downstream gene . In all these cases , intron retention was detected but most of these transcripts were spliced and polyadenylated at the 3’-end ( S12 Fig ) . Transcription in those RNAs ends at the polyadenylation signal of the gene . The opposite orientation of the gene leads to the transcription of chimeric RNAs that possess the snRNA sequence at the 5’ end and an antisense RNA of the downstream gene at the 3’-end . Our experiments with chimeric sn-mRNAs whose tagged coding mRNA is in sense to the snRNA revealed that the assayed U1 or U2 derived sn-mRNAs are not translated into either the protein coded by the gene or peptides starting from previous ATGs within the chimeric sn-mRNAs . Interestingly , SL derived sn-mRNAs can be translated into proteins . This mechanism is different from trans-splicing in which SLs are transcribed , processed to their mature form and fused to the 5’-end of mRNAs from far genomic regions that may even be located on different chromosomes [19] . In the assayed case , disruption of the Integrator complex results in the lack of 3’-end processing of the sls-2 . 8/snRNA , the transcription of the downstream intergenic region and the coding gene named Y75B8A . 23 . The single intron in this gene is mostly spliced and the transcript is polyadenylated . Taken together , our results show that the capacity to translate these chimeric sn-mRNAs depends on the nature of the snRNA . U-snRNAs have a specific secondary structure oriented to their function in mRNA splicing while SLs might confer stability and enhance the translation of mRNAs containing an sls’ in their 5’ region [19] . Although only SL derived sn-mRNAs can be translated into proteins or peptides , long chimeric U1- and U2-derived and other chimeric sn-mRNA might have an epigenetic effect on gene regulation . This effect could happen directly upon the genes included in the sn-mRNA , but it could also affect other genes by different means such as sequestering microRNAs or epigenetic regulation [55] . In addition to this function in sn-RNA processing , the Integrator RNAPII-associated complex plays a critical role in synchronous activation of gene expression during metazoan development by regulating transcriptional elongation . It is estimated that half of mammalian genes are regulated by pause and release of RNAPII [31–34] . Consistently , our genome-wide analysis reveals that the transcriptional effect caused by disruption of the Integrator complex is not restricted to genes located downstream of the snRNA loci . Likely due to the direct effect of the Integrator complex on gene transcription regulation and as a consequence of the generation of chimeric sn-mRNAs , Integrator disruption affects the transcription of a wide range of genes located away from the snRNA loci . Thus , knockdown of either the Catalytic or the Holder Class subunits of the Integrator complex causes upregulation of genes coding for a large set of kinases ( 23% of the total kinome ) and phosphatases ( 38% of the total phosphatome ) . This suggests the unchaining of a dramatic change in the normal regulatory state of the organism’s signaling pathways . In addition to this general effect , knockdown of individual subunits , such as ints-1 and ints-5 , affects specific sets of genes , indicating a particular function in transcriptional regulation of extracellular proteins . Other subunits share effects on specific sets of genes involved in mitochondrial activity ( ints-6 and ints-9 ) , gap junction activity ( ints-7 and ints-8 ) or neuronal activity ( ints-1 and ints-11 ) . This suggests that specific subunits of the Integrator complex might have additional functions beyond the 3’-end processing of the snRNAs , as occurs in humans [31 , 56] . Our findings indicate that Integrator complex downregulation in C . elegans triggers non-conventional transcription of genes located downstream of the snRNA loci , generating long chimeric sn-mRNAs . As a result of this and of the direct role of the Integrator complex in transcriptional regulation [31–34] , the transcriptomic profile of key regulators in signaling transduction such as kinases , phosphatases and other specific genes , is altered . In humans , alteration in phosphorylation pathways results in serious outcomes in the form of diseases , especially cancer . Phosphorylation-related mutations are highly enriched as tumor “drivers” [57] . Indeed , the tyrosine kinase family encompasses the greatest number of oncoproteins . Altered phosphorylation of proteins involved in cell cycle , apoptosis or cell adhesion pathways corrupt these mechanisms leading to a strong correlation with cancer . As a consequence , kinases offer an enormous potential as targets for drugs in therapies against cancer [58] . Since the characterization of Ints6 , named at that time as DICE1 ( deleted in cancer 1 ) , as a tumor suppressor in lung carcinomas [59 , 60] , mutations in the different Integrator complex subunits have been reported as involved in multiple kind of tumors [61] . Recent studies show that the Integrator complex is regulated to control the initiation and release of paused RNAP II at immediate early genes ( IEGs ) following stimulation with epidermal growth factor ( EGF ) in HeLa cells [31 , 56] . This raises the possibility that the Integrator complex could be regulated under specific circumstances to produce these chimeric sn-mRNAs that we have observed and to activate a physiological transcriptional response . In this scenario , human tumors that harbor mutations in the Integrator complex might be constitutively activating a similar anomalous transcriptional program to the one described in our C . elegans model .
C . elegans strains were maintained on Nematode Growth Medium ( NGM ) agar plates seeded with a lawn of E . coli OP50 . Bristol N2 was used as the WT strain . The nematodes were grown on these plates at 15° , 20° or 25°C , depending on the purpose of the experiment . The ints-6 ( t1903 ) thermosensitive mutant was regularly grown at 15°C and shifted to 25°C 12h before the experiments , when required . The following strains were used: JCP294 ints-6 ( t1903 ) IV JCP301 jcpSi3 [pJC50 ( ins-37p::ins-37::eGFP::ins-37UTR , unc-119 ( + ) ) ] II; unc-119 ( ed3 ) III JCP341 jcpSi10 [pJC51 ( ints-6p::ints-6::3xFLAG::eGFP::ints-6UTR , unc-119 ( + ) ) ] II; unc-119 ( ed3 ) III JCP343 jcpSi12 [pJC55 ( W04G5 . 8p::W04G5 . 8::eGFP::W04G5 . 8UTR , unc-119 ( + ) ) ] II; unc-119 ( ed3 ) III JCP378 jcpSi19 [pJC56 ( eft-3p::ints-6::3xFLAG::eGFP::ints-6UTR , unc- 119 ( + ) ) ]II; unc-119 ( ed3 ) III JCP383 ints-6 ( tm1615 ) IV; jcpSi10 [pJC51 ( ints-6p::ints-6::3xFLAG::eGFP::ints-6UTR , unc-119 ( + ) ) ] II JCP387 jcpSi24 [pJC57 ( H27M09 . 5p::H27M09 . 5::3xFLAG::eGFP::H27M09 . 5UTR , unc-119 ( + ) ) ]II; unc-119 ( ed3 ) III JCP394 jcpSi31 [pJC58 ( Y75B8A . 23p::Y75B8A . 23::3xFLAG::eGFP::Y75B8A . 23UTR , unc-119 ( + ) ) ]II; unc-119 ( ed3 ) III JCP405 jcpSi37 [pJC60 ( F08H9 . 3p::F08H9 . 3::3xFLAG::eGFP::F08H9 . 3UTR , unc-119 ( + ) ]II; unc-119 ( ed3 ) III JCP462 ints-6 ( jcp1 ) [ints-6::3xFLAG] JCP479 jcpSi53 [pJC63 ( 3-tags-in-3-frames ( HA:MYC::TY ) in the snRNA coding gene F08H9 . 10 , unc-119 ( + ) ) ] II; unc-119 ( ed3 ) III JCP504 jcpSi55 [pJC64 ( 3-tags-in-3-frames ( HA::MYC::TY ) in the snRNA coding gene W04G5 . 11 , unc-119 ( + ) ) ] II; unc-119 ( ed3 ) III JCP590 ints-2 ( jcp15 ) [ints-2::3xFLAG] JCP614 ints-3 ( jcp8 ) [ints-3::3xFLAG] JCP625 ints-9 ( jcp10 ) [ints-9::3xFLAG] JCP626 ints-5 ( jcp21 ) [ints-5::3xFLAG] JCP630 ints-7 ( jcp22 ) [ints-7::3xFLAG] JCP643 ints-11 ( jcp31 ) [ints-11::3xFLAG] JCP645 ints-13 ( jcp33 ) [ints-13::3xFLAG] Worms were monitored on NGM plates under a Leica dissecting microscope ( MZ16FA ) . Gravid hermaphrodites were dissected . 2- to 4-cell stage embryos were mounted on 4% agar pads in water and sealed with Vaseline petroleum jelly . Imaging was performed at 25°C . Differential interference contrast microscopy ( DIC ) was performed on a motorized fluorescent Leica microscope ( DM6000B ) equipped with a Hamamatsu Orca-ER C10600 camera and fitted with DIC optics . The appropriate filters were selected for fluorescent microscopy . Images were captured using the open source Micro-manager software ( www . micro-manager . org ) and processed with XnView software and ImageJ software . Synchronized populations of C . elegans eggs , larvae or adults were freeze-cracked , fixed with -20°C cold methanol for 2 min and cold acetone for 4 min . Next , samples were dried at RT and recovered by adding a drop of PBS containing 0 . 1% Tween ( PBST ) for 5 min . Once eggs or worms were properly prepared , they were blocked for 15 to 30 min in 1% BSA PBST blocking solution . Subsequently , samples were incubated either O/N at 4°C or at RT using FLAG antibody in 1% BSA PBST ( F1804 Sigma , 1:500 dilution ) followed by 1 or 2 hours incubation with the secondary antibody Alexa Fluor 633 in 1% BSA PBST ( Invitrogen A-21050 , 1:500 dilution ) . Finally , samples were mounted using VECTASHIELD Antifade Mounting Medium ( H-1000 Vector laboratories ) with DAPI ( 1 μg/ml ) . U2OS cells were cultured and incubated on pre-treated poly-L-Lysine coated coverslips ( Sigma ) to improve adhesion . Cells were washed with PBS/Ca2+Mg2+ ( 1 mM ) and fixed with 4% paraformaldehyde in PBS/Na+K+ ( 1 mM ) for 30 min at RT with gentle agitation . Next , two washes with PBS ( 1 mM ) were performed for 5 min each and then cells were permeabilized with 0 . 1% Triton X-100/PBS for 10 min at RT with gentle shaking . Next , cells were washed with 0 . 2% PBS-BSA for 10 min at RT . Subsequently , cells were incubated with Ints6 antibody ( Bethyl Laboratories , 1:50 dilution ) in 0 . 2% PBS-BSA for 1 h at RT in a humid chamber . Afterwards , cells were washed with PBS ( 3 times , 7 min each ) and incubated with the secondary antibody ( CyTM3-conjugated AffiniPure Goat Anti-Rabbit IgG , 115-165-003 , Jackson ImmunoResearch Laboratories , 1:500 dilution ) in 0 . 2% PBS-BSA for 30 min at RT in the dark . After this incubation , cells were washed with PBS ( 3 times , 5 min ) and incubated with DAPI ( 2 μg/ml ) in PBS for 5 min , all in the dark . Finally , cells were washed with Milli-Q water ( 3 times ) and coverslips were mounted on the slides using the SlowFade Antifade kit ( Invitrogen ) . To immunoprecipitate INTS-6 , and co-immunoprecipitate its interactors , protein extracts from the JCP378 strain ( jcpSi19[pJC56 ( eft-3p::ints-6::3xFLAG::eGFP::ints-6UTR , unc-119 ( + ) ) ]II;unc-119 ( ed3 ) III ) were used and extracts from N2 worms were the negative control . IPs/Co-IPs were performed with ANTI-FLAG M2 Magnetic Beads ( Sigma ) . Protein extracts were filtered through a 5 . 0 μm filter and subsequently through 0 . 45 μm filters to remove any remaining cell debris and particulates that could interfere with protein binding . In each IP/Co-IP reaction , 150 μl of the ANTI-FLAG M2 Magnetic Beads were incubated with 30 mg of protein extract . Immediately afterwards , the beads were equilibrated with TBS buffer ( 375 μl per IP/Co-IP reaction: 50 mM Tris HCl , 150 mM NaCl , pH 7 . 4 ) . This step was repeated , leaving the beads with a small amount of buffer . Then , the protein extract was incubated with the equilibrated beads for 3–4 h or O/N , always at 4°C in a rotating rack with gentle mixing . Once the binding step was complete , the beads were collected and the supernatants were removed , followed by the washing steps . The beads were washed with TBS buffer ( 1500 μl per IP/Co-IP reaction ) three sequential times for 10 min on a rotating rack at 4°C to remove all non-specifically bound proteins . INTS-6::3xFLAG::eGFP fusion protein and consequently its interacting proteins were eluted from the magnetic beads either by boiling samples in SDS-PAGE sample buffer ( 100 μl ) or by competitive elution with 3xFLAG peptide ( 400 ng/μl 3xFLAG peptide in TBS ) for 1 h at RT in a rotating rack with gentle mixing ) . Finally , eluates were precipitated using TCA . Eluted IPs were run on SDS-PAGE ( Mini-PROTEAN™ TGX™ Precast Gels , Any kDa ) . Next , gels were stained with Coomassie Blue and bands were excised . The CIC bioGUNE proteomics platform ( https://www . cicbiogune . es/org/plataformas/Proteomics ) performed the proteomic analysis . Proteins were digested with Trypsin from each gel band and analyzed by LC-MS/MS: Liquid Chromatography-Mass Spectrometry/Mass Spectrometry . Worms ( usually from 8 to 10 NGM plates ) were harvested with M9 buffer and collected in 50 ml Falcon tubes . They were washed several times , allowing them to settle to the bottom between washes . After the last washing step , as much supernatant as possible was removed . Then , a double volume of lysis buffer ( 50 mM Tris HCl pH 7 . 4 , 150 mM NaCl , 1 mM EDTA , and 1% Triton X-100 ) , containing 1x protease inhibitors ( Complete EDTA-free Protease inhibitor , Roche ) and 1x phosphatase inhibitors ( PhosSTOP , Roche ) was added to each worm pellet . Next , samples were ground in liquid nitrogen using a pre-chilled mortar and pestle . Ground worms were thawed on ice followed by centrifugation at 4°C ( 15000 rpm , 15 min ) to eliminate any non-soluble tissue or cellular remains . The supernatants were transferred to fresh Eppendorf tubes . 30 μg of protein extract per sample were loaded onto the polyacrylamide gels ( Mini-PROTEAN TGX Precast Gels ) after boiling for 5 min in Laemmli buffer ( 80 mM Tris-HCl pH 6 . 8 , 5 mM DTT , 2% SDS , 7 . 5% glycerol , 5 mM EDTA , 0 . 002% bromophenol blue ) . Samples were run using the Mini- PROTEAN Tetra Cell or the Criterion™ Cell electrophoresis system ( Bio-Rad ) at a constant voltage of 120 V in SDS-PAGE running buffer ( 25 mM Tris , 200 mM glycine , 0 . 1% ( w/v ) SDS ) until the tracking dye reached the bottom of the gel . Precision Plus Protein Dual Color Standards ( Bio-Rad ) were used as the size reference . For antibody-specific detection of proteins , samples were separated by SDS- PAGE and transferred to a 0 . 45 μm nitrocellulose membrane ( Protran BA 85 , GE Healthcare ) in transfer buffer ( 25 mM Tris , 192 mM glycine , 10% methanol ) for 90 min at 4°C and a constant voltage of 90 V using the Mini Trans-Blot Electrophoretic Transfer Cell ( Bio-Rad ) or Criterion Blotter . After transfer , the membrane was blocked in TBS-T ( 49 mM Tris base 102 mM NaCl , 5 . 4 mM KCl , 0 . 05% ( v/v ) Tween-20 , pH 8 ) with 5% ( w/v ) non-fat dry milk ( Sveltesse Nestlé ) for 60 min with gentle rocking . To detect the protein of interest , the membrane was incubated with the primary antibody ( HA 6E2 , Cell Signaling 1:1000; GFP Living Colors GFP Monoclonal 632381 Clontech 1:1000; FLAG F1804 Sigma 1:1000; TY1 SAB4800032 Sigma 1:1000; MYC 9B11 Cell Signaling 1:1000; actin ( I-19 ) sc-1616 Santa Cruz Biotechnology 1:1000 ) diluted in TBS-T milk for 120 min at RT or O/N at 4°C . Afterwards , the membrane was washed three times with TBS-T for 10 min each and then incubated with the respective horseradish peroxidase ( HRP ) conjugated secondary antibody in TBS-T milk ( α-mouse HRP linked GE Healthcare NA931 1:2500; α-goat IgG HRP linked 805-035-180 Jackson ImmunoResearch 1:5000 ) for 60 min at RT . The membrane was washed twice more with TBS-T for 10 min each time and then once with TBS only . To chemiluminescently detect the protein of interest , ECL Blotting Detection Reagents ( GE Healthcare ) and Amersham Hyperfilms ECL ( GE Healthcare ) were used according to the manufacturer’s instructions . Films were developed manually: 1 min in developing solution ( Agfa developer G153 ) , 1 min in fixing solution ( Agfa fixer G-345 ) and then rinsed in water . Polyacrylamide gels were stained with Coomassie blue using the Colloidal Blue Staining Kit ( Invitrogen ) according to the manufacturer’s instructions . Polyacrylamide gels were silver stained using Silver Stain for Mass Spectrometry ( Thermo Scientific ) according to the manufacturer’s instructions . Plates seeded with the corresponding RNAi clones were used to feed synchronized WT L1 worms . RNAi clones were obtained from either the ORFeome Library [62]: ZC376 . 6 ( ints-2 ) , Y51A2D . 7 ( ints-5 ) T23B12 . 1 ( ints-12 ) R02D3 . 4 ( ints-13 ) or the Ahringer Library [63]: F47C12 . 3 ( ints-10 ) . The RNAi clones C06A5 . 1 ( ints-1 ) , Y92H12A . 4 ( ints-3 ) , W04A4 . 5 ( ints-4 ) , F08B4 . 1 ( ints-6 ) , D1043 . 1 ( ints-7 ) , Y48G10A . 4 ( ints-8 ) , F19F10 . 12 ( ints-9 ) , F10B5 . 8 ( ints-11 ) were cloned from cDNA using the following primers: ints-1 ( Fw 5’-AAACCACGAGTTGGACAAGG-3’ , Rv 5’-TCAAATCAATCGGCATTTCA-3’ ) , ints-3 ( Fw 5’-TTCGCCAAAATGTGAAACAA-3’ , Rv 5’-AGACGTAGGTCAGCGAGGAA-3’ ) ints-4 ( Fw 5’- CGGATCCCAGAAGAATCGTA-3’ , Rv 5’-CGTCATCACTTGCATCATCC-3’ ) ints-6 ( Fw 5’- CTCGTTTGAATCCACAAGCA-3’ , Rv 5’-TGAGCTTTTGAGGCATGTTG-3’ ) ints-7 ( Fw 5’- TGTGAATGCGATGCTTCTTC-3’ , Rv 5’ ACATGTACGGGCAGTTGTCA-3’ ) ints-8 ( Fw: 5’-TTACTAAGCTTCCATAGATCGCCGTAATCGT-3’ , Rv: 5’- TTACTCTCGAGGTGAGTGGGCCGTGAAGTAT-3’ ) , ints-9 ( Fw: 5’-TATATCAAAGCCCGCGAATC-3’ , Rv: 5’- GGTCTCATCCGGTTTTCAA-3’ ) ints-11 ( Fw: 5’- AAAAAGGTTGTCGGATGTGC-3’ , Rv: 5’- GCTTCGGTTGAGCAGAAATC-3’ ) All RNAi clones were verified by sequencing . 5 ml LB medium containing ampicillin ( 100 μg/ml ) was inoculated with a single bacterial colony and incubated at 37°C for 8 h with constant shaking . 400 μl of the bacterial culture was spread on 90 mm NGM RNAi feeding plates ( NGM plates with 100 μg/ml ampicillin , 12 . 5 μg/ml tetracycline , 1 mM IPTG ) and incubated O/N at RT to grow a bacterial lawn and induce dsRNA expression . The next day , synchronized L1 populations were transferred to RNAi feeding plates . The WT strain fed with a clone carrying the empty L4440 vector was used as an RNAi negative control . Functioning of RNAi was assessed by detection of RNA-dependent RNA polymerase mediated amplification of the gene transcripts subjected to RNAi silencing , and western blot of representative subunits of the three transcriptional classes ( S8 Fig ) . Worms from 3 to 5 plates were washed off with M9 buffer and collected in 50 ml Falcon tubes , allowing them to settle to the bottom of the tube . Worm pellets were subsequently washed with M9 buffer until no bacterial remains were visible . Next , worm pellets were transferred to Eppendorf tubes and as much supernatant as possible was removed . RNA extractions were performed using the mirVana™miRNA isolation kit ( Ambion ) following the manufacturer’s protocol for total RNA isolation . Worm tissue homogenization was carried out with the assistance of a polytron pre-chilled with liquid nitrogen . 7 μg of total RNA was denatured for 5 minutes at 95°C and loaded onto denaturing 10% polyacrylamide gels containing TBE-Urea ( Bio-rad ) . Electrophoresis run for about 150 min at 150 V . RNA was transferred to positively charged nylon membranes ( Hybond-N+ , Roche ) . After brief washing using 2 × SSC , the transferred blots were cross-linked under short-wave UV light . After prehybridization at 50°C for 1 hour in Church Buffer ( 0 . 36M Na2HPO4 , 0 , 14M NaH2PO4 , 7% SDS , 1mM EDTA ) , the blots were subjected to hybridization with Digoxigenin-labeled DNA probes overnight at 50°C . The membranes were washed as follows: twice for 5 minutes at room temperature in 2× SSC , three times 10 minutes at 50°C in 2× SSC containing 0 . 4% SDS , 10 minutes at room temperature in 1× Washing Buffer ( DIG Wash and Blocking Buffer Set , Roche ) . Membranes were blocked for 30 min at room temperature in 1× Blocking Buffer ( DIG Wash and Blocking Buffer Set , Roche ) , then incubated with anti-Digoxigenin AP Fab fragments ( Roche ) diluted ( 1/10000 ) in 1× Blocking Buffer at room temperature for 30 min and then , they were washed twice ( 15 minutes each time ) at room temperature in 1× Washing Buffer . Membranes were equilibrated in 1× detection buffer ( DIG Wash and Blocking Buffer Set , Roche ) incubate with several drops of CDP-star ( Roche ) . First , RNA was treated with DNase to eliminate any DNA contamination . In each sample , a total reaction of 10 μl contained: 500 ng RNA , 1μl RQ1 RNase-Free DNase ( Promega ) , 1x RQ1 DNase 10X Reaction Buffer and DEPC water . Reactions were incubated for 30 min at 37°C . Reactions were stopped by adding 1 μl STOP solution ( Promega ) and incubating them for 10 min at 65°C . cDNA synthesis was performed using Transcriptor First Strand cDNA Synthesis Kit ( Roche ) from 500 ng of total RNA according to the manufacturer’s instructions . Samples were stored at 4°C for immediate use or at -20°C for longer periods . To study 3’ end processing , PCR amplification was performed using the GoTaq DNA Polymerase ( Promega ) . The primers used were: sls-2 . 8 Fw 5’-GCTGTCGTTTCGATCTCTCG-3’; Y75B8A . 23 Rv 5’-TGTCGTGAGTAGGTGTGCAA-3’; H27M09 . 8 Fw 5’-GTGTGGCAGTCTCGAGTTGA-3’; H27MO9 . 5 Rv 5’-TTGAACCTTTTCGTCGGAAC-3’; F08G2 . 9 Fw 5’-TGGAACCTAGGGAAGACTCG-3’; ins-37 Rv 5’-TTGAACTTGTCCGGGATTCT-3’; W04G5 . 11 Fw 5’- ATTTTTGGAACCCAGGGAAG-3’; W04G5 . 8 Rv 5’-GTGGAGATTTCTGCGACACA; F08H9 . 10 Fw 5’- TGACCTATGTGGCAGTCTCG-3’; F08H9 . 3 Rv 5’- TCGACAATCTCATTCCGACA-3’; act-1 Fw 5’- CCAGGAATTGCTGATCGTATG-3’; act-1 Rv 5’-GGAGAGGGAAGCGAGGATAG-3’ The final concentrations in each PCR reaction were: 1x GoTaq Reaction Buffer ( 1 . 5 mM MgCl2 ) , 0 . 2 mM dNTPs , 0 . 2 μM upstream primer and downstream primer , 2 . 5 units GoTaq DNA Polymerase plus the required amount of DNA template ( <500 ng ) in each case . For the upstream primers that target the snRNAs , the concentration used was 0 . 8 μM . Reactions were performed using GeneAmp PCR System 9700 thermal cyclers ( Applied Biosystems ) . PCR conditions were adjusted in each reaction based on the DNA fragment to be amplified and the primer pairs used , but all reached 40 amplification cycles . Sequencing libraries were prepared by following the TruSeq RNA Library ( LS ) Preparation Kit v2 instructions ( Illumina Inc . , San Diego , CA ) from 1 ug of total RNA that was previously depleted using the RiboZero ( Human/Mouse/Rat ) protocol . All libraries were run in a HiSeq1500 PE100 lane in Rapid mode , pooled in equimolar amounts to a 10 nM final concentration . The library concentration was measured via qPCR using the KAPA library quantification kit for Illumina sequencing platforms ( Kapa Biosystems , Wilmington , MA ) before high throughput sequencing . Bioinformatic analysis was performed as described [48]: The quality of RNAseq results was initially assessed using FastQC ( http://www . bioinformatics . babraham . ac . uk/projects/fastqc/ ) . The raw reads were trimmed , filtered for those with a Phred quality score of at least 25 and all adapters were removed with Trimmomatic software [64] . Clean reads were aligned versus the N2 Caenorhabditis elegans reference genome ( release WBcel235 . 85 , http://www . ensembl . org/Caenorhabditis_elegans/Info/Index ) using Tophat2 [65] with default parameters . Resulting alignment files were quality assessed with Qualimap2 [66] and sorted and indexed with Samtools software [67] . After taking a read count on gene features with the FeatureCounts tool [68] , quantitative differential expression analysis between conditions was performed both by DESeq2 [69] and edgeR [70] implementations to compare the groups in pairs . Both implemented as R Bioconductor packages and performed read-count normalization by following a negative binomial distribution model . In order to automate this process and facilitate all group combination analysis , the SARTools pipeline [71] was used . All resultant data was obtained as HTML files and CSV tables , including density count distribution analysis , pairwise scatter plots , cluster dendrograms , Principal Component Analysis ( PCoA ) plots , size factor estimations , dispersion plots and MA- and volcano plots . Resulting tables , including raw counts , normalized counts , Fold-Change estimation and dispersion data for each of the analysis methods ( DESeq2 and edgeR ) were annotated with additional data from Biomart ( https://bioconductor . org/packages/release/bioc/html/biomaRt . html ) , WormBase ( http://www . wormbase . org ) and org . Ce . eg . db ( https://bioconductor . org/packages/release/data/annotation/html/org . Ce . eg . db . html ) databases . Final tables also include the associated gene name , Ensembl Transcript and protein information , GO Term ID and names , EntrezID , UniprotTrEMBL information and Human ortholog ID and gene name data . In order to control the False Discovery Rate ( FDR ) , p-values were amended by Benjamini-Hochberg ( BH ) multiple testing corrections [72] . Those features showing corrected p-values below the 0 . 05 threshold were considered up- or down-regulated genes . To reinforce downstream analysis and discard false-positive over/under-expressed genes , common up- and down-regulated features were extracted from DESeq2 and edgeR tables . The CRAN packages eVenn ( https://www . rdocumentation . org/packages/eVenn/versions/2 . 4 ) and pheatmap ( https://CRAN . R-project . org/package=pheatmap ) were used to graphically represent Venn diagrams and heatmap plots showing these common features . Gene Ontology enrichment analysis was performed for common up/down regulated genes by using the clusterProfiler package [73] through its enrichGO tool . This tool uses a hypergeometric BH model to obtain adjusted q-values . Each GO category ( Biological Process–BP- , Molecular Function–MF , and Cellular Component–CC- ) was represented in bar plots , showing its relative abundance and associated q-value . Similarly , KEGG and Reactome pathway analysis was conducted using clusterProfiler ( enrichKEGG ) and ReactomePA [74] tools . The KEGG pathway maps were obtained with the Pathview package [75] . By tacking DESeq2 expression values , the regularized log2 transformation ( rlog ) data was represented as a 2-dimensional Principal Component Analysis ( PCA ) plot . Sample-to-sample Euclidean distances were calculated from the rld data and represented in a heatmap , showing the adjacent clustering information [48] . Lists of up- and downregulated genes are available online for comparison and easy visualization by loading the following datasets onto the web version of the Upset application ( http://caleydo . org/tools/upset/ ) : Upregulated genes: https://raw . githubusercontent . com/CharoLopez/upset-data/master/Up_regulated_all . json Downregulated genes: https://raw . githubusercontent . com/CharoLopez/upset-data/master/Down_regulated_all . json | The gene transcription profile determines the developmental state of an organism . During embryogenesis , aging , starvation or any lifecycle stage , organisms express specific sets of genes that must be turned off at other stages to maintain the correct metabolic and differentiated state of the cells . Mutations that disrupt control of signaling pathways may give rise to certain types of tumors . This happens with mutations in genes coding for the Integrator complex , a multi-protein complex involved in the processing of small nuclear RNAs ( snRNAs ) . Here , we uncover a major mechanism underlying gene expression changes in mutants affecting the Integrator complex . Using a Caenorhabditis elegans model , we describe how the lack of snRNA processing leads to transcription of genes located downstream of the snRNA loci . This primary alteration is not restricted only to those genes but has a broad effect on the expression of other genes involved in the regulation of signaling pathways by protein phospho-modification . | [
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"organ... | 2019 | Disruption of the Caenorhabditis elegans Integrator complex triggers a non-conventional transcriptional mechanism beyond snRNA genes |
Between August 2012 and April 2013 the Career Development Fellowship programme of the Special Programme for Research and Training in Tropical Diseases ( World Health Organization ) underwent an external evaluation to assess its past performance and determine recommendations for future programme development and continuous performance improvement . The programme provides a year-long training experience for qualified researchers from low and middle income countries at pharmaceutical companies or product development partnerships . Independent evaluators from the Swiss Tropical and Public Health Institute and the Barcelona Institute for Global Health used a results-based methodology to review the programme . Data were gathered through document review , surveys , and interviews with a range of programme participants . The final evaluation report found the Career Development Fellowship to be relevant to organizers’ and programme objectives , efficient in its operations , and effective in its training scheme , which was found to address needs and gaps for both fellows and their home institutions . Evaluators found that the programme has the potential for impact and sustainability beyond the programme period , especially with the successful reintegration of fellows into their home institutions , through which newly-developed skills can be shared at the institutional level . Recommendations included the development of a scheme to support the re-integration of fellows into their home institutions post-fellowship and to seek partnerships to facilitate the scaling-up of the programme . The impact of the Professional Membership Scheme , an online professional development tool launched through the programme , beyond the scope of the Career Development Fellowship programme itself to other applications , has been identified as a positive unintended outcome . The results of this evaluation may be of interest for other efforts in the field of research capacity strengthening in LMICs or , generally , to other professional development schemes of a similar structure .
The gap in research capacity strengthening ( RCS ) has been widely recognized as a major stumbling block for development in low and middle-income countries ( LMICs ) and is manifested in the absence of trained and experienced candidates with skills , knowledge , networks , and confidence to build careers in research [1–3] . Both cause for and consequence of this deficit in RCS is the phenomenon called ‘brain drain’: young skilled candidates for a research career go abroad to find better research environments and working conditions . Hence , this continuing exodus to industrialized countries results in reduced numbers of trained scientists in LMICs [4] . This is observable also in health-related research areas where LMICs continue to lag behind industrialized countries although in these very places key improvements in public health stand to be achieved through evidence-based clinical research . Founded to promote high-quality clinical research in LMICs , the Career Development Fellowship ( CDF ) programme began in 1999 as a partnership between the World Health Organization Special Programme for Research and Training in Tropical Diseases ( WHO/TDR ) and one pharmaceutical company [5] . During the initial phase of the programme ( 1999–2009 ) , WHO/TDR awarded one fellowship per year to nine candidates originating from seven LMICs . The fellows were all placed with GlaxoSmithKline Biologicals , the Belgium-based division of the global vaccine and pharmaceutical research , development , and production company . This early phase ( Phase I ) assisted WHO/TDR to identify and develop appropriate procedures and mechanisms to support and administer the fellowship programme . In the second period ( Phase II—2008–2013 ) a grant from the Bill & Melinda Gates Foundation enabled WHO/TDR to scale-up the programme over a four-year period and to engage the participation of additional pharmaceutical companies and product development partnerships ( PDPs ) as host institutions . An overview of fellows awarded throughout the programme period 1999–2013 is provided in Table 1 . The CDF programme provides qualified scientists from LMICs with three core supports: ( i ) a grant that covers the administrative costs of the fellowship , including attendance at one professional scientific meeting during fellowship; ( ii ) a 12-month assignment in the clinical department of a pharmaceutical company or PDP ( including necessary training resources such as clinical supervision and mentoring ) ; and ( iii ) an annual face-to-face alumni meeting and networking opportunities through a dedicated career development website [6] . The programme addresses the research fields of product development ( PD ) and clinical trials ( CT ) , which are not widely taught at academic centres . Between 1999 and 2012 , at the time when the evaluation was launched , 27 fellows had completed this unique programme , which facilitates the development of career elements essential to becoming a leader in clinical research . The programme’s ultimate goal is for fellows to return to their home institutions equipped with specialized skills , experience and access to vibrant networks within the international research community . Fellows benefit their home institutions and , by extension , their home countries , by pursuing high-level research careers and influencing their local environments . As part of the TDR framework for performance assessment [7] , two institutions–the Swiss Tropical and Public Health Institute ( Swiss TPH , Basel , Switzerland ) and the Barcelona Institute for Global Health ( ISGlobal , Barcelona , Spain ) –were commissioned to undertake an external and independent evaluation of the WHO/TDR CDF programme , “to evaluate the outcome and potential impact of the project in order to provide the evidence to assist on recommendations and future decision making . ” This article summarizes the approach and results of the evaluation , and indicates how the programme could be adapted in the interest of further scaling-up and extension . Similar initiatives may benefit from the experience of the CDF programme .
In order to assess the WHO/TDR CDF programme in its various aspects , the evaluators employed a results-based monitoring and evaluation approach [8] . This encompassed an initial assessment of inputs , examined activities and outputs , and culminated in a review of outcomes and impact , for which indicators were defined . The evaluation was designed to assess the programme from the following four perspectives: i ) Relevance: Does the CDF programme address relevant challenges , needs , and gaps for fellows and their home institutions and countries ? ; ii ) Effectiveness: Does the programme deliver training and capacity development effectively ? ; iii ) Efficiency: Does the programme implement its activities in an efficient manner ? ; and iv ) Impact and Sustainability: Has the CDF programme contributed to developing clinical research and product development capacity for LMIC researchers , institutions , and countries , and will it continue to do so ? A log frame was developed encompassing the four aspects discussed above , including detailed descriptions and objectives for each of the dimensions to be evaluated , outputs and/or outcomes , and defining indicators and data sources ( Table 2 ) . This log frame underwent several rounds of external review and refinement before the development of evaluation tools used to collect data from programme participants . The initial evaluation design was hampered by the fact that benchmarks had not been established at the outset of the CDF programme , i . e . the “training needs” for home institutions’ purposes or “training gaps” of fellows . These baseline indicators could only be included in the evaluation as retrospective assessments by the survey participants . For this , the evaluation team developed prelisted ranges of possible bottlenecks , gaps , or professional activities for the persons filling in the survey to select from , with the opportunity to add others in free text . For instance , the evaluators surveyed fellows regarding their pre-fellowship skills and knowledge through a questionnaire . Open access TDR documentation and relevant websites as well as internal documents provided by CDF management served the evaluation team as written sources . The following groups of people involved in the programme were addressed in different ways: ( i ) the CDF management team at WHO/TDR , ( ii ) representatives of home institutions , ( iii ) representatives of host institutions , and ( iv ) fellows . Using the log frame , three versions of a comprehensive questionnaire were developed to be administered to representatives of home institutions , host institutions , and fellows in order to retrieve information about various aspects of the programme from multiple perspectives ( Table 3 ) . Based on both the log frame and survey results , four versions of a focus group discussion catalogue were developed , one for each of the three surveyed groups and an additional one for CDF management . An attempt was made to contact and invite all home institution , host institution , and fellow participants in the programme to participate in the evaluation ( 27 fellows , 16 host institution representatives , 25 home institution representatives , Table 4 ) . Out of those successfully contacted , a fairly good response rate was achieved from programme fellows ( about 78% ) . Host institution representatives were somewhat less responsive ( 56% ) . Responses from nine out of sixteen representatives gave perceptions ranging from critical to enthusiastic , as mirrored in the participation in the alumni meetings . In contrast , such diversity of views could not be obtained from representatives of home institutions where only three out of ten responded . The low response rate from home institution representatives ( 12% ) was likely due to the lack of a specified focal person for the CDF programme or to the original programme liaison no longer being available . We must consider that results drawn from the home institution group may be biased in the sense that the ones complying were possibly the mentors most involved in the programme . However , this fact strongly left its mark in the recommendations for the programme’s future regarding the involvement of home institution representatives . The evaluation was carried out between August 2012 and April 2013 and covered the programme from 1999 through 2012 . In a first phase , documents were screened and the log frame developed , after which the CDF management team was interviewed to refine the evaluation strategy . Following a piloting phase , questionnaires were administered to the three participant groups . Analyses were followed by in-depth focus group discussions and interviews with representatives from all three groups and the CDF management team before the final report was drafted . Questionnaires were administered online . The surveyed groups were adequately informed of the procedures , and general and personalized reminders were delivered in a timely manner . Quantitative responses were collected and compiled to inform the next phase of the evaluation , which included an in-depth analysis of survey results in order to determine which aspects of the programme would be qualitatively addressed through stakeholder interviews . Preliminary and final results were discussed with all stakeholders involved ( including the donor organization and CDF management ) via telephone interviews and in-person meetings , and were presented during the third CDF programme alumni meeting . Results and recommendations from the evaluation were translated into a reporting document that was presented on various occasions from a ‘lessons learned’ standpoint . Results of the evaluation are also briefly described on the TDR website [9] .
The CDF programme exemplifies all elements included in TDR’s mission statement , mandate , and objectives . It is consistent with TDR’s strategic commitment to develop innovative knowledge , solutions , and implementation strategies on health needs [10–12] , and translates these concepts into practice . The CDF scheme supports evidence-based decisions and so contributes to the development and implementation of new or improved interventions as well as to the translation of innovation , knowledge , solutions , and implementation strategies to policy and practice in addressing development goals and improving health in disease endemic countries . Applicants to the programme and the fellows selected fall into the scope of the programme based on their scientific backgrounds . Fellows originate from target countries and regions ( Fig 1 ) . Programme participants are placed at host institutions that contribute to the goals stated in the programme . Fellows surveyed identified major bottlenecks for their home institutions and reported whether they were , in their view , addressed by the CDF programme ( Table 5 ) . Twelve persons ( 57% ) estimated that the CDF programme addresses the bottlenecks of home institutions well or very well . The fellows’ placements were–through joint efforts–good matches between the needs of the fellows’ home institutions and the fellowship opportunity offered by the host company . In consequence of this perfect fit strategy , and due to a preference for high quality over quantity of participants , some CDF programme fellowship positions were not filled . Home institution representatives stated that the CDF programme led to alleviation of institutional scientific isolation , mainly through the networking aspect of the programme , which facilitates long-lasting relations through post-training communication and collaboration on several levels: between fellows , between fellows and their host institutions , and between fellows’ home institutions and the developing CDF programme network . Bottlenecks identified by host companies encompassed institutional staff knowledge ( 8/9 ) , project management ( ( 7/9 ) , and lack of collaboration with other institutions/companies ( 6/9 ) . Host companies’ representatives reported–similar to the fellows’ perceptions–adequate targeting of the CDF programme to existing bottlenecks , both to theirs and the ones of the fellows . In addition , they valued the programme’s contribution to improving both “administration” and “documentation” higher . Taken altogether , the evaluation found the CDF programme to be highly relevant with regard to TDR objectives and the programme’s own stated objectives . The training components covered by the various fellowship placements include i ) scientific knowledge ( clinical pharmacology , good clinical practice , good laboratory practice , biostatistics , microbiology/molecular biology , medicine and clinical trial design ) , ii ) technical skills ( project planning and management , evidence-based study implementation , ethics and ethical clearance , regulatory compliance , monitoring and evaluation , quality control , meeting organisation and presentation ) , and iii ) cross-cutting skills ( see below ) . CDF participants emphasized the following as major successes of the programme: i ) a practical approach to addressing research skill needs on the scientific , technical , and cross-cutting levels; and ii ) the involvement of the fellow in a wide range of professional activities through a strong link to the host company and to a larger international research network . Mirroring the fellows’ individual research capacity needs , the pre-training gaps identified are summarized in Table 6 . More than 90% of fellows reported that their skills and competencies in product development or clinical trials were either better ( 29% ) or much better ( 62% ) following their participation in the fellowship . The hands-on experience provided was highly valued . When asked whether they would prefer more theoretical training or more hands-on experience , 76% of the fellows chose the latter . In addition to scientific skills , fellows reported considerable improvement in cross-cutting skills such as ( in % ) : study implementation ( 90 ) , regulatory issues ( 90 ) , documentation ( 86 ) , monitoring & evaluation ( 81 ) , project planning ( 81 ) , management and leadership ( 76 ) , problem-solving ( 71 ) , quality control ( 67 ) , ability to acquire new knowledge ( 62 ) , collaborative practice ( 62 ) , administration ( 57 ) , social networking ( 48 ) , and evidence-based implementation ( 38 ) . The networking aspect of the CDF programme was brought forward through three platforms , i ) the online platform and ii ) the alumni network , and iii ) alumni meetings . Initially developed as a communication platform site for fellows and launched under the name “Continuing Professional Development”[13] , the online portal became crucial for programme participants . It soon evolved into a career development tool now known as the Professional Membership Scheme ( PMS ) , which all fellows are invited to join . It was built in partnership with WHO-TDR in order to capture the development of core competencies as current and former fellows progress through their careers . The PMS is now embedded in the Global Health Network [6] , a virtual professional community comprising a collection of interconnected specialist research sites linked through a digital hub , much like an online science park . Its success was one of the positive unintended outcomes from the CDF programme noted by the evaluation team . During the period from mid-October 2012 to mid-June 2013 , PMS web pages received an average of 25 unique visits and 248 views per month from various stakeholders . Fellows reported that some of the most important uses of the website are networking ( 64% ) , retrieving programme information ( 64% ) , and searching for advanced training options ( 36% ) . Interestingly , 58% of fellows reported that the alumni network helps them to alleviate scientific isolation . Growing attendance at the annual CDF alumni meeting , which brings together CDF management , fellows and both host and home institution representatives , may be seen as an indicator of increased networking activity ( Table 7 ) . Taken together , the results show evidence that the CDF programme delivers the intended training and capacity development in an effective manner . A clear measure of the efficiency of the CDF programme was the successful transition from Phase I to Phase II and the expansion of the programme to include more fellows , host companies , and home institutions from LMICs . Activities were implemented efficiently . In general , programme deadlines were met , although delays caused by external factors like visa application procedures and contract processes in companies , were seen in all recruiting rounds . The selection process was identified as very transparent by all stakeholders involved . Each round of selection has seen more eligible applications ( minimum double ) than positions offered . CDF management has opted for quality in each round , leaving positions empty rather than accepting fellows that are not optimal matches . The support and flexibility of host institutions to facilitate the integration of the fellows , both at the cultural and working levels , is noteworthy . Once training placements are over , a smooth reintegration of fellows is necessary in order for LMIC-based home institutions to reap the benefits of the CDF programme . However , 38% of fellows reported that their reintegration was “problematic , ” and three fellows reported that problems with re-entry were not resolved , despite the initial agreement between home institutions and TDR to reintegrate fellows at least at their previous level of employment . Such findings emphasize the home institution stakeholders’ crucial role as participants and supporters of the fellowship programme in order for it to sustain long-term impact . This evaluation reviewed the short-and mid-term impacts of the CDF programme . There were three major indicators for impact: ( i ) enhancement of research/scientific activities; ( ii ) improvement in research environment at institutional and national level; and ( iii ) engagement in high-level scientific collaboration . The indicators include the amount and quality of fellows’ training , research , and networking activities; number of publications and conferences; and involvement in PD and CT post-training . A crucial assessment of the impact and sustainability was the involvement in high-level scientific activities towards the end of and after the CDF fellowship period . The data in Table 8 suggest that fellows are equipped with skills for leadership and an ability to conduct projects in an international context . An interesting aspect to highlight is the level of involvement of fellows in national and international collaborations both during ( 52% ) and after ( 81% ) the training period . Anecdotal ( at this stage ) cases showed that sustainability was supported best when home institutions gave a high level of attention to the post-training reintegration period of their fellow . Also , first evidence indicates an important role of the home institution key persons towards harmonized communication between fellows , host , and home institution personnel . So far , the CDF programme has contributed to developing PD and CT capacity for LMIC researchers , institutions , and countries , and has made good progress towards strengthening research capacity . The evaluation highlighted the following general recommendations: i ) continue and expand the CDF programme , ideally in partnership with other research organisations with similar supporting objectives; ii ) develop a reintegration process that includes a re-entry grant scheme , overlapping fellows at host companies , and securing academic credit for programme participation; and iii ) involve home institutions , primarily by better understanding and defining the roles and responsibilities of the different stakeholders in the CDF programme .
One fundamental cause of LMIC’s limitation in terms of clinical research remains the insufficient number of experienced researchers with: i ) necessary knowledge and skills , ii ) access to scientific networks , and iii ) the confidence to design and lead their own research programmes [14] . The present external evaluation found that the CDF programme , as designed , enables fellows to acquire essential knowledge and skills . Many fellows reported that their skills and competencies for PD and CT are ( much ) better following the fellowship , and that the hands-on training they received was critical for their career development . However , other institutional-level factors such as: i ) research infrastructure , ii ) the translation of individual capacity into institutional research capacity strengthening , and iii ) an environment that allows trained researchers to stay in or return to low and middle income home countries , remain key [2 , 3 , 15] . The CDF programme stipulates the return of fellows to their home institution for at least one year after completion of the fellowship period . All but one ( at the time of the submission of the manuscript , 41 fellows had completed their fellowship , Table 1 ) were able to comply with this stipulation . Similar findings have been made for the majority of other TDR postgraduate grantees [16] . The evaluation found that the programme has created a context that enables former fellows to build on their network of peers , mentors , and supervisors . As such , the CDF programme is surely not contributing to ‘brain drain’ . Through the evaluation , it became clear that the home institution has a central role in ensuring successful reintegration . It is the responsibility of home institution representatives to provide an appropriate environment in which returning fellows can share their new skills . The rather low percentage of fellows who reported improvement in evidence-based implementation is of concern , emphasizing the crucial role of the home institution . In cases where home institutions valued the return of their fellow as an opportunity to develop their institutional , local , and national research environment , they were able to successfully translate the individual fellowship into sustainable RCS . It is , however , the fellows’ responsibility to commit to sharing what they have acquired through the fellowship: experience , expertise , methods and tools , teaching skills , contacts , and networks . The fellowship experience does not end upon return to a home institution . On the one hand , regular alumni meetings allow CDF programme peers and mentors to meet . On the other hand , the web-based portal is continuously and increasingly being utilized , particularly following its evolution into the PMS ( embedded in the Global Health Network and used by many professionals in health-related research and development ) , through which relevant career competencies can be documented and displayed . Rooted in , but grown beyond application within the CDF programme only , the PMS was revealed by the external evaluation as an unintended outcome that heavily contributes support to CDF alumni and , beyond , to the international clinical research community . Above all , both the personal and work-related contacts between fellows and host institutions made through the programme prove to be major sources for continued and future collaborations ( for 57% of fellows ) across disciplines and sectors , nationally and internationally . Surely , capacity-building approaches are most promising when driven by the spirit of shared responsibility and guiding principles shared by donors and partners from different sectors in the North and South [5 , 17–20] . The period ( post-2013 ) following the independent evaluation of the WHO/TDR CDF programme , has seen considerable progress . With regard to the successful reintegration of fellows , a three-step strategy has been developed including: ( i ) define clear responsibilities , and a mentor , at the home institution; ( ii ) campaign at home institutions to stress the relevance of returning fellows and how they add value to the institutional , national , and regional research strategy; and ( iii ) design a reintegration scheme process to support fellows’ return to their home institutions . During the 1999–2012 period covered by the evaluation , 27 fellows from 16 different countries , mainly from Africa with a minority from Asia and Latin America , participated in the programme . At the time of submission of this manuscript , an additional 16 fellows have been trained in 10 host institutions , including three PDPs and two research institutions ( one of them located in Zimbabwe ) . The programme has expanded to a total of 42 current selected fellows , 18 host organisations , and now covers eight different disease areas . More qualified female candidates have been encouraged in the call description and during the application period to enter the selection process in order to adjust the gender balance in the long term . In the course of the evaluation , selected fellows raised both during interviews and alumni meetings that any form of internationally recognized academic accreditation of the CDF would add to career prospects . Also , on sub-Saharan African institution is not sending candidates due to lack of academic recognition and instead send their staff for academic degrees . The issue of accreditation remains unresolved for the moment as the programme will need to make efforts to meet the lifelong learning requirements set out in the Bologna Declaration for acquisition of credits in non-higher education contexts [21] . Subsequent to the evaluation period , and in line with the evaluation’s recommendations , the new phase of the CDF programme was jointly launched in 2014 by TDR and the European & Developing Countries Clinical Trials Partnership ( EDCTP ) , which signed a partnership agreement in March 2014 for a clinical development fellowship allowing both to be , “more efficient with the funding provided by our donors” [22] . Beginning this year , an additional 25 fellows per year will be placed at some 20 high-level product development organisations [23 , 24] through this partnership . We look forward to the clinical research collaborations and networks that will emerge from this endeavour . | The asymmetry of research training between high and low and middle income countries ( LMICs ) and the resulting need for research capacity strengthening ( RCS ) in under-resourced regions has long been established . In 1999 , the World Health Organization ( WHO ) , through the Special Programme for Research and Training in Tropical Diseases ( TDR ) , launched the Career Development Fellowship ( CDF ) programme , in which LMICs’ candidates spend one year in a pharmaceutical company to be trained in product development and clinical research and then return to their home institution . Between August 2012 and April 2013 , the programme underwent an independent external evaluation from the perspectives of relevance , effectiveness , efficiency , and impact , and sustainability . In addition to analysis of data and documentation , programme managers , current and former fellows , home and host institutions participated in the evaluation through surveys and interviews . In the current article , the external evaluators , along with CDF programme managers , discuss the results of the evaluation in the broader context of individual and institutional RCS , underlining CDF successes , including transmitting essential skills and networks , and addressing the challenge and fundamental importance of integrating fellows , home and host institutions into an on-going relationship that solidifies and amplifies the benefits of the one-year experience . | [
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... | 2016 | Research Capacity Strengthening in Low and Middle Income Countries – An Evaluation of the WHO/TDR Career Development Fellowship Programme |
The Drosophila testis is a well-established system for studying stem cell self-renewal and competition . In this tissue , the niche supports two stem cell populations , germ line stem cells ( GSCs ) , which give rise to sperm , and somatic stem cells called cyst stem cells ( CySCs ) , which support GSCs and their descendants . It has been established that CySCs compete with each other and with GSCs for niche access , and mutations have been identified that confer increased competitiveness to CySCs , resulting in the mutant stem cell and its descendants outcompeting wild type resident stem cells . Socs36E , which encodes a negative feedback inhibitor of the JAK/STAT pathway , was the first identified regulator of niche competition . The competitive behavior of Socs36E mutant CySCs was attributed to increased JAK/STAT signaling . Here we show that competitive behavior of Socs36E mutant CySCs is due in large part to unbridled Mitogen-Activated Protein Kinase ( MAPK ) signaling . In Socs36E mutant clones , MAPK activity is elevated . Furthermore , we find that clonal upregulation of MAPK in CySCs leads to their outcompetition of wild type CySCs and of GSCs , recapitulating the Socs36E mutant phenotype . Indeed , when MAPK activity is removed from Socs36E mutant clones , they lose their competitiveness but maintain self-renewal , presumably due to increased JAK/STAT signaling in these cells . Consistently , loss of JAK/STAT activity in Socs36E mutant clones severely impairs their self-renewal . Thus , our results enable the genetic separation of two essential processes that occur in stem cells . While some niche signals specify the intrinsic property of self-renewal , which is absolutely required in all stem cells for niche residence , additional signals control the ability of stem cells to compete with their neighbors . Socs36E is node through which these processes are linked , demonstrating that negative feedback inhibition integrates multiple aspects of stem cell behavior .
Stem cell niches are complex environments that provide support for stem cells through molecular signals . Several well-characterized niches provide not just one but multiple signals which stem cells must integrate and interpret in order to remain at the niche and self-renew [1] . How this integration is achieved is not well understood at present . Furthermore , in order to maintain the appropriate number of stem cells and the homeostatic balance between self-renewal and differentiation , it is necessary that self-renewal cues be present in limiting amounts or that their activity be dampened to prevent excessive accumulation of stem cells . One general feature of many signal transduction pathways is the presence of feedback inhibitors [2–4] . These are dampeners of signaling , transcriptionally induced by the signaling itself , that prevent signal levels from being aberrantly high . One such family of feedback inhibitors is the Suppressor of Cytokine Signaling ( SOCS ) proteins , which were identified as inhibitors of JAK/STAT ( Janus Kinase/Signal Transduction and Activator of Transcription ) signal transduction , and are SH2- and E3-ligase domain-containing proteins ( Fig 1A and [2] ) . The SH2 domain binds phosphorylated ( i . e . , activated ) signal transduction components and the E3-ligase targets them for degradation by Ubiquitin-dependent proteolysis . In mammals , SOCS proteins can thus inhibit several tyrosine kinase-dependent signaling pathways , including JAK/STAT and Mitogen-Activated Protein Kinase ( MAPK ) [2 , 5] . The Drosophila testis is an ideal model system to study questions of signal regulation and integration in stem cells [6] . The testis niche , called the hub , supports two stem cell populations . The first , germ line stem cells ( GSCs ) , gives rise to sperm after several transit-amplifying divisions leading up to meiosis . The second , somatic cyst stem cells ( CySCs ) , gives rise to cyst cells , the essential support cells for germ line development . Many ligands for signaling pathways are produced by the hub , including the JAK/STAT pathway agonist , Unpaired ( Upd ) , the Hedgehog ( Hh ) pathway ligand Hh and the Bone Morphogenetic Protein ( BMP ) homologs Decapentaplegic ( Dpp ) and Glass Bottom Boat ( Gbb ) [7–11] . The latter two signals are also produced by CySCs and are required in GSCs for self-renewal , indicating that CySCs constitute part of the niche for GSCs along with the hub [10 , 12] . CySCs require JAK/STAT and Hh activity for self-renewal [8 , 13–15] . CySCs and GSCs compete for space at the niche , a phenomenon that was revealed by the analysis of testes lacking the JAK/STAT feedback inhibitor Socs36E [16 , 17] . In these animals , excessive JAK/STAT activity was detected in CySCs , and Socs36E mutant CySCs displaced the resident wild type GSCs . Additionally , we recently showed that CySCs with sustained Hh signaling or sustained Yorkie ( Yki ) activity also outcompeted neighboring wild type GSCs , indicating that several signaling pathways can control niche competition [18] . Moreover , we showed that prior to out-competing GSCs , mutant CySCs displaced neighboring wild type CySCs , indicating that both intra- ( CySC-CySC ) and inter-lineage ( CySC-GSC ) competition take place in the testis . While the two types of competition appear related , in that one precedes the other , there are instances in which only intra-lineage competition takes place [19] . While the competitive phenotype of Socs36E mutant CySCs was ascribed to increased JAK/STAT signaling [16 , 17] , we were surprised to find that clonal gain-of-function in JAK/STAT signaling in CySCs did not induce competitive behavior , and we concluded that loss of Socs36E did not mimic increased JAK/STAT signaling in CySCs [18] . Here , we address whether other mechanisms could account for the competitive behavior of Socs36E mutant CySCs . Because SOCS proteins can inhibit MAPK signaling in cultured cells and in Drosophila epithelial tissues [5 , 20 , 21] , we examined if Socs36E repression of MAPK signaling underlied the Socs36E competitive phenotype . Indeed , we find that Socs36E inhibits MAPK signaling in CySCs during self-renewal , and that gain of MAPK activity induces CySCs to outcompete wild type CySCs and GSCs at the niche . We dissect the genetic relationship between Socs36E and the MAPK and JAK/STAT pathways and show that loss of Socs36E can compensate for decreased self-renewal signaling within CySCs . Thus , we show that CySCs integrate multiple self-renewal signals through the use of a feedback inhibitor that controls at least two signaling pathways regulating stem cell maintenance at the niche .
Loss of Socs36E in the somatic lineage leads to the displacement of GSCs at the niche by mutant CySCs [16] . Although Socs36E is a well-described inhibitor of JAK/STAT signaling [22–25] , we previously determined that elevating JAK/STAT pathway activity in CySCs did not cause the displacement of GSCs [18] . Therefore , we asked whether another signaling pathway known to be inhibited by Socs36E—the MAPK pathway—could be responsible for the niche colonization phenotype by Socs36E mutant CySCs in the testis . Previous work has found that over-expression of Socs36E inhibited MAPK activity and conversely Socs36E knock down enhanced MAPK-dependent tumorigenesis [20–22 , 24] . Increased MAPK activity in Socs36E-depleted cultured cells suggested that Socs36E directly regulated the MAPK pathway [20] , but whether this occurs in vivo is yet to be established . MAPK signaling is activated by several extracellular ligands , the best characterized of which are epidermal growth factors ( EGFs ) , acting through the EGF receptor ( Egfr ) ( reviewed in [3] ) . Upon ligand binding to Egfr , intracellular adaptors recruit the Ras guanine exchange factor ( GEF ) Son of Sevenless ( Sos ) . Sos activates Ras and initiates a phosphorylation cascade resulting in MAPK ( called Rolled ( Rl ) in Drosophila ) activation and subsequent gene transcription alterations through several ETS domain-containing transcription factors ( Fig 1B ) . EGF ligands are present in the testis; germ cells produce Spitz ( Spi ) while somatic cells express vein [26 , 27] . First , we tested whether MAPK signaling levels were regulated by Socs36E in the testis . We induced mutant clones for Socs36E and stained for phosphorylated MAPK ( dpERK ) , an established readout for pathway activity [28] . In testes with control clones , we observed dpERK staining in CySCs as well as in differentiating cyst cells ( Fig 1C , arrow for stem cell clone and arrowhead for unmarked CySC ) . The dpERK staining in CySCs is dependent on Egfr activity because the dpERK signal was autonomously lost in Egfr mutant clones ( Fig 1D , compare arrow to arrowheads ) . By contrast , we found that dpERK staining was autonomously increased in Socs36E mutant CySCs ( Fig 1E , compare arrows to arrowhead ) . Because dpERK staining was variable , we compared the fluorescence intensity of the marked clone with that of its immediate wild type CySC neighbor ( Fig 1F ) . This analysis revealed a significant decrease in dpERK intensity in Egfr mutant clones ( P = 0 . 0008 ) and a significant increase in Socs36E mutant clones ( P = 0 . 0071 ) . While a previous study reported that Socs36E did not regulate dpERK using Socs36E mutant testes [17] , our clonal analysis provides better resolution and allows for direct comparison of mutant and wild type CySCs in the same tissue . Taken together , these data suggest that Socs36E negatively regulates the MAPK pathway in normal CySC function , in addition to its known role in repressing JAK/STAT activity . Next , we sought to establish whether increased MAPK signaling in somatic cells in the testis could cause CySCs to outcompete GSCs for space at the niche and recapitulate the loss of GSCs observed in Socs36E mutant testes ( S1 Table and [16] ) . We used the somatic cell driver Traffic jam ( Tj ) -Gal4 to hyper-activate MAPK in all CySCs and their lineage . In controls , we found 13 . 6 GSCs contacting the hub , and the nuclei of CySCs , marked with Zfh1 , were visible behind the GSCs ( Fig 2A and 2F ) . When we over-expressed either a dominant-active form of the EGF receptor ( λTop ) or of MAPK , Rolled ( RlSEM ) , we observed CySCs contacting the hub directly in the place of GSCs ( Fig 2B and 2F ) . We counted the number of GSCs in these genotypes and found that hyper-activation of MAPK in CySCs resulted in a significant loss of GSCs non-autonomously ( Fig 2F , S1 Table , 13 . 6 in control vs 9 . 4 in UAS-λTop and vs 9 . 8 in UAS-RlSEM , P<0 . 0013 and P<0 . 0025 , respectively ) . We note that we did not see an increase in βPS-integrin when MAPK signaling was hyper-activated in CySCs or when Socs36E was lost from these cells ( S1 Fig ) . Additionally , when we over-expressed a very strong activator of the pathway , RasV12 , using Tj-Gal4 we observed a dramatic loss of GSCs ( 1 . 5 GSCs in UAS-RasV12 testes ) ( Fig 2C and 2F , S1 Table ) , indicating that the strength of competition between CySCs and GSCs depends on the level of MAPK activation in CySCs . Finally , as these experiments tested CySC-GSC competition using lineage-wide over-expression , we wanted to determine whether a single CySC clone with increased MAPK activation could outcompete wild type CySCs ( CySC-CySC competition ) and GSCs ( CySC-GSC competition ) for space at the niche . We used the MARCM technique [29] to generate control clones , or clones that over-expressed either RlSEM or RasV12 . At 14 days post clone induction ( dpci ) , we observed that wild type clones labelled a variable fraction of CySCs , consistent with CySCs undergoing stochastic loss and replacement ( Fig 2D and [18] ) . However , clones in which MAPK was hyper-activated had replaced most wild type CySCs by 14 dpci ( Fig 2E ) and also had outcompeted resident GSCs , leading to a significant reduction in GSC numbers ( Fig 2G ) . These phenotypes closely resemble the effects that we and others observed in Socs36E mutant clones ( see below and [16] ) . Specifically , we observed 13 . 9 GSCs in testes with control clones and 8 . 4 GSCs in testes with Socs36E clones ( P<5 . 2x10-8 ) . In contrast , gain-of-function of JAK/STAT signaling in CySC clones did not lead to GSC loss [18] . Together , our results suggest that both CySC-CySC and CySC-GSC competition induced by Socs36E loss is due primarily to the increase in MAPK activity in these cells , rather than that of JAK/STAT . The results presented above suggest that CySCs undergo MAPK signaling and are responsive to changes in levels of its activity . Indeed , labelling with dpERK antibody reveals that the MAPK signaling pathway is active in wild type CySCs ( Fig 1C and [26 , 27] ) . Although MAPK has been shown to be required during cyst cell differentiation in the testis [26 , 27 , 30 , 31] , its role in the CySCs themselves is unclear . Previous work has reported that persistent Egfr or raf mutant clones are not recovered , but there is a debate as to whether this reflects a requirement for MAPK signaling in CySCs during self-renewal [17 , 27 , 30] . In order to clarify the role of MAPK in CySCs , we addressed whether MAPK signaling affected CySC numbers and self-renewal . First , we examined testes from flies carrying a temperature-sensitive mutation in Egfr in trans to a loss-of-function allele ( referred to as Egfrts ) . When shifted to the restrictive temperature , these testes displayed the previously-described phenotype of a block in germ cell development , resulting in many small germ cells throughout the testes and a complete lack of differentiated spermatid fibers ( Fig 3A and 3B and [27 , 30] ) . Notably , these testes also have fewer somatic cells near the hub ( Fig 3A and 3B ) . We labelled these somatic cells with Zfh1 to mark the CySCs and their offspring and with Eya to mark differentiated somatic cells . In Egfrts testes , Eya expression was observed in somatic cells adjacent to the hub , suggesting that CySCs differentiate early in the absence of MAPK signaling ( Fig 3B , arrows ) , corroborating prior observations [30 , 32] . We counted the number of CySCs ( defined as Zfh1-positive , Eya-negative cells ) in these samples and found that there were significantly fewer CySCs in Egfrts testes compared to control ( Fig 3G , S1 Table , P<2 . 7x10-12 ) . It was important to exclude Eya-expressing cells from the Zfh1 pool in this analysis because a prior study using only one somatic marker did not note a difference in somatic cell number when MAPK was decreased [32] . To determine whether the requirement for MAPK in maintenance of CySCs is autonomous to the somatic lineage , we used Tj-Gal4 to over-express an RNAi against MAPK ( Fig 3D ) . These testes displayed a phenotype similar to that which we observed in testes in which the entire animal was mutant for Egfr ( Fig 3B ) . In Tj>MAPK RNAi testes , Eya-expressing cells were present close to the hub ( Fig 3D” , arrow ) . However , in control Tj>+ testes , Eya-expressing cells were located several cell diameters from the hub ( Fig 3C” , arrow ) . We counted significantly fewer Zfh1-positive , Eya-negative CySCs when MAPK signaling was inhibited autonomously within the somatic lineage ( Fig 3H , S1 Table , P<2 . 3x10-12 ) . Moreover , as expected , proper germ cell development was inhibited in Tj>MAPK RNAi testes , resulting in an accumulation of small early germ cells ( Fig 3F ) . Conversely , when we hyper-activated MAPK in somatic cells by over-expressing λTop or RlSEM , there were significantly more Zfh1-expressing , Eya-negative cells ( Fig 3H , S1 Table , P<1 . 4x10-11 for UAS-λTop and P<1 . 0x10-7 for UAS-RlSEM ) . Taken together , these experiments indicate that MAPK acts autonomously within the somatic lineage to regulate CySC numbers . As not all CySCs were lost when we inhibited MAPK within the whole somatic lineage , we generated mutant clones for components of the MAPK pathway to determine whether CySC clones with compromised MAPK signaling were able to self-renew . We generated both positively-marked and negatively-marked clones of several alleles of Egfr and the Drosophila Ras , Ras85D , and scored the presence of marked stem cells at 2 dpci , to verify that mutant clones could be induced , and at 7 dpci , to assess the ability of the mutant clones to self-renew at the niche . Egfr or Ras85D mutant GSCs were recovered and maintained at similar rates to control GSCs ( Fig 4F , Table 1 for negatively-marked clones , S2 Table for positively-marked clones ) . However , CySCs mutant for Egfr or Ras85D were recovered less well at 2 dpci and were not maintained by 7 dpci ( Fig 4F , Tables 1 and S2 ) . We note that another group reported that Ras85DΔC40B null mutant clones were recovered at higher rates than control clones [17] . However , our results that CySCs lacking Ras85D function do not self-renew are supported by our use of multiple alleles of several pathway components ( see below ) . Moreover , our results are consistent with prior reports that persistent Egfr or raf mutant somatic clones were not recovered [27 , 30] . We determined the fate of the clones that were induced but not recovered at later time points . We were able to detect Egfr mutant clones that expressed the differentiation marker Eya by 2 dpci ( Fig 4E , arrow marks a positively-labeled clone ) , suggesting that Egfr mutant clones differentiate rapidly . Thus , our results indicate that MAPK activity is required autonomously in CySCs for self-renewal and that MAPK-deficient stem cells are rapidly lost from the niche and differentiate . The fact that somatic knock down of MAPK reduced CySC numbers by ~45% ( Fig 3H ) , whereas all MAPK pathway mutant clones were lost , strongly suggests that CySCs lacking MAPK activity are primarily lost as a result of competition by their wild type CySC neighbors . Therefore , we conclude that MAPK signaling regulates the ability of CySCs to compete for space at the niche . We have shown that Socs36E represses MAPK activity in addition to its known role in repressing JAK/STAT signaling . Therefore , we sought to clarify the relationship between Socs36E and these two signaling pathways in CySC self-renewal , in particular to establish which pathway was the functionally relevant target of Socs36E regulation . We used the MARCM technique to generate GFP-expressing clones lacking both Socs36E and MAPK activity and monitored the ability of these clones to self-renew and compete with wild type CySCs for niche occupancy . CySCs mutant for either of two alleles of Socs36E ( Socs36EEY and Socs36EPZ ) self-renewed better than control clones at all time points , indicating that they are less likely to be lost through neutral competition ( Fig 5J and 5K , Table 2 and [18] ) . We generated control clones expressing a dominant-negative form of Ras ( RasN17 ) and determined that , while they were recovered at 2 dpci , few marked CySCs were found at 7 dpci ( Fig 5A and 5J , Table 2 , Fisher’s exact test P<0 . 0001 , FRT40A , UAS-RasN17 compared to control FRT40A ) , consistent with our earlier observations that MAPK-deficient clones cannot be maintained in the niche . Surprisingly , Socs36E mutant clones expressing RasN17 were able to self-renew and were recovered robustly at 7 dpci ( Fig 5B , arrows , Fig 5J , Table 2 , Fisher’s exact test P<0 . 0001 , Socs36EEY FRT40A , UAS-RasN17 compared to FRT40A , UAS-RasN17 ) . One caveat of this experiment could be that MAPK activity was incompletely blocked by expression of RasN17 . Therefore , we generated clones that were doubly mutant for Socs36E and a component of the MAPK pathway . Sos encodes a Ras GEF and is located on the same chromosome arm as Socs36E ( 2L ) , enabling the generation of clones doubly mutant for Socs36E and Sos ( i . e . , Socs36E mutant cells that are deficient for MAPK signal transduction ) . First , we confirmed that Sos mutant clones were unable to self-renew using two independent alleles ( Fig 5C , 5D and 5K , Table 2 ) . In both cases mutant clones were induced and observed at 2 dpci ( Fig 5C , arrow ) , but no mutant CySCs were recovered at 7 dpci ( Fig 5D ) , suggesting that like other components of the MAPK pathway , Sos is required for CySC self-renewal . To verify that the lack of self-renewal observed in Sos mutant clones was due to loss of MAPK activity in these cells , we generated Sos mutant MARCM clones in which we over-expressed RasV12 , a dominant-active form that acts downstream of Sos . CySCs of this genotype were recovered at 60% of control rates , compared to 0% for Sos alone ( Fig 5E , arrows , Table 2 , Fisher’s exact test P<0 . 0001 , sosx122 FRT40A , UAS-RasV12 compared to sosx122 FRT40A ) , indicating that increasing MAPK pathway activity downstream of Sos is sufficient to rescue self-renewal in CySCs . We took advantage of the possibility of rescuing Sos mutant CySCs to determine whether cell death played a role in eliminating clones lacking MAPK activity . We expressed the baculovirus caspase inhibitor P35 to prevent apoptosis in Sos mutant MARCM clones and scored for CySC clones at 7 dpci . Blocking apoptosis did not increase recovery of Sos mutant CySCs ( Table 2 ) . Thus , caspase-dependent cell death cannot account for the loss of MAPK signaling-deficient CySCs . Next we analyzed CySC clones that were doubly mutant for Socs36E and Sos ( Fig 5F , 5I and 5K , Table 2 ) . These mutant CySC clones were readily recovered at 7 dpci ( Fig 5F , arrow , Fisher’s exact test P<0 . 0001 , Sosx122 , Socs36EPZ FRT40A compared to Sosx122 FRT40A ) , like CySCs lacking Socs36E and over-expressing dominant-negative RasN17 . These clones persisted for at least 2 weeks ( Fig 5I and 5K ) , indicating long-term stem cell function . However , we noted an important difference between Socs36E single mutant and Sos , Socs36E double mutant CySC clones . Socs36E single mutant clones maintained constant clone recovery rates , indicating that they have a robust ability to bias neutral replacement and colonize the niche ( Fig 5K , Table 2 ) . Indeed , by 14 dpci , most Socs36E clones had entirely replaced all wild type CySCs at the niche ( 33/42 clones were fixed , meaning that they had colonized the entire niche , Fig 5H and S3 Table ) . However , recovery rates of Sos , Socs36E double mutant clones decreased over time , similar to the normal turnover observed in control clones ( Fig 5K , Table 2 , Fisher’s exact test at 7 dpci P = 0 . 0026 , Sosx122 , Socs36EPZ FRT40A compared to Socs36EPZ FRT40A ) . In contrast to Socs36E mutant clones , Sos , Socs36E double mutant CySCs were not able to outcompete their neighbors and few mutant CySCs were present at the niche at 14 dpci . In these testes , wild type CySCs outnumbered mutant CySCs , and no Sos , Socs36E clones were fixed , indicating that they had not colonized the niche ( Fig 5I , S3 Table , Fisher’s exact test for fixed clones P<0 . 0001 , Sosx122 , Socs36EPZ FRT40A compared to Socs36EPZ FRT40A ) . Finally , we note that as in the case of Hh- and Yki-induced competition , the CySC-CySC competition caused by Socs36E mutation could be suppressed by removing one copy of string ( stg ) , which encodes the Drosophila Cdc25 protein and is a limiting factor for entry into mitosis [33] . Whereas 79% of Socs36E clones were fixed at 14 dpci , only 45% of Socs36E clones were fixed when stg was reduced ( S3 Table , Fisher’s exact test for fixed clones P = 0 . 0183 , Socs36EPZ FRT40A compared to Socs36EPZ FRT40A; stg/+ ) . Consistent with their increased competitiveness towards CySCs , Socs36E single mutant clones also out-competed resident GSCs for niche space , significantly reducing GSC numbers ( S3 Table , 8 . 4 GSCs/testis with Socs36E mutant clones versus 13 . 9 GSCs/testis with control clones at 14 dpci , P<5 . 2x10-8 ) . As in the case of Hh- and Yki-induced CySC-GSC competition , the GSC reduction caused by Socs36E mutant CySCs could be suppressed by removing one copy of string ( stg ) ( S3 Table , 12 GSCs/testis for Socs36E clones in a stg/+ background vs . 8 . 4 GSCs/testis for Socs36E clones in a background that was wild type for stg , P<0 . 00032 ) . In contrast to Socs36E single mutant clones , the Sos , Socs36E double mutant clones did not out-compete GSCs ( S3 Table , P<0 . 17 ) . Notably Sos , Socs36E double mutant CySCs displayed elevated levels of stabilized Stat92E protein ( Fig 5G , arrow ) , indicating that the JAK/STAT pathway was activated in these cells . This latter observation suggests that elevating JAK/STAT signaling is not sufficient to confer competitive ability on CySCs , consistent with our prior clonal results [18] . Next , we examined whether JAK/STAT pathway activity was required for self-renewal and/or competitiveness downstream of Socs36E . Unfortunately , there is no known JAK/STAT pathway component encoded by a gene on chromosome 2L , precluding double mutant analysis . However , we used the JAK/STAT target and effector chinmo , located on 2L , as a proxy for JAK/STAT activity in CySCs [34] . As previously described , chinmo mutant CySC clones were unable to self-renew and were likely out-competed by wild type neighbors ( S2B and S2D Fig and [34 , 35] ) . Importantly , chinmo Socs36E double mutant clones were recovered frequently ( S2C and S2D Fig ) , indicating that removing Socs36E from chinmo mutant CySCs restored their ability to compete with neighbors . Additionally , these double mutant clones over-proliferated and formed ectopic masses of somatic cells ( S2C Fig , arrow ) , suggesting they were mis-specified , consistent with work showing that chinmo is required to maintain the male identity of CySCs [35] . In order to assess directly the role of JAK/STAT signaling downstream of Socs36E , we used the MARCM technique to inhibit pathway activity in Socs36E mutant clones . We used two approaches: first we expressed a dominant-negative form of the receptor Domeless ( Dome [36] ) , called DomeΔcyt , and second we expressed an RNAi transgene against the transcription factor Stat92E . In control clones , knocking down JAK/STAT activity with either approach led to a marked loss of self-renewal: by 7 dpci very few clones expressing DomeΔcyt were maintained and clonal depletion of Stat92E was sufficient to abolish self-renewal ( Fig 6A , 6B and 6H , Table 2 ) . Socs36E mutant CySCs that expressed DomeΔcyt were recovered with high frequency , similar to controls ( Fig 6C , arrows , Fig 6H , Table 2 , Fisher’s exact test P<0 . 0001 , FRT40A , UAS-DomeΔcyt compared to Socs36EEY FRT40A , UAS-DomeΔcyt ) . Socs36E mutant CySCs depleted for Stat92E were recovered at 7 dpci ( Fig 6D , arrows , Fig 6H , Table 2 , Fisher’s exact test P<0 . 0001 , FRT40A , UAS-Stat92E RNAi compared to Socs36EEY FRT40A , UAS-Stat92E RNAi ) , albeit at rates that were lower than control clones . Since control clones lacking Stat92E were never covered at 7 dpci , it is notable that removing Socs36E from these cells resulted in a moderate but significant rescue of self-renewal . The more robust rescue of self-renewal of Socs36E , UAS-DomeΔcyt CySCs could be due to incomplete pathway inhibition . To address this possibility , we tested whether Stat92E activity was indeed lacking in these clones by staining testes carrying Socs36E , UAS-DomeΔcyt or Socs36E , UAS-Stat92E RNAi clones with an antibody against stabilized , activated Stat92E [34] . In Socs36E mutant clones alone , as expected , we observed increased Stat92E protein ( Fig 6E’ , arrow , compare with wild type CySC , arrowhead ) . In most Socs36E mutant CySCs expressing DomeΔcyt , we observed a lack of Stat92E staining ( Fig 6F’ , arrow ) . However , a few of these CySCs displayed reduced but detectable Stat92E immunoreactivity ( Fig 6F’ , arrowhead ) , suggesting that there may be residual JAK/STAT signaling in these clones . However , in Socs36E mutant CySCs expressing Stat92E RNAi , we never observed any Stat92E protein ( Fig 6G’ arrow , compare with wild type CySCs , arrowheads ) , indicating robust inhibition of JAK/STAT signaling in these clones . Although we cannot exclude the possibility that there may be some remaining Stat92E protein below the threshold of detection , these results suggest that Socs36E mutant CySCs are capable of renewing in the absence of Stat92E , although at reduced rates .
The data presented here implicate MAPK signaling as a major regulator of CySC competition for niche access and establish that the competitiveness of CySCs lacking Socs36E is derived primarily from their increased MAPK activity . The ability of a stem cell to self-renew reflects not only intrinsic properties but also extrinsic relationships with its neighbors [37] . For instance , if a cell is unable to compete for space at the niche then it will be no longer able to receive short-range niche signals and will be more likely to differentiate . Conversely , if a cell is more competitive for niche space , this cell and its offspring will replace wild type neighbors and colonize the entire niche [18 , 38 , 39] . Our data show that CySCs with increased MAPK signaling out-compete neighboring stem cells in CySC-CySC as well as CySC-GSC competition and that CySCs with reduced MAPK activity are themselves out-competed . We favor the interpretation that MAPK regulates primarily competitiveness rather than self-renewal because while MAPK mutant clones are lost from the niche , lineage-wide inhibition of the pathway does not result in a complete loss of stem cells . This contrasts with the role of JAK/STAT signaling in CySCs . Stat92E mutant CySCs are lost and lineage-wide pathway inhibition results in pronounced and rapid stem cell loss [8 , 12 , 13 , 40] . Based on these results , we argue that JAK/STAT signaling in CySCs primarily controls their intrinsic self-renewal capability while MAPK signaling regulates their competitiveness . Interestingly , there are important similarities between Hh and MAPK function in CySCs in that CySCs lacking Hh signal transduction are out-competed and those with sustained Hh activity out-compete wild type neighbors [14 , 15 , 18] . Lastly , we note that CySCs mutant for the tumor suppressor Hippo ( Hpo ) ( which leads to sustained Yki activation ) or Abelson kinase ( Abl ) also have increased competitiveness [18 , 19] , suggesting the existence of multiple inputs controlling the ability of stem cells to stay in the niche at the expense of their neighbors . In the future , it would be interesting to determine if genetic hierarchies exist between competitive pathways or if they independently converge on similar targets . One outstanding question is how altering the competitiveness of CySCs affects the maintenance of the germ line . In the case of Socs36E , MAPK , Hh and Hpo , the competitive CySC displaces not only wild type CySCs but also wild type GSCs ( this study and [16 , 18] ) . While these observations suggest that out-competition of CySCs and GSCs is linked , the result that Abl mutant CySCs only compete with CySCs and not with GSCs indicates that these two competitive processes are separable genetically [19] . It is well established that Egfr/MAPK signaling is required in somatic cells for their proper differentiation and for their encystment of the developing germ line [26 , 27 , 30 , 31] . In this study , we identity an additional function for Egfr/MAPK in the somatic stem cells , specifically that this pathway regulates competitiveness of CySCs , with each other and with GSCs . Regarding the latter , it is possible that the loss of GSCs when somatic cells have high MAPK signaling is linked to their possibly increased encystment by these cells . Indeed , recent work has shown that Egfr activity in CySCs regulates cytokinesis and maintenance stem cell fate in GSCs [41] . It is tempting to speculate that increased somatic Egfr activity leads to increased encystment of GSCs and loss of stem cell fate in GSCs . MAPK may play a conserved role in niche competitiveness as mouse intestinal stem cells that acquire activating mutations in Ras bias normal stem cell replacement dynamics and colonize the niche [38 , 39] . Interestingly , the activating ligand Spi is produced by germ cells [26 , 27] , suggesting that the germ line coordinates multiple behaviors in the somatic cell lineage . In addition to transducing signals from the germ line , CySCs also receive ligands from hub cells ( including Hh and the JAK/STAT ligand Upd ) and they have to integrate these various stimuli . If unmitigated , the combined effect of all of these signals could produce highly competitive CySCs , with overall negative effects on niche homeostasis . Our data are consistent with a model in which the induction of Socs36E by the primary self-renewal pathway ( JAK/STAT ) results in the restraint of a competitive trigger ( MAPK ) in CySCs . In this way , Socs36E acts to integrate signals from different sources and maintain homeostatic balance between resident cell populations that share a common niche ( Fig 7 ) .
For a full list of genotypes , see S1 Text . The following stocks are described in fFybase ( flybase . org ) : Egfrtsla; FRT42D EgfrIK35 ( Egfrf2 , Flybase ) ; FRT82B Ras85Dx7b; FRT82B Ras85D ΔC40B; Sosx122 FRT40A ( gift of N . Baker ) ; chinmo1 FRT40A; Socs36EPZ1647 FRT40A ( referred to as Socs36EPZ , gift of E . Matunis ) ; Socs36EEY06665 FRT40A ( referred to as Socs36EEY ) ; Traffic jam ( Tj ) -Gal4; UAS-RlSEM;UAS-λTop 4 . 4 [42]; UAS-Rl RNAi ( MAPK RNAi , VDRC#43123 ) ; UAS-Ras85DN17 ( gift of D . Montell ) ; UAS-DomeΔcyt ( gift of J . Hombria ) ; UAS-Stat92E RNAi ( BL# 33637 ) . FRT42D Egfr124A and Sose26D FRT40A were gifts of J . Treisman . Egfrts is Egfrtsla in trans to Egfr124A . The Sosx122 Socs36EPZ1647 FRT40A double mutant was generated by recombination and the presence of both mutations confirmed by the lack of complementation with Sose26D and the presence of β-gal . Crosses were maintained at 25°C except Tj-Gal4 crosses , which were raised at room temperature and males were shifted to 29°C after eclosion for 10 days to achieve maximum Gal4 activity . For Figs 1C–1F , 4A–4D and 4F , negatively marked clones were generated by FLP/FRT [43] and clones were scored by the absence of GFP . For Figs 2D , 2E , 2G , 4E , 5 and 6 , positively marked clones were generated by the MARCM technique [29] and clones were scored by the expression of GFP . All clones were induced randomly using hs-FLP by heat shocking males at 37°C for one hour . Since these techniques rely on mitotic recombination , within the somatic lineage , clones can only be induced in CySCs , which are the only mitotic somatic cells . Within the germ-line , clones can be induced in GSCs and their transit-amplifying offspring , but only GSC clones will persist . Statistical analyses were carried out using Graph Pad Prism and MS Excel . Immunohistochemistry was performed as previously described [34] , except in the case of dpERK antibody , for which testes were dissected and fixed in 10 mM Tris-HCl , pH 6 . 8 , 180 mM KCl , 50 mM NaF , 10 mM NaVO4 and 10 mM β-glycerophosphate as described in [26] and then treated as in the case of other antibodies . We used the following primary antibodies: guinea pig anti-Tj ( 1:3000 , gift of D . Godt ) , rabbit anti-Zfh1 ( 1:5000 , gift of R . Lehmann ) , rabbit anti-Stat92E ( 1:1000 ) , rabbit anti-Phospho-p44/42 MAPK ( Erk1/2 ) ( Thr202/Tyr204 ) ( 1:200 , Cell Signaling #9101 ) , mouse anti-βPS Integrin , mouse anti-Eya , mouse anti-Fas3 ( all 1:20 , Developmental Studies Hybridoma Bank ( DSHB ) , created by the NICHD of the NIH and maintained at The University of Iowa , Department of Biology , Iowa City , IA 52242 ) , goat anti-Vasa ( 1:100 , Santa Cruz Biotechnology ) , rabbit anti-GFP ( 1:500 , Life Technologies ) , chicken anti-GFP ( 1:500 , Aves Labs ) . | Niches are specialized local environments that support stem cell self-renewal through the local production of short-range signals . In many tissues , resident stem cells compete with each other for niche access . Stem cells that receive multiple self-renewal cues have to integrate these discrete signals to prevent excessive competition or premature differentiation . Negative feedback inhibition between signaling pathways can provide a cohesive node through which such signals are interpreted . Here we show that a negative feedback inhibitor of the JAK/STAT pathway ( Socs36E ) acts as a brake on a second signaling pathway ( MAPK ) . Stem cells lacking Socs36E have sustained MAPK signaling , which endows these cells with superior competitive skills that allow them to displace their normal neighbors and take over the entire niche . Our results show that dampening niche-derived signals is critical in maintaining balance in stem cell niches . | [
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"tec... | 2016 | Socs36E Controls Niche Competition by Repressing MAPK Signaling in the Drosophila Testis |
It is estimated that a large proportion of amino acid substitutions in Drosophila have been fixed by natural selection , and as organisms are faced with an ever-changing array of pathogens and parasites to which they must adapt , we have investigated the role of parasite-mediated selection as a likely cause . To quantify the effect , and to identify which genes and pathways are most likely to be involved in the host–parasite arms race , we have re-sequenced population samples of 136 immunity and 287 position-matched non-immunity genes in two species of Drosophila . Using these data , and a new extension of the McDonald-Kreitman approach , we estimate that natural selection fixes advantageous amino acid changes in immunity genes at nearly double the rate of other genes . We find the rate of adaptive evolution in immunity genes is also more variable than other genes , with a small subset of immune genes evolving under intense selection . These genes , which are likely to represent hotspots of host–parasite coevolution , tend to share similar functions or belong to the same pathways , such as the antiviral RNAi pathway and the IMD signalling pathway . These patterns appear to be general features of immune system evolution in both species , as rates of adaptive evolution are correlated between the D . melanogaster and D . simulans lineages . In summary , our data provide quantitative estimates of the elevated rate of adaptive evolution in immune system genes relative to the rest of the genome , and they suggest that adaptation to parasites is an important force driving molecular evolution .
Hosts face an ever-changing array of parasites to which they must adapt , and parasites are widely believed to be one of the most important and universal selection pressures in natural populations . Consistent with this view , immune genes in several taxa are known to evolve faster than other genes , and sometimes significantly faster than the neutral rate – a signature of adaptive evolution [1] , [2] , [3] . Indeed , many studies of one or a few immune genes have identified the action of positive selection in Drosophila , including Relish [4] , the Scavenger Receptors [5] RNAi genes [6] , TEPs [7] , Persephone [8] and others [2] . More recently , complete genome sequencing of multiple Drosophila species found that immune-related genes have high rates of amino-acid substitution , and are more likely to show evidence of adaptive evolution than other genes [1] , [9] . Here we go beyond the yes/no detection of selection , to quantify the additional adaptation that occurs in proteins of the immune system over and above that which occurs in the rest of the genome . The rate at which natural selection fixes new mutations can be estimated by comparing the amount of polymorphism within populations to divergence between species at synonymous and nonsynonymous sites [10] , [11] , [12] , [13] , [14] . Approaches of this kind have been used to estimate the genome-wide rate of adaptive evolution , and found that it is often surprisingly high [10] , [13] , [15] , [16] , [17] . However , the nature of the selection pressures underlying this evolution remains unknown . One approach to answering this question is to compare estimated rates of adaptive evolution between proteins with different functions . Moreover , focussing on genes where we have a strong expectation of elevated positive selection also has a further benefit; there is an ongoing debate about the extent to which the high genomic estimates represent artefacts of processes such as population demography [15] , [18] , [19] , and testing the a priori hypothesis that immunity genes will have increased adaptive rates can address this issue . To assess the role of pathogens and other parasites as a cause of molecular evolution we have resequenced population samples of most of the best-characterised immunity genes in the Drosophila melanogaster genome , together with position-matched ‘control’ genes with no known immune function . This provides a quantitative estimate of the impact of parasite-mediated selection on the rate of adaptive evolution , and suggests that immunity genes have double the genome-average rate ( Figure 1 ) . We found that this was not caused by a generally elevated rate in immunity genes . Instead , most immunity genes show similar rates of adaptive evolution to the rest of the genome , with only a small subset evolving under very intense selection ( Figure 2 ) . These genes tend to be concentrated in a few pathways , which we argue are likely to be hotspots of host-parasite coevolution ( Figure 3 ) . Interestingly , these pathways are known to be suppressed by pathogens , and this suggests that active parasite-suppression of the immune system is an important cause of this adaptive evolution . Furthermore , when independent lineages are compared , similar genes show accelerated rates of adaptation ( Figure 4 ) . This suggests that despite their dynamic nature , host-parasite interactions may create similar selective pressures in related species , leading to replicable signatures at the molecular level .
The proportion of amino acid substitutions that were fixed by natural selection ( denoted α ) can be estimated using extensions of the McDonald-Kreitman test [16] , which compares non-synonymous and synonymous changes , and contrasts within-species polymorphism to fixed differences between species . We have extended existing maximum likelihood approaches [15] , [23] , [24] to estimate separate α values for immunity and non-immunity genes , and for different classes of immunity genes ( see Materials and Methods ) . We found that the proportion of substitutions attributable to positive selection in immune genes is approximately 50% greater than the genome average . Based on the divergence between D . simulans and D . melanogaster and polymorphism in Kenyan populations of both species , we estimated that 65% of amino acid substitutions in immunity genes have been fixed by selection ( 95% bounds bootstrapping across genes within categories: 55–72% , Figure 1A ) . This is significantly higher than our estimate for non-immunity genes , which is very close to previous genome-wide estimates ( reviewed in [10] ) ( α = 41%; 95% bounds are 31–50%; difference from immunity genes: p = 0 . 004 , inferred by bootstrapping ) . The effect remained highly significant when data from all populations were combined , though absolute estimates of α were slightly lower ( immune: α = 58%; non-immune: α = 33%; p = 0 . 004; Figure S10 ) . Since the exclusion of rare variants led to slightly higher estimates of α ( Figure S16 ) , this effect is probably caused by the enlarged sample size containing a higher proportion of ( low-frequency ) mildly-deleterious non-synonymous variants , which can cause α to be underestimated [23] . Estimates of α in the Greek ( Athens ) populations had greater variance and failed to detect a significant difference between immunity and non-immunity genes ( Figure S10B ) , as might be expected because the relatively low genetic diversity of this population means we have little statistical power to accurately infer α [14] . The proportion of amino acid substitutions fixed by selection ( α ) will clearly be affected by the number of substitutions not fixed by selection , i . e . , the number of effectively neutral substitutions fixed through genetic drift . Therefore , it is possible that the higher α of immunity genes does not reflect any increase in the absolute number of adaptive substitutions per non-synonymous site ( denoted a [16] ) . This possibility has been little explored , because a , unlike α , is difficult to estimate as a multi-gene average , and because single-gene estimates of either statistic tend to be imprecise . Here we use an approach that allows us to obtain relatively stable estimates of a for individual genes ( see Materials and Methods ) , which can then be averaged across immune and non-immune genes . Using Kenyan populations of D . melanogaster and D . simulans , we estimated that since their common ancestor , selection has fixed an average of 10 . 6×10−3 adaptive substitutions per non-synonymous site in immunity genes , but only 5 . 7×10−3 in other genes ( difference between immunity and control genes: p = 0 . 02; Figure 1B ) . This difference in the absolute number of adaptive substitutions corresponds to 50% increase in the proportion ( α ) described above , and suggests that natural selection is fixing adaptive substitutions in immunity genes at nearly double the genome average rate . The high rate of adaptive evolution that we found in immunity genes could be driven either by a general elevation in the strength of selection across all immunity genes , or by a few key genes experiencing intense selection pressures . To investigate this , we examined the distribution of a across genes . Although mean a is higher for immunity genes than other genes ( Figure 1B ) , the modal class is the same , i . e . , centred on zero in both cases ( Figure 2A versus Figure 2B ) , and the difference in mean is driven by a subset of immune genes with unusually high a ( Figure 2C; this results in a significantly higher variance for immunity genes ) . The wider distribution of a across immunity genes suggests that most of these genes experience similar selection pressures to the rest of the genome , while a small subset are under substantially stronger selection . This is consistent with the analyses of D . simulans genome sequences that found little evidence that immunity genes as a group are outliers in terms of recurrent adaptive evolution [17] . Thus it appears that host-parasite arms races may involve a relatively small subset of the immune system . This analysis could be confounded if our estimates were less accurate for immune genes than control genes , but this is unlikely for two reasons . First , the immunity genes tend to be longer than control genes , which will reduce the variance of a estimates and make our analysis conservative ( Figure 2C ) . Second , the pattern remains significant and quantitatively almost identical if the analysis is restricted to genes with more than 500 non-synonymous sites ( Figure S17 , S18 ) . Clues as to the nature of the selection pressures acting on immune genes can be gained from looking at which functional classes of immune gene are experiencing the strongest selection [1] , [2] . To examine how selection pressures differ between immune genes with different functions , we classified the genes in two different ways . First , we classified genes according to the branch of the immune system in which they function: the humoral , cellular , melanisation and antiviral RNAi responses . We found little variation between the first three categories ( α = 51% , 62% and 63%; per-site a = 0 . 009 , 0 . 010 and 0 . 012 , respectively ) , and individually no category was significantly different from non-immunity genes ( Figure 1A and Figure 1B ) . However , RNAi genes were an exception to this , showing approximately twice the proportion of adaptive substitutions as compared to non-immune genes ( α = 88% vs . 41%; p<0 . 001 ) , and seven times the number of adaptive substitutions per site ( a = 0 . 042 vs . 0 . 0057; p<0 . 001; Figure 1 ) . This is consistent with previous results , which found that some RNAi genes evolve rapidly under positive selection [6] , [25] . Second , we classified immune genes ( excluding those involved in RNAi ) according to their mode of action: pathogen recognition , signalling cascade , and antimicrobial peptides ( AMPs ) . This categorisation gave a superior fit to the data according to model selection techniques ( see Materials and Methods , and Table S2 ) and was also a significantly better fit than randomly assigning genes to categories of the same size ( randomization test: p<10−3 ) . Using this alternative categorisation , no group was significantly higher than non-immune genes , although signalling molecules did have a marginally higher α but not a ( estimated α = 57% vs . 41%; p = 0 . 085 ) . Consistent with previous results [26] , [27] , AMPs showed no evidence of adaptive evolution ( were not detectably different from α = 0; Figure 1A ) , undergo significantly less adaptive evolution than RNAi , signalling and cellular recognition genes ( p<0 . 014 in each case ) , and undergo marginally less adaptive evolution than non-immune genes ( estimated α = −13% vs . 41%; p = 0 . 082 ) . Alternative analyses using other populations and outgroups resulted in a qualitatively identical pattern ( Figures S10 , S11 , S12 , S13 , S14 , S15 ) , except that the use of D . yakuba as an outgroup resulted in the signalling molecules having a significantly higher α than the controls ( p<0 . 031; Figure S14A and S14B ) . Because the high rate of adaptive evolution in immune system genes is caused mainly by a subset of genes under very strong selection ( Figure 1 and Figure 2 ) , we investigated how these genes are distributed across the immune system ( Figure 3 ) . The two main signalling pathways in the immune system are the Toll and IMD pathways , and of these the IMD pathway has a much higher rate of adaptive evolution than the Toll pathway ( IMD: mean estimated a = 0 . 023; Toll: mean a = 0 . 009; difference between Toll and IMD p = 0 . 039 by bootstrapping within classes ) . Within the Toll pathway , the extracellular molecules are under stronger selection than the cytoplasmic ones ( extracellular: mean a = 0 . 015 , cytoplasmic: mean a = 0 . 005 , p = 0 . 033 ) . The antiviral RNAi genes again show strong adaptive evolution [6] ( mean estimated a = 0 . 032 ) . Elsewhere , TEP I and PGRP-LD are also under exceptionally strong selection [1] , [7] . It has been suggested that the phagocytosis receptor Dscam , which can produce up to 18 , 000 differently spliced isoforms , may allow Drosophila to mount specific immune responses [28] , [29] . However , despite having over 22 kbp of coding sequence from Dscam , we were unable to find any evidence of adaptive evolution in this gene , indicating that this gene is not subject to arms-race selection . If the immune system adapts to parasites in similar ways in related species , then we would expect to see the same genes experiencing positive selection in different lineages [30] . Alternatively , each species could respond differently , resulting in different genes being positively selected in different lineages [30] . To address this question , we estimated the rate of adaptive evolution separately for each of the lineages leading to D . simulans and D . melanogaster from the common ancestor of the two species . The pattern of α ( and a ) across different pathways and functional categories of genes was very similar between the two lineages ( Figures S12 , S13 ) , suggesting that the broad distribution of selection pressures between immune functions is the same . For example , in both lineages antiviral RNAi genes have the highest rates of adaptive evolution and antimicrobial peptides have the lowest rates . Estimates of a along these individual lineages are associated with high levels of noise due to the short length of the branches; furthermore , the measurement error will be negatively correlated across the two lineages . Despite these sources of error , however , the data show a significant positive correlation in immunity gene a estimates between the two lineages ( Figure 4 ) , and this suggests that individual genes , and not just categories of gene , are under similar selection pressures in both lineages . This correlation was not significantly different to that that found in the non-immunity genes , indicating that there is no greater tendency for parasites to cause lineage specific selection than other selective agents ( Figure 4 ) . The analyses presented above can identify selection that has occurred over millions of years , but recent selective sweeps can also be detected though reductions in genetic diversity . In both D . melanogaster and D . simulans there was no significant difference in the diversity of synonymous sites ( πs ) between immunity and non-immunity genes ( Kenyan D . melanogaster: πs = 1 . 60% vs . 1 . 55%; Kenyan D . simulans: 2 . 46% vs . 2 . 62%; Figure S19 , Figure S20 , Table S3 ) . Furthermore , if the immune genes are split into functional categories , only the diversity of the antiviral RNAi genes is significantly lower than the control genes ( D . melanogaster πs = 0 . 80% , p<0 . 001; D . simulans πs = 1 . 01% , p<0 . 001 . Figure S19 , Figure S20 , Table S3 ) . This is consistent with RNAi genes having the highest rates of adaptive substitution in the immune system , and suggests a high proportion of them may have recently experienced selective sweeps in both species . Furthermore , none of the immune genes had unusually high levels of polymorphism , suggesting host-parasite coevolution in Drosophila has not resulted in the ancient polymorphisms like those seen in vertebrate MHC genes and some plant resistance genes [31] , [32] . It is known that flies are infected by different parasites in different populations , and this could lead to local adaptation where different alleles of a gene are favoured in different populations [33] , [34] , [35] , [36] , [37] . However , we could not detect any differences between immune genes and the controls in the amount of population structure in either D . melanogaster or D . simulans ( Figure S21 ) providing no evidence to suggest that local adaptation of immune genes is common . However , it should be noted that our statistical power to detect genetic structure may be extremely low , and the effects of local adaptation on patterns of nucleotide variation may be small [38] . We also compared the amino acid diversity ( πa ) of the immunity and control genes , as this may reflect differences in selective constraint or the effects of balancing selection . In all eight populations πa was slightly higher in the immune genes , and in three populations the difference was significant ( Figure S22 , Figure S23 , Table S3 ) . Compared to the control genes , immune signalling molecules tend to have lower amino acid diversity , while antimicrobial peptides and recognition molecules in the cellular immune system have significantly higher amino acid diversity ( Figure S22 , S23 ) . These differences correspond to the estimated number of substitutions occurring by genetic drift ( Figure S24 ) , but not to differences in πs , implying that they are caused by differences in selective constraint , rather than long-term balancing selection maintaining amino acid polymorphisms .
We have found that the rate of adaptive substitution in immunity genes is nearly double the genome average . This is the first quantitative estimate of the rate at which natural selection drives protein evolution in genes of the immune system relative to the genome as a whole , and confirms that adaptation to parasites is an important force driving evolution . There are several reasons why parasites may be a powerful selection pressure . Firstly , parasites can cause high rates of mortality and morbidity , and therefore have a large impact on the fitness of their hosts . Secondly , the direction of parasite-mediated selection continually changes , due to coevolutionary arms races between hosts and parasites [39] , and ecological factors altering the composition of the parasite community . Finally , parasites generally have shorter generation times , and ( in the case of viruses ) elevated mutation rates , potentially giving them an edge in the ‘arms-race’ . This means that hosts may often be maladapted to their current set of parasites , and therefore under strong selection to evolve resistance . We have also found that the high rate of adaptive substitution of immunity genes is driven by a small subset of immune genes under strong selection , while the majority of immunity genes have similar rates of adaptive evolution to the rest of the genome . This suggests that rapid ‘arms-race’ coevolution may only involve a small subset of molecules in the immune system . Since there is a tendency for these strongly-selected genes to cluster by pathway or protein-family , these clusters may reflect hotspots for coevolutionary interaction with parasites . By examining the function of these groups of strongly-selected genes , we can gain clues regarding the underlying molecular processes that drive this coevolution . It is striking that almost all of these genes fall within the IMD signalling pathway and the antiviral RNAi pathway ( Figure 3 ) . It is known that both signalling pathways and RNAi are targeted by parasite molecules that suppress the immune response , and it has been suggested that this suppression may cause much of the adaptive evolution seen in immunity molecules [1] , [2] , [4] , [25] , [40] . The Toll pathway tends to have lower rates of adaptive evolution . It is unclear why this is , although it may reflect the pathogens with which it interacts , or constraint from its other functions in development [41] . In contrast to the signalling pathways , the PGRPs and GNBPs that act as receptors for the Toll and IMD pathways are not positively selected , possibly reflecting their role in binding to highly conserved pathogen molecules [7] . Unlike many other organisms ( especially vertebrates [42] ) , AMPs in Drosophila show less adaptive evolution than most genes . This contrasts with the high rate of AMP gain and loss in the Drosophila phylogeny [1] , and suggests that whatever process favours the duplication of AMPs does not result in strong selection on their protein sequence . Our results also imply that AMPs may be weakly constrained , with genetic drift fixing amino acid substitutions at a relatively high rate . This may be a consequence of gene duplication , as duplicated genes often have elevated rates of amino acid substitution [43] . It is interesting to note that components of the antiviral RNAi pathway also mediate defence against transposable elements [44] , [45] , [46] , and these ‘genomic parasites’ may be an important selective force on these genes [25] . Indeed , several RNAi genes with no reported anti-viral function [25] , [47] , [48] , and other genes involved in chromatin function [17] , show evidence of rapid adaptive evolution in Drosophila . At the phenotypic level , many organisms show evidence of convergent evolution , with different species evolving similar adaptations in response to similar selection pressures . However , it is unclear whether convergence is also common in molecular evolution , or whether molecular evolution is idiosyncratic , with each species following a unique evolutionary pathway [30] . One way to address this question is to test whether the same genes are evolving adaptively in different species [30] . At a broad level , we found that similar functional classes of immunity genes tend to have elevated rates of adaptive evolution in both the D . melanogaster lineage and the D . simulans lineage . At a finer scale , the rate of adaptive evolution of individual genes is correlated in the two lineages ( despite the very high levels of noise associated with these single-lineage estimates ) . Because this correlation was not significantly different in immunity genes and our control genes , this suggests the fluctuating selection pressures associated with host-parasite coevolution do not result in unusually high rates of lineage-specific selection . Together these results suggest that the immune system of these two closely related species experience similar selection pressures , and adapt to those selection pressures in similar ways . Previous studies on immunity genes have applied various tests of adaptive evolution , and found that a higher than average fraction of immunity genes test ‘positive’ ( e . g . , [1] , [2] ) . However , the statistical power of these tests will depend on factors such as selective constraint and gene length , and these could differ between immunity and non-immunity genes , even if their rates of adaptive substitution were identical . Furthermore , such confounding factors will be even more important if adaptive substitution is frequent across the genome , meaning that a large proportion of all genes evolve under some degree of positive selection [10] . Therefore a particular strength of the current approach , which can compare the estimated rates of adaptive evolution across different groups of genes , is that it provides quantitative estimates of the effect size rather than simply counting the number of ‘significant’ tests . Estimates of the rate of adaptive substitution based on the McDonald-Kreitman test have been subject to some recent criticism as they can be influenced by factors such as population demography [18] , [19] . However , it seems unlikely the differences observed here are artefacts . First , we compared loci where we have a strong a priori expectation of adaptive substitution to position-matched control loci . Second , we found no significant differences in the rate at which genetic drift causes non-adaptive evolution at these loci , such as could mislead the tests ( Figure S24 ) . Finally , false signatures of adaptive substitution can occur in populations that have experienced bottlenecks or recent expansions , and yet the signal we observed was much stronger in the ancestral Kenyan populations ( Figure S10A ) , and weakest in the more derived populations ( Figure S10B ) , while quantitative estimates of a differed surprisingly little between datasets . As new sequencing technologies result in ever larger datasets , this approach promises to be a powerful way to identify the selection pressures driving molecular evolution . Our data not only confirm that parasites are an important driving force in molecular evolution [1] , [2] , they quantify the magnitude of this effect , and show that the rate of adaptive protein evolution in immunity genes is nearly twice the genome average . This elevated rate in the immune system is due to a subset of genes evolving under intense positive selection , and many of these genes are strongly selected in both D . melanogaster and D . simulans , suggesting that our results may reveal general principles of immune system evolution . In particular , some of the most strongly selected genes may be targeted by parasite suppressors the immune response , and this may be a key battlefield in coevolution . These data add to the growing evidence that much adaptive protein sequence evolution is driven by co-evolutionary conflicts within or between genomes [49] , [50] .
Flies were sampled from six populations of D . melanogaster and two populations of D . simulans , covering both their original range in Africa and more recent global expansion . In each population we extracted genomic DNA from four female flies that were either collected from the wild or were the progeny of crosses between pairs of isofemale lines ( i . e . we sampled eight chromosomes from each population ) . Targeted genes were amplified by PCR in ∼5 kbp products , and the PCR products from each population were then mixed together , purified on a gel , and sequenced using the Solexa-Illumina sequencing platform to high coverage ( mean >130-fold; Figure S1 ) . The 36 bp sequencing reads were aligned to the D . melanogaster or D . simulans genome using MAQ [51] allowing for up to 2 mismatches per read , which resulted in 5–16 million mapped reads in each population . The sites were then assigned to coding or non-coding sequence using the genome annotation , and coding sites were classified as synonymous or non-synonymous . Positions with less than 20-fold coverage were excluded , as were genes represented by less than 100 bp; however , our results were not strongly affected by the exclusion of sites with less than 50-fold or 100-fold coverage ( Figure S25 ) . Full details of the Solexa-Illumina sequencing , together with a detailed comparison with traditional Sanger sequencing , are given in Text S1 . A full listing of loci , their positions and polymorphism counts are given in Table S1 . To estimate the rate of adaptive substitution , we used a multi-locus , maximum likelihood extension of the McDonald-Kreitman test . This method is based on Welch 2006 ( ref . [15] , see also [23] , [24] ) , but contains several new features and models . Software that implements the new methods is available on request from the authors , or from http://tree . bio . ed . ac . uk/software/ . We compared non-synonymous and synonymous divergence between D . melanogaster and D . simulans with polymorphism from both species . For each locus , the six observations ( dN , dS , and pN and pS for each species ) , were assumed to have the following expected values:where lS and lN are the number of synonymous and non-synonymous sites , λ = μt is the expected neutral divergence between the species , θi = 4Neμ is the expected neutral polymorphism for species i , ni is the number of alleles sampled for species i ( taken here to be 8 per sampled population ) , and f is the fraction of non-synonymous mutants that are effectively neutral [15] . The parameters of greatest interest here , α or a , quantify the multiplicative or additive deviation of the observed dN from its expectation under neutrality and purifying selection . Positive estimates of either α or a are consistent with adaptive protein evolution , while negative values result either from sampling error , or from the presence of mildly deleterious mutations ( which violate the assumptions of the test , contributing to pN but rarely reaching fixation [16] , [52] ) . This violation can be mitigated by excluding low frequency synonymous and non-synonymous polymorphisms , as this is expected to remove the great majority of mildly deleterious mutations while leaving the neutral pN/pS ratio unaltered [52] , [53] . To explore this phenomenon , we repeated our analyses excluding all putative polymorphisms with an estimated minor-allele frequency below a range of threshold frequencies ( Figure S3 ) . Our results were qualitatively unaltered , and so in the main text we report only results with all sampled polymorphisms included in the counts . To estimate the model parameters it was assumed that observed quantities were Poisson distributed around their expected values [15] , [23] , [24] . This distribution is derived under the assumption that substitutions and polymorphisms occur as independent events , but this assumption can be violated , e . g . , by linked selection causing the clustering of substitution events in time . We used three approaches to reduce the impact of such violations . First , for some parameter types ( selective constraint f and/or adaptive substitution a ) , we assigned separate parameters to each locus , making the extent of stochastic variation irrelevant to the parameter estimates obtained . Second , we obtained confidence intervals by bootstrapping across loci , rather than using the curvature of the likelihood surface . Third , we used model-selection criteria that allow for un-modeled over-dispersion ( such as that arising from the clustering of events in time ) . To avoid over-parameterization associated with assigning large numbers of locus-specific parameters , we assumed that λ ( the neutral mutation rate multiplied by divergence time ) took a single value across all loci . To model neutral polymorphism , we exploited the correlation between θ at a locus , and its local recombination rate [54] , by fitting the model θ = mr+b , where r is the local D . melanogaster recombination rate [55] . Maximum likelihood estimates of m and b were then obtained for each of the two species . This model has the advantage of providing appropriate estimates of θ for loci where the synonymous polymorphism is not at equilibrium , such as after a recent selective sweep . Model selection techniques ( see below ) also showed that it was significantly preferred to models in which θ did not vary between loci , and in which each locus had a separate parameter . Importantly , however , estimates of a were very similar under all three parameterizations ( Figure S26 ) . Given our chosen model , a data set of k loci was used to fit k+5 nuisance parameters , plus the a or α values of interest . To choose between different parameterizations of the likelihood model ( see Table S2 ) we used the Akaike Information Criterion , corrected for finite sample size and over-dispersion in the count data [56] . This criterion is given by QAICc = −2lnL/c+2K+K ( K+1 ) / ( n-K-1 ) where lnL is the maximized likelihood for the model , K is the number of parameters it contains , and n is the number of data points ( taken to be 6 times the number of loci ) . The factor c is the correction for overdispersion , and was estimated by c = ( 2lnLfull-2lnLsat ) /nfull , where “full” denotes the largest model in the set of models being compared , and “sat” denotes the saturated model , in which the expected values of all data points were set to their observed values . The conditional likelihood of each model was obtained by converting the QAICc values into Akaike weights [56] . To compare estimates of adaptive substitution along two independent lineages , we used a variant of the method above , including polymorphism from a single species , and polarizing substitutions on to the D . melanogaster or D . simulans branch based on the inferred ancestral sequence . Ancestral sequences were inferred using maximum likelihood under a codon-based model and the tree ( ( ( Dmel , Dmel ) , ( Dsim , Dsim ) ) , ( ( Dyak ) , ( Dere ) ) ) using PAML [57] . Genetic diversity was quantified in two ways . First , an estimate of θ derived from the number of polymorphic sites , calculated exactly as Watterson's θw under the assumption that all eight chromosomes in each population were sampled [58] . Although sites with low read depth may not sample all chromosomes , even at 20-fold coverage ( our minimum threshold for inclusion ) given equal representation of the chromosomes there is >90% chance that at least 7 of the 8 chromosomes have been sampled . Given the observed read depths this effect would lead us to underestimate Watterson's θ by less than 0 . 5% of its correct value for most loci ( Figure S9 ) . Second , an estimate of θ based on π ( the average number of pairwise differences per site ) was calculated from read frequencies ( rather than allelic frequencies ) at each site based on the assumption that read frequencies should reflect underlying allele frequencies . In fact , although significantly correlated , read frequencies do not provided a good estimate of allele frequencies in our data ( Pearson's ρ = 71; Figure S4 , see Text S1 for a full discussion ) . However , when averaged over multiple sites , π based on read-depth is extremely highly correlated with that based on true allele frequencies from Sanger sequence data , suggesting that this is an excellent measure of diversity ( Pearson's ρ = 0 . 90; Figure S26 ) . The degree of population structure was quantified using a sequence-based estimate of FST derived from πs calculated within and between populations: FST = ( πtotal–πsub ) /πtotal [e . g . 59] where πsub is the average genetic diversity of a gene within a population and πtotal is diversity across all populations . Averages across genes were calculated as the ratio between the mean of the numerator and the mean of the denominator for those genes , rather than the mean of the ratios . The significance of differences between classes of genes in FST and genetic diversity was assessed by bootstrapping . Genes were re-sampled with replacement within each category , and the statistic was recalculated 1000 times to produce a null distribution . | All organisms are attacked by an ever-changing array of pathogens and parasites , and it is widely supposed that the ensuing host–parasite “arms race” must drive extensive adaptive evolution in genes of the immune system . Here we have taken advantage of new sequencing technologies and analytical approaches to quantify the amount of adaptation that is occurring in immunity genes relative to the rest of the genome . We sampled two species of fruit fly ( D . melanogaster and D . simulans ) from eight different populations around the world , and sequenced 136 immunity and 287 non-immunity genes from these samples . Based on the differences in the sequences between the two species , and the genetic diversity within each species , we have estimated that natural selection drives twice as much change in immune-related proteins as in proteins with no immune function . Interestingly , the rate of adaptation is also more variable among immunity genes than among other genes in the genome , with a small subset of immunity genes evolving under intense natural selection . We suggest that these genes may represent hotspots of host–parasite coevolution within the genome . | [
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] | 2009 | Quantifying Adaptive Evolution in the Drosophila Immune System |
West Nile virus ( WNV ) , a zoonotic pathogen naturally transmitted by mosquitoes whose natural hosts are birds , has spread worldwide during the last few decades . Resident birds play an important role in flavivirus epidemiology , since they can serve as reservoirs and facilitate overwintering of the virus . Herein , we report the first experimental infection of magpie ( Pica pica ) with two strains of West Nile virus , lineages 1 ( NY-99 ) and 2 ( SRB Novi-Sad/12 ) , which are currently circulating in Europe . Magpies were highly susceptible to WNV infection , with similar low survival rates ( 30% and 42 . 8% ) for both lineages . All infected magpies developed viremia detectable at 3 days post-infection with titers above those necessary for successful transmission of WNV to a mosquito . Neutralizing antibodies were detected at all time points analyzed ( from 7 to 17 days post-infection ) . WNV genome was detected in the brains and hearts of all magpies that succumbed to the infection , and , in some of the surviving birds . WNV-RNA was amplified from swabs ( oral and cloacal ) at 3 , 6 and 7 days post-infection and feather pulps , from 3 to 17 days post-infection , of infected animals . Even more , infectious virus was recovered from swabs up to 7 days post-infection and from feather pulps up to 10 days post infection . Sham-infected control animals were negative for viremia , viral RNA , and antibodies . These results suggest that the magpie , which is one of the most abundant corvid species in Europe , could represent a source of WNV transmission for birds and humans . Our observations shed light on the pathogenesis , transmission , and ecology of WNV and can benefit the implementation of surveillance and control programs .
West Nile virus ( WNV ) is an arbovirus ( arthropod-borne virus ) belonging to the family Flaviviridae , genus Flavivirus , which is maintained in an enzootic cycle involving several species of birds and mosquitoes appertaining mainly to the Culex pipiens complex [1] . Occasionally , it can infect humans and horses , which are considered dead end hosts due to the low viremic titers that they develop , which are insufficient to infect mosquitoes and maintain the transmission cycle [2] . While the majority of human WNV infections are sub-clinical , approximately 20% of patients suffer West Nile fever with flu-like symptoms and 1% develop a severe neuro-invasive potentially fatal disease [1] . After reaching the American continent in 1999 , WNV spread around the globe , and is now considered the most widespread arthropod-borne virus in the world , and the main causative agent of arboviral encephalitis in the U . S . [3] . In Europe , only lineage 1 strains were circulating until isolation of a lineage 2 strain from a goshawk in Hungary in 2004 [4] . Since then , lineage 2 strains have been responsible for high bird mortality and dozens of human deaths in several Southeast European countries [5] . The pivotal role of migratory birds in spreading WNV infection is well documented [1] . Resident birds are also considered important in maintaining WNV circulation in nature [6] . In fact , WNV overwintering in Europe by local birds and mosquitoes has been suggested [4] . Pathogen transmission dynamics are related to host-feeding patterns of mosquitoes , resident or migratory bird behavior , and host susceptibility to the virus . For instance , the high mortality rates observed in WNV lineage 1 infected birds in Israel and North America , particularly among corvids [7] , have not been observed in Europe , where lineage 1 viruses are circulating [8] . Nevertheless , WNV has been isolated from dead birds in Europe , including Passeriformes like magpie ( Pica pica ) [4 , 6 , 9–13] . During initial epidemics in the US , crows were considered to play an important role as virus amplifiers . It has been proposed that corvids , including magpie , are also involved in a WNV endemic cycle in human habitats in Europe [14] . Viral RNA has been detected in 5 to 9% of the tested population [12 , 15] and neutralizing antibodies in 1 . 5 to 11% [16–18] . However , in order to consider the magpie as an infectious source of WNV transmission , it should develop a competent viremia and/or shed virus , aspects that have never been explored before . Therefore , herein we described the first experimental infection of magpie , one of the most abundant corvid species in Europe , in order to examine the pathogenesis of WNV infection in this host and elucidate the possible role of these birds in WNV ecology .
Magpies were captured under permit 346760 of the regional government of the autonomic Community of Castilla-La Mancha using walk-in cage traps in several hunting locations in South-Central Spain . Traps were checked daily and , upon capture , birds were aged based on plumage and molt patterns , sampled , and tested for the presence of WNV-RNA by consensus flavivirus real time RT-PCR [19] , and the presence of flavivirus antibodies by a commercial blocking ELISA ( Ingenzim West Nile Virus , Ingenasa , Madrid , Spain ) , respectively . Birds that tested negative were transferred to mosquito-net covered flight cages at the experimental farm of the IREC ( Instituto de Investigación en Recursos Cinegéticos , Ciudad Real , Spain ) . Captures took place between April and June 2017 and only individuals younger than a year old were selected . Magpies were maintained for a maximum of two months in groups of ten in mosquito-proof flight cages with ad libitum food and water supply , and were weighed and re-tested on a monthly basis to confirm well-being and lack of exposure to Flaviviruses . A final group of 34 magpies was transported to the biosafety level 3 ( BSL-3 ) facilities at INIA ( Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria , Madrid , Spain ) , where they were housed in 3 separate cages ( 11–12 birds/cage ) . Each cage was equipped with a net-covered flight cage with several fixed and one free-swinging perch with artificial grass cover , rough cardboard lining on the ground , and enough space for free flight . After one week of adaptation , animals were weighed and blood was collected via the jugular vein for preinoculation serology ( 0 days post-inoculation , d . p . i . ) . Then , they were subcutaneously inoculated in the neck with 5x103 plaque forming units ( p . f . u . ) /bird of WNV diluted in 100 μl of Eagle Minimum Essential Medium ( EMEM , BioWhittaker , Lonza , Verviers , Belgium ) . One group of magpies ( n = 12 ) was infected with NY-99 WNV lineage 1 strain ( GenBank accession no . KC407666 ) , another group ( n = 11 ) with SRB Novi-Sad/12 WNV lineage 2 strain ( GenBank accession no . KC407673 ) , and the third negative control group ( n = 11 ) was similarly sham-inoculated with medium . Two magpies per group were euthanized at 3 d . p . i . by intravenous injection of a sodium pentobarbital overdose . All animals were handled in strict accordance with the guidelines of the European Community 86/609/CEE and the protocols were approved by the Committee on Ethics of animal experimentation of our Institution ( INIA permit number 2017–01 ) . Food and water were provided ad libitum throughout the experiment . Magpies were monitored daily for clinical signs , and those showing severe clinical signs were anesthetized and euthanized , as were all surviving animals at the end of the experiment ( 17 d . p . i . ) . At 3 , 7 , 10 and 17 d . p . i . , all surviving animals were weighed and sampled ( blood , feathers , and oropharyngeal and cloacal swabs ) . Blood samples ( 0 . 5 ml per bird ) were collected via the jugular vein and allowed to coagulate at 4°C overnight prior to centrifugation at 5 , 000 rpm for 10 minutes . Pulps of growing feathers and swabs were placed into 0 . 5 and 1 ml of EMEM medium , respectively . Any dead or euthanized magpie was subjected to full detailed necropsy and tissues were collected , splitted , and stored in PBS at -80°C and in 10% buffered neutral formalin for further virological and histopathological analyses , respectively . Additionally , swabs and feather pulps were also collected from dead and euthanized birds . WNV-specific neutralizing antibodies to both viral lineages ( 1 and 2 ) were detected in serum samples by plaque reduction neutralization test ( PRNT ) on Vero cells using twofold serial serum dilutions , as previously described [6] . Titers were calculated as the reciprocal of the serum dilution , diluted at least 1:20 , which reduced plaque formation ≥ 90% ( PRNT90 ) relative to samples incubated with negative control pooled sera . Collected sera , feather pulps and oral and cloacal swabs were also tested for infectious WNV by plaque assay on Vero cell culture as previously reported [20] . Tissues ( brains and hearts ) were thawed and weighed prior to homogenization in a TissueLyser II mixer ( 2 minutes at 30 cycles/s; Qiagen , Germany ) in 0 . 75 ml EMEM . Resulting homogenates were clarified by centrifugation ( 12 , 000 rpm for 5 minutes ) and stored at -80°C . Viral RNA from the processed tissues , as well as from oropharyngeal and cloacal swabs and feather pulps , was extracted using a QIAcube extractor ( Qiagen , Germany ) according to the instructions provided by the manufacturer . Lineage 1 viral RNA was detected by real-time RT-PCR as previously described [21] using a High Scriptools-Quantimix Easy Probes kit ( Biotools ) . For lineage 2 WNV-RNA quantification primers used were forward , 5’-CAGACCACACTCTAGTG-3’ , and reverse , 5’-CCCACGCGGCCATAA-3’; enclosing nucleotides 10691 to 10793 of the WNV , SRB-Novi Sad/12 strain [GenBank accession no . KC407673; [6]] . Viral RNA was quantified as genomic equivalents ( GE ) to p . f . u . by comparison with RNA extracted from previously titrated samples [20] . WNV-RNA was amplified from samples of surviving animals by conventional RT-PCR ( SuperScript One Step RT-PCR system , Invitrogen , Carlsbad , CA , USA ) as described [20] using specific primers ( forward , 5’- CCTTGGAATGAGCAACAGAGACTT -3’ , and reverse , 5’- GTGTCAATGCTTCCTTTGCCAAAT -3’; enclosing nucleotides 985 to 1320 of WNV NY99 strain , GenBank accession no . KC407666 ) and bidirectionally sequenced ( Macrogen , The Netherlands ) . Statistical analyses were performed using Graph Pad Prism for Windows , version 6 ( Graph Pad Software , Inc . , San Diego , CA , 2005 ) . Kaplan-Meier survival curves were analyzed by a log-rank test . Mean survival time ( MST ) was calculated for every group of inoculated magpies . Two-way analysis of variance ( ANOVA ) with Bonferroni’s correction for multiple comparisons was used to evaluate the weight differences of the animals along the experiment . Unpaired t-test was used to compare viremia between the groups infected with the two lineages used . Statistically significant differences are indicated by asterisks * ( p<0 . 05 ) , ** ( p<0 . 01 ) , *** ( p<0 . 001 ) .
A low survival rate was observed in magpies infected with WNV lineage 1 ( 30% ) and 2 ( 42 . 8% ) , with mean survival times of 6 . 7 ( range 6–8 ) and 6 . 5 ( range 6–7 ) d . p . i . , respectively ( Fig 1 ) . Clinical signs in magpies that died included lethargy , ataxia , inability to fly , and leg paralysis . Death typically occurred less than 10 hours after first showing clinical signs . Surviving , infected birds were lethargic 6–12 days post-infection . All infected magpies showed significant weight loss until they died . Surviving birds gained weight from 7 d . p . i . while control magpies did so throughout the course of the experiment ( Fig 2 ) . Serum samples were tested for infective virus at different time points ( 3 , 7 , 10 and 17 d . p . i . ) and positive samples were only detected at 3 d . p . i . in infected magpies , with high titers ( range 5x103-8x108 p . f . u/ml ) in both viral lineages ( Fig 3 ) . WNV-specific neutralizing antibodies were detected in all tested infected magpies from one week after infection until the end of the experiment , regardless of the infecting isolate ( Fig 3B ) . PRNT90 titers ranged from 1 . 6x102 to 1 . 35x103 , showing a discreet tendency to increase in surviving animals . All samples from sham-inoculated magpies resulted negative . Two magpies per group were euthanized at 3 d . p . i . to analyze the presence of WNV-RNA in their hearts and brains . Viral genome was detected in all samples from infected magpies , except in the brain of one lineage 1 infected bird ( Fig 4 ) . The initial organ sampling schedule was abrogated due to the high mortality observed , so only organs from animals that succumbed to the infection and from surviving birds at the end of the experiment were analyzed . All samples from dead birds were positive regardless of the infecting lineage . WNV-RNA was also detected in the brains from two surviving magpies , each one infected with a different lineage , as well as in the hearts of all surviving animals infected with lineage 1 virus , and in one of the three lineage 2 infected surviving birds ( Fig 4 ) . These results were confirmed by conventional RT-PCR and sequencing . WNV-RNA was also amplified from swabs ( oral and cloacal ) of infected birds from 3 to 7 d . p . i . , peaking at 6 d . p . i . with no viral genomes detected in oropharyngeal or cloacal swabs after 7 days d . p . i ( Fig 5A and 5B , upper panels ) . Presence of infectious virus was also analyzed in some representative WNV-RNA positive samples ( Fig 5A and 5B , lower panels ) . On the other hand , almost all feather pulps from infected birds were WNV-RNA positive from 3 to 10 d . p . i . , and in some of them virus could be also recovered ( Fig 6 ) . Notably , at 17 d . p . i . , feather samples from one magpie of each group were also WNV-RNA positive , results that were confirmed after sequencing of RT-PCR amplicons . The two magpies that died at 12 and 13 d . p . i . had WNV-RNA in their brains , hearts , and feathers , but not in their swabs . Both individuals had a severe fungal pneumonia and airsacculitis , presumably caused by Aspergillus sp . that has to be considered the primary cause of death .
Magpie is one of the most abundant corvids in Europe [22] and it is suspected to be a source of WNV transmission . Using experimental infections , we showed that magpies are highly susceptible to fatal and nonfatal infections with WNV , elicit neutralizing antibodies , and develop viremic titers higher than those necessary for the successful transmission of WNV to mosquitoes [23] . Additional demonstration of infectious virus and viral genome in swabs and feather pulps , point to this species as a likely source of viral transmission . WNV is maintained in nature in an enzootic cycle between its natural hosts -birds- and mosquitoes . In countries with continental climate , the enzootic cycle is apparently disrupted during the winter season , as vectors die or enter in diapause . Nevertheless , WNV vertical transmission in vectors has been described , indicating that the virus can overwinter in vertically infected mosquitoes [24] , as well as in those horizontally infected that survive the increasingly warm and short winters . Besides overwintering in mosquitoes , WNV can also be re-introduced by migratory birds . Moreover , climate change alters the migratory behavior of some wild bird species , some of which have now become residents in Europe , or shortened their migratory routes by wintering in Southern Europe instead of migrating to the African continent . In this new scenario , the idea of local birds acting as competent reservoirs that amplify and/or maintain the virus during winter in Europe is gaining force [14] . Magpie is widely distributed in Europe , representing the most abundant resident corvid , which is highly adapted to human environments . Additionally , magpie is one of the feeding preferences of Culex pipiens , a bridge WNV vector to humans [25] . However , only three isolations of WNV from magpies have been reported in Europe . In 2004 , a WNV lineage 1 was isolated from a magpie in the Camargue , France [11] , and in 2008 the virus was isolated from three magpies in Italy [12 , 26] . Lineage 2 strain was detected in Greece in a hunter-harvested magpie in an area where human cases had occurred [27] . Despite briefing of hunters for recognition of encephalitis or collection of dead birds , no sick or dead magpies were recovered in the outbreak area . In contrast , WNV epidemics in America were associated with high bird mortality [28] , leading to considerable declines in populations of related North American magpie species [28 , 29] . A similar picture has been observed for American crows ( Corvus brachyrhynchos ) suffering substantial die-offs from WNV while no reports of mortality exist for the European carrion crows ( Corvus corone ) , even though recent experiments have shown that both species are highly susceptible to WNV [30 , 31] . We have performed the first experimental infection of magpies with 2 lineages of WNV isolates currently circulating in Europe . Birds were infected with the viral lineage 1 prototype NY-99 , known to be highly pathogenic for the American magpie [29] , and the SRB Novi-Sad/12 lineage 2 strain isolated from a dead Northern goshawk ( Accipeter gentilis ) during recent outbreaks in eastern Europe [6] . A high susceptibility to WNV infection was observed in magpies infected with both viral strains with survival rates of 30% and 42 . 8% for lineage 1 and 2 , respectively . All infected magpies lost weight and elicited high neutralizing antibodies titers . In addition , all of them had high virus titers 3 d . p . i . ( 5x103-8x108 p . f . u/ml ) , demonstrating magpies can be a likely source of vector feeding transmission . In fact , host competence for WNV has been experimentally established based on viremia levels that range above 104 to 105 p . f . u/ml [30] , and it should also be noted that the reduced mobility observed in viremic birds might increase exposure to host seeking mosquitos . The high mortality rate recorded here is in line with previous data obtained from experimentally infected North American black-billed magpie ( Pica hudsonia ) [30] . In our experimental infection , the viral dose administered ( 5x103 p . f . u/magpie ) was in the range of the amounts inoculated by feeding mosquito [23] . Although lower pathogenicity with lower mortality from WNV in the European avian community has been proposed [8] , our data suggest that the susceptibility of the European magpie to WNV could be underestimated . In fact , under the experimental conditions here reported , the development of signs prior to death took less than 24–48 hours . The detection of infectious virus and viral RNA in feather pulps and cloacal and oral swabs suggests that magpie could also act as a source of horizontal transmission of WNV , not only within bird communities , but also to horses and humans , since they are highly adapted to human habitats and frequently forage on pastures . Even more , infections of birds after ingestion of infected animals has been described [30] and , therefore , death magpies can also be a source of WNV transmission for scavenger birds . Two lineage 2 infected magpies died 12 and 13 d . p . i . ( 5 and 6 days later than any of the other infected animals ) . They succumbed presumably to aspergillosis , although WNV was also present in their brains , hearts and feathers . Aspergillus fumigatus , the primary cause of avian aspergillosis is an ubiquitous opportunistic pathogen and the combination of high concentrations of spores in the environment and impaired immunity of the bird are considered factors leading to the development of clinical disease [32] . Confinement , handling for sampling collection , and concomitant West Nile virus infection may more probably have contributed to development of the disease in these two individuals . In summary , magpie is highly susceptible to West Nile virus infection; it amplifies the virus to sufficient levels to transmit it to other hosts and sheds it in considerable amounts , which probably contributes to maintain the viral life cycle . Since magpie lives close to human population , it should be a priority target in surveillance programs . | Birds play an important role in the epidemiology of flaviviruses such as West Nile virus ( WNV ) since birds are natural hosts and facilitate hibernation of the virus in periods of absence of mosquitoes that transmit the virus . Since it has been proposed that magpies play an important role in an endemic WNV cycle in human habitats in Europe , we conducted the first experimental infection of magpie with the two WNV lineages currently circulating in Europe . We observed high susceptibility of magpie to WNV infection with virus titers higher than those necessary for the successful transmission of WNV to a mosquito and often resulting in death . Likewise , we detected elevated titers of neutralizing antibodies in all the samples tested as well as the viral genome in the organs , oropharyngeal and cloacal swabs and feather pulps of the infected animals . Our results suggest that the magpie , which is one of the most abundant corvid species in Europe , could be a source of WNV transmission to other birds and humans , which expands the knowledge about WNV pathogenesis , transmission and ecology , that benefits monitoring and control programs . | [
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"r... | 2018 | High susceptibility of magpie (Pica pica) to experimental infection with lineage 1 and 2 West Nile virus |
Detection and sequencing of chikungunya virus ( CHIKV ) genome was performed using a combination of a modified reverse transcription loop-mediated isothermal amplification ( RT-LAMP ) method and a MinION sequencer . We developed the protocol for drying all the reagents for the RT-LAMP in a single reaction tube . Using this system , the CHIKV genome was effectively amplified under isothermal conditions , and used as a template for MinION sequencing with a laptop computer . Our in-house RT-LAMP method and MinION sequencing system were also validated with RNAs and serum samples from recent outbreaks of CHIKV patients in Brazil . The obtained sequence data confirmed the CHIKV outbreaks and identified the genotype . In summary , our established inexpensive on-site genome detection and sequencing system is applicable for both diagnosis of CHIKV infected patients and genotyping of the CHIKV virus in future outbreak in remote areas .
Chikungunya is a mosquito-borne febrile disease caused by chikungunya virus ( CHIKV ) , which is a positive strand RNA virus belonging to the genus Alphavirus . In the last decade , serious outbreaks of chikungunya have been reported . These appeared to have started on the coast of Kenya in 2004 [1] , and subsequently spread to islands in Indian ocean [2] , India [3] , parts of Southeast Asia [4] , as well as temperate Mediterranean areas of Europe [5–7] . Since late 2013 , chikungunya outbreaks have been also reported in the Caribbean , United States , Mexico , Central America , and Brazil [8–11] . Millions of people have been affected during these outbreaks , and infection is now a major public health concern [11–13] . CHIKV are phylogenetically classified into three major genotypes , the West Africa , the Asia , and the East-Central South Africa ( ECSA ) . The Asian genotype was the cause of recent outbreaks in the Caribbean and United States [14] . In Brazil , both Asian and ECSA genotypes have been reported , with the outbreak in Rio de Janeiro beginning in 2014 , being attributed to the ECSA genotype [9 , 15 , 16] . It has been reported that phylogenetic diversification and infectivity differences among CHIKV may be correlated [17] and the evolution of CHIKV may be related to the viral adaptation in mosquito vectors and mammalian hosts . Since the clinical features of chikungunya are similar to other mosquito-borne febrile diseases , including Dengue fever , Zika fever and malaria , the differential diagnosis of these diseases based on clinical signs is difficult in endemic regions . Definitive diagnosis of chikungunya is based on serology , virus isolation , or genome detection by reverse transcription PCR ( RT-PCR ) from patient-derived samples . However , these laboratory diagnostic techniques are expensive and require well-equipped facilities that are not generally available in remote areas and thus are seldom performed in routine clinical practice . Loop-mediated isothermal amplification ( LAMP ) is a very sensitive , “user-friendly” , and time-efficient nucleic acid amplification method [18 , 19] . The method can be applied to detect genomes of RNA viruses if reverse transcriptase is included in the reaction ( reverse transcription LAMP; RT-LAMP ) . Using the LAMP and RT-LAMP methods , amplification reaction of nucleic acids can be performed under isothermal conditions without expensive or sophisticated equipment . The Bst polymerase , a necessary enzyme for the LAMP reaction , is known to be highly tolerant to inhibitory molecules in clinical samples [20] , and therefore it is applicable for the samples without the requirement of the purification steps of nucleic acids . We have already found that all the reagents for the LAMP can be dried and kept in reaction tubes , and that patient blood can be used directly in the LAMP reaction without purification of nucleic acids . This simplified protocol has been applied for diagnosis of Human African Trypanosomiasis and Malaria [21 , 22] . This dried-LAMP ( named CZC-LAMP ) can be stored at the ambient temperature for prolonged periods , which is useful in the remote areas where cold-chains are not available . Recently , a portable-type next generation sequencer , the MinION has been developed . The sample preparation from DNA or cDNA can be completed within 10 min in the simplest protocol . The library preparation for sequencing can be performed with a magnet separator and simple isothermal incubator , and thus only minimal equipment is required . Notably as most of the newly emerging infectious disease outbreaks have been reported in the remote areas with resource-poor settings , portable , affordable and disposable MinION provides a promising tool for rapid identification and epidemiological analysis on site . In this study , we combined our field-friendly RT-LAMP system and the MinION technology , to successfully achieve viral sequencing in a simple and rapid way . Specifically , the genomes of CHIKV in a drop of human blood would be amplified with the dried RT-LAMP method , and the products sequenced by the MinION . This simple sequencing work-flow is likely to be applicable to investigate outbreaks of various infectious diseases in remote areas .
Chikungunya virus SL11131 ( AB455493 ) and SL10571 ( AB455494 ) , which are members of the ECSA genotype , were passaged in Vero cells . These viruses were isolated from serum of a Japanese patient returning from Sri Lanka in 2006 [23] , who provided written informed consent for their use . CHIKV-S27 which is an African prototype ( NC_004162 ) also belongs to the ECSA genotype . These viruses were provided by Dr . Takasaki ( National Institute of Infectious Diseases , Japan ) and stored in -80 oC freezer at a biosafety level ( BSL ) -3 until use . The in-house dried CHIKV RT-LAMP system was produced by using a trehalose vitrification technique based on a previous report [21] with several modifications . Trehalose ( FUJIFILM Wako Pure Chemical , Osaka , Japan ) was prepared by dissolving in deionized distilled water ( 2 mol/L = 2M ) in 85 oC for 1 hour . The Trehalose solution ( 1 . 6 μl , 2M ) , deoxyribonucleotide triphosphates ( dNTPs ) ( 1 . 4 μl , 25mM each ) ( Nippon Gene , Tokyo , Japan ) , WarmStart RTx reverse transcriptase ( 0 . 25 μl , 15 U/μl ) ( New England Biolabs Inc . , Ipswich , MA ) , RNase inhibitor ( 0 . 1 μl , 40 U/μl ) ( Takara Bio Inc . , Shiga , Japan ) , and Bst 2 . 0 WarmStart DNA polymerase ( 0 . 05 μl , 120 U/μl and 0 . 25 μl , 8 U/μl ) ( New England Biolabs Inc . ) were then mixed . We used two different concentrations of Bst 2 . 0 WarmStart DNA polymerase to adjust the glycerol amount in the reaction mixtures , which resulted in an effective drying time and enzyme stability [21] . The enzyme mixture solution ( 3 . 65 μl ) was placed inside of the tube lid . The LAMP primer sets for CHIKV [24] were prepared in deionized distilled water . FIP and BIP ( 0 . 4 μl each ) , F3 and B3 ( 0 . 05 μl each ) , and FLF and BLP ( 0 . 2 μl each ) , trehalose ( 0 . 7 μl , 2M ) , and the colori-fluorometric indicator ( CFI ) ( 1 μl ) were mixed and the mixture solution ( 2 . 35 μl ) was placed at the bottom of the same reaction tube as the enzyme mixture solution had been placed on the lid . CFI consists of 3 mM hydroxyl-naphtol blue ( HNB; MP Biomedicals , Aurora , OH ) and 0 . 35% v/v GelGreen ( 10 , 000X solution in DMSO , Biotium , Hayward , CA ) dissolved in distilled water . The tubes were air dried with a fan in a grove box connected with an ultra-low dew point air dryer ( QD20-50; IAC Co . , Kawasaki , Japan ) for 12 hours . The tubes with dried mixture solutions were kept with molecular sieves 3A ( FUJIFILM Wako Pure Chemical ) in an aluminium bag at ambient temperature . The reaction tubes with the dried mixture solutions were stored for at least 2 months at ambient temperatures and were emplaced in the RT-LAMP reaction for CHIKV genome detection . Prior to the reaction , reaction buffer ( 23 μl ) , consisting of 20 mM Tris-HCl ( pH8 . 8 ) , 50 mM KCl , 6 mM MgSO4 , and 10 mM ( NH4 ) 2SO4 in 0 . 1% TritonX-100 , and 2 μl template was added . For the templates , 1 μl of extracted RNA with or without 1 μl of whole blood from healthy human volunteer were used . RNAs were extracted from CHIKV when titers were determined by plaque forming units per ml ( PFU/ml ) . Thereafter , the tubes were turned upside down for 2 minutes to mix and reconstitute the dried enzyme reagents . The RT-LAMP reaction was achieved at 63 oC for 45 minutes . Because CHIKV-CZC-LAMP contains gelgreen which emits green fluorescent , the reaction could be monitored by the FAM channel with real-time PCR detection system ( CFX96; Bio-Rad , Philadelphia , PA ) . One cycle of the amplification was set as 1 min , and reaction speed ( min ) was estimated to be equal to the threshold cycle ( Ct ) value . The specificity was judged by Tm ( melting temperature ) value . Our assay was also validated using clinical samples . Serum samples were obtained from clinically suspected patients collected in 2016 and 2018 , and which had been stored at the Flavivirus Laboratory , Oswaldo Cruz Institute/Oswaldo Cruz Foundation ( Fiocruz ) in Rio de Janeiro which is a Brazilian Ministry of Health Regional Reference Laboratory for arboviruses ( LABFLA ) . Viral RNAs from those sera were extracted by the QIAamp viral RNA mini kit ( Qiagen , Germany ) . Detection of the genome of CHIKV by the CHIKV-CZC-LAMP was performed using directly the serum or RNA from sera samples ( 2 μl ) with 23 μl of the reaction buffer in each tube . For incubation of the samples , the portable incubator ( BSR-miniT100H , Bio Medical Science , Tokyo , Japan ) was used , and detection of the fluorescent signal from reactive samples was confirmed using the hand-made blue-green LAMP reaction detector as described in our previous report [21] . To compare the sensitivity of the RT-LAMP system , quantitative real-time PCR ( qRT-PCR ) [25] was conducted with the Express One-step SuperScript qRT-PCR system ( Invitrogen , Carlsbad , CA ) and the StepOnePlus Realtime-PCR System ( Thermo Fisher Scientific , Waltham , MA ) . The reaction cycle was set as 50 oC for 15 minutes for reverse transcription , 95°C for 2 minutes for initial denaturation , followed by 45 cycles of 95°C for 15 seconds , and 60°C for 60seconds . The sequences of primer sets for the qRT-PCR were as follows; Forward primer 6856F: 5’-TCACTCCCTGTTGGACTTGATAGA-3’ , reverse primer 6981R: F: 5’-TTGACGAACAGAGTTAGGAACATACC -3’ , and 6919 FAM-MGB probe: 5’-FAM-AGGTACGCGCTTCAAGTTCGGCG -MGV-3’ [25] . The genomic DNA Sequencing kit SQK-MAP-006 ( Oxford Nanopore Technologies , Oxford Science Park , UK ) was initially used for CHIKV SL10571 and S27 in which one sample was analyzed in an individual flow-cell . Briefly , LAMP products ( 2 μl ) were directly used for end-repairing and dA-tailing using the NEBNext Ultra II End Repair/dA-tailing module ( New England Biolabs ) by incubation at 20°C for 5 minutes , then 65 oC for 5 minutes , and subsequently purified by Agencourt AMPure XP beads ( Beckman Coulter , Brea , CA ) with a magnetic stand . Adaptors ( Oxford Nanopore Technologies ) were then ligated at room temperature for 10 minutes by Blunt/TA Ligase Master Mix ( New England Biolabs ) . The resultant adapter-ligated DNA was purified by Dynabeads MyOne Streptavidin C1 ( Thermo Fisher Scientific ) . Thereafter , samples were eluted with elution buffer ( Oxford Nanopore Technologies ) and the resultant libraries were applied to the MinION Flow-Cell ( R7 . 0 ) with buffer and fuel mix , and the Genomic DNA 48-hour sequencing protocol was used with the MinKNOW software ( Oxford Nanopore Technology ) . The base call was performed with the Metrichor Agent ( https://metrichor . com ) and only “passed” sequences with high reliability were used for the subsequent analysis . As for LAMP products from clinical samples , a 1D Native barcoding genomic DNA kit ( with EXP-NBD103 and SQK-LSK108 , Oxford Nanopore Technologies ) was applied according to the manufacture’s instruction . Briefly , LAMP products ( 2 μl ) were ligated with each barcode by Blunt/TA Ligase Master Mix ( New England Biolabs ) , purified by AMPure XP beads , then all samples were pooled and applied to the single MinION Flow-Cell ( R9 . 4 , FLO-MIN106 ) in MK1b device . The obtained FAST5 data from MinION were converted into fastq file formats using albacore . 1 . 2 . 6 ( Oxford Nanopore Technologies ) . MinION sequence data have been submitted to the DNA Data Bank of Japan ( DDBJ ) under the following DDBJ Sequence Read Archive ( DRA ) accession number: DRA007513 . The local BLASTN program was employed with obtained reads as queries against CHIKV SL11131 ( AB455493 ) reference at position of 10 , 317–10 , 472 , which corresponds to the region between F2 to B2 region of LAMP reaction ( E value <1e-5 , identity >90% ) . All the blastn hit regions were clipped and aligned to CHIKV SL11131 by Bowtie2 [26] , and IGV and IGVtools [27] were used to visualize and obtain count data from bam files . The obtained count data were analyzed by MicroSoft Excel ( S1 Table ) , and consensus sequences were constructed . For further sequence analysis , the F2 and B2 regions were omitted , since during LAMP amplification those sequences would be replaced by the primer sequence completely . For the remaining 117 bp region ( 10 , 336–10 , 452 ) , the population for the major nucleotides was interpreted as a consensus of the sequence , if the proportion of the main allele was supported by more than 70% of the total coverage . For bi-allelic loci , a non-primer allele was considered as a genuine allele taking into account for the primer effect , that partially replaced the original mutation into the primer sequence ( LF/LB and F1/B1 ) . The consensus sequences were aligned , and phylogenetic trees were constructed by a Neighbor joining ( NJ ) -based method with the reference CHIKV sequences [11 , 16 , 28 , 29] using MEGA7 software [30] . Ethical approval for the diagnostic procedure was obtained from both the Ethical Screening Committee of Fiocruz ( CAAE ) : 90249218 . 6 . 1001 . 5248 ( 2 . 998 . 362 ) , and the Graduate School of Veterinary Medicine/ the Research Center for Zoonosis Control , Hokkaido University ( approved number: 28–2 ) . Because patient data was anonymized , informed consent was not required . Also , there is a transference agreement of LAMP and MinION protocols between Hokkaido University and Flavivirus Laboratory to support the virological surveillance performed by the LABFA , and all validation test using Brazilian sample were performed at this laboratory .
We have established the dried RT-LAMP system for the detection of the CHIKV genome ( CHIKV-CZC-LAMP ) from clinical samples , containing dried-reagents in the reaction tubes , and which were stored at ambient temperatures for prolonged periods . The CHIK-CZC-LAMP system consists of reaction tubes , reaction buffer , an isothermal incubator , and a LED LAMP reaction detector ( Fig 1A ) . The sensitivity of the CHIKV-CZC-LAMP was evaluated using purified RNAs from CHIKV pulsed with or without 1 μl of human blood . Positive reactions were recognized as yellow fluorescence under a LED detector , and a positive reaction could also be recognized as change of sample color from violet to sky blue by the naked eye ( upper column in Fig 1B ) . As for blood-added samples , the reaction tubes were briefly centrifuged to recognize fluorescence more clearly ( lower column in Fig 1B ) . The time of positivity to obtain an amplification signal by the CHIKV-CZC-LAMP was correlated with Ct values . The time required for the amplification of 50 PFU CHIKV genome was 15 to 18 min in the absence of blood , and 18 to 23 min for CHIKV genome in the presence of blood ( Fig 1C ) . The reaction time of CHIKV genome with blood was delayed a few minutes compared to that without blood , suggesting that the delay of the reaction time may be due to inhibitory effect of the reaction by blood or more probably fluorescent masking effect by the blood color . The sensitivity of CHIKV-CZC-LAMP using CHIKV genome at the end points of the reaction ( 45 minutes ) was <50 PFU CHIKV per reaction , showing 100% sensitivity ( 95% CI: 54%-100% ) at 50 PFU of CHIKV , and 33% ( 95% CI: 4%-78% ) at 5 PFU of CHIKV . Addition of blood to the CHIKV genome had no inhibitory effect on sensitivity of the RT-LAMP , showing 100% sensitivity ( 95% CI: 54%-100% ) at 50 PFU of CHIKV , and 50% sensitivity ( 95% CI:12%-88% ) at 5 PFU CHIKV ( Fig 1C ) . The sensitivity and Ct value of qRT-PCR system [25] was also determined using the same RNA templates , and was revealed to be <0 . 5 PFU CHIKV per reaction ( Fig 1C ) . The feasibility of established CHIKV-CZC-LAMP for patient sample diagnosis was validated at Flavivirus Laboratory using both RNA and serum samples of patients with chikungunya collected and kept at the Fiocruz . RNA and serum from healthy individuals ( n = 4 ) were also tested as healthy endemic controls . All the RNA samples were tested by qRT-PCR , and the Ct values were determined ( Table 1 ) . The RNA samples and crude sera from the same patient were examined by the CHIKV-CZC-LAMP . Among 33 CHIKV qRT-PCR positive samples , 23 RNA samples and 19 serum samples were positive for CHIKV-CZC-LAMP , showing 70% ( 95% CI: 0 . 51–0 . 84 ) sensitivity for RNA samples and 58% ( 95% CI: 0 . 39–0 . 75 ) sensitivity for serum samples . No positive reaction was observed in the 4 endemic healthy control RNA and serum samples , demonstrating 100% specificity ( 95% CI: 0 . 40–1 . 00 ) ( Table 1 ) . CHIKV-CZC-LAMP-detectable RNA samples had Ct values from 12 . 21 to 31 . 1 by qRT-PCR . The results were correlated with the analytical sensitivity of the CHIKV-CZC-LAMP using RNA from CHIKV , which was 33% at 5 PFU , corresponding to 30 . 60 of Ct value of qRT-PCR ( Fig 1C ) . In addition , CHIKV-CZC-LAMP showed positive reactions using crude serum from serologically positive samples which had Ct values from 12 . 21 to 28 . 43 ( Table 1 ) . The amplified products by CHIKV-CZC-LAMP from clinical samples , and CHIKV-SL10571 and -S27 laboratory strains , were sequenced with MinION ( Table 2 ) . After mapping the reads to the reference sequence of CHIKV SL11131 , the mapped read number of each nucleotide was retrieved , and consensus sequences were generated based on the population . The population for the major nucleotides was interpreted as a consensus of sequence although the presence of viral quasispecies could not be excluded . Because the clinical samples were sequenced with the recent version of R9 . 4 , the average length of read and outputs were much higher in those samples than laboratory strains CHIKV-SL10571 and -S27 strains which were sequenced with the earlier version R7 . 0 . ( Table 2 ) . Both platforms showed good coverage with no low coverage regions ( >416 coverage at minimal , as shown in Table 1 and S1 Table ) . The lack of 3→ 5 exonuclease activity in Bst DNA polymerase may result in error amplification estimated to be about 1 x 10−4 [31] . In addition , it has been reported that sequence accuracy of the current version of the MinION R9 . 4 flowcell with 2D chemistry is 97% [32] which is still of course low compared to the conventional Sanger-sequence or other next generation sequencers . Nevertheless , the high sequence coverage renders enough discriminative power for single nucleotide variations ( SNVs ) calling with high confidence . The obtained consensus sequences were aligned , and phylogenetic trees were constructed based on 117 nucleotides of the LAMP target region ( Fig 2 ) . The sequences from two laboratory strains , CHIKV-SL10571 , and African prototype CHIKV-S27 showed a 100% match with deposited sequence data , demonstrating the proof-of-concept of our analysis . For the chikungunya patient samples from Rio de Janeiro , the obtained sequences all clustered within ECSA genotype in the phylogenetic analysis ( Fig 2 ) , confirming the circulation of the ECSA genotype in Rio de Janeiro during the epidemics in 2016 and 2018 .
The present study describes the development of a one-step , easy and “user-friendly” gene amplification assay for the rapid detection of CHIKV , named CHIKV-CZC-LAMP . The analytical sensitivity of CHIKV-CZC-LAMP was <50 PFU per reaction both from purified RNA and RNA sample pulsed with human blood . This sensitivity of the CHIKV-CZC-LAMP was not as high as the standard qRT-PCR method which showed higher sensitivity , detecting <1 PFU per reaction [25] . CHIKV-CZC-LAMP also showed less sensitivity than qRT-PCR using clinical samples . However , improvement on the sensitivity by selection of different primer sets will be expected to allow detection of relatively low viral load samples . In the current study , we used already established primer sets [24] which targeted E1 region of CHIKV genome . Other primer sets targeting the 6K-E1 regions [33] have recently been reported , which need to be comparatively validated . Also , a second set of reaction accelerating primers ( stem primes ) could increase the sensitivity of the amplification [34] . For the diagnosis of CHIKV infection , RNA detection methods are recommended before 6 days post symptom onset [35] . The viral titer has variation based on the individuals or the viral genotypes . It has been reported that high viral load of CHIKV ( 107−109 viral particles/ml ) was detected in the patient’s blood in a recent outbreak [12] , which will be easily detected in our LAMP-MinION system . After 5 days of the onset of symptom , additional serological assays are recommended , since the viral genome amount is expected to be low at this point [12 , 35] . The negative results by our system in some CHIKV-infected patients who had relatively low viral load may be caused by delayed onset of symptoms after CHIKV infection . The undetectable RNA samples showed Ct value of >23 . 95 in qRT-PCR , which is estimated to be about 500 PFU virus per reaction , demonstrating that CHIKV-CZC-LAMP could detect only high viral titer samples in the early phase of the CHIKV infection . Therefore , we propose that our system can be combined with a serological diagnostic test . Nevertheless , the developed CHIKV-CZC-LAMP could be considered to be superior to a qRT-PCR method in terms of feasibility as there is no need for significant technical skills or expensive equipment . In addition , it has the advantages of easy transportability and low cost . This system will also be useful for diagnosis not only of chikungunya but also other established and newly emerging infections of RNA viral diseases in the field setting as a point of care test . In Brazil , the first autochthonous cases of the Asian and ECSA genotypes were reported in 2014 in Oiapoque and Feira de Santana , respectively [9–11] . In 2016 , an outbreak of chikungunya was reported in Rio de Janeiro , and ECSA genotype was reported in this epidemic [10 , 16] . In this study , we decided to evaluate our CHIK-CZC-LAMP combined with MinION portable sequencer using samples from chikungunya patients in Rio de Janeiro , to determine if on-site diagnosis and on-site genotyping was feasible or not . It was demonstrated that molecular diagnosis and on-site sequencing from clinical samples in resource-limited region was possible . The sequence data obtained from the chikungunya samples revealed that those were the ECSA genotype , which was consistent with the previous report [10 , 16] . As a proof-of-concept , laboratory CHIKV strains ( SL10571 and S27 ) were also sequenced and showed absolute match with the sequences deposited in GenBank . The reliability of LAMP diagnosis is also complemented by sequencing , as the LAMP method is known to cause frequent non-specific amplification induced by primer dimers [36] . We confirmed the sequence of CHIKV genome in the samples , demonstrating that the obtained sequence information gave us definitive and reliable information from epidemic clinical samples . Distinguishing CHIKV from Dengue fever or Zika fever is also important , but it is often challenging in the clinical setting , as these viruses share the same vectors , and have similar presenting clinical symptoms . The evidence of the co-circulation of dengue virus ( DENV ) , Zika virus ( ZIKV ) and CHIKV has been also reported [37 , 38] . Hence , multiplex or panel of diagnostics for those arbovirus infections will be also required . The RT-LAMP methods for ZIKAV and DENV has been available [39 , 40 , 41] , and the MinION analysis from DENV RT-LAMP had been also been established [42] . Thus , we anticipate such multiplex assays will soon be established . The re-emerging ECSA genotype was reported to have adapted to the Aedes albopictus mosquito , and to produce more virus particles in that mosquito population [43] , which might result in large urban epidemics . It was also reported that ECSA genotype CHIKV infection provoked high viral load in the patient [12] , suggesting that adaptive mutation in the CHIKV envelope causes high replication efficiency [17] . In addition , acquired immunity after CHIKV infection has been reported to be critical for further protection against CHIKV infection [44] . The immunity is long-lasting and suggested to be cross-reactive based on the combination of the genotypes ( serotypes ) [45] . Therefore , cohort studies with identification of pathogen lineages will be required for a better understanding of chikungunya epidemics . Many lineages from diverse geographical areas have potential to spread out to other geographies through travel , vectors , and reservoir animals . However , in most of the epidemic cases , sequence analysis under resource-limited conditions remains very challenging . The present study demonstrated that by combining LAMP and MinION , sequencing on site is very feasible . The blood or serum sample are directly applied to CHIK-CZC-LAMP system without RNA extraction and RT-LAMP can be performed with a battery driven portable incubator and without the need for specialized equipment . The same device can be used for MinION sample preparation , and sequencing can be done with a laptop computer . Therefore , the system is not dependent on stable electricity . The estimated cost of CZC-LAMP system was approximately one dollar per tube , which is much more affordable than RT-PCR or qRT-PCR . The MinION platform requires lower initial costs than other sequencer devices , and can read multiplexed samples , although we only used small sample size in this study . If more sample numbers are applied in one flow cell , the cost for the system will also be reduced . In summary , a one-step , easy gene amplification assay for CHIKV genome detection was successfully developed . The assay was evaluated with RNA and serum samples from 16 CHIKV serology positive patients during recent chikungunya outbreaks in Rio de Janeiro , Brazil . In combination with MinION sequencing technology , we also identified the CHIKV genotypes with a laptop computer . The developed CHIK-CZC-LAMP diagnostics and MinION sequencing workflow will certainly contribute to future outbreak analysis in resource limited settings . | Chikungunya virus has re-emerged as an important pathogen causing several outbreaks in the world . As the clinical symptoms of chikungunya is similar to other mosquito-borne febrile diseases , the definitive diagnosis of the disease is based on the detection of viral genome from the patient blood . Loop-mediated isothermal amplification ( LAMP ) is a method that rapidly amplify nucleic acids under isothermal condition . In the present work , a simple dried format LAMP test for chikungunya diagnosis was developed which can be directly amplified from human blood . Combining with the portable sequencer MinION sequencing system , a method to identify the viral genotype was also established . The developed on-site diagnosis and genotyping system is easy to perform , sensitive , and rapid . Therefore , it offers great promise as a routine simple tool for diagnosis and disease management of chikungunya . | [
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"viru... | 2019 | Field diagnosis and genotyping of chikungunya virus using a dried reverse transcription loop-mediated isothermal amplification (LAMP) assay and MinION sequencing |
Cell cycle control must be modified at meiosis to allow two divisions to follow a single round of DNA replication , resulting in ploidy reduction . The mechanisms that ensure meiosis termination at the end of the second and not at the end of first division are poorly understood . We show here that Arabidopsis thaliana TDM1 , which has been previously shown to be essential for meiotic termination , interacts directly with the Anaphase-Promoting Complex . Further , mutations in TDM1 in a conserved putative Cyclin-Dependant Kinase ( CDK ) phosphorylation site ( T16-P17 ) dominantly provoked premature meiosis termination after the first division , and the production of diploid spores and gametes . The CDKA;1-CYCA1 . 2/TAM complex , which is required to prevent premature meiotic exit , phosphorylated TDM1 at T16 in vitro . Finally , while CYCA1;2/TAM was previously shown to be expressed only at meiosis I , TDM1 is present throughout meiosis . These data , together with epistasis analysis , lead us to propose that TDM1 is an APC/C component whose function is to ensure meiosis termination at the end of meiosis II , and whose activity is inhibited at meiosis I by CDKA;1-TAM-mediated phosphorylation to prevent premature meiotic exit . This provides a molecular mechanism for the differential decision of performing an additional round of division , or not , at the end of meiosis I and II , respectively .
In the germ line of sexually reproducing organisms , a specialized cell division—meiosis—ensures ploidy reduction in the gametes . Accomplishment of meiotic chromosome segregation requires extensive modifications of cell cycle progression compared to mitosis: ( i ) a longer prophase where crossovers occur between homologues [1] , and ( ii ) two rounds of chromosome segregation , preceded by a single round of DNA replication . Cyclin-dependent kinases ( CDKs ) promote progression through both meiosis and mitosis , and a central regulator of their activity is the anaphase-promoting complex/cyclosome ( APC/C ) , a conserved multi-subunit E3 ubiquitin ligase that triggers the degradation of multiple substrates , including cyclins [2] . The modifications of the cell cycle machinery required for meiosis are not fully understood , but the general perception is that during prophase I , the activity of CDK-cyclin complexes increase slowly until peaking at the onset of the first division . This activity drops when cyclins are degraded by the APC/C to allow the segregation of homologous chromosomes at anaphase I . This decay is not complete , although it is sufficient to allow spindle disassembly , entry into a second meiotic division and the avoidance of intervening DNA replication . CDK-cyclin activity increases again at meiosis II , followed by a complete abolishment of this activity by the APC/C that allow sister chromatids to segregate to opposite poles and meiosis termination ( reviewed in [2–4] ) . Thus , one critical aspect of the meiotic cell cycle is the meiosis I to meiosis II transition , where CDK activity has to decrease to trigger meiotic spindle disassembly , but be kept at a sufficiently high level to prevent DNA replication . Further , the mechanisms that ensure the entry into a second division must be turned off at the end of meiosis II to avoid the entry into a third division and ensure meiotic exit . The proteins and mechanisms that regulate these key meiotic transitions are very diverse among the studied eukaryotes ( Saccharomyces cerevisiae , Schizosaccharomyces pombe , Drosophila melanogaster , Mus musculus , Xenopus laevis and Arabidopsis thaliana ) , even though they all directly modify the CDK-cyclin-APC/C module [5–13] . A . thaliana has at least five cell cycle CDKs ( CDKA;1 , CDKB;1 , CDKB1;2 , CDKB2;1 and CDKB2;2 ) and more than 50 cyclins , of which only a few have clear meiotic functions . CDKA;1 is a major cyclin-dependent kinase that drives meiotic progression in plants [14] . Though the core cyclins ( s ) that directly regulate meiotic progression remain to be identified , several cyclins have been shown to play a role at meiosis . The cyclin SDS is required for the formation of meiotic crossovers and acts together with CYCB3;1 in suppressing premature cell wall synthesis [15–17] . TAM , an A-type cyclin ( CYCA1;2 ) is essential to prevent meiosis termination at the end of the first division [14 , 18 , 19] . In the tam1 null mutant a single division occurs at meiosis , leading to the production of diploid gametes . The same defects are observed in osd1 . OSD1 is an APC/C inhibitor and is the functional equivalent of Mes1 in fission yeast and Emi2/Erp1 in vertebrates [18 , 20–22] . Another component involved in meiotic progression is SMG7 , a protein involved in nonsense-mediated RNA decay ( NMD ) in somatic tissues that is also essential for progression through anaphase II [23 , 24] . Finally , TDM1/MS5 ( THREE DIVISION MUTANT1/MALE STERILE 5 ) is required for meiotic exit , its mutation leads to a failure to terminate meiosis after meiosis II and to an aberrant third division [25 , 26] . TDM1 is a protein of unknown function that shares limited structural similarities with the APC/C subunit CDC16/Cut9/APC6 [20] . Intriguingly , the expression of a non-destructible version of TAM , whose wild type version is only expressed during the first division [23] , dominantly provokes the entry into a third meiotic division , mimicking the phenotype of the recessive loss-of-function tdm1 mutant [20] . This suggested that TAM and TDM1 could be functionally related , but the nature of this relationship and the role of these two proteins were elusive . In this study we shed new light on the role and regulation of TDM1 during the meiotic cell cycle . We propose that TDM1 stimulates the APC/C to promote termination of meiosis , this activity of TDM1 being inhibited at meiosis I by CDKA;1-TAM phosphorylation to prevent premature termination of meiosis . These molecular data exemplify how CDK phosphorylation is important for the integrity of the meiotic program in plants .
To identify genes controlling meiotic progression , a genetic screen was designed based on the idea that mutations that prevent a second meiotic division—such as osd1 and tam–would restore the fertility of mutants that have unbalanced chromosome segregation only at the second meiotic division [18 , 21] . This is the case for spo11-1 rec8 double mutants , in which the first meiotic division resembles a mitosis ( balanced segregation of sister chromatids to opposite poles ) but the second division is unbalanced and leads to aneuploid gametes and hence very limited fertility [27] . Mutations in OSD1 or TAM , that lead to meiotic exit before meiosis II , are indeed able to restore fertility of spo11-1 rec8 [18 , 21] . Thus , a forward genetic screen based on the restoration of fertility of spo11-1 rec8 was performed to identify genes whose mutations provoke a similar phenotype to osd1 or tam . Despite their meiotic segregation defect and reduced fertility , spo11-1 rec8 plants produced a sufficient amount of seeds for mutagenesis with ethylmethane sulfonate ( EMS ) . The M1 generation , that is presumably heterozygous for any given EMS-induced mutation , was self-fertilized and harvested in bulks of ~5 plants to produce M2 families . M2 families were screened for increased fertility compared to spo11-1 rec8 non-mutagenized controls . Three bulks segregated plants with increased fertility . Analysis of male meiotic products stained by toluidine blue showed that in all three cases , fertile plants produced dyads of spores , instead of tetrads that are observed in wild type , or polyads as observed in spo11-1 rec8 ( Fig 1 ) suggesting that the second meiotic division did not occur in those plants . Sequencing of candidate genes ( OSD1 and TAM ) identified recessive mutations in TAM in two of the three families . The identified mutations were a splicing site in exon 7 ( TAIR10 chr1:29082522 C>T ) and a mutation in the 5’UTR region which introduced an upstream out of frame start codon ( TAIR10 chr1:29084174 G>A ) . A complementation test showed that they were allelic , confirming that the mutations in TAM caused the dyad phenotype and the restoration of fertility . The third family ( spo11rec8 ( s ) -40 ) had no mutation in OSD1 and TAM and is the focus of this study . Chromosome spreads showed that the four fertile M2 plants from the same bulk identified in the screen ( spo11 rec8 ( s ) -40 ) were tetraploids ( Fig 1 ) . This suggested that the causal mutation was dominant and caused the production of male and female diploid gametes in the M1 plant . Whole genome sequencing of DNA pooled from two sister plants with ~100X coverage revealed the presence of 1 , 144 SNPs compared to wild type . However , only 15 SNPs appeared as homozygote . These few homozygote SNPs were located throughout the genome suggesting that they were present in the spo11-1 rec8 line before mutagenesis , rather than resulting from fixation of de novo EMS-induced mutations . The fact that all other detected mutations were heterozygote further suggested that the causative mutation could be dominant . This dominant mutation would have been phenotypically expressed in the M1 plant , and due to combining this mutation with spo11 rec8 , this would lead to the production of diploid clonal gametes . This phenocopies a spo11-1 rec8 osd1 triple mutant ( MiMe , [21] ) , hence maintaining heterozygosity of EMS induced mutations from the M1 plant in the tetraploid M2 plants . Candidate causal mutations were then looked for among the heterozygote SNPs . Among these 1 , 129 mutations , 341 were predicted to affect a coding sequence ( non-sense , missense or splicing site ) . A mutation in TDM1 ( TAIR10 Chr4:11185795 G>A ) resulting in an amino acid change ( TDM1-P17L ) , appeared as a good candidate as the potential causal dominant mutation as TDM1 was previously shown to be essential for meiotic exit at the end of meiosis II . To test this hypothesis , a genomic clone containing TDM1 ( including promoter and terminator ) that is able to complement tdm1-3 mutant ( Table 1 ) was mutated to recreate the mutation identified in the screen ( TDM1-P17L ) . When introduced in spo11-1 rec8 plants , the TDM1-P17L clone was able to restore fertility of primary transformants ( Table 1 . spo11-1 rec8: 0 . 1 seeds per fruit ( n = 197 ) , spo11-1 rec8 TDM1-P17L#15: 25 seeds per fruit ( n = 15 ) , spo11-1 rec8 TDM1-P17L#67: 48 seeds per fruit ( n = 10 ) , compared to 50 to 60 in wild type ) and led to the production of dyads ( Fig 2 , Table 1 ) . This shows that the mutation in TDM1 is indeed the cause of restoration of fertility and dyad production in spo11rec8 ( s ) -40 . Analysis of meiotic chromosome spreads in spo11-1 rec8 TDM1-P17L primary transformants ( Fig 3 ) showed a mitotic-like first division , with 10 univalents aligned at metaphase I and sister chromatids segregated at anaphase I ( like in spo11-1 rec8 ) , and an absence of the second division . Next , the ploidy level of spo11-1 rec8 TDM1-P17L primary transformants offspring was explored . Self-pollination of spo11-1 rec8 TDM1-P17L produced only tetraploids ( 4n ) ( Table 2 ) . When spo11-1 rec8 TDM1-P17L was crossed with wild type as male or female , all of the progeny were triploid ( Table 2 ) . Altogether these results demonstrated that spo11-1 rec8 mutants transformed by TDM1-P17L produce male and female mitosis-like derived spores , which result in functional diploid clonal gametes . When introduced into wild type plants or tdm1-3 mutants , the TDM1-P17L genomic clone provoked the production of dyads in primary transformants ( Table 1 , Fig 2 ) . Male meiotic chromosome spreads showed that these plants had a wild type first division and an absence of the second meiotic division ( Fig 3 ) . Among progeny derived from self-pollinations of the primary transformants ( which were diploid ) , only tetraploids and triploids were found ( Table 2 ) . When TDM1-P17L plants were fertilised with wild-type pollen grains , diploid and triploid plants were produced ( Table 2 ) . In summary , the TDM1-P17L mutation confers a similar meiotic defect as the recessive null osd1 or tam mutations [18 , 21] , leading to premature exit from meiosis before the second division and consequently to the production of diploid male and female gametes . TDM1 belongs to a family of proteins conserved in plants . The A . thaliana genome contains five other genes having significant sequence similarity with TDM1 ( S1 and S2 Figs ) . The function of these TDM1-like genes is so far unknown . The closest homologue , TDM1-like1 ( AT5G44330 ) , originates from a duplication found in the Brassicaceae ( S1 Fig ) . Two T-DNA mutants in which TDM1-like1 is disrupted ( GABI_750C08 and GABI_173C10 ) were indistinguishable from wild type with respect to fertility and the formation of proper tetrads . Furthermore , the tdm1 tdm1-like1 double mutants were indistinguishable from tdm1 with meiocytes attempting a third division . Taken together , this suggests that TDM-like1 is not essential for meiosis . Sequences analyses showed that the TDM1-P17L mutation is in a highly conserved five amino-acid domain in the TDM1 and TDM1-like 1 protein subfamily ( S1 Fig ) , and not present in the other TDM1-like proteins . Especially , P17 and the adjacent T16 are conserved in all members of the TDM1 and TDM1-like 1 subfamily ( S1 Fig ) . This TP motif defines a minimum consensus CDK phosphorylation site with T16 being the phosphate receptor [28] . To test the role of this putative phosphorylation site , we created versions of TDM1 in which we substituted T16 with a non phosphorylatable alanine ( designated TDM1-T16A ) , or by deleting the entire conserved domain between amino acid 14 and 19 ( TDM1-Δ14_19 ) . When introduced into wild-type plants , both TDM1-T16A and TDM1-Δ14_19 led to the production of meiotic dyads instead of tetrads in primary transformants , recapitulating the effect seen in TDM1-P17L ( Table 1; Fig 2 ) . Moreover , when introduced into tdm1-3 mutants , TDM1-Δ14_19 also provoked the production of dyads ( Table 1 ) . However , mutating tyrosine 14 ( TDM1-Y14A ) or proline 18 ( TDM1-P18A ) , two less conserved amino acids ( S1 Fig ) , did not affect meiosis in the wild type and was able to complement the tdm1-3 mutation ( Table 1 ) , suggesting that tyrosine 14 and proline 18 tyrosine do not play crucial roles in the function of TDM1 . In summary , expression of TDM1-P17L , -T16A and -Δ14_19 mutations are equally able to dominantly confer premature meiosis exit . As TPs are potential phosphorylation sites , these results suggest that TDM1 is regulated by phosphorylation to ensure the meiosis I to meiosis II transition . Previous work revealed a role for the cyclins TAM and SDS in meiosis and both were found to build active complexes with CDKA;1 [20 , 23] . In vitro kinase assays showed that TDM1 is phosphorylated by CDKA;1-TAM and not , or to very low levels , by CDKA;1-SDS , while both kinase complexes were active against a generic substrate ( Fig 4A and 4B ) . Mass spectrometry of in vitro phosphorylated TDM1 identified peptides that contained a phosphorylated T16 residue corroborating that TDM1 is indeed a substrate of CDKA;1-TAM ( Fig 4D and 4E ) . Conversely , we did not identify any peptides containing phosphorylated T16 when performing mass spectrometry analyses of TDM1-P17L treated with CDKA;1-TAM ( S3 Fig ) . This , together with the meiotic defect conferred in vivo by TDM1-T16A and TDM1-P17L , suggests that the TDM1 function is regulated by CDKA;1-TAM through T16 phosphorylation . In addition to T16 , mass spectrometry revealed three other phosphorylated sites upon treatment with CDKA;1-TAM , S62 , S253 , and S320 . This is consistent with the observation that TDM1-T16A and TDM1-P17L can be still phosphorylated in vitro by CDKA;1-TAM ( Fig 4C ) . Peptides including phosphorylated version of these three amino acids were also identified when TDM1-P17L was treated with CDKA;1-TAM . However , the residues S253/P254 or S320/T321 are poorly conserved in the TDM1 proteins of flowering plants ( S1 Fig ) . In addition , the expression of a non-phosphorylatable version of TDM1 at S62 ( TDM1-S62A ) in the null tdm1 mutant restored meiosis ( Table 1 ) , demonstrating that the S62 residue does not play an essential role in regulating meiosis progression in vivo . Our previous work showed that CDKA;1-TAM also phosphorylates OSD1 in vitro [20] , suggesting that CDKA;1-TAM could regulate the function of OSD1 in addition to TDM1 . OSD1 possesses seven potential CDK-dependent phosphorylation sites ( [S/T]-P ) . The expression of OSD1 mutated in all seven sites ( S/T to A ) , complemented the null osd1 mutant ( S4 Fig ) , suggesting that the phosphorylation of OSD1 by CDKs does not play an essential role in meiotic progression . To further investigate the role of TDM1 and its interactions with the other components of the meiotic cell cycle we performed epistasis analyses ( Table 1 , Figs 5 and 6 , Table 3 ) . When the TDM1-P17L construct was introduced into tam-2 or osd1-3 mutants , the transformants produced dyads at the end of a regular meiosis I ( Table 1 and Fig 5A–5D ) , as occurs in each single mutant . Thus the TDM1-P17L mutation does not modify the osd1 or tam null mutant defects , and vice versa . SMG7 is essential for progression through meiosis II , the smg7-1 mutant being arrested at anaphase II [23 , 24] . When the TDM1-P17L construct was introduced into smg7-1 , the transformants had the smg7-1 phenotype ( Table 1 and Fig 5E–5H ) : meiosis was indistinguishable from wild type until metaphase II ( Fig 5E and 5F ) but an anaphase II arrest was observed with an incapacity to distribute the chromatids ( Fig 5G and 5H ) , resulting in complete sterility . This shows that the smg7-1 mutation is epistatic on the TDM1-P17L mutation . Finally , we co-transformed wild type plants with TDM1-P17L and a non-destructible version of TAM ( TAMΔD [20] ) , and selected primary transformants carrying either both or one of the transgenes ( Figs 5 and 6 ) . Plants transformed only with TAMΔD ( n = 6/6 ) were sterile , and meiocytes went through both meiosis I and meiosis II , before attempting a third meiotic division characterized by the formation of four spindles ( Fig 6A–6D ) , as previously described [20] . Plants transformed with only TDM1-P17L ( n = 8/9 ) were fertile and produced dyads following the first division ( Fig 6E–6G ) , as described above . Plants transformed with both TDM1-P17L and TAMΔD , were sterile and showed a meiotic defect that differed from both single transformants: In these plants ( n = 8/8 ) , meiocytes went through normal meiosis I until telophase I ( Figs 5I–5K and 6H–6I ) . Neither regular meiosis II nor meiosis III were observed , but aberrant meiosis II-like figures ( Figs 5L–5N and 6J and 6K ) with uncondensed and stretched chromosomes , ending with chromosomes being scattered throughout the cell and complete sterility . This shows that expressing TDM1-P17L in TAMΔD prevents the occurrence of a second and third meiotic division and , conversely , that the expression of TAMΔD in TDM1-P17L prevents meiotic termination after meiosis I , forcing cells entering into an aberrant meiosis II . While TAM is specifically expressed in meiosis I , TDM1 was identified as a protein required for meiotic exit [23] . Nevertheless , our genetic and biochemical data indicate that TAM and TDM1 functionally interact and that TDM1 may also be expressed in meiosis I . To examine TDM1 expression , we generated transgenic plants carrying the entire TDM1 gene fused to a β-glucuronidase ( GUS ) reporter at the C terminus . GUS histochemical assays revealed that TDM1::GUS was specifically expressed in anthers of flower buds whose size correspond to the meiotic stage ( Fig 7A and 7B ) . Further , a TDM1::Myc genic fusion was constructed and shown to be able to complement the tdm1 mutation . Myc Immuno-localization on male meiocytes showed that TDM1::Myc was present throughout both meiotic divisions , from mid-prophase to the tetrad stage ( Figs 7C–7L and S6 ) . This data indicates that expression of TAM and TDM1 partially overlaps in meiosis I[23] . TDM1 contains a four-TPR ( tetratricopeptide repeat , 61–224 ) domain with structural similarity to the TPR domains of the APC/C subunits APC6 , APC3 , APC and APC8 [20] , suggesting that TDM1 could interact with or be a subunit of the APC/C . To test this hypothesis we used Y2H experiments to assess interaction of TDM1 with different APC/C subunits ( Fig 8A ) . In this assay , TDM1 interacted with itself and this interaction was mediated by its N terminal ( 1–294 ) , which contains the TPR domains . TPR domains are known to mediate protein-protein interactions , and notably self-dimerization in the case of the TPR-containing APC/C subunits [29 , 30] . In addition , TDM1 interacted with the APC/C core component CDC27b ( HOBBIT/APC3b ) and the APC/C activator CDC20 . 1 . No interaction was detected between TDM1 and the other core subunits tested ( CDC27a , APC5 , APC6 , APC7 , APC8 , APC10 , APC11 ) , nor other APC/C activators ( CDC20 . 3 , CCS52A1 , CCS52A2 , CCS52B or SAMBA ) , nor OSD1 nor PANS1 [31] . TDM1 interactions with itself and with CDC20 . 1 were confirmed by bimolecular fluorescent complementation ( BiFC ) experiments ( Fig 8B–8I ) . Finally , the TDM1-P17L version was able to interact in yeast two-hybrid with itself , CDC20 . 1 , HOBBIT and TDM1 , like wild type TDM1 ( Fig 8A ) , suggesting that TDM1 Pro17 is not essential neither for TDM1 dimerization nor for the interaction of TDM1 with the APC/C .
Control of APC/C activity is fundamental to regulate cell cycle progression and termination . The APC/C ubiquitinates cyclins and other targets to trigger their degradation by the proteasome promoting anaphase I and cell cycle exit . Here we propose that TDM1 is an APC/C meiotic component . TDM1 shares structural similarities with the TPR-containing APC/C subunits [20] . TDM1 interacts with itself , suggesting that it acts as a homodimer like the TPR-containing APC/C subunits . Interestingly , a tdm1 mutation ( ms5-2 ) that truncates the protein by 112 amino acids at the C-terminus , leaving intact the TDM1-TDM1 interaction domain , dominantly provokes the loss of function phenotype [26] , which is compatible with TDM1 acting as a homodimer because the non-functional version of TDM1 would sequester functional versions of TDM1 into a non-functional complex . Further , yeast two-hybrids and BiFC assays showed that TDM1 interacted directly with the APC/C activator CDC20 . 1 and with the TPR-containing APC/C core component CDC27b ( HOBBIT ) . Together with the absence of meiotic exit provoked by the tdm1 loss-of-function , this suggests that TDM1 could promote meiotic termination by activating the APC/C and/or by modifying its specificity ( Fig 9 ) . This would trigger the elimination of the remaining cyclins from the cell and promote the exit from the meiotic program . As TDM1 is present throughout meiosis , its activity must be negatively regulated to prevent premature meiotic termination before the end of meiosis II . Here we showed that mutating the TDM1 Proline 17 ( TDM1-P17L ) or its adjacent Threonine 16 ( TDM1-T16A ) dominantly provokes an exit from meiosis after the first division , mimicking the phenotype of the recessive tam loss-of-function mutant . Further , TAM in combination with the cyclin-dependent kinase CDKA;1 is able to phosphorylate TDM1 at T16 in vitro . In addition , the TDM1 protein is present throughout meiosis , while TAM is present only at meiosis I . We also showed that TDM1 interacts directly with the APC/C . Altogether , this strongly suggests a model in which CDKA;1-TAM inhibits TDM1 at meiosis I through T16 phosphorylation , preventing premature meiotic exit ( Fig 9 left panel ) . At the second division , the absence of TAM would reactivate TDM1 , and this would promote meiotic exit at the end of meiosis II via APC/C activation ( Fig 9 right panel ) . This model accurately predicts the premature meiosis exit observed in tam , TDM1-T16A or TDM1-P17L , and the failure to exit from meiosis after the second division of tdm1 or TAMΔD , and is further supported by epistasis data ( Table 3 ) : as predicted , ( i ) the null tdm1 mutation in tam suppressed premature exit from meiosis and provoked the entry into a third meiotic division; ( ii ) Expressing TDM1-P17L in tam did not modify the premature meiotic exit seen in both single mutants; ( iii ) Expressing TAMΔD in tdm1 did not modify the three-division meiotic defect observed in both single mutants . This simple two-component model would predict that expressing both TAMΔD and TDM1-P17L should lead to a single meiotic division , because the expression of a non-destructible version of TAM would not affect the precocious meiotic exit provoked by TDM1-P17L , which is resistant to TAM-mediated regulation . However , in plants expressing both TDM1-P17L and TAMΔD , meiosis did not end after meiosis I like in TDM1-P17L , but arrested at an aberrant stage . This suggests that TAM has target ( s ) other than TDM1-P17 ( see below also ) . Given the diversity in the regulation of APC/C across kingdoms [2] , the mechanisms of APC/C regulation for meiosis termination seem far from universal . TDM1 seems to be the functional equivalent to Mfr1/Fzr1 in fission yeast . Termination of meiotic divisions is dependent on activation of the APC/C by Mfr1/Fzr1 . It has been shown that Cuf2 up-regulates Mfr1/Fzr1 in meiosis II to activate APC/C activity , degrade cyclins and lead to exit from meiosis . Both cuf2 , fzr1 and cuf2 fzr1 mutants undergo an aberrant third division where chromatids try to divide again [32] . As TDM1 is also present in meiosis I , it must be kept latent to avoid premature meiosis termination . We propose that only the relief of its inhibition at the second meiotic division promotes APC/C activity to completely shut off CDK activity . Differences in the mode of regulation of these genes in fission yeast and plants would have converged to deliver the same outcome of APC/C regulation and meiosis termination ( Fzr1 is regulated at the transcriptional level by the transcription factor Cuf2 , while TDM1 is regulated post-translationally by CYCA1;2/TAM ) . OSD1 , a functional homolog of the fission yeast Mes1 [7] , promotes entry into the second meiotic division through APC/C inhibition [20 , 22] . In addition , OSD1 and TAM act in a synergistic manner to promote the transition from prophase to the first meiotic division , since the double mutant tam osd1 is not able to enter the first meiotic division [18] . Here we showed that , in contrast to tam , expression of TDM1-P17L in osd1 , does not affect the entry into meiosis I as meiocytes exit meiosis after the first division , as in osd1 mutants . This suggests that TAM promotes entry into meiosis I independently of both TDM1-P17 and OSD1 . TAM can phosphorylate OSD1 in vitro [20] . However , a version of OSD1 mutated in its seven potential CDK-dependent phosphorylation sites ( [S/T]-P ) is still functional , raising doubts about the relevance of this phosphorylation in vivo . Further work is required to identify the additional target ( s ) , beyond TDM1-P17L , of TAM . SMG7 is essential for the progression of meiosis II , as smg7-1 arrests at anaphase II . SMG7 has been proposed to control meiosis II exit through TDM1 , as the tdm1 mutation suppresses the smg7 anaphase II arrest [20 , 23] . Neither tam [23] nor the TDM1-P17L mutations [this study] alleviate the anaphase II arrest in smg7 mutant , which is consistent with our model ( tam or TDM1-P17L is predicted to not affect meiosis II progression ) . By contrast , these double mutants show that smg7 abolished the premature meiotic exit in tam and in TDM1-P17L plants . This suggests that smg7 promotes meiotic progression by regulating TDM1 in parallel to TAM . Although SMG7 has been described as a nonsense-mediated RNA decay factor , previous work has shown that its role at meiosis is not mediated through this activity [33] . More work is required to understand the meiotic role of this multifaceted protein . The regulation of TDM1 by CDKA;1-TAM-mediated phosphorylation determines the differential fate of first or second division meiocytes , ensuring the entry into the second meiotic division and meiotic exit . This sheds new light on the importance of CDK-mediated phosphorylation in meiotic cell cycle regulation . In addition to providing new insight into the regulation of meiotic progression , the identification of a dominant mutation that leads to premature meiotic exit , and that leads to the production of clonal gametes when combined with spo11 rec8 , could facilitate the development of synthetic clonal reproduction through seeds ( apomixis ) [34] .
The mutant alleles used in this study were spo11-1-3 , rec8-2 , osd1-3 , tam-2 , tdm1-3 , smg7-1 and were genotyped as in [20 , 21 , 24] . They are all in the same genetic background ( Col-0 ) . The TDM1-like1 alleles ( GABI_750C08 and GABI_173C10 ) were obtained from the NASC . Plants were identified by PCR using primers as follow: GABI_750C08U ( 5’-AGGTTTCTTGACTCCACCACC-3’ ) , GABI_750C08L ( 5’-CCGCTACTGTTGCTTGTTCTC-3’ ) and Lb ( 5’- CCCATTTGGACGTGAATGTAGACAC-3’ ) . EMS mutagenesis was performed as previously described [35] . The M1 plants that are presumably heterozygous and chimeric for EMS mutations were self-fertilized and harvested in bulks of ~5 to produce M2 families . About 2000 M2 families ( 400 bulks ) were screened for increased fertility compared to spo11-1 rec8 non-mutagenized control . Whole genome sequencing was done by Illumina Highseq 2000 100pb paired ends ( The Genome Analysis Centre , Norwich ) . A list of SNPs was generated compared to the reference genome of A . thaliana TAIR10 ( cultivar Columbia ) using the mutdetect pipeline [36] . Male meiotic products observation , chromosomes spreads , and ploidy measurement were carried out using the techniques described in d'Erfurth et al [37] . A TDM1 genomic fragment was amplified by PCR using TDM1 U ( 5′-GACATCGGCACTTGCTTAGAG 3′ ) , TDM1 L ( 5′-GCGATATAGCTCCCACTGGTT-3′ ) . The amplification covered 986 nucleotides before the ATG and 537 nucleotides after the stop codon . The PCR product was cloned , by Gateway ( Invitrogen ) , into the pDONR207 vector ( Invitrogen ) , to create pENTR-TDM1 , on which directed mutagenesis was performed using the Stratagene Quickchange Site-Directed Mutagenesis Kit . The mutagenic primers used to generate mutated version of TDM1 were: TDM1-P17L: ( 5′GAGTTTACTATACTCTGCCGCCGGCGAGAAC-3′ ) ; T16A: ( 5′CTCCACCTGGAGTTTACTATGCCCCGCCGCCGGCGAGA -3′ ) ; TDM1-Y14A: ( 5’-CCACCTGGAGTTGCGTATACTCCGCCGCGGCG-3′ ) ; and TDM1-Δ14–19 ( 5’-CCACCTGGAGTTGCGAGAACAAGTGATCATGTGGC-3’ ) ; and their respective reverse complementary primers . The mutagenic primers used to generate mutated version of OSD were OSD1_T198A_U: GGAAGAAGCTGGCTTCATCGCACCCGAGAAGAAGC; OSD1_ T105A_U: GTTGCCTTCTTGGTATCCAAGAGCACCTCTACGCG; OSD1_T224A_U: GGCGGAGATCCAGAAGTTGAAGAGCGCTCCTCAAGCTA; OSD1_S40A_U: CACGGCTTAGTTTGATTGAAGCTCCGGTGAATCCAG; OSD1_S146A_U: GTTGGTGTTCTTGAAGCTCCAGTACCACTGTCAGG; OSD1_T160A_U: AAATGCTCGATGGTCGCTCCTGGACCATCTGTGGG; OSD1_T73A_U: TGGCAGAGGTGGTCACGCTCCATTTAGATTGCCAC; and their respective reverse complementary primers . To generate binary vectors for plant transformation , an LR reaction was performed with the binary vector for the Gateway system , pGWB1 . The resulting binary vectors , pTDM1 , pTDM1-P17L , pT16A , pTDM1-Y14A and pTDM1-Δ14–19 were transformed using the Agrobacterium-mediated floral dip method on wild type plants and plant populations segregating for spo11-1 rec8 , osd1-3 , tam-2 or tdm1-3 mutation . Transformed plants were selected on agar plates containing 20 mg/L hygromycin and relevant homozygous mutants were identified among primary transformants through genotyping . The TAMΔD construct [20] was cloned into the binary vector pMDC123 ( www . arabidopsis . org ) that confers BASTA resistance ( BAR gene ) and introduced in Agrobacterium . Double transformation of Col-0 wild type by TAMΔD and TDM1-P17L was performed by mixing the two agrobacterium cultures before floral dipping . Transformants were first selected in vitro on 20 mg/L hygromycin and then genotyped by PCR for the BAR gene to identify double transformants . Among 174 hygromycin resistant primary transformants , 85 were also transformed by the TAMΔD/BAR transgene . TDM1 cDNA was amplified by PCR using 5’-ATGTGTCCCTGCGTAGAGCGT-3’ and 5’-CTACATCTCTGCGGTTTTAAGCTC-3’ . CDC20 . 1 cDNA was amplified by PCR using 5’-ATGGATGCAGGTATGAACAAC-3’ and 5’-TCAACGAATACGATTCACG-3’ TDM11-294 cDNA was amplified by PCR using 5’- ATGTGTCCCTGCGTAGAGCGT -3’ and 5’- TATTTCTGCTAACATTTCG -3’ . TDM1295-434 cDNA was amplified by PCR using 5’-CGAAATGTTAGCAGAAATAGA-3’ and 5’-CTACATCTCTGCGGTTTTAAGCTC-3’ . PCR product was cloned by Gateway ( Invitrogen ) into the pDONR221 vector ( Invitrogen ) to create pENTR . The other APC/C clones were described in [38] . The mutagenic primers used to generate mutated version of TDM1-P17L were the same as described above . LR reactions were done into the pDEST32 ( bait ) and pDEST22 ( prey ) vector ( Invitrogen ) . Y2H interaction was performed by mating , as described previously [38] into the yeast strain AH109 and Y187 ( Clontech ) . Protein interactions were tested in planta using BiFC assays [39] in leaf epidermal cells of N . benthamiana [40] . N-terminal fusions , using the pENTR clones described above for Y2H , with two YFP complementary regions ( YFPN + YFPC ) were co-infiltrated in N . benthamiana leaves and scored after 3 or 4 days for fluorescence as described in [41] . YFPN::DEF and YFPC::GLO [42] were used as positive controls for interaction . Each experiment was replicated at least three times . Observations were made using a Leica SP5 II AOBS Tandem HyD confocal laser-scanning microscope . Optical sections were collected with a Leica HCX PL APO CS 20 . 0x0 . 70 IMM UV water objective upon illumination of the sample with a 514-nm argon laser line with an emission band of 525–570 nm for the YFP . The specificity of the YFP signal was systematically checked by determining the fluorescence emission spectrum between 525 and 600 nm with a 10-nm window and under an excitation at 514 nm . Images were processed using Leica LASAF and Adobe Photoshop software . Target genes were subcloned into an entry vector and recombined into the destination vector pGGWA [43] by using LR Clonase II ( Life Technologies ) . Error-free destination clones were confirmed by sequence analyses . E . coli BL21-AI cells ( Life Technologies ) were transformed with the resulting destination clone and grown in LB medium containing 100 μg/ml ampicillin until OD600 = 0 . 6 at 37°C . The culture was transferred to 18°C and grown for 30 min . The production of the fusion protein was induced by adding 0 . 3 mM isopropyl-β-d-thiogalactopyranoside ( IPTG ) and 0 . 2% arabinose overnight at 18°C . Cells were harvested by centrifugation and re-suspended in Ni-NTA binding buffer ( 50 mM NaH2PO4 , 100 mM NaCl , 10% ( v/v ) glycerol , 25 mM imidazole , pH 8 . 0 ) containing protease inhibitors ( cOmplete ETDA-free; Roche ) , and lysed by sonication ( Digital Sonifier 450D , BRANSON ) . After addition of Triton X-100 to 0 . 2% ( w/v ) , the cell slurry was incubated at 4°C for 20 min then clarified by centrifugation . The supernatant was passed through a column packed with Ni-NTA resin ( Qiagen ) , which was washed sequentially with Ni-NTA binding buffer , and eluted with Ni-NTA elution buffer ( Ni-NTA binding buffer containing 200 mM imidazole ) . The eluate was applied onto a column packed with glutathione agarose ( Sigma ) , which had been equilibrated by Ni-NTA buffer . The column was washed sequentially with Ni-NTA binding buffer followed by kinase buffer ( 50 mM Tris-HCl , pH 7 . 5 , 10 mM MgCl2 , 1 mM EGTA ) , then the fusion protein was eluted with kinase buffer containing 10 mM glutathione . After CDK complexes were expressed and purified by using a system as described [44] , ATP was added to 2 mM , and the complexes were incubated for 1 h at 30°C . The reaction was then further purified with a column packed with Strep-Tactin sepharose resins ( IBA ) , which had been equilibrated with kinase buffer . CDK complexes were eluted with kinase buffer containing 2 . 5 mM desthiobiotin . The aliquoted complexes were frozen in the liquid nitrogen and stored at -80°C . The kinase assays were carried out with CDKA;1-TAM or CDKA;1-SDS as a kinase , 2 μg of histone H10 ( NEB ) or purified GST-TDM1 ( WT , T16A , and P17L ) -His6 as a substrate , 92 . 5 kBq of [γ-32P]ATP ( PerkinElmer ) per reaction in kinase buffer with a final volume of 20 μl . After incubation for 30 min at 30°C , the reactions were stopped by adding Laemmli sample buffer ( Bio-rad ) and boiled . Samples were separated on 12% ( for Histone ) or 7 . 5% ( for TDM1 ) TGX gels ( Bio-rad ) , and after the gels were stained with Bio-Safe™ Coomassie G-250 Stain ( Bio-rad ) , they were dried with HydroTech™ Gel Drying System ( Bio-rad ) . Radioactive proteins were detected using a Typhoon™ FLA-7000 system ( GE Healthcare ) . To identify phosphorylation sites on TDM1 , kinase reactions were carried out with CDKA;1-TAM , 2 μg GST-TDM1-His6 , 1 mM ATP ( Sigma ) per reaction in kinase buffer with a final volume of 20 μl . After incubation for 1 h at 30°C , the reactions were stopped by adding Laemmli sample buffer and boiled . Samples were separated on 7 . 5% TGX gels , and the gels were stained with Bio-Safe™ Coomassie G-250 Stain . An LTQ-Orbitrap XL ( Thermo Fisher Scientific ) coupled with an EASY-nLC 1000 ( Thermo Fisher Scientific ) was used for nano-LC-MS/MS analyses as described previously [45] . | Meiosis is a fundamental process for sexually reproducing organisms that creates genetic diversity within populations . A key feature of meiosis is the reduction of the number of chromosomes , from two sets to one set , prior to fertilization . This reduction in chromosome number is due to two cell divisions following a single round of DNA replication . In this study , we analysed the mechanism which controls the number of cell divisions , ensuring that meiotic termination occurs after the second meiotic division , and not at the end of the first division . We used the model plant Arabidopsis thaliana to show that the gene TDM1 has a central role in regulating meiotic cell divisions . The integrity of the gene affects whether one , two or three meiotic divisions will occur . We further explain the relationship between TDM1 and its regulator the cyclin TAM , and how they work together to produce reproductive cells with a reduced number of chromosomes . This tightly controlled mechanism ensures the transmission of the correct number of chromosomes from one generation to the next . | [
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"pr... | 2016 | TDM1 Regulation Determines the Number of Meiotic Divisions |
A fundamental property of cell populations is their growth rate as well as the time needed for cell division and its variance . The eukaryotic cell cycle progresses in an ordered sequence through the phases and and is regulated by environmental cues and by intracellular checkpoints . Reflecting this regulatory complexity , the length of each phase varies considerably in different kinds of cells but also among genetically and morphologically indistinguishable cells . This article addresses the question of how to describe and quantify the mean and variance of the cell cycle phase lengths . A phase-resolved cell cycle model is introduced assuming that phase completion times are distributed as delayed exponential functions , capturing the observations that each realization of a cycle phase is variable in length and requires a minimal time . In this model , the total cell cycle length is distributed as a delayed hypoexponential function that closely reproduces empirical distributions . Analytic solutions are derived for the proportions of cells in each cycle phase in a population growing under balanced growth and under specific non-stationary conditions . These solutions are then adapted to describe conventional cell cycle kinetic assays based on pulse labelling with nucleoside analogs . The model fits well to data obtained with two distinct proliferating cell lines labelled with a single bromodeoxiuridine pulse . However , whereas mean lengths are precisely estimated for all phases , the respective variances remain uncertain . To overcome this limitation , a redesigned experimental protocol is derived and validated in silico . The novelty is the timing of two consecutive pulses with distinct nucleosides that enables accurate and precise estimation of both the mean and the variance of the length of all phases . The proposed methodology to quantify the phase length distributions gives results potentially equivalent to those obtained with modern phase-specific biosensor-based fluorescent imaging .
The cell cycle is one of the most fundamental processes in biology . Through this process , a parental cell transmits to its two daughter cells genetic and epigenetic information by accurately replicating its DNA and evenly apportioning all nuclear and extranuclear contents . The mechanism of cell cycle regulation is tailored to ensure accurate cellular content replication , but seems to be less constrained by how long it takes to complete this process successfully . Several check points exist that ensure that chromosomes are faithfully copied and that the parental cell has enough material in order to produce two viable isogenic daughter cells . Meeting the conditions of each of these check points takes variable time and delays the completion of the cell cycle . Yet , how long the cells take on average to complete the cell cycle is an important biological property . In unicellular organisms , the average intermitotic time is a direct measurement of the organism's fitness , while in multicellular organisms , the regulation of the rate of cell division is critical for development , stem cell maintenance , tissue or organ homeostasis , wound healing , and immunity . The temporal organization of the cell cycle is therefore under tight regulation , likely reflecting a fine balance between accuracy in information transmission and speed . The average cell cycle time has been estimated at the population level by measuring the growth curve of exponentially proliferating cell cohorts , under conditions in which cells can be counted and cell death is negligible compared to the population wide growth rate . Under conditions in which cell counting is not possible or in which cell death rates cannot be neglected ( e . g . , homeostasis , immune reactions , cancer growth ) , indirect estimates for the average division time or the average death time are typically inferred e . g . , through the rate of increase of cells arrested in mitosis after administration of colchicine , the fraction of labelled mitotic figures after pulse labelling ( FLM method ) , and from long-term labelling and delabelling time-series of deuterium or bromodeoxyuridine ( BrdU ) tracing experiments [1]–[3] . For growing cell populations these estimates depend on assumptions about the shape of the intermitotic time distribution [4] . The latter , when analyzed at a single-cell level , e . g . , by time-lapse imaging , shows significant variability in otherwise seemingly homogeneous cell populations . This observation led more than forty years ago to the development of one of the first stochastic cell cycle models [5] . Smith and Martin proposed at that time that cell's life comprehends an state and a phase . Whereas the time cells spend in the state was assumed to be exponentially distributed , the time cells spend in the phase was , in this simplest scenario , a fixed delay . Experimental validation was provided by time-lapse imaging of growing cell cultures , measurements of fraction of labelled mitoses and fractions of sibling pairs with age difference greater than a specified value [6] . Even though later studies [7]–[10] have shown that the model assumptions do not exactly match experimental data , its simplicity and mathematical tractability makes the Smith-Martin model even today a popular theoretical model [6] , [11] . In the last ten years , 5- ( and 6 ) -Carboxyfluorescein diacetate succinimidyl ester ( CFSE ) dilution assays in concert with a whole set of advanced modeling techniques [12]–[14] allowed to estimate the average duration , as well as inter-cellular variability in more complex scenarios with division time densities in vitro or in vivo after adoptive cell transfer . Especially generation structure , activation times and generation dependent cell death were included in these models and subsequently estimated in the context of lymphocyte proliferation . Inter-cellular variability not only of division times but also of death times were confirmed directly in long-term tracking of single HeLa cells [15] and B-lymphocytes [10] . The latter study provided extensive quantitative data on the shape of age-dependent division and death time distributions which are required to calibrate e . g . , the Cyton [16] or similar models . A review on these , and alternative stochastic cell cycle models is given in [4] . At a higher temporal and functional resolution the eukaryotic cell cycle is structured into four distinct phases: 1 ) the phase during which organelles are reorganized and chromatin is licensed for replication , 2 ) the phase in which the chromosomes are duplicated by DNA replication , 3 ) the phase which serves as a holding time for synthesis and accumulation of proteins needed in 4 ) the phase , or mitosis , which is marked by chromatin condensation , nuclear envelope breakdown , chromosomal segregation , and finally cytokinesis , which completes the generation of two daughter cells in phase [17] . Considering explicitly cell cycle phases in mathematical models of cell division probably dates back to the discovery that is replicated mainly during a specific period of the cell cycle . Already in their seminal paper , Smith and Martin related the state to the phase and the phase to the and possibly to some part of the phase . Subsequent studies that explored phase-resolved cell cycle models , majoritarely rooted in the field of oncology and cancer therapy , include [18]–[25] . As in the present work , most of these studies relied on flow cytometry data generated by labelling selectively cells that are synthesizing using nucleoside analogs ( e . g . , BrdU , iodo-deoxyuridine ( IdU ) or ethynyl-deoxyuridine ( EdU ) ) , together with a fluorescent intercalating agent to measure total DNA content ( e . g . , 4 , 6- diamidino-2-phenylindole ( DAPI ) , and propidium iodide ( PI ) ) , in order to test the model assumptions and draw conclusions about the cells and conditions under consideration . Here we present a simple stochastic cell cycle model that incorporates temporal variability at the level of individual cell cycle phases . More precisely , we extend the concept underlying the Smith-Martin model of delayed exponential waiting times to the cell cycle phases . We first demonstrate that the model is in good agreement with published experimental data on inter-mitotic division time distributions . We then show , based on stability analysis , that phase-specific variability remains largely undetermined when measurements are taken on cell populations under balanced growth ( i . e . , growth under asymptotic conditions in which the expected proportions of cells in each phase of the cycle are constant ) . We prove that by properly measuring proliferating cells under unbalanced growth , one can with at least three well placed support points , assuming noise-free conditions , uniquely identify the average and variance in the completion time of each of the cell cycle phases . When comparing our model with two experimental data sets obtained from conventional pulse-labelling experiments of distinct proliferating cell lines , we find that , while the kinetics extracted from these experiments are well approximated by the predictions of the proposed model , the information content is insufficient to determine accurately all the parameters . Finally we propose a modification of the prevailing experimental protocol , based on dual-pulse labelling with and , for example , that overcomes this shortcoming .
The eukaryotic cell cycle is defined as an orderly sequence of three phases distinguished by cellular DNA content , termed and A dividing cell is supposed to proceed , under this minimalist view , from one phase to another in a fixed order , until reaching the end of phase . Here it completes cytokinesis generating two genetically identical daughter cells that are by definition in phase ( Fig . 1 A ) . We assume that the completion time of any phase ( i . e . the time lapse between the entry to and exit from that given phase ) is a random variable which is distributed according to a delayed ( or shifted ) exponential density function ( Fig . 1 B ) , ( 1 ) where is the reciprocal of the rate of the exponential ( measured in units ) and is the fixed delay ( in units ) , and denotes the Heaviside step function whose value is zero for negative argument , i . e . , for and one for positive argument . Notice that with a slight abuse of notation we denote here the random variable ( subscript of density function ) and the value it assumes ( the argument of the function ) by the same symbol This will allow us to denote the probability density function and the cumulative probability distribution of the random variable by and respectively , and to define the complementary cumulative distribution The delay in Eq . 1 ‘ensures’ that a cell that enters a specific phase will remain therein for at least time units ( e . g . hours ) before proceeding to the next phase . Besides this fixed minimal time additional less predictable effects that affect the completion of the processes associated to a phase are assumed to be exponentially distributed with both mean and standard deviation given by The phase specific mean completion time , denoted in the following by is then with standard deviation and coefficient of variation The Laplace transform of Eq . 1 is given by ( 2 ) where is the transformed variable corresponding to the time lapse The temporal organization of the cell cycle is defined by the vector of phase-specific completion times , which in turn depend on the parameter vectors and The cell cycle length , understood as the time lapse between the entry into until exit out of is the random variable Its probability density function is the convolution of the three underlying delayed exponential distributions and corresponds to the delayed hypoexponential distribution . Explicit expressions can be computed using the inverse Laplace transform of the product of the Laplace transforms of the three densities given by Eq . 2 , i . e . , ( 3 ) In case that all entries in are distinct , we get ( 4 ) in which the indices and iterate over the three phases and is the sum of the elements in In Fig . 1 B we plot the shape of the phase specific completion time distribution defined by Eq . 1 , which illustrates that the probability for a cell to complete a given phase in less than time units is zero under this model . A graphical representation of the cell cycle model is provided in Fig . 1 A . Notice that each phase can have distinct parameter values and for the completion time distribution . As a first validation , we compared the empirical frequency of undivided cells as a function of time after ‘birth’ ( reported by [5] ) with the respective probability according to the model which we denote as ( Fig . 1 C ) . As a second test , we fitted the cell cycle length density given by Eq . 4 to data extracted from video-tracking of in vitro proliferating B cells [10] . The delayed hypoexponential distribution ( shown in Fig . 1 D ) , but also the delayed log-normal and the delayed gamma distribution ( not shown ) with parameter values proposed in [10] , reproduce closely the measured division time histogram . While the two latter depend on three parameters each , the hypoexponential distribution depends on six parameters , that remain largely undetermined given this kind of data . A proliferating cell population that obeys the probability model specified in the previous section can be represented by a non-Markov multidimensional random process , whose evolution depends on its history . There exist an infinite number of possible histories or realizations of the population size dynamics We focus here on a specific important subset , namely those under balanced growth . Under balanced growth a cell population grows exponentially with mean growth rate and constant mean proportions of cells in the three phases where e . g . , The expectation operator is defined over all possible realizations of the process . We will now derive explicit expressions for and a transcendental equation that defines the growth rate . A first step in obtaining the constant frequencies of cells in each of the phases consist in computing the ratio between the cells that complete a given phase and the total number of cells inside the same phase at time This phase-specific quantity , denoted here by represents the asymptotic efflux rate constant , which will be useful , as we will see , to construct a transition probability matrix The latter will enable us to employ methods from linear algebra to solve the steady state condition . Suppose for example that a cohort of cells entered a given phase at time Then the density of cells leaving this phase at time will be Similarly if a cohort of cells entered this phase at time then a proportion will remain in it until time Recalling that the influx of cells into a given phase is proportional to and that is the complementary cumulative distribution of which is Laplace transformed to we integrate over all past entries and finally take the ratio to obtain ( 5 ) ( 6 ) ( 7 ) While the second equality is a consequence of the definition of the Laplace transform , the third equality follows by substituting using Eq . 2 . For a phase without a delay , i . e . , the last expression simplifies to the familiar mass action principle , where the transition probability is directly proportional to the decay rate Assuming that cells are immortal and recalling that division occurs as cells proceed from to we build up the transition probability matrix as follows ( 8 ) The balanced growth condition can now be formulated in matrix form ( 9 ) where the growth rate is an eigenvalue of and the proportions vector is the corresponding eigenvector . It can be shown that there exists a single dominating real positive eigenvalue for ( see Materials and Methods ) whose associated normalized eigenvector is ( 10 ) The uniqueness and existence of a dominating positive real root ultimately motivates our focus on balanced exponential growth , as any immortal proliferating cell population with sufficient nutrients and space will eventually enter this stationary phase . The time it takes , either starting with a single cell or a synchronized cell cohort to enter this state depends on the cell cycle parameters . The exponential growth rate is the unique real positive root of the characteristic equation which writes as ( 11 ) It is easy to see that the denominator in Eq . 11 is always positive . To determine a non-trivial it remains to solve the transcendental equation in the numerator ( 12 ) Numerical solutions to this equation can be computed using e . g . , the Newton-Raphson root finding algorithm , with fast convergence if the initial value is set to where is the average cell cycle length , i . e . , the sum of the elements in This first guess is a naive estimate for assuming that cells divide according to a deterministic division time identical to the average of the hypoexponential density defined in Eq . 3 . The predicted fractions of cells in each of the phases can be compared to frequencies extracted experimentally from bivariate analysis of cell populations transiently exposed to nucleoside analogs and subsequently examined both for the intensities of the signals due to incorporated nucleoside analog and total DNA content [26] ( e . g . the so called BrdU-DAPI staining dot plot ) . The question that we want to address in this section is: What can potentially be learned about the parameters of the model , given this type of experimental data ? By definition , the measured frequencies will sum to one , and therefore we have for three populations effectively only two equations but six model parameters . This makes it impossible to identify all the parameter values , irrespective of the number of samples we take . It is however possible to derive analytical expressions for the upper and lower bounds for both the parameters and the average completion time of each phase . Consider the experimentally determined frequencies , denoted by Substituting the vector by in Eq . 10 and solving for each phase specific parameter we obtain ( 13 ) where is a phase specific element of the vector ( 14 ) The phase specific parameters and respectively the reciprocal rate and delay , are by definition greater or equal to zero . These conditions propagate into Eq . 13 which allows us to specify boundaries for and First notice that is , for each phase , a monotonically decreasing function of with a maximum at and a zero crossing at The maximum and the root represent the upper bounds for and respectively , while the lower bounds are zero for both . We thus have for each phase ( 15 ) The mean phase-specific completion time , the sum of the reciprocal rate and the delay is also bounded , with an interval given by ( 16 ) This result is derived from the fact that is concave having its unique minimum at which follows from setting the derivative to zero . This implies that is a monotonically decreasing function in the interval with the corresponding extrema specified above . It is important to note that the intervals defined by Eqs 13–16 depend on the average growth rate which is in general not known . Formally if one specific pair of parameter vectors and explains the measured frequencies with growth rate the scaled parameter vectors and mimic equally well the same data for arbitrary positive however with a reduced growth rate This can be easily verified by substituting these expressions in Eq . 10 and Eq . 12 . The direct consequence is that remains undefined . However for the relative average time a cells spends e . g . , in phase the growth rate cancels out . Using the fact that and the appropriate series expansion for the natural logarithm , the widths of the intervals bounding and for each phase can be written as: ( 17 ) From this it is straight-forward to show that This implies that by using measurements of the phase-specific stationary cell frequencies to infer the phase-specific completion times results in estimates of the mean value that are more precise than the estimates of the standard deviation Notice that the width of the intervals can be interpreted as a naive lower bound for the uncertainty about the respective parameter values . For the two data sets analyzed in this article ( see details in next section ) , we computed the intervals for the phase-specific standard deviations that were on average times wider than the intervals for the expected phase-specific completion times Balanced growth analysis does not allow to distinguish between fixed ( ) and purely exponentially distributed ( ) completion times even if is known . This follows from Eq . 15 because possible values for the standard deviation include and and the latter requires , according to Eq . 16 , the delay to be null . The incapacity to resolve the values of and is overcome if one selects and follows a subpopulation within which the proportions of cells in each phase are transiently different from the balanced growth proportions . Consider a simple thought experiment that consists in taking a population under balanced growth and labelling all the cells that are in a specific phase , say which can be either or Initially all the cells are in the same phase but as time passes by the labelled cells progress through the cell cycle and eventually distribute over the three phases . The labelled cell subpopulation which is initially not balanced will return asymptotically to balanced growth conditions , restoring the corresponding proportions of cells in the three phases . We refer to this transient dynamics of a selected subpopulation as transient unbalanced growth . It turns out that measuring the transient dynamics of this subpopulation yields information that potentially allows to distinguish between a fixed and a purely exponentially distributed phase completion time . More specifically , a mathematical proof will show that taking samples at three well chosen time points ( support points ) permits under ideal conditions accurate estimation of the average and the variability in the time required to complete the phase The initial average fraction of cells in phase which are selectively labelled at time is determined by Eq . 10 . To predict when the labelled cells will have completed we need to specify when they entered this phase . For the time before labelling the average influx into is proportional to . For the time after the labelling , because by definition all labelled cells entered phase before ( otherwise they would not be labelled ‘as being in phase ’ ) , the entry of cells is zero . Hence , the average influx to the labelled subpopulation is proportional to where denotes the Heaviside step function . Let us assume that within the subpopulation of labelled cells and their progeny one could identify how many phases a cell or a cohort of cells went through since the labelling event , and let count the number of phases since labelling . In close analogy to expression Eq . 5 we compute the time-dependent exit-rate density distribution for cells with as ( 18 ) where , for convenience , we interpreted and will interpret in the following both as a phase and a phase index . As before , the third row follows from the definition of the Laplace transform setting . On the left-hand side , the arrow from 0 to 1 represents the transition from the initial phase ( ) to the next phase ( ) , corresponding to the completion of the initial phase In contrast to Eq . 5 , the denominator accounts for the cells that entered or initiated phase sometime in the past , and did not complete this phase until the instant of labelling ( and not at time as in Eq . 5 ) , while the numerator , except for the altered average influx , remains unchanged . After computing and substituting using Eq . 2 , Eq . 18 yields for ( 19 ) It follows that the accumulated average cell flux that at time has completed and progressed to the next phase is given by ( 20 ) which for approaches one , reflecting the fact that all cells will eventually complete The Laplace transform of Eq . 20 writes as where is , as before , the transformed variable corresponding to Within a cohort of cells isolated for instance in phase , i . e . , the accumulated average cell flux out of the subsequent phase can then be derived recalling Eq . 2 and using the properties of the inverse Laplace transform as ( 21 ) For an arbitrary cell cohort originally in the accumulated average flux , completing phases and entering the phase since isolation , can be written in general as ( 22 ) in which denotes a function which returns an appropriate phase index . For and it is defined as where is the modulo operation , and is a vector of cell cycle phase indices . The function thus returns , for increasing , in a cyclical fashion , the cell cycle phase indices , starting with for Notice that Eq . 21 corresponds to Eq . 22 for and Analytical expression for Eq . 22 , although solved relatively easily with modern algebra software , can become quite cumbersome for values of larger than six . In our case , deriving the expressions for up to a value of five was sufficient to simulate the experiments . Because we want to compare the model predictions with experimentally measured cell frequencies , more interesting than the accumulated fluxes are the expected proportions of cells inside each phase over time . These can be computed using Eqs 20–22 , closely following the methodology outlined in [11] , [12] . For the fraction of cells initially in phase we have ( 23 ) where the lower index 0 in indicates that this expression describes cells which completed zero phases since The first term on the right hand side corresponds to the fraction of cells in phase at divided by which accounts for the total population growth during the same interval . The second term stands for the fraction of cells that remained in phase up to time relative to the initial number of cells in this phase . By evaluating the integral in Eq . 20 , substituting in Eq . 23 and letting as before , without loss of generality , the time of partition be zero , we get for ( 24 ) Expressions for cells initially in or phase can be obtained by substituting by the respective phase . If there were no cell division ( i . e . , ) we could readily obtain the average fraction of cells that completed phases at time as the difference between the cells that entered the phase , i . e . , , and those that left it , i . e . , divided by To account for cell division , we need to multiply this difference by an additional term which increases by a factor 2 each time cell cohorts make a transition from This term is defined , for each case , as follows: and where the brackets in the exponent represent the floor operator . In general we get for all consecutive phases for cells initially in phase the relatively manageable expression ( 25 ) As for Eq . 24 , the resulting solutions are defined as piecewise-continuous functions in time . Also notice that most expressions in this section can be written in more compact , but less intuitive , vector form , by dropping the initial phase index and using bold vector notation as before . In this section we will show that data from the transient kinetics generated by our thought experiment allows to accurately estimate the average and the variability in the individual completion times . The proof is based on the analytical expressions derived in the previous section , and also on the assumption that the kinetics are acquired under the ideal conditions of large population sizes and no measurement errors . The latter condition , although clearly unrealistic , can always be approached in practice by increasing the number of samples at each support point . For the sake of generality , consider a subpopulation of cells that are in an arbitrary phase and are labelled at Assuming that the ‘label’ does not in any way affect the cell cycle of the cells , the parameters and of the labelled subpopulation are the same as those of the full population under balanced growth . Under these conditions , we can obtain using Eq . 13 and Eq . 14 with the fractions of the full population observed at time Substituting in the upper row of Eq . 24 and solving for to find ( 26 ) where denotes an arbitrary time point that lies in the interval , and is the experimentally determined equivalent of Eq . 24 . This shows that the balanced growth rate is fully determined by only two support points , one immediately after the partition at and a second at an arbitrary This also makes clear that placing more support points in the interval does not increase knowledge about nor the parameter values , under ideal conditions . Importantly the uncertainty about the phase-specific variability discussed in previous sections remains . By replacing the same expression for in the second row of the right-hand side of Eq . 24 we get ( 27 ) After experimentally acquiring and the phase specific and this expression will depend on a single unknown One can show that Eq . 27 is solved by a unique This follows from the fact that the right hand side of Eq . 27 is a monotonically decreasing function in with corresponding values lying in the interval while the left hand side is positive by definition . Substituting the solution for into Eq . 13 yields the remaining parameter vector Taken together this proves that in theory samples of the three cell cohorts and taken at three support points , a first at a second at and a third at are sufficient to determine all the parameters of the model . The thought experiment analyzed so far , although conceptually simple , poses a series of experimental challenges , that make a one-to-one realization difficult . The technical difficulties lie mostly in initially separating the cells according to their phase and in following these cells as they enter the subsequent phases . A widely used technique , namely DNA-nucleoside-analog pulse-chase labelling experiments , generates nevertheless to a certain extent comparable data . The latter achieves the initial phase-specific partitioning by exposing during a short time window proliferating cells with a nucleoside analog ( e . g . , BrdU , IdU or EdU ) that gets selectively incorporated into the DNA of cells that are actively replicating their genome . Measuring subsequently by simultaneously the DNA content and the amount of incorporated nucleoside analog per cell permits to discern the three phases and immediately after the pulse . In addition , due to the permanent staining property of the nucleoside analogs , it is possible to follow , up to a certain degree , the labelled and unlabelled cell cohorts over time . Several dies , such as Hoechst 33342 , the dihydroanthraquinone analog DRAQ5 , DAPI , and PI are commonly available to stain DNA content in cells [27] , and can be used in combination with nucleotide analogs . In theory , this method would largely correspond to the hypothetical experiment that we analyzed so far . In practice however , the overlap of the subpopulations in the scatter plots prevents the exact determination of the frequencies of cells described by Eq . 24 and Eq . 25 . For example labelled cells that have completed the phase but remain in phase are indistinguishable from those that did not complete the initial phase yet . As has been reported previously , only four different sub-populations can be identified with reasonable accuracy [26] . These are: where the corresponding populations in our thought experiment are indicated in brackets . This shows that computing Eq . 25 up to is sufficient to describe a complete in silico BrdU pulse labelling experiment . The reason is that , using current protocols , fluorescence of labelled cells becomes indistinguishable from background as soon as the cells divide a second time . In other words , cells that leave population by dividing a second time join population ( see Fig . 2 ) . For the experimental data , analyzed in the next section , the fraction of labelled cells that completed two cell divisions during the 12 hours time frame of the experiment is negligible . The population is the only sub-population that matches directly the type of data considered before and its temporal evolution follows as such Eq . 24 . The remaining three populations in contrast represent mixtures of cell cohorts whose kinetics could be described individually by Eqs 24–25 . By analyzing two data sets from samples of single pulse-labelling experiments , we tested the model and the effect of population intermixing on the identification of the model parameter values . The two cell lines considered were in vitro cultured human glioblastoma cancer cells ( for details see Materials and Methods ) and in vitro cultured Chinese hamster cells ( courtesy G . Wilson ) . We will refer to these data as the and the data sets . Both data sets consist of samples taken from asynchronously dividing cell populations at several time points after a single BrdU pulse , with sample sizes ranging from 5000 to 50000 cells each . Data points represent simultaneous measurements of BrdU as well as DAPI or PI ( DNA content ) in a single cell by fluorescent activated cell sorting . As a preliminary test we minimized the residual sum of squares i . e . , least-squares fitting , of adequate mixtures of Eq . 24 and Eq . 25 to extracted frequencies at different time points after the pulse . We found that , for properly chosen parameter values , both data sets were reasonably well approximated by the model predictions ( Fig . 3 A ) . While this indicated that the model captured some of the relevant temporal characteristics of cell cycle progression , a subsequent analysis revealed that an infinite number of different parameter combinations fitted the measured frequencies with the same minimal ( not shown ) . This implies that there exist , given the available data , no single best-fit parameter combination , but a whole region in parameter space that can explain the data equally well . When we then interrogated the same data by approximate maximum likelihood ( ML ) estimation , using a simple likelihood function ( see Materials and Methods ) , we found again that relative large regions in parameter space mapped to the same ML ( see Fig . 3 B ) . It turned out that these regions were entirely superimposed onto the lines defined by Eq . 13 and Eq . 26 ( dashed lines ) . These lines define what could have potentially been learned in our thought experiment with only two support points , one at and a second at . In both experiments , ML parameters associated with the phase were spread out almost everywhere along these lines ( Fig . 3 B , gray regions ) . Parameters related to the phase were more concentrated but still in the case of the data a substantial region of ML estimates were observed . Finally the region for the phase parameters approached that of a point estimate for both data sets . The spread of the ML estimates suggests that even in the ideal case of large population size and noise-free data , the specific choice of the support points in these experiments does not allow to determine uniquely neither the delay nor the standard deviation for all the phases . In contrast the average completion time for each phase and the total division time can be estimated with relatively high precision . To better quantify the uncertainty of these estimates , Bayesian 99% credibility regions ( CR ) were computed by the Markov chain Monte Carlo method ( MCMC ) using the same likelihood function as before ( Fig 3 C ) . CRs followed mainly the same trends as the regions observed in the ML estimates , covered however as expected a larger volume . An exception was the ‘blown up’ CR of the phase parameter for the cell line , for which the ML estimates wrongly insinuated a well defined point estimate . In Table 1 we summarized the obtained Bayesian summary statistics . One can see that the intervals for the average duration of each phase are narrow compared to those for the individual parameters and . In both cases the data allows for a deterministic phase ( ) , while for the data set variability in is a necessary characteristic to reproduce accurately the data . Notably , when contrasting the two cell lines , are the short phase of Chinese hamster cells and the approximately two times more extended phase of the human glioblastoma cell line . It is out of the scope of this paper to interpret or relate these differences to cell line specific conditions . More importantly in this context is the fact that the information of the analyzed data is too sparse to narrow down all the parameter values even under noise-free conditions . The information extracted from the and data sets is apparently insufficient to pinpoint all six parameters related to the three phases of our simple cell cycle model . This is disappointing especially because the number of support points largely exceeds the three ideally required , and the support points seem to include at least for the U87 data set one at a second at and a third at A potential explanation for this poor resolution in the estimates is the previously mentioned intermixing of the cell population clusters in the BrdU versus DAPI scatter plots compared to the ideal conditions discussed earlier . The cluster overlap in the data makes it impossible to measure directly the frequencies of most of the populations , including the cell cohorts described by Eq . 24 . In order to approach the conditions assumed in the thought experiment by avoiding the loss of information caused by the intermixing , we devised an extension of the current single pulse protocol , which places a second pulse immediately before measuring or fixing each sample ( see Fig . 4 , top ) . The second pulse is expected to expose the cells with a further nucleoside analog that can be distinguished from the first one by Depending on the cell cycle kinetics and the length of the measuring period , the additional pulse increases the number of classifiable populations from four up to nine distinct populations . To appreciate the additional populations identified by double pulse labelling , data from a single pulse-chase labelling experiment was artificially colored , to mimic the expected FACS output from proliferating cells labelled according to the protocol described before . In Fig . 4 , besides the gates defining the populations and cells that have incorporated the second label are drawn in red . For the time immediately after the pulse ( i . e . , ) , no extra information is gained by the second pulse . However , already two hours later , one additional population can be discerned . Twelve hours after the first pulse , seven population , instead of three , can be recognized . Thus by resolving the four initial population according to the cell cycle phases , it is possible to measure the kinetics of nine subpopulations ( and ) . Because all these kinetics depend on the cell cycle parameters , each of them can in principle tell us something about the phase completions times . However some information is redundant . For example if and are measured , then is defined by the total fraction of cells in phase , because Similarly from one can deduce by knowing the frequency of cells in phase . Double-label experiments using pairs of nucleoside analogs like BrdU , IdU and EdU , also in combination with radioactive tritiated thymidine ( ) , have been explored in several cancer cell proliferation studies [19] , [28]–[31] . In recent years , dual pulse experiments using BrdU in combination with EdU have become more common . Studies relying on this method estimated changes in replication , inferred mitochondrial DNA bio-genesis and stained proliferating cells in the bone marrow in vivo [32]–[34] , in general with the aim to increase the statistical power of the conventional methods . To assess if the latter method would allow quantifying more accurately and precisely the parameters of the model , we generated in silico data mimicking the output of a hypothetical dual pulse experiment using Eq . 24 and Eq . 25 ( see Fig . 5 A ) . We found that by employing the redesigned protocol with the same replicates and time points as in the corresponding data sets , we could reduce the regions corresponding to the ML up to point estimates ( Fig . 5 B ) . Furthermore , the uncertainties due to noise became also significantly smaller ( Fig . 5 C ) . Pooling this artificial data according to the output expected from a single pulse experiment , reproduced again the uncertainties seen in Fig . 3 C ( not shown ) . Together this indicates that the redesigned dual pulse protocol provides parameter estimates with higher accuracy and precision . Real dual pulse labelling experiments will however be needed to confirm these theoretical predictions . The cell cycle model introduced here is deliberately simple and neglects cell loss . In this section , we ask whether the estimates of its parameters are reasonable when some of the simplifying assumptions of the model do not hold . Specifically , we ask how accurate are the mean and standard deviation of the phase completion times estimated using this simple model if the true completion times were not distributed as a delayed exponential function or if there was concurrent phase-specific cell loss . Empirical measurements [35] indicate that the cycle phase time for the S phase is distributed closer to a delayed hypoexponential or a delayed gamma distribution ( see below ) rather than the caricatural delayed exponential . Therefore , an important question which arises is how much do the estimates of the average and standard deviation in phase durations obtained with this simple model depend on the true underlying distribution ? While many different scenarios could be tested we opted to fit a delayed hypoexponential density with two decay and one delay parameter to direct in vitro measurements of and phase durations employing fluorescent biosensors ( Fig . 6 A-B , [35] ) . Using the obtained best-fit estimates , we then performed in silico dual-pulse labelling experiments , in which the phase durations were drawn in the case of the and phase from delayed hypoexponential density functions ( Fig . 6 C ) . Finally we fitted the simple model , i . e . , Eq . 24 and Eq . 25 , which is based on delayed exponential distributions , to this data , to see if we could recover the original averages and standard deviations despite using the ‘wrong’ caricatural model . Both summary statistics ( i . e . , mean , standard deviation ) of phase durations could successfully be re-estimated ( Fig . 6 D ) . Although generalizing this finding lies out of the scope of this article , it suggests that even if the true underlying distribution is not a delayed-exponential function , important quantities like the average and standard deviation of the phase durations may still be estimated with the simple model developed herein . It also indicates that BrdU labelling experiments with a realistic number of samples are unlikely to have the power to discriminate between delayed exponential and more complex density distributions . We now turn to the issue of how much the presence of phase-specific cell death ( or loss in general ) , which is unaccounted for in our model , affects the accuracy of the estimates of the mean and standard deviation of the phase durations . To this end , we will first introduce the extensions necessary to describe cell death in the model . We rely on the fact that if the probability of death per cell cycle is less that 50% , the average population size will asymptotically grow exponentially with an effective growth rate where This implies that the arguments used to analyze exponential growth without death remain valid for a model that allows moderate levels of cell death . To consider death , we assume that cells have two possible fates per phase , either they progress to the next phase or they die . Let as before , be the phase completion time density , conditioned however on the cell being alive at time And let be the phase-specific time to death density conditioned on the cell having not progressed to the next phase . Then , as e . g . , in [36] , assuming that both events compete with each other ( i . e . , whatever fate happens first , prevents the other ) , the resulting density becomes ( 28 ) Consider now a scenario of an exponentially growing population , in which cell death occurs exclusively during phase Let us assume further that the phase specific time to death density is a simple exponential density with mean Using straight-forward probabilistic arguments , we can compute analytically , for this simple scenario two important quantities , namely the probability to die in this phase ( ) , and the expected value of the effective completion time , distributed as We get Note that , in this simple case , is also the probability to die per division cycle . Evaluating Eq . 18 using instead of we obtain for Eq . 24 , ( 29 ) where and represent the equivalents of and we had previously defined for the case of no cell loss . The former quantities , which now depend on are derived applying to Eq . 5 the same substitution as above . Expressions equivalent to Eq . 10 and Eq . 11 are obtained along the same lines . These become however rather lengthy and are therefore omitted here . Eq . 29 reproduces accurately in simulated BrdU pulse labelling experiments , if death occurs , as specified above ( see Fig . 7 A for an example with and ) . The differences between the analytical predictions for with 30% death and without death ( denoted by ) are , for the parameter sets that we tested , relatively small , and vanish as expected , as tends to zero ( see Fig . 7 B for computed at one specific time point ( h ) for different values of ) . To further test , how much both cell death and a completion time with a shape distinct from a delayed exponential may jointly affect parameter estimates , we simulated BrdU pulse labelling experiments , where two major assumptions underlying Eq . 24 were simultaneously violated . First , we assumed a delayed gamma distribution ( with shape parameter of two ) for the completion time of each phase . Second , we considered cell death during phase , and adjusted such that was either zero or The population size ( starting with five cells ) took about twice as much time to grow to a similar size for compared to a the scenario without death ( see Fig . 7 C , middle column , for five independent simulations ) . In addition , the variability in the population sizes between the simulations appeared higher for increased death rates . In contrast , when estimating the mean and variance of by non-linear least squares fitting using Eq . 24 , the marked changes seen in the population kinetics where not paralleled by changes in the estimates . Both the mean and the variance were accurately determined in both cases ( see Fig . 7 C , right column ) . Taken together , this suggests , that the estimates for the mean and variance of using Eq . 24 , at least for the reasonable parameter values that we tested , are relatively robust to simultaneous changes in the shape of the completion time , and moderate levels of cell death .
In this article , we propose a simple stochastic model that aims at approximating the time it takes for a cell to accomplish the sequential phases of the cell cycle , by defining the completion time in each phase as a delayed exponential density distribution . At first sight this might seem a gross oversimplification of all the processes involved . However , when compared with experimental data , this simplistic model performs surprisingly well . While the observation that the model reproduces closely the experimental time series has to be interpreted with care , we think its success can be explained by the fact that the probability rule captures simultaneously two important regimes of complex biochemical processes that qualitatively differ in their completion time distribution . As was shown recently by Bel et al . [37] the completion time for a large class of complex theoretical biochemical systems , including models for DNA synthesis and repair , protein translation and molecular transport , simplify either to deterministic or to exponentially distributed completion times , with a very narrow transition between the two regimes depending on the rate parameters . These are precisely the ‘ingredients’ of the delayed exponential distribution . Under this light our model could be naively interpreted as a sensor that measures approximately the relative contribution of delay and decay processes in each of the cell cycle phases . However , whereas delays connected in series form again a delay , this is not true for decays . Sequentially coupled decays form a process with hypoexponential distributed completion times with a shape similar to the frequency distribution of cell cycle phase completion time reported in [35] . Thus a more flexible model for the completion time of each phase could be a hypoexponential distribution of the family that we are currently using to model the total cell cycle length distribution ( i . e . , Eq . 3 ) . If instead , processes are not connected in an ordered series but rather concurrent , the times for all the processes to complete is dominated by the largest delay or the smallest decay parameter . It is tempting to interpret the relative weight of constant delay and exponential decay ( i . e . , the coefficient of variation ) as a measure of the precision of the processes regulating each phase , which in turn might reflect a selective pressure on timing . Tighter pressure might reduce the coefficient of variation , as our results suggest for the phase when compared to the remaining phases . Yet , this might also reflect the conjunction of many parallel and independent process such as replication forks whose number is expected to increase the timing precision by the law of large numbers . In fact , the mean time and the variance of the phase are shorter in the early phase of the embryo when cells display a higher number of replication forks in which the DNA polymerase progresses at the same rate [38] . An important simplification of our model consists in the assumption that cell loss by death , differentiation or immigration is small compared to population wide division rates , such that we can neglect it when fitting the model to experimental data . The main reason to adopt this approach was simplicity and the fact that the available data sets did hardly permit the determination of the possibly large number of additional parameters . While for the U87 LIFE/DEAD discrimination was performed , the markers used for gating are specific for late stages of apoptosis or necrosis typically after membrane integrity is lost and therefore do not necessarily reflect the true fraction of dying cells . The fraction of dead cells identified and excluded by this method was typically low . In case that experimental conditions would however suggest substantial cell loss , the model is flexible enough to be adapted without major technical difficulties , along the lines of Eq . 28 and Eq . 29 . For instance , when the number of new-born cells equals the number of dying cells , solving the model analytically turns out to be easier , because And given that the apoptotic state ( e . g . , defined by Annexin-V staining ) would be measured simultaneously with nucleoside incorporation and DNA content , this could open up the possibility to assess the duration of apoptosis in vivo . These potential extensions not withstanding , it is reassuring that considering concurrent phase-specific cell death of up 30% may not change the estimates of the mean and standard deviation of the phase completion time obtained using a caricatural model that neglects cell death , as our results indicate . Another fundamental abstraction of our model is that the completion times for the cell cycle phases of a given cell are uncorrelated , which also implies uncorrelated division times of parental cells and siblings . Even though positive correlation in division times between parental and daughter cells [10] and between siblings [36] has been observed recently in vitro by direct long-term microscopy of activated proliferating B cells , Schultze et al . reported many years ago for in vivo murine crypt epithelial cells the lack of correlation of completion times of a cell through successive phases [31] . It remains to be shown experimentally how much of the correlation or lack of correlation is due to cell type or environment . In any case , it would be interesting to extend the present model to include correlation in phase completion times . The live cell biosensor-based fluorescent imaging strategy exploited in [35] allows for direct quantification of the stochastic timing of the cell cycle phases . It is worth comparing the estimates of cell cycle phase-specific completion times obtained with this direct method with those provided by the indirect pulse labelling method . The mean phase completion time was reported for the lines NCI-H292 and HeLa cell line to be 8 . 2 and 8 . 4 hours respectively with standard deviation of 0 . 5 and 2 . 9 hours ( extracted from Fig . 2 in [35] ) , which lie in the range of the estimates we obtained , despite the different human cell lines that have been analyzed ( Table 1 ) . In principle , pulse labelling with nucleoside analogs can be used in vivo to quantify the stochasticity of the cell cycle in anatomical places that are currently not feasible to visualize by multiphoton microscopy , given that a sufficiently large ( over 1000 ) and representative sample of cells can be harvested . Our method therefore provides , concerning the and phases , very similar information as these imaging methods , yet it has a much wider application scope . In comparison with the Smith and Martin cell cycle model , that assumes a single variable phase [5] , we have proposed a more complex model with three variable phases . A question can be raised whether a less complex model with variability in only one or two of the three phases would reproduce equally well our BrdU pulse labelling data . This could simplify the analysis and reduce the issue of parameter identification . One might , for example , consider a scenario , similar to the double transition probability model analyzed in [39] , in which the and the phase have delayed exponentially distributed durations , while the durations of and phase are fixed . It is easy to see that such a less complex model is embedded into our model , as it suffices to set while assuming that the variability in the duration of the phase is generated entirely during the phase . Clearly , from a data fitting perspective , and especially for the V79 data set , the simpler embedded model and the larger model would perform equally well . This can be read directly from Fig . 3 , as the set of approximate ML estimates for includes values that are equal or close to zero . However , the interpretation of the V79 data set based on these two models would be fundamentally different . For instance , by relying on the deterministic model , one would be lead to conclude that the phase duration is for every cell about 9 hours . By allowing however for possible interpretations of the data encompass the latter case , but in addition include scenarios in which some cells complete their phase in about 7 hours , while other cells may take far longer . Even though the original data does not permit to discriminate between these models , simulated dual pulse labelling experiments indicate that this is in principle feasible . Finally , in view of the experimental data provided by Hahn et al . ( [35] , used in Fig . 6 B ) , the scenario of variable S-phase duration with is well justified . On the other hand , we distinguished only three cell cycle phases , although the cell cycle is typically structured into at least four biologically distinct phases . This simplification stems from the fact that quantification of DNA content by flow cytometry cannot discriminate between cells in the and phase . Additional biomarkers , such as pS780 reported by Jaccoberger et al [40] , could be used together with DNA content dyes and nucleoside analogs in extended labelling protocols to identify the four main cell cycle phases . Extending the model to distinguish accordingly a fourth phase would be rather straightforward mathematically . Despite restricting the model to three phases , it is worth noticing that we are extending the work of Cain and Chau [39] , [41] , who studied both balanced and non-balanced growth conditions , assuming one and two random transitions , mapped respectively to part of and the remaining cell cycle phases . Also , we extend the work of Larsson et al . [42] who were able to infer the variation in the completion times of and based on the histograms of DNA content . Long-term labelling with BrdU has been used in vivo to study disease progression of infected rhesus macaques with the simian immunodeficiency virus [1] , [43] and due to toxicity more rarely in HIV-1 [44] . These studies typically targeted turnover rates of T lymphocytes subpopulation over a time period of several weeks and provided average birth and death rate estimates . In contrast , the method outlined here measures cell proliferation at a much short timescale and has the potential to yield phase specific estimates of both the average and the variability of completion times . We anticipate that valuable complementary information about SIV and HIV infection could be gained using the redesigned protocol proposed here , especially in the light of the known modulation of the cell cycle checkpoints by accessory viral proteins [45] . Recently , in a computational ‘tour de force’ , Falcetta et al . [25] used a stochastic model of cell cycle progression with discrete age-structure to derive qualitative conclusions about the mechanism of action of several anti-cancer therapies . This model was able to mimic ( in their wording ‘rendering’ ) quantitative data on single BrdU pulse labelling assay and time-lapse imaging . The empirical distribution cell cycle lengths they reported is akin to the hypoexponential family in our model , however , the distributions of phase lengths remain implicit in their simulation framework , in which time is discrete and the parameters are transition probabilities per time step . This prevents knowing how uncertain are the estimates of the phase length variances based on single pulse labelling using their approach . Dual pulse labelling with a pair of thymidine analogs has been used before to study cell cycle kinetics [19] , [28] , [31] . What is common to those studies is scheduling the two consecutive pulses by fixing the time lapse between the pulses , irrespectively of the time at which cell samples are collected for cell cycle phase analysis . It is worth stressing that , according to the present study , specially when the second pulse is timed according to each individual sample ( i . e . adjusting accordingly the interval between pulses ) one can harness the potential of the model to quantify the mean and variance of the phase-specific time . Making the second pulse at a fixed minimal time before collecting cells for analysis allows to resolve cellular cohorts , which would otherwise be confounded . New technologies like the one developed by Hahn et al . [35] but also the ubiquitination-based cell cycle indicator , termed ‘Fucci’ [46] will greatly increase our understanding of phase resolved cell cycle progression and unveil its epigenetic and stochastic variability in isogenic cell populations . To translate this knowledge gained mainly from in vitro cell cultures into an in vivo context , long term ( greater than 12 hours ) and continuous multi-photon imaging may be required . This however is technically very demanding , and may remain prohibitive for cells deep inside tissues despite major technological advances in the field . The methodology presented here allows to measure phase specific cell cycle progression variability in vivo by relatively simple technical means . Even though nucleoside analogs are potentially carcinogenic , the adverse effects of low dose pulse labelling remain typically undetectable . Determining accurately cell cycle progression variability in mouse models of cancer might become a crucial step in understanding the high variability in susceptibility to cell cycle specific anti-cancer drugs .
Here we will show that a cell population that follows the stochastic model specified before will eventually enter a stationary exponential growth phase . The requirement for such an asymptotic behavior is , recalling Eq . 11 , that the complex valued function ( 30 ) has for positive valued elements of the vectors and a unique positive real root which represents the upper bound of the real part of any of its other potentially infinite number of roots . The complex numbers that solve Eq . 30 correspond , according to our model , to the stationary phase growth rate of the proliferating cell population . In case that is real , the population is growing exponentially , while if is purely imaginary growth is oscillating . In general , roots have both non-zero real and imaginary parts , which leads to oscillations with growing or decaying amplitude . If for real and we write the real and imaginary part of are computed as ( 31 ) where ( 32 ) For to be a root of both real and imaginary part have to vanish . We restrict our analysis to the positive complex half plane , i . e . since we are interested in growing and not contracting cell populations . Due to the symmetries in the trigonometric functions and and and one can easily see that if is a root , its complement is also a root . We can thus reduce the analysis even further to values with positive imaginary parts . If for fixed we plot in the complex plane as a parametric function of we get a spiral with the distance from a center point given by ( 33 ) Crucially , as is a monotone increasing function of the spiral never crosses itself . For the imaginary part of vanishes as expected because and For this special case is obviously monotone decreasing with and restricted to the interval This means that the spiral can only ‘start’ in the interval between one and minus infinity . Taken together , this implies that if for and fixed the real part of is positive , then there exist a single ‘opportunity’ to cross the origin , while if negative there exists none . At the border where the real part is zero ( Fig . 8 C ) , the corresponding value of is the only positive real root . Due to the monotonicity of any value of greater than the positive real root will result for in which does not admit for any solution . The different possible scenarios are exemplified in Fig . 8 . | Among the important characteristics of dividing cell populations is the time necessary for cells to complete each of the cell cycle phases , that is , to increase the cell's mass , to duplicate and repair its genome , to properly segregate its chromosomes , and to make decisions whether to continue dividing or enter a quiescent state . The cycle phase times also determine the maximal rate at which a dividing cell population can grow in size . Cell cycle phase completion times largely differ between cell types , cellular environments as well as metabolic stages , and can thus be considered as part of the phenotype of a given cell . Our article advances the methods to quantitatively characterize this phenotype . We introduce a novel phase-resolved cell cycle progression model and use it to estimate the mean and variance of the cycle phase completion times based on nucleoside analog pulse labelling experiments . This classic workhorse of cell cycle kinetic studies is revamped by our approach to potentially rival in accuracy and precision with modern phase-specific biosensor-based fluorescent imaging , while superseding the latter in its application scope . | [
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"computational... | 2014 | Quantifying the Length and Variance of the Eukaryotic Cell Cycle Phases by a Stochastic Model and Dual Nucleoside Pulse Labelling |
The Epstein-Barr virus ( EBV ) nuclear proteins EBNA3A , EBNA3B , and EBNA3C interact with the cell DNA binding protein RBPJ and regulate cell and viral genes . Repression of the CDKN2A tumor suppressor gene products p16INK4A and p14ARF by EBNA3A and EBNA3C is critical for EBV mediated transformation of resting B lymphocytes into immortalized lymphoblastoid cell lines ( LCLs ) . To define the composition of endogenous EBNA3 protein complexes , we generated lymphoblastoid cell lines ( LCLs ) expressing flag-HA tagged EBNA3A , EBNA3B , or EBNA3C and used tandem affinity purification to isolate each EBNA3 complex . Our results demonstrated that each EBNA3 protein forms a distinct complex with RBPJ . Mass-spectrometry revealed that the EBNA3A and EBNA3B complexes also contained the deubquitylation complex consisting of WDR48 , WDR20 , and USP46 ( or its paralog USP12 ) and that EBNA3C complexes contained WDR48 . Immunoprecipitation confirmed that EBNA3A , EBNA3B , and EBNA3C association with the USP46 complex . Using chromatin immunoprecipitation , we demonstrate that WDR48 and USP46 are recruited to the p14ARF promoter in an EBNA3C dependent manner . Mapping studies were consistent with WDR48 being the primary mediator of EBNA3 association with the DUB complex . By ChIP assay , WDR48 was recruited to the p14ARF promoter in an EBNA3C dependent manner . Importantly , WDR48 associated with EBNA3A and EBNA3C domains that are critical for LCL growth , suggesting a role for USP46/USP12 in EBV induced growth transformation .
Epstein-Barr Virus ( EBV ) is a herpesvirus that establishes lifelong asymptomatic infection in up to 95% of the human population [1] . In vitro , EBV infection of resting B lymphocytes drives them to proliferate as lymphoblastoid cell lines ( LCLs ) [2 , 3] . The EBV genome resides in LCLs as a non-integrated episome and expresses a limited gene repertoire called latency III , which includes genes encoding six nuclear proteins ( EBNA1 , 2 , 3A , 3B , 3C , and LP ) , three integral membrane proteins ( LMP1 , 2A , and 2B ) , and more than 30 micro RNAs ( miRs ) [1] . Latency III driven B lymphocyte proliferation in vivo is normally controlled by a vigorous cytotoxic T cell response [4] . In the absence of an effective immune response or in collaboration with various environmental or genetic co-factors , EBV latent infection can result in malignancies , including Burkitt and Hodgkin lymphomas , post-transplant lymphoproliferative disease ( PTLD ) , as well as nasopharyngeal and gastric carcinomas [1] . Extensive genetic and biochemical data support the model that EBV latency III gene expression usurps growth and survival signaling pathways in B lymphocytes normally triggered by antigen recognition and CD4+ T cell co-stimulation [1 , 5] . LMP1 expression results in constitutive NF-kB activation that is essential for LCL outgrowth and survival . The ability of LMP1 to self-associate allows it to activate , in a ligand independent manner , molecules that transduce signals from receptors in the TNF superfamily [6 , 7 , 8 , 9 , 10] . The other two latent membrane proteins , LMP2A and LMP2B , are not required for LCL transformation in vitro [11] . The ability of LMP2A to engage B cell receptor signaling molecules may be important for maintaining viral latency or for the growth and survival of EBV infected cells in vivo [12 , 13] . EBNA2 is an acidic transactivator that is targeted to promoters through an interaction with the RBPJ DNA binding protein , a component of the Notch signaling pathway [14] . EBNA2 and its co-activator EBNALP are the first genes expressed during EBV latent infection and result in upregulation of promoters including c-myc , EBV LMP1 , LMP2A , and EBNA essential for latency III transformation [15 , 16] . Global analysis of EBNA2 and RBPJ binding in LCLs has implicated EBF1 and other B cell transcription factors as pioneering factors for EBNA2 binding of promoters and enhancers [17] . In contrast , the role of the EBNA3 proteins in LCL transformation is less clearly defined . The EBNA3 protein family is defined by a ~300 aa region of homology in their N-termini; there are no known homologs outside of EBV and the closely related primate lymphocryptoviruses . EBNA3A , EBNA3B , and EBNA3C share a common exon structure consisting of a short 5’ exon and a longer 3’ exon arranged in a tandem array that likely arose from triplication of a single ancestral EBNA3 gene [1] . Reverse genetic analyses have demonstrated that EBNA3C is essential for LCL transformation , while EBNA3B is dispensable [18 , 19] . The requirement for EBNA3A is probably intermediate . EBNA3A truncation or conditional inactivation abrogated transformation in multiple studies . However , LCLs have been generated under appropriate conditions , using feeder cells , with an EBV genome deleted for EBNA3A [20] . The most convincing evidence of the unique requirement for EBNA3A and EBNA3C derives from LCLs in which either EBNA3A or EBNA3C has been rendered conditional by fusion to a mutant estrogen hormone binding domain [21 , 22 , 23 , 24 , 25] . In this system , LCL growth arrest induced by EBNA3A inactivation could be rescued only by exogenous EBNA3A expression , but not by expression of additional EBNA3B or EBNA3C [22 , 23] . Similarly , EBNA3C inactivation results in termination of LCL growth that can only be restored by EBNA3C [21 , 24 , 25] . Cell cycle effects of the EBNA3 proteins , particularly EBNA3C , have been documented in many other systems . EBNA3C can overcome serum deprivation and disrupt the G1 checkpoint in REFs , NIH3T3 , and U2OS cells [26] . Additionally EBNA3C overexpression can disrupt mitotic spindle checkpoints and produce aneuploidy [26 , 27] . EBNA3A and EBNA3C can cooperate with HRAS in classical transformation assays [28] . Multiple mechanisms of EBNA3C mediated effects have been suggested , including inhibition of accumulation of the CDK inhibitors p27KIP1 and p16INK4A , Rb degradation via the SCF ubiquitin ligase , c-myc stabilization , and binding to , and inactivation of cyclinA-CDK complexes [29 , 30 , 31] . In LCLs , the growth effects of EBNA3A and EBNA3C appear to be primarily due to suppression of the CDKN2A gene products p16INK4A and p14ARF [32] . Conditional inactivation of either EBNA3A or EBNA3C results in p16INK4A and p14ARF accumulation and cessation of growth . Moreover , siRNA knockdown of both gene products restores growth despite EBNA3A or EBNA3C inactivation . EBNA3A and EBNA3C effects appear to be at the level of CDKN2A transcription as changes in protein levels are accompanied by concomitant increases in mRNA and a substantial reduction of the repressive H3K27me3 modification at the CDKN2A promoter [32] . Furthermore , p16INK4A null B lymphocytes can be transformed into LCLs in the absence of functional EBNA3C protein [33] . Although the mechanism by which EBNA3A and EBNA3C cooperatively suppress CDKN2A is unknown , gene co-regulation by the EBNA3 proteins appears to be frequent . In LCLs , 52 out of 287 genes reported as EBNA3A regulated were found to also be regulated by EBNA3C [34] . In Burkitt lymphoma cells , EBNA3A and EBNA3C are both required for suppression of BIM , a pro-apoptotic Bcl-2 family member [35] . Genome-wide analysis of EBNA3A , EBNA3B , and EBNA3C effects in BL31 cells infected with recombinant EBV genomes [36] , suggested that about half of the cell genes differentially expressed as a result of deletion of one EBNA3 ORF are similarly affected by deletion of at least one of the other EBNA3s . In that study , overlap among genes regulated by EBNA3B and EBNA3C was particularly extensive [36] . A large number of interacting proteins have been suggested to be important for EBNA3 activities . Of these , RBPJ is the best established as a mediator of transcriptional and LCL growth promoting effects [37 , 38 , 39 , 40] . RBPJ is bound by the conserved N-terminal EBNA3 domain , which unlike Notch and EBNA2 , does not interact with the RBPJ’s beta-trefoil domain . Instead , the EBNA3s bind to the N-terminal rel-homology domain ( NTD ) of RBPJ [37 , 41] . Although biochemical assays suggested that the EBNA3-NTD interaction could inhibit RBPJ DNA binding , genome-wide co-localization between EBNA3 proteins and RBPJ has been demonstrated by ChIP-seq [42 , 43] . Genetic analyses have demonstrated that interaction of both EBNA3A and EBNA3C with RBPJ is essential for CDKN2A promoter repression and maintenance of LCL growth [32 , 33] . A second cell protein important for CDKN2A regulation is CtBP1 , which interacts with the C-terminal regions of EBNA3A and EBNA3C [44 , 45 , 46] . Mutation of the CtBP1 binding sites in EBNA3A and EBNA3C impairs their ability to support LCL growth . By contrast , RBPJ binding mutants are completely defective in maintenance of LCL growth [21 , 24] . The strength of evidence supporting a role for other interacting proteins in mediating EBNA3 growth effects varies considerably . Although significant progress has been made in mapping the EBNA3 domains critical for LCL growth , for most interacting proteins , mutations within the EBNA3 proteins that selectively disrupt their binding have yet to be identified . In parallel with efforts to correlate ongoing genetic analysis of the EBNA3 proteins with interacting protein binding , we set out to devise a means of purifying endogenous EBNA3 complexes from LCLs and to determine their protein constituents . To that end , recombinant EBV genomes in which DNA encoding a flag-HA ( F-HA ) epitope is inserted in-frame to the C-terminus of the EBNA3A , EBNA3B , or EBNA3C ORF were constructed . These genomes were used to transform primary B-lymphocytes into three cell lines: EBNA3A-F-HA , EBNA3B-F-HA , and EBNA3C-F-HA LCLs . Using tandem affinity purification and LC/MS/MS , we characterized the protein composition of endogenous EBNA3A , EBNA3B , and EBNA3C complexes in these LCLs . Here we show that each EBNA3 protein is associated with the USP46 and USP12 deubiquitylase ( DUB ) complexes , and that the domains of the EBNA3A and EBNA3C proteins that bind these DUBs are important for maintenance of LCL growth . In the presence of EBNA3 proteins , RBPJ and the USP46/USP12 enzymes become associated and , when purified , these EBNA3 containing complexes exhibit DUB activity . Using CRIPSR/Cas9 we provide evidence that USP46 is essential in 721 LCLs , but dispensable in 293T cells . Further , using chromatin immunoprecipitation we demonstrate increased binding of WDR48 to the p14ARF promoter in the presence of functional EBNA3C protein . We propose a model in which EBNA3s serve as adaptor proteins between USP46/USP12 and RBPJ , recruiting these DUB complexes to chromatin to regulate transcription .
In order to study endogenous EBNA3 complexes from LCLs , we generated recombinant EBV genomes in which flag and HA epitope tags are fused in-frame with the carboxyl-terminus of EBNA3A , EBNA3B , or EBNA3C , using a previously described EBV BACmid [47] . These recombinant EBV genomes were used to transform B lymphocytes into LCLs , designated EBNA3A-F-HA LCL , EBNA3B-F-HA LCL , and EBNA3C-F-HA LCL , respectively , and collectively referred to as the EBNA3-F-HA LCLs . Additionally , a control a wild-type LCL was created using the unmodified EBV BACmid as the transforming genome . Western blotting of the three EBNA3-F-HA and wild-type LCLs revealed that RBPJ , EBNA1 , EBNA2 , EBNALP , and LMP1 levels in whole cell extracts were indistinguishable among the different cell lines ( S1 Fig ) . The epitope tagged EBNA3 proteins were expressed at levels comparable to their wild-type counterparts and , as expected , migrated as slightly higher apparent molecular weights than the untagged proteins . Interestingly , the EBNA3B-F-HA LCL was hypomorphic for EBNA3C expression and exhibited a slower rate of growth than the other LCLs . A similar reduction in EBNA3C expression and rate of growth was previously reported in an LCL in which the EBNA3B gene was replaced by a chloramphenicol cassette [48] . Thus , the fusion of flag-HA tags to each of the EBNA3 open reading frames resulted in transformation competent EBVs that express latency proteins at levels comparable to those seen in wild-type LCLs . RBPJ immunoprecipitation efficiently retrieves four of the six EBV nuclear proteins ( EBNA2 , EBNA3A , EBNA3B , and EBNA3C ) from LCL lysates ( Fig 1 , left panels ) . Although previous work had suggested that EBNA2 and EBNA3C exist in distinct complexes [49] , efforts to further investigate whether EBNA3 proteins exist in distinct complexes have been hampered by varying degrees of cross-reactivity of among available EBNA3A , EBNA3B , and EBNA3C antibodies [43] . Using flag immunoprecipitation on EBNA3A-F-HA LCL lysates , we found that EBNA3A and RBPJ were efficiently precipitated , but no EBNA1 , EBNA2 , EBNA3B , EBNA3C or LMP1 was detectable ( Fig 1 , right panels ) . Immunoprecipitations using flag resin on EBNA3B-F-HA or EBNA3C-F-HA LCL lysates produced similar results: RBPJ and the tagged EBNA3 protein were readily detectable , but other EBV latency proteins were not . Control immunoprecipitations for HA ( Fig 1 , left panel ) and flag ( Fig 1 , right panel ) from wild-type LCLs , did not precipitate any detectable RBPJ , EBNA2 , or EBNA3 proteins . Thus , EBNA2 , EBNA3A , EBNA3B , and EBNA3C all associate with the same cell DNA binding protein , but appear to form distinct RBPJ complexes in LCLs . In order to identify proteins that associate with EBNA3A , EBNA3B , or EBNA3C under physiologic conditions , we purified EBNA3 complexes by tandem affinity purification ( TAP ) from LCLs expressing flag-HA tagged EBNA3A , EBNA3B or EBNA3C and from the wt LCL as a control . For each LCL , LC/MS/MS fingerprinting identified between 63–174 peptides of the epitope tagged EBNA3 protein and 95–148 peptides corresponding to RBPJ ( Table 1 ) . For each EBNA3-F-HA LCL , the purified protein complex contained peptides from the corresponding epitope tagged EBNA3 protein and no peptides that mapped to the other untagged EBNA3 proteins expressed in that LCL . No peptides from other EBV proteins , such as EBNA1 , EBNA2 , and EBNALP , were detected in any complexes . Additionally , 14 peptides corresponding to CtBP1 were detected in the EBNA3A complex , but not in the EBNA3B , EBNA3C or control TAPs ( Table 1 ) . We also detected 105 , 99 , and 3 total peptides corresponding to WDR48 in purified EBNA3A , EBNA3B , and EBNA3C complexes , respectively . Importantly , in the EBNA3A and EBNA3B complexes we also detected the known WDR48 associated proteins WDR20 ( 28 and 4 peptides , respectively ) , USP46 ( 15 and 8 peptides , respectively ) , and USP12 ( 7 and 4 peptides , respectively ) ( Table 1 ) . Thus , analysis of tandem affinity purified EBNA3 complexes lends further support to model that each EBNA3 protein , while highly associated with RBPJ , exists in a distinct complex that does not contain other EBV nuclear proteins . Further , our results identify the USP46 ( and USP12 ) deubiquitinases and their associated chaperones WDR48 and WDR20 as members of the EBNA3A and EBNA3B protein complexes in LCLs . Because USP46 and USP12 are highly homologous ( ~90% identity ) and we could confirm a robust association with USP46 by Western blotting ( discussed below ) , we chose to focus our subsequent attention on the USP46/WDR48/WDR20 complex . Given that the EBNA3 proteins share other protein binding partners ( e . g . , RBPJ and CtBP1 ) and because we had previously identified WDR48 as a cell protein that interacts with flag-EBNA3C aa365-545 [50] , the small amounts of this protein detected in the EBNA3C was unexpected , particularly given the much larger amounts associated with EBNA3A and EBNA3B . We considered the possibility that WDR48 became dissociated from EBNA3C during complex purification . Therefore EBNA3C complexes were rapidly immunoprecipitated from EBNA3C-F-HA LCLs and blotted for associated proteins . Co-precipitated RBPJ was readily detected , as were members of the USP46 complex , including USP46 , WDR48 , and WDR20 ( Fig 2 ) . Additionally CtBP1 , a known EBNA3C interacting protein [45] was detectable under these conditions even though it was not detected in the TAP-MS experiments . Thus , EBNA3C also appears to target the USP46 complex; however , this complex appears to be less stably associated with EBNA3C-F-HA than it is with either EBNA3A-F-HA or EBNA3B-F-HA . Although originally described as an endosomal protein [51] , WDR48 has subsequently been isolated from other cellular compartments , including as a chaperone for USP1 in the nucleus as a component of the Fanconi anemia DNA repair pathway [52] . Lehoux et al . , found that USP12 and USP46 fused to red fluorescent protein were predominantly cytoplasmic in C33A cervical carcinoma cells , but were recruited to the nucleus via an interaction with the HPV E1 helicase [53] . Because our TAP lysis procedure extracted both nuclear and cytoplasmic proteins ( S2 Fig ) , we wanted to ensure that the USP46/WDR48/WDR20 complex subcellular localization was compatible with formation of a complex with EBNA3 proteins . To this end , we fractionated LCLs into cytoplasm , membrane , nucleoplasm , chromatin , and cytoskeletal components ( Fig 3 ) . Fraction purity was monitored by immunoblotting for control proteins of known localization: alpha-tubulin ( cytoplasm ) , BRG1 ( chromatin associated ) , histone H2B ( chromatin ) , and lamin B ( nuclear matrix/cytoskeleton ) . The EBNA3 proteins were predominantly found in the nucleoplasm with a small quantity stably associated with chromatin . Notably , a significant amount of the WDR48 , WDR20 , and USP46 proteins were found in the nucleoplasm , with the balance being extranuclear . To more directly assess whether EBV latent gene products might affect USP46 localization , we compared fractions derived from EBV negative BL41 cells with fractions derived BL41 cells super-infected with EBV ( S3 Fig ) . Although USP46 was present in the nucleoplasm regardless of EBV status , we consistently observed an increase in nucleoplasmic USP46 levels in EBV positive BL41 cells to varying degrees . We did not observe significant changes in the levels of nucleoplasmic WDR48 or WDR20 in response to EBV infection; however , USP46 was increased in the membrane fraction upon EBV infection ( S3 Fig ) , similar to that seen in LCLs compared to uninfected BL41 cells . As expected , RBPJ was present in the nucleoplasm and , to a much lesser extent , the chromatin fraction in LCLs and BL41 cells . We consistently observed a portion of the cellular RBPJ in the cytoskeletal fraction of LCLs , but not in BL41 cells , regardless of EBV infection . This may reflect RBPJ association with the nuclear cytoskeleton ( matrix ) as has been previously reported [54] . In summary , these cell fractionation experiments are compatible with EBNA3 proteins associating with a USP46 DUB complex that resides in the nucleoplasm of B lymphocytes . To further study the association of the EBNA3 proteins with members of the USP46 complex , Flag tagged EBNA3A , EBNA3B or EBNA3C was co-expressed with Xpress tagged WDR48 , WDR20 , or USP46 . Under these conditions , each of the EBNA3 proteins interacted with WDR48 , WDR20 , and USP46 . In each case , consistent with our LC/MS/MS data , the interaction with WDR48 was the most robust ( Fig 4A and 4B ) . We speculated that the EBNA3 proteins may target the USP46 complex primarily via interactions with WDR48 and that , in our overexpression assay , only a small portion of the WDR20 or USP46 was complexed by endogenous WDR48 . To test this , we also assessed the ability of each EBNA3 protein to co-precipitate USP46 in the presence of additional WDR48 protein ( Fig 4B ) . This markedly increased the retrieval of USP46 , suggesting a central role for WDR48 in mediating interaction with the EBNA3 proteins . In order to test whether the EBNA3 proteins could recruit this DUB complex to RBPJ complexes , we evaluated the ability of RBPJ and WDR48 to co-immunoprecipiate . In LCLs , WDR48 could be weakly detected in RBPJ immunoprecipations; however in EBV negative BL41 cells , no binding above background could be discerned ( Fig 5 ) . These results are consistent with a model in which EBNA3 proteins serve as adaptor proteins to recruit WDR48 to RPBJ in LCLs . In order to map the EBNA3 residues that mediate interaction with WDR48 , additional immunoprepicitation assays were conducted using EBNA3 deletion mutants . Each EBNA3 protein was split into 3 approximately equal fragments , an N terminal region encompassing the RBPJ binding motif , a central region , and a C-terminal domain . These 3 fragments were fused to Flag and co-expressed with WDR48 in 293T cells . This revealed that EBNA3A aa524-944 , EBNA3B aa394-938 , and EBNA3C aa365-545 interacted with WDR48 as well or better than the full-length proteins ( S4 Fig ) . We further mapped the EBNA3A and EBNA3C interactions using internal deletion mutants that have been assessed for their ability to support LCL growth [21 , 23] ( Fig 6A and 6B ) . These data revealed that EBNA3A aa827-944 was critical for WDR48 interaction . In the case of EBNA3C , small deletions of aa447-500 or 501–544 disrupted interaction with WDR48 , as did mutation of the EBNA3C SUMO interaction motif ( E3C509mSIM ) . Importantly , EBNA3C mutants that were defective for WDR48 association correspond to mutants that are intermediate for supporting LCL growth ( Fig 6C ) . Thus , the EBNA3 domains responsible for association with WDR48 , while not as critical as the RBPJ association domains are important for EBNA3 mediated growth effects in LCLs . In the Fanconi anemia DNA repair pathway , SUMO interaction motifs ( SIMs ) of the FANCI protein associate with sumo-like domains ( SLDs ) within the WDR48 C-terminus [55] . Because EBNA3s proteins all contain SIMs [56] , and our mapping data implicated the EBNA3C SIM in WDR48 binding , we speculated that these SLDs might be important for EBNA3A , EBNA3B , and EBNA3C interactions with WDR48 . We first evaluated WDR48 aa1-634 , a previously described mutant ( also called WDR48ΔSLD2 ) that is unable to associate with FANCI [55] . Flag-EBNA3A was able to associate with this mutant comparable to wildtype WDR48 ( Fig 7A ) . By contrast , EBNA3B aa394-938 , which associates strongly with full length WDR48 , did not bind to WDR48 aa1-634 . The strong association of EBNA3C aa365-545 with full length WDR48 was almost completely lost with WDR48 aa1-634 ( Fig 7A ) . To further define the EBNA3A binding site , we constructed additional WDR48 truncation mutants: WDR48 aa1-535 , which removes the spacer region between SLD1 and SLD2 and WDR48 aa1-430 , which is deleted for both SLDs . Both WDR48 truncation mutants interacted with EBNA3A and WDR48 aa1-430 interacted as efficiently as full length WDR48 ( Fig 7B ) . Thus , EBNA3B and EBNA3C require the WDR48 SLD2 domain for binding , but EBNA3A can associate with the WDR48 N-terminus , independent of the SLDs . Because EBNA3A aa827-944 , which is essential for WDR48 binding , also contains two CtBP1 binding motifs [44] , we sought to determine whether CtBP1 binding was separable from WDR48 binding . Deletion of EBNA3A aa920-944 ( EBNA3A 1–919 ) had no effect on CtBP1 or RBPJ binding , but dramatically impaired WDR48 association in co-immunoprepicitation assays ( Fig 8 ) . Immunoprecipitations also confirmed that the previously described EBNA3A CtBP1 binding mutant [44] retains the ability to bind to WDR48 with efficiency comparable to wild type EBNA3A ( Fig 8B ) . These data demonstrated that EBNA3A aa827-944 contain distinct binding sites for CtBP1 and WDR48 . In order to assess whether WDR48 binding by EBNA3A might be important for LCL growth , we tested EBNA3A aa1-919 , which binds CtBP1 but not WDR48 to binding , in EBNA3A-HT LCL growth complementation . For these experiments EBNA3A-HT cells were transfected with EBNA3A , EBNA3A mutant or control GFP expression plasmid , split , maintained in growth media lacking 4HT and compared with control transfected cells grown in the presence of 4HT ( Fig 9A and S5 Fig ) . In the presence of 4HT ( closed square ) or transcomplemented wt EBNA3A ( closed diamond ) , LCL growth continued , whereas transcomplementation with EBNA3A CtBP1 binding mutant ( open diamond ) resulted in modestly impaired growth . By contrast , EBNA3A mutants impaired for RBPJ or WDR48 association were unable to maintain LCL growth under these conditions . Western blotting for p16 expression demonstrated that EBNA3A mutants defective for supporting LCL growth , including the WDR48 binding mutant ( EBNA3A aa1-919 ) were impaired for suppression of p16 expression , compared to wild type EBNA3A ( Fig 9B ) . We assessed the ability of purified EBNA3 complexes to cleave ubiquitin from the 7-amino-4-methlcoumarin ( AMC ) fluorophore ( Fig 10A ) in an effort to determine whether USP46/USP12 deubiqutinase activity is activated or inhibited by association with EBNA3 proteins . EBNA3 complexes , isolated by TAP from EBNA3A-F-HA LCL ( closed circle ) , EBNA3B-F-HA LCL ( closed triangle ) , or EBNA3C-F-HA LCL ( closed square ) or control WT LCL ( X ) , were incubated with Ub-AMC reaction buffer and fluorescence intensities were measured by fluorometer . For each EBNA3 complex , but not wt control , fluorescence intensity increased with time during the assay , consistent with DUB activity within each EBNA3 complex . Interestingly , the amount of USP46 isolated from EBNA3C-F-HA LCLs was comparable to that seen in EBNA3A or EBNA3B complexes ( Fig 10B ) further confirming the association of the USP46 DUB complex with EBNA3C in LCLs . In order to assess the requirement for USP46 expression in LCLs , we attempted to knockout USP46 expression using CRISPR/Cas9 gene editing . We cloned two guide RNA ( gRNA ) sequences into the pX330 vector , which allows simultaneous expression of the Cas9 nuclease and a gRNA , and transferred this dual expression cassette into pCEP4 to allow hygromycin selection . Each construct was transfected into the 721 LCL and , as a control , 293T cells . We identified multiple 293T cell populations in which no expression of USP46 could be detected ( Fig 11 ) . In some cases , low level USP46 expression was detectable , which probably reflects the oligoclonal nature of this experiment . By contrast , we observed no more than an ~50% reduction in USP46 levels in the 721 LCLs ( Fig 11 , top panels ) . To ensure that the CRISPR/Cas9 mediated gene editing had worked as intended we PCR amplified and sequenced the targeted region for each USP46 gRNA from one cell population ( c3 in each case ) . Sequencing results demonstrated that in each population , at least one allele had undergone an in-frame deletion ( S6 Fig ) , which would be predicted to abrogate further Cas9 cleavage , but not disrupt the USP46 open reading frame . These sequencing results confirm that the USP46 gene was successfully edited in the 721 LCLs . As a further test , we performed an independent replicate of our USP46 CRISPR/Cas9 knockout in both cell lines ( S7 Fig ) . In 293T cells , 22 of 39 clones were successfully knocked out for USP46 expression , whereas we did not observe any USP46 knockout among 39 randomly selected clones in the 721 LCL . This difference was highly statistically significant ( p < 10–8 ) by a two-tailed Fishers exact test . Our results suggest that the USP46 gene is dispensable in 293T cells , but our inability to generate USP46 null LCLs using the same approach despite evidence for Cas9 mediated cleavage , implicates USP46 in LCL growth or survival . To more directly assess whether EBNA3C interaction with the USP46/WDR48/WDR20 , could recruit the DUB complex to chromatin , we performed chromatin immunoprecipitation ( ChIP ) assays for WDR48 in EBNA3C-HT LCLs and assayed for enrichment using qPCR [42] . We first examined the EBNA3C binding site within the p14ARF promoter that was recently reported to mediate recruitment of repressor complexes to this promoter . We observed an increase in ChIP signal in the presence of 4HT over that seen with 4HT withdrawal ( Fig 12 ) . As controls , we examined two additional sites located near the EIF2AK3 and PPIA genes which are bound by cell transcription factors , but not by EBNA3C [42 , 57] . At each of these locations , we observed no enrichment for in the permissive ( 4HT+ ) condition relative to the EBNA3C inactivation state ( 4HT- ) . Total levels of WDR48 and USP46 were unchanged upon 4HT withdrawal ( Fig 12B ) thus increased signal in the WDR48 ChIP at the p14 was not attributable to increased expression of the constituents of the USP46/WDR48/WDR20 complex by EBNA3C . These results are consistent with an EBNA3C dependent recruitment or stabilization of USP46/WDR48/WDR20 complex binding at the p14ARF promoter .
In this manuscript , we report the first detailed characterization of EBNA3A , EBNA3B , and EBNA3C complexes from LCLs . Despite an extensive literature on putative EBNA3 interacting proteins , endogenous EBNA3 complexes have not previously been isolated and subjected to LC/MS/MS analysis . The use of epitope tags permitted tandem affinity purification of these complexes and minimized the chances that observed differences in composition were attributable to differences in the antibodies used . Our approach has additional advantages in that the proteins were expressed at endogenous levels from their native promoters . Further , because these recombinant EBV genomes were able to transform primary B lymphocytes into LCLs , the epitope tags did not disrupt EBNA3 interactions essential for the transformation process . Despite the large array of proteins reported to interact with each EBNA3 , we identified only a limited number of proteins specifically associated with the EBNA3s through the TAP procedure . This limited overlap between binary protein-protein interaction screens and protein complex composition is consistent with results from large scale protein interactome mapping efforts [58] . It is a formal possibility that the purified EBNA3 complexes associated with these cell proteins during the purification procedure . We view this as unlikely since it requires the simultaneous assumption that the interaction is sufficiently strong to be maintained during the TAP procedure , but does not occur endogenously despite these proteins being present in the same subcellular fraction . One important caveat for our analysis is that the TAP procedure , while highly specific , can be insensitive for weak interacting partners . Thus , the complexes defined in our study may be most appropriately described as EBNA3 “core” complexes . Each EBNA3 complex was found to include RBPJ , a transcription factor in the Notch signaling pathway that is critical for EBNA3A and EBNA3C function in maintaining LCL growth . It was previously known that EBNA2 and EBNA3C exist in separate RBPJ complexes [49] . Our results demonstrate that all four RBPJ-interacting EBNAs ( 2 , 3A , 3B , and 3C ) form distinct RBPJ complexes . This has several important implications for the mechanisms by which EBNA3 proteins can act to regulate transcription and their ability to modulate EBNA2 activation [38 , 59 , 60 , 61] . First , despite binding to a distinct domain in the RBPJ N-terminus , EBNA3 proteins are able to exclude EBNA2 which interacts with the RBPJ beta trefoil domain . Further , because EBNA3A and EBNA3C must each interact with RBPJ to maintain LCL growth via p16 promoter repression , it is likely that two different RBPJ molecules , and hence , binding sites are required . Although we did not detect stable interactions among the EBNA3 complexes , it is conceivable that interactions described by other investigators at these promoters are required for cooperative gene regulation observed among EBNA3 proteins [62] . Indeed , it is tempting to speculate that EBNA3 proteins exert their cooperative effects by exploiting paired RBPJ sites in the human genome that are important for activation of specific genes by intracellular Notch [63] . Interactions with other transcription factors are also likely to be important for observed differences in EBNA3A , EBNA3B , and EBNA3C binding observed in ChIP-seq experiments [42 , 57] . Although we initially embarked on these experiments with the expectation of identifying unique EBNA3A , EBNA3B , and EBNA3C interacting partners , we unexpectedly identified another shared EBNA3 target: the USP46 and USP12 deubiqutinases ( DUBs ) and their chaperones WDR48 and WDR20 [64 , 65] . Because we did not find peptides corresponding to other EBNA proteins in these complexes , each EBNA3 protein appears to target RBPJ and the USP46/USP12 DUB complexes independently . Each EBNA3 bound most strongly to WDR48 , and USP46 binding was enhanced by WDR48 co-transfection , consistent with WDR48 being the primary mediator of EBNA3 binding to the USP46/USP12 DUB complexes . It is notable that EBNA3B and EBNA3C target the WDR48 SLD2 domain , whereas EBNA3A interacts with the WD repeats of WDR48 . Thus , the EBNA3 proteins bind to WDR48 via their highly divergent C-termini and do not all target the same WDR48 subdomains . Whether these distinct strategies for targeting the WDR48 protein are an accident of positive selection or account for differences between EBNA3A and EBNA3C’s roles in LCL growth is not clear . These binding site differences would allow for chromatin associated EBNA3A and EBNA3C to simultaneously bind ( and potentially stabilize ) a single WDR48 molecule , but we found no evidence for stable binding of both EBNA3A and EBNA3C in a single complex in our LC/MS/MS data . Nevertheless , we find that USP46/WDR48/WDR20 is a component of endogenous EBNA3 complexes purified from LCLs and is bound by domains of EBNA3A and EBNA3C that are important for p16 suppression and LCL growth . Taken together these findings suggest that these DUB complexes are specifically targeted by EBNA3 proteins as part of the EBV lymphocyte transformation strategy . The ubiquitin specific proteases USP12 and USP46 are close paralogs , that are 89% identical over 357 residues and are both regulated by the WDR48 and WDR20 chaperones [64 , 66] . Although the physiologic role of these enzymes is incompletely understood , they appear to exhibit partially overlapping substrate specificity [64] . The more distantly related USP1 is also regulated by WDR48 , but WDR20 is unique to USP12/USP46 complexes . We did not detect any USP1 peptides in our complexes , suggesting that the even though the EBNA3 proteins interact strongly with WDR48 , they are selective for USP12 and USP46 complexes , possibly due to stabilizing interactions with WDR20 or the enzymes themselves . Although the PHLLP1 and PHLLP2 phosphatases have been reported to be components of the USP46 and USP12 complexes , they were not detectably associated with EBNA3 complexes , likely because these phosphatases are predominantly membrane-associated [67 , 68 , 69 , 70] . Because regulation of the steady state levels of the PHLLP phosphatases by USP46/USP12 is a critical regulatory step in the Akt signaling pathway , EBNA3 proteins might influence PHLLP protein levels , and hence , alter Akt signaling by binding this DUB complex . However , PHLLP1 was not detectable in our LCLs and we found no evidence that PHLLP2 levels or Akt phosphorylation were effected by EBNA3C inactivation ( S8 Fig ) . Further , USP46 complexes were present in both the cytoplasm and the nucleoplasm of LCLs and this distribution was also observed in EBV negative B cells . Membrane associated USP46 and USP12 complexes have been implicated in regulating membrane trafficking of receptors , including Notch1 and GLR1 [71 , 72] . Although it is possible that EBNA3 proteins could affect this regulation , we do not favor this hypothesis as the levels of membrane associated USP46 in LCLs are not reduced , but slightly higher than that observed in EBV negative BL41 cells . Our inability to derive USP46 null LCLs using CRIPSR/Cas9 gene editing is consistent with USP46 playing an essential role in LCL growth or survival that it does not play in 293T cells . Based on our observation that WDR48 plays a dominant role in mediating EBNA3 association with the USP46 DUB complex and binds to EBNA3A and EBNA3C domains that are important for regulation of p16 , we suspect that the DUB complex interaction is important for transcriptional effects of the EBNA3 proteins . We considered the possibility that this interaction contributes to the long half-life of EBNA3 proteins; however the steady state levels of EBNA3A and EBNA3C WDR48 binding mutants were not detectably different than wild type ( Fig 6 ) and there was no detectable difference in protein turnover in the presence of cyclohexamide ( S9 Fig ) . Instead , our results suggest that EBNA3 proteins act as adaptor molecules to target USP46 complexes to promoters via interactions between RBPJ and other transcription factors . This is supported by our observation that WDR48 is recruited the p14ARF promoter in an EBNA3C dependent manner . Given the central role of ubiquitylation in transcriptional activation , we favor the hypothesis that the EBNA3 proteins recruit the DUB complex to remove ubiquitin molecules from other nuclear proteins . However , we are unaware of any unbiased methods for determining the substrates of DUB complexes . Using a candidate substrate approach , we investigated the possibility that ubiquitylation of histone H2A ( H2A-Ub ) and H2B ( H2B-Ub ) were targeted by these complexes as has been previously described in Xenopus [73] . However , we found global levels of H2A-Ub and H2B-Ub to be unaffected by the presence EBNA3 proteins ( S10 Fig ) . We did observe decreased H2A-Ub at the p16 promoter upon EBNA3C-HT inactivation ( S10 Fig ) and no change in H2B-Ub levels . This is consistent with decreased polycomb repression of p16 upon EBNA3C-HT inactivation , but not consistent with recruitment of USP46 to chromatin playing a direct role in deubiquitylating histones at the p16 promoter . In summary , we find that the USP46/USP12 DUB complexes are a highly associated with EBNA3 proteins in LCLs , interact with domains of EBNA3A and EBNA3C essential for LCL growth , and that DUB activity is preserved in these complexes . The substrates upon which these DUBs act upon in LCLs remain to be determined , despite our efforts to identify effects of the EBNA3 proteins on several candidates . Although we have focused on transcriptional effects of the EBNA3 proteins , it is likely that their ability to associate with the USP46/USP12 DUB complexes explains other observed EBNA3 activities as well , most notably their effects on the stability of cell proteins , including Mdm2 , cyclin D , Gemin3 , IRF4 or aurora kinase B [27 , 30 , 31 , 74 , 75] . We believe that the identification of the USP46/USP12 DUBs as components of the EBNA3 complexes in LCLs represents a significant advance in our understanding of the multitude of roles played by EBNA3 proteins in B lymphocyte transformation .
Lymphoblastoid cell lines ( LCLs ) described in this manuscript were derived by EBV transformation of peripheral blood B lymphocytes from de-identified donors , with written informed consent , which is approved by Partners IRB based on Helsinki recommendations . 293T ( obtained from Elliott Kieff , Harvard Medical School ) , a human cell line transformed by adenovirus 5 and SV40 large T antigen [76] , was cultured in Dulbecco’s modified Eagle’s ( Gibco ) medium . The “wild-type” LCL , created with an unmodified EBV BACmid , was a generous gift from Fred Wang [47] and the 721 LCL was obtained from Bill Sugden [77] . LCLs and P3HR1 ZHT cells [78] , a type II EBV cell line , were cultured in RPMI 1640 ( Gibco ) . Media was supplemented with L-glutamine ( Gibco ) , penicillin-streptomycin ( Gibco ) and 10–15% FetalPlex ( Gemini Bio-Products ) . pBS-XS-EA contains the XbaI-SalI fragment from the EBV B95-8 genome containing the EBNA3C region , in which the C-terminus of the EBNA3C ORF is mutated to create EcoRI and AvrII sites ( GATTCGATTAAGGGGATCCTAGG ) . pBS-EBNA3C-flag-HA-CAT was created from pBS-XS-EA by inserting an oligo encoding the flag and HA epitopes ( AATTGGATGAATTCGCGGCCGCTGGAGGAGACTACAAGGACGACGATGACAAGTCGGCCGCTGGAGGATACCCCTACGACGTGCCCGACTACGCCTAGGACGCGT annealed to CTAGACGCGTGGATCCGCATCAGCCCGTGCAGCATCCCCATAGGAGGTCCGCGGCTGAACAGTAGCAGCAGGAACATCAGAGGAGGTCGCCGGCGCTTAAGTAGG ) and a PCR product containing an FRT flanked CAT gene amplified from pKD3 using the primers ( CACTGAATTCCTAGGTAGGTGTAGGCTGGAGCTGCTTCGAAG and TTGAATGAACGCGTCATATGAATATCCTCCTTAG ) . pSG5-EBNA3A-flag-HA-CAT and pSG5-EBNA3B-flag-HA-CAT were created by cloning the NotI/MluI fragment from pBS-EBNA3C-flag-HA-CAT into pSG5-EBNA3A and pSG5-EBNA3B which had been modified to create NotI/MluI sites allowing the flag-HA tag to be fused in-frame with the EBNA3A or EBNA3B ORFs , respectively . Plasmids for expression of EBNA3A and EBNA3C mutants have been previously described [21 , 23] . pSG5-flag-EBNA3A 1–919 was constructed by PCR amplify of pSG5-Flag-EBNA3A using primer pairs EBNA3A-C919 ( ACAACAGCTGGCGGCCGCTACCTTCTAGTTTCAGGGCCTGTGACATTTTGGCCAC ) and EBNA3A-N543 ( CTCAGGGAATGGCATACCCATTAC ) , digested with NotI/BssHII and recloned into pSG5-Flag-EBNA3A or pCEP-Flag-EBNA3A . pSG5-Flag-E3A mCtBP1 was constructed same as previously described [44] . Flag-tagged EBNA3B 1–938 , 1–544 , and 394–938 were constructed by PCR amplification from pSG5-EBNA3B [23] using appropriate pairs of the following primers: E3B-N1 ( TTGTACAAAACTGCAGGCATGAAGAAAGCGTGGCTCAG ) , E3B-C938 ( AACTTTGTACGCGGCCGCTTACTCATCGTTCGATGTTTCAGAAG ) , E3B-C544 ( TCACTCTCTAGCGGCCGCTAACCGGTGAAGACACAAGGGCCTC ) , and E3B-N394 ( CTGCCGTACACTGCAGCAGTATACGGCAGGCCCGCGGTG ) , and cloned into the PstI/NotI sites of pSG5-flag [79] . The expression construct for Xpress-tagged-WDR48 was a kind gift of Jae Jung [51] . WDR48 1–634 ( ΔSLD2 ) was constructed using WDR48-N399 ( GCAAAGTGGATTTTGAAGATG ) and WDR48-C634 ( AGTTCAATTGCGGCCGCCTACAACACAGCAATATCTTCTTC ) . Resulting PCR product was digested with HpaI/NotI and recloned into pcDNA4-WDR48 . WDR48 1–535 ( ΔSLD2 ) was constructed using WDR48 C535stop ( TGTTTCATTAAGCGGCCGCTACGTTAACTAACCCCCGGAATCTCGGCAGAGCAGC ) and WDR48-N295 ( GCACCAGTTCTCAAGATGGAGC ) . Resulting PCR product was digested with EcoRI/NotI and recloned into pcDNA4-WDR48 . Then WDR48 1–535 ( ΔSLD2 ) was digested with HpaI and religated to make WDR48 1–430 ( ΔSLD1/2 ) . Xpress tagged USP46 and WDR20 were constructed by amplifying the ORFs from ORFome 5 . 1 Entry clones ( generous gifts of Marc Vidal ) using the following primers: TGTACAAAAGGTACCTATGACTGTCCGAAACATCGCCTC + CTTTGTACTCGAGCGGCCGCTATTCTCTTGACTGATAGAATAAAATATATC and TGTACAAAAAGGTACCTATGGCGACGGAGGGAGGAGGGAAG + AACTTTGTACTCGAGCGGCCGCTAAGGATTAAAACTTACCACTTTACCAG , respectively . Resulting PCR products were KpnI/NotI digested and cloned into pCDNA4-HisMax-A ( Invitrogen ) . All constructs were verified by sequencing . In frame C-terminal flag-HA tags were fused to the EBNA3A , EBNA3B , or EBNA3C ORFs as follows: DNA fragments containing these fusions were either excised as an Xba/SalI fragment ( from pBS-E3C-flag-HA-CAT ) or PCR amplified ( from pSG5-E3A-flag-HA-CAT and pSG5-E3B-flag-HA-CAT ) , using the E3A-F ( TGACGTGGTCCAACATCAGC ) and E3A-R ( GCGTATTATCAGTGGGTGGAATGGAGGGGGACACACTTCTACACCTTTGCCATATGAATATCCTCCTTAG ) or the E3B-F ( ACTCCCATGCAGCTGGCACTAAGGGC ) and E3B-R ( CCCCGCAGTCTGTTGCCCCAGGGTTCATCCCAGTTCTTGTTACATGGGCGCATATGAATATCCTCCTTAG ) primers . Fragments were recombined into an EBV-BACmid derived from the B95-8 genome using an inducible lambda red recombinase as previously described [47] . Following transient expression of FLP recombinase , single colonies were plated and screened for excision of the CAT gene . Recombinant EBV-BACmids were transfected into P3HR1 ZHT cells , selected with puromycin and induced for replication by addition of 4HT . Viral supernatant were collected and used to transform peripheral blood B cells in to lymphoblastoid cells as previously described [23] . LCLs were screened by PCR for recombinant genomes containing the flag-HA fused in frame to the appropriate EBNA3 open reading frame and the absence of co-infecting P3HR1 genomes . The following antibodies were used for Western blotting , immunoprecipitations and chromatin immunoprepicitation: mouse monoclonal antibodies against HA . 11 ( 16B12 , Covance ) , Flag ( M2 and M5 , Sigma ) , Xpress ( R910-25 , Invitrogen ) , alpha-tubulin ( B-5-1-2 , Sigma ) , Glyceraldehyde-3-Phosphate Dehydrogenase ( GAPDH; 6C5 , Millipore ) , Beta Actin ( Sigma , A5441 ) , BRG1 ( PA5-17008 , Thermo Scientific ) , LaminB ( sc-6216 , Santa Cruz ) , RBPJ ( Hyb-T6709 , Cosmo Bio Co ) , CtBP1 ( Q13363 , Millipore ) , WDR48 ( HPA038421 , SIGMA or rabbit polyconal sera , a kind gift of Alan D’Andrea ) , WDR20 ( A301-657A , Bethyl Laboratories ) , USP46 ( HPA007288 , Sigma ) , p16 ( clone JC8 , sc-56330 , Santa Cruz Biotechnology ) , NF-kB p65 ( 8242 , Cell Signalling ) , EBNA1 ( 13-156-100 , Advanced Biotechnologies ) , EBNA2 ( PE2 , [80] ) , EBNA3A ( F115P , Exalpha Biologicals ) , EBNA3B ( F120P , Exalpha Biologicals ) , EBNA3C ( A10 , [81] ) , LMP1 ( S12 , [82] ) , EBNALP ( 4D3 , a kind gift of Yasushi Kawaguchi [83] ) . histone H2A ( #07–146 , Millipore ) , Ub-Histone H2A ( #05–678 , Millipore ) , histone H2B ( 07–371 , Millipore ) , Ub-Histone H2B ( #07–371 , Millipore ) , PHLPP1 ( A300-660A , Bethyl ) , PHLPP2 ( A300-661A , Bethyl ) , Akt ( #4691 , Cell signaling ) , p-Akt ( #4060 , Cell signaling ) , and GFP ( sc-5384 , Santa Cruz ) . Approximately 6x108 cells from an LCL transformed with either a wild-type EBV BAC or a recombinant EBV BAC expressing either EBNA3A-F-HA , EBNA3B-F-HA , or EBNA3C-F-HA were lysed in 10ml of TAP lysis buffer ( 1% ( v/v ) Igepal CA-630 , 50mM TrisHCl [pH7 . 5] , 140mM KCl , 10mM NaF , 1 . 5mM EDTA , and 5% glycerol ) containing 10mM β-ME and EDTA-free Complete protease inhibitor ( Roche , Mannheim , Germany ) . Lysed cells were incubated at 4°C for 30 minutes with constant rotation before being cleared by two rounds of centrifugation at 8500rpm for 10 minutes and one spin at 10 , 000rpm for 20 minutes . Supernatants were diluted as required to match total protein concentration as measured by Bradford assay ( BioRad ) . A 50μl aliquot was saved for Western blot analysis and the remaining supernatants were incubated with 60μl of anti-Flag M2 agarose ( Sigma ) for 4 hours at 4°C with rotation . The beads were washed extensively with TAP lysis buffer before being eluted with 60μl of 0 . 4mg/ml Flag peptide ( Sigma ) in TAP buffer twice at 4°C for 30 minutes with shaking and once at 37°C for 30 minutes with shaking . Elutions were passed through Bio-Spin columns ( Bio-Rad ) to remove entrained agarose beads and pooled . Agarose-conjugated HA beads , 25μl per sample , ( F7 , Santa Cruz Biotechnology ) were added to the pooled elutions and incubated overnight at 4°C with constant rotation . The beads were washed three times with TAP lysis buffer and eluted with 30μl of 0 . 4mg/ml HA peptide ( Covance ) twice at 37°C for 30 minutes with shaking; elutions were spun through Bio-Spin columns and pooled for LC/MS/MS analysis . Eluted samples ( 50μl ) were mixed with 10μl of 4X LDS Loading Buffer ( Invitrogen ) separated on a 10% Bis Tris NuPAGE MOPS gel ( Invitrogen ) . Gels were fixed in destain solution ( 50% methanol and 7 . 5% acetic acid ) , rehydrated , stained with Simply Blue Safestain ( Invitrogen ) , cut horizontally into one slice per sample , and destained until transparent . Gel slices were reduced with DTT , alklyated with iodoacetamide , and then rinsed with three alternating washes of 50 mM ammonium bicarbonate and acetonitrile . Each slice was then digested with trypsin by resuspending in 50mM ammonium bicarbonate/10% acetonitrile/5 . 5g/mL trypsin and incubating at 37°C for 24 hours . Peptides were extracted with one rinse of 50mM ammonium bicarbonate/10% acetonitrile followed by one rinse of 50% acetonitrile/0 . 1% formic acid , lyophilized , then rehydration in 20μL 96% water , 4% methanol , and 0 . 2% formic acid . Digested samples were loaded into 96-well plates for mass spectrometry analysis on a LTQ-Velos Orbitrap XL ( Thermo Fisher Scientific ) instrument . For each run , 10μL of each re-constituted sample was injected onto an Easy nLC system configured with a 10cmx100um trap column and a 25cm x 100um ID resolving column ( Thermo Scientific ) . Buffer A was 96% water , 4% methanol , 0 . 2% formic acid and Buffer B was 10% water , 90% acetonitrile , and 0 . 2% formic acid . Samples were loaded at 5μL a minute for 9 minutes , and a gradient from 0–60% B at 375nl/minute was run over 70 minutes , for a total run time of 115minutes ( including regeneration , and sample loading ) . Velos-Orbitrap ( Thermo Scientific ) was run in a standard data dependent Top 10 configuration at 60K resolution for a full scan , with monoisotopic precursor selection enabled , and +1 , and unassigned charge state rejected . MS2 fragmentation and analysis was performed in the ion trap using CID fragmentation . Peptides were identified using SEQUEST ( Thermo Fisher Scientific ) through Protein Discoverer , version 1 . 2 . MS/MS data were searched using 10ppm mass accuracy on precursor m/z and a 0 . 5Da window on fragment ions . Fully enzymatic tryptic searches with up to three missed cleavage sites were allowed . Oxidized methionines were searched as a variable modification and alkylated cysteines were searched as a fixed modification . Sequential database searches were performed using the NCBI RefSeqHuman FASTA database . Peptides for each charge state were filtered to a false discovery rate ( FDR ) of 1% . Subcellular fractionation was performed using the subcellular protein fractionation kit ( Thermo Scientific Pierce ) according to the manufacturer’s instructions . For each fraction , an amount corresponding to that derived from 400 , 000 cells was resolved by SDS-PAGE and probed for EBNA3 proteins , RBPJ , and components of the USP46 complex ( USP46 , WDR20 , and WDR48 ) . Fraction purity was assessed by probing for tubulin , BRG1 , Histone H2B , and LaminB . In vitro deubiquitination assays using TAP purified EBNA3s complex and Ub-AMC ( U-550 , Boston Biochem ) as a substrate were performed in 100uL of reaction buffer ( 20 mM HEPES-KOH at pH 7 . 8 , 20 mM NaCl , 0 . 1 mg/mL BSA , 0 . 5 mM EDTA , 20mM beta-mercaptoethanol ) . Fluorescence signal was monitored in VICTOR X5 multilabel plate reader ( Perkin Elmer ) . Transfected 293T cells which were harvested from 10cm tissue culture dishes or ten million of LCLs were lysed into IP lysis buffer ( 1% ( v/v ) Igepal CA-630 , 40mM TrisHCl [pH7 . 5] , 150mM NaCl , and 10mM MgCl2 ) supplemented with fresh 0 . 015mg/mL aprotinin ( Sigma ) , 0 . 5mM PMSF , and 1ug/ml Leupeptin . Lysates were incubated at 4°C for 30 minutes with rotation and cleared by centrifugation at 10 , 000x g for 15 minutes . Supernatants were pre-cleared by rotating with Sepharose ( Sigma ) for 1 hour at 4°C and then incubated with anti-Flag M2 agarose , anti-HA magnetic beads ( cloneTANA2 , MBL ) , or protein A/G for 2 hours at 4°C with rotation . The beads were washed extensively with IP lysis buffer and either eluted with 0 . 4 mg/ml Flag peptide or 0 . 4mg/ml HA peptide in IP lysis buffer at 37°C for 30 minutes with shaking or resuspend into SDS sampling buffer . The proteins were analyzed by Western blotting . Total-cell lysates or immunoprecipitated proteins were separated by sodium dodecyl sulfate ( SDS ) -polyacrylamide gel electrophoresis , blotted onto nitrocellulose membrane , and probed with appropriate antibodies . After extensive washing , horseradish peroxidase conjugated secondary antibodies ( Jackson Immuno Research ) were applied . After incubation for 1–2 hours the membrane was washed again , and developed with chemiluminescence reagent ( Perkin Elmer ) . Western blots were exposed on film and visualized on a KODAK Image Station 4000R ( Kodak Molecular Imaging Systems ) . Five million of EBNA3AHT-infected LCLs [23] were transfected with 2ug of oriP plasmid DNA expressing EBNA3A , EBNA3A mutant or control plasmid . LCLs were harvested during log-phase growth , washed with complete medium , resuspended in 100ul of buffer V with DNA in a cuvette , transfected using program X-001 of Amaxa Nucleofector ( Lonza ) , and cultured for 3 days in LCL-conditioned medium with 4-hydroxy-tamoxifen ( 4HT ) . Cells were then washed with PBS twice , and cultured in complete medium with or without 4HT . Every 4 to 7 days , cell numbers were counted , cultures were split , and the total numbers of viable cells relative to those of the initial culture were calculated . Cas9 mediated editing of the USP46 gene was accomplished by cloning either of two targeting 20mers for the gRNA ( CRISPR-1 in exon3: AAACTTGCTGACGTGCCTGG and CRISPR 2 in exon4: TATTGCGGACATCCTTCAGG ) [84 , 85] into the pX330 plasmid [86] . The Cas9 expression cassette and gRNA were excised from pX330 by PciI/ NotI digestion and cloned into pCEP4 with a modified polylinker sequence , which allowed for hygromycin selection via an self-maintaining episomal plasmid . Five million of EBV transformed LCLs were harvested during log-phase growth , resuspended in 100ul of buffer Ingenio with pCEP-CRISPR-USP46 plasmid , transfected using program U-001 of Amaxa Nucleofector , and cultured for 2 days in RPMI1640 complete medium . 20 , 000 cells were plated on 96 well culture plate using RPMI1640 complete medium with 300ug/ml hygromycin for one month . For 293T cells , one million cells were transfected with pCEP-CRISPR-USP46 plasmid using Effectene , recovered for 48 hours , and then subjected to hygromycin selection . Hygromycin resistance cells were harvested and screened with DNA PCR using primer pairs ( CRISPR-1 F: GGTGAGCTGGACTCCAATACAGGG and R: GCCAGCTCTTCCTTTTGAGGAGAT or CRISPR-2 F: GGAGGCAGAGGTTGCAGTGAACTG and R: GCAATCACATGCAACATAGCGTAC ) and Western blotting analysis . These primers were also used for Sanger sequencing of PCR products . USP46 Western blot signals were quantified and normalized to tubulin signal . For statistical analysis any cell line exhibiting >25% normalized USP46 signal was considered positive . ChIP assays were performed as described previously [87] . Briefly , 2x106 cells per ChIP were fixed in 1% ( wt/vol ) formaldehyde and sonicated using cup horns sonication system ( Qsonica ) . After extract clearing by centrifugation , supernatants were diluted and incubated with protein G agarose with salmon sperm DNA ( Millipore ) for 1 h with rotation at 4°C . Protein G agarose was pelleted and supernatants were used in ChIP experiments . One or two micrograms of antibody were added per 2x106 cells , followed by incubation overnight at 4°C with rotation . Purified DNA was quantified using gene specific primers and iTaq universal SYBR green supermix ( Bio-Rad ) using a CFX96 touch real-time PCR detection system ( Bio-Rad ) . Primers used for these experiments were as follows: p16 TSS [32] , p14ARF [42] , EIF2AK3 ( F: CTTCCGGACGCAATTACCAATGAG and R: GTAGGAAAGGTATTCCGGGAACTG ) or PPIA [57] . | Epstein-Barr virus ( EBV ) is a gammaherpesvirus implicated in the pathogenesis of multiple malignancies , including Burkitt lymphoma , Hodgkin lymphoma , post-transplant lymphoproliferative disease ( PTLD ) , nasopharyngeal carcinoma , and gastric carcinoma . EBV infection of resting B-lymphocytes drives them to proliferate as lymphoblastoid cell lines ( LCLs ) , an in vitro model of PTLD . LCLs express a limited EBV gene repertoire , including six nuclear proteins ( EBNA1 , 2 , 3A , 3B , 3C , and LP ) , three integral membrane proteins ( LMP1 , 2A , and 2B ) , and more than 30 micro RNAs . EBNA2 and the EBNA3 proteins are transcription factors that regulate viral and cell gene expression through the cell DNA binding protein RBPJ . In this study , we established LCLs transformed by recombinant EBV genomes in which a Flag-HA epitope tag is fused in-frame to the C-terminus of EBNA3A , EBNA3B or EBNA3C . Using these LCLs , we purified endogenous EBNA3 complexes and identified the USP46 deubiquitinating enzyme ( DUB ) and its associated chaperones WDR48 and WDR20 as EBNA3 binding proteins . We find that EBNA3s interact primarily with the WDR48 protein and that loss of WDR48 interaction with EBNA3A or EBNA3C impairs LCL growth . This study represents the first characterization of EBNA3 complexes from LCLs and implicates the USP46 DUB complex in EBNA3 mediated gene regulation . | [
"Abstract",
"Introduction",
"Results",
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"Methods"
] | [] | 2015 | The EBNA3 Family of Epstein-Barr Virus Nuclear Proteins Associates with the USP46/USP12 Deubiquitination Complexes to Regulate Lymphoblastoid Cell Line Growth |
Orientia tsutsugamushi is an intracellular α-proteobacterium which resides in trombiculid mites , and is the causative agent of scrub typhus in East Asia . The genome sequence of this species has revealed an unprecedented number of repeat sequences , most notably of the genes encoding the conjugative properties of a type IV secretion system ( T4SS ) . Although this observation is consistent with frequent intragenomic recombination , the extent of homologous recombination ( gene conversion ) in this species is unknown . To address this question , and to provide a protocol for the epidemiological surveillance of this important pathogen , we have developed a multilocus sequence typing ( MLST ) scheme based on 7 housekeeping genes ( gpsA , mdh , nrdB , nuoF , ppdK , sucD , sucB ) . We applied this scheme to the two published genomes , and to DNA extracted from blood taken from 84 Thai scrub typhus patients , from 20 cultured Thai patient isolates , 1 Australian patient sample , and from 3 cultured type strains . These data demonstrated that the O . tsutsugamushi population was both highly diverse [Simpson's index ( 95% CI ) = 0 . 95 ( 0 . 92–0 . 98 ) ] , and highly recombinogenic . These results are surprising given the intracellular life-style of this species , but are broadly consistent with results obtained for Wolbachia , which is an α-proteobacterial reproductive parasite of arthropods . We also compared the MLST data with ompA sequence data and noted low levels of consistency and much higher discrimination by MLST . Finally , twenty-five percent of patients in this study were simultaneously infected with multiple sequence types , suggesting multiple infection caused by either multiple mite bites , or multiple strains co-existing within individual mites .
Scrub typhus is a zoonotic disease endemic in Southeast Asia caused by Orientia tsutsugamushi , a Gram-negative obligate intracellular coccobacillus . The number of new cases in East Asia has been estimated at approximately one million per year [1] . It is transmitted by the bite of larval stages of trombiculid mites ( “Chiggers”; Leptotrombidium spp . ) , which more typically feed on small rodents . The disease commonly presents as an acute febrile illness within 7–10 days of being bitten . The clinical features include fever , headache , myalgia , lymphadenopathy and an eschar at the site of the bite . Disease severity and manifestations vary widely from asymptomatic to fatal , and show marked geographical differences , with reported fatality rates in the pre-antibiotic era ranging from 3% in Taiwan to 40% in Japan [2] . It is not known whether these geographical differences reflect genetic variation in the bacteria , the host , or both . Strains of O . tsutsugamushi are typically distinguished serologically on the basis of the 56 kDa-outer membrane protein encoded by ompA , which is known to be highly polymorphic within the natural population . Despite the importance of this pathogen , little is known of the population diversity or the role of homologous recombination in driving the microevolution of this species . This question is relevant for the development of markers aimed at epidemiological surveillance , but is also of evolutionary interest given the unusual mode of molecular evolution and distinctive intracellular niche of this species . The genome of O . tsutsugamushi strain Boryong reveals a massive proliferation of repeated non-functional genes , including 359 copies of the conjugative transfer ( tra ) components of a type IV secretion system ( T4SS ) , and >400 transposases [3] . These duplications may facilitate extensive intragenomic rearrangement and possibly homologous recombination , although direct population-based evidence for this is currently lacking . Obligate intracellular bacteria are generally considered unlikely to undergo high rates of homologous recombination as strict vertical ( transovarial ) transmission from mother to offspring will lead to co-evolution of host and symbiont , and will restrict the opportunities for different lineages to meet , and hence recombine . This picture , which largely stems from extensive studies on the aphid symbiont Buchnera , has recently been challenged by convincing evidence of high rates of homologous recombination and host promiscuity in the α-proteobacterial reproductive parasite Wolbachia [4] . Current evidence implicates horizontal transmission between hosts to explain the lack of host specificity among different Wolbachia strains , and to provide the opportunity for different lineages to recombine [5] . Furthermore , the high proliferation of IS elements in the Wolbachia genome coincides with high rates of intragenomic rearrangements [6] . Here we have examined the role of homologous recombination in shaping the population structure of O . tsutsugamushi through the development of a multilocus sequence typing ( MLST ) scheme that can be performed directly on DNA extracted from patient blood . MLST is a powerful tool for the study of bacterial evolution [7] , global epidemiological surveillance ( e . g . Streptococcus pneumoniae , Neisseria meningitidis ) and for monitoring the emergence of resistant strains ( e . g . methicillin-resistant Staphylococcus aureus ) [8] . We applied our MLST scheme to an incident series of scrub typhus infections in North and Northeast Thailand . We estimated the rate of homologous recombination expressed as the ratio of the likelihood that a given nucleotide site will change by a recombinational replacement of the region spanning the site against the likelihood that the site will change by de novo mutation ( r/m ) . Comparisons with the equivalent estimates in other species point to very high rates of homologous recombination in O . tsutsugamushi . We have also shown some evidence for local clonal expansion and mixed infection , and have compared our results with those based on the highly polymorphic outer membrane protein encoded by ompA , which is currently used to distinguish strains .
This study was conducted according to the principles expressed in the Declaration of Helsinki . The study protocol was approved by the Ethics Committee of the Faculty of Tropical Medicine , Mahidol University , Thailand ( Approval Number: MUTM 2006-053 ) . This retrospective study used the leftover sample . The subsequent data were analyzed anonymously . Eighty-four patients presenting to Udon Thani general hospital , Northeast Thailand between October 2000 and December 2001 with scrub typhus were identified using PCR , as previously described [9] . Five millilitres of blood was drawn on admission for molecular diagnostics . The study also included 20 strains isolated previously from patients in Udon Thani and Tak province ( Northern Thailand ) that were maintained in laboratory culture . The bacterial reference strain Kato , DNA of reference strains Gilliam , Karp and a patient DNA ‘Sido’ were obtained from the Australian Rickettsial Reference Laboratory , Geelong , Australia . DNA was extracted from admission blood samples and in vitro cell culture as previously described [10] . The housekeeping gene candidates were selected from shotgun sequencing of O . tsutsugamushi strain UT 76 ( Udon Thani , Thailand ) , which was conducted at the Wellcome Trust Sanger Institute , UK ( ftp://ftp . ensembl . org/pub/traces/orientia_tsutsugamushi_ut76 ) . Using the incomplete assembly , contiguous genes homologous to 19 orthologous housekeeping genes from 8 related rickettsial species ( Rickettsia typhi , R . conorii , R . prowazekii , R . felis , Ehrlichia ruminantium , Anaplasma marginale , Wolbachia pipientis strain wMel , Bartonella henselae ) were identified using BLASTN [11] and annotated using Artemis software [12] . Seven housekeeping gene loci were selected: gpsA , mdh , nrdB , nuoF , ppdK , sucD , and sucB . Fourteen primer pairs from these loci were designed using PrimerSelect ( DNASTAR Lasergene , USA ) ( Table 1 ) . O . tsutsugamushi DNA was amplified using nested PCR , as follows . The first PCR round contained 200 µM dNTP , 1× PCR buffer , 1 . 5 mM MgCl2 , 0 . 05 unit of Taq DNA Polymerase ( Promega , USA ) and 5 µl extracted DNA ( total volume 50 µl ) . The amplification profile for all loci with the exception of gpsA was as follows: 94°C for 4 minutes ( 1 cycle ) , followed by 35 cycles of 94°C for 30 sec , 55°C for 30 sec , 72°C for 30 sec and 1 cycle of 72°C for 5 minutes . An annealing temperature of 50°C was used for gpsA . Five µl of the first PCR product was then used in a second PCR amplification profile using a 50°C annealing temperature for sucD , nrdB , sucB , nuoF , ppdK and 45°C for mdh and gpsA . PCR product clean up using QIAquick PCR purification kit ( QIAGEN , Germany ) was followed by sequencing reactions in forward and reverse directions using the second PCR primer . The PCR sequencing methods used ABI PRISM® BigDyeTM Terminator Cycle Sequencing Kits with AmpliTaq DNA polymerase ( FS enzyme ) ( Applied Biosystems , USA ) , following the protocols supplied by the manufacturer . The PCR sequencing product was precipitated and then resuspended in loading buffer and subjected to electrophoresis in an ABI 3730XL sequencer ( Applied Biosystems , USA ) . MLST was defined for 84 DNA samples that had been extracted from EDTA blood and shown previously to be positive by PCR for O . tsutsugamushi , 21 DNA samples extracted from in vitro O . tsutsugamushi isolates , 3 DNA samples extracted ( Karp , Gilliam and Sido strain ) , and 2 whole genome sequences available from GenBank . Forward and reverse sequence traces for each locus were compared using SeqMan® II ( DNASTAR Lasergene , USA ) . Allele numbers for each locus were assigned to each unique sequence in the order in which they were discovered , to give an allelic profile for each strain in the order gpsA-mdh-nrdB-nuoF-ppdK-sucD-sucB . Each allelic profile was assigned a sequence type ( ST ) , again numbered sequentially as new allelic profiles were found . The allele and profile frequencies were analysed using the software START version 2 . The diversity index ( Simpson's index of diversity ) was calculated as previously described [13] , [14] . The genetic relatedness on the basis of allelic profile of all samples ( patient samples and reference strains ) was analysed and displayed using e-BURST ( https://eburst . mlst . net ) . The DNA sequences from all 7 loci were concatenated in the locus order used to define allelic profile . A neighbour-joining tree based on the concatenated sequences was constructed using MEGA version 4 . 0 . An estimate of the ratio of recent recombination to mutation events ( r/m ) with clonal complexes was made by comparing the sequences of mismatched alleles in clonal founders and single locus variants [15] . Other tests for recombination were performed using the RDP suite of programs [16] . The entire 56- kDa protein gene ( 1 . 5 kb ) of the 22 in vitro isolates used in this study has been sequenced and reported previously [17] , [18] . Comparisons were made between the 56kDa gene sequence data and MLST using BioNumerics ( Applied Maths , Belgium ) . Simpson's index of diversity was calculated for each of these datasets . To verify that the double nucleotide peaks seen on sequencing were due to multiple gene products from two or more alleles of polymorphic genes present in the patient sample ( indicative of mixed infection with multiple strains of O . tsutsugamushi ) , restriction enzyme analysis and PCR cloning were performed . For restriction enzyme ( RE ) analysis , an enzyme was chosen to cut or not cut the PCR product once at a polymorphic site . PCR products of locus gpsA from 2 strains ( no . 37 and 70 ) were digested with DdeI ( Promega , USA ) and NcoI ( NEB , England ) respectively , as recommended by the manufacturer . The restriction enzyme pattern was analysed by gel electrophoresis . For PCR cloning , the PCR products from locus gpsA of strain no . 37 and 70 were blunt-end cloned to pGEM®T easy vector and transformed into E . coli JM109 . 10–20 white colonies were selected and DNA was extracted and characterized . The clones of size greater than the vector ( approximately 500-bp insert size ) were further digested with EcoRI enzyme ( NEB , England ) to excise the cloned fragment and the products examined by gel electrophoresis . Clones with inserts of around 500 bp were digested with DdeI and NcoI . Selected clones that gave differing RE patterns were further verified by PCR and sequencing of the insert . Sequence trace was examined using SeqMan®II .
The O . tsutsugamushi MLST scheme was developed for direct application to clinical blood samples . Most of the samples ( 77% ) used in the current study were EDTA blood samples from patients with scrub typhus , an approach necessitated by the difficulty of isolating this slow growing bacterium in cell culture . Although DNA extracted directly from patients' blood may contain low concentrations of bacterial DNA , it was possible using a nested PCR approach to produce amplicons in concentrations that were sufficient to sequence . In this study there were 3 patient samples ( UT125 , UT144 , UT196 ) on which MLST was performed both on DNA extracted from the original blood sample and from the organism grown in cell culture . The sequence types from both sources were identical ( data not shown ) . This demonstrates that the MLST sequence type can be determined directly from a patient blood sample when in vitro culture is not available . A total of 108 DNA samples ( 24 isolates extracted from in vitro cell culture and 84 PCR positive EDTA samples ) were amplified and sequenced at all seven loci ( 2700-bp in total for each strain ) . The expected size of the final PCR products and the length of each sequenced gene fragment are shown in Table 1 . Eighty-seven of the 108 DNA samples analysed had clear sequence reads in both directions at all 7 loci ( Table S1 ) , but 21 DNA samples , all amplified directly from patient blood ( 25% of patient samples processed in this way ) , repeatedly showed double peaks at one or more nucleotide positions at one or more loci . This was not seen with any of the 24 DNA samples extracted from cultured isolates . The number of polymorphic sites at each ambiguous locus varied from 1 to 13 , and the number of ambiguous loci per strain varied from 1 to 7 . The polymorphic sites in some strains were also found in other strains at the same positions . Forty-nine sequence types ( STs ) were identified among the 89 samples ( 84 Thai patient samples , 1 Australian patient sample , 2 reference samples and 2 in silico genomes available from GenBank ) for which the sequencing was unambiguous at all loci ( Table S1 ) . Of these , 24 STs were represented by only a single strain . The most common ST was ST29 , which accounted for 17 strains ( 19 . 1% ) , followed by ST2 ( n = 7 ) , ST6 ( n = 5 ) , ST33 ( n = 4 ) , ST34 ( n = 4 ) , ST27 ( n = 3 ) , ST1 , 9 , 10 , 13 , 30 and 38 ( n = 2 ) . The number of alleles at each locus ranged from 18 to 23 . There was a high degree of genetic diversity with a Simpson's index of diversity of 0 . 95 ( 95% CI 0 . 92–0 . 98 ) . The ratio of non-synonymous to synonymous nucleotide changes ( dN/dS ) was calculated for all 7 gene loci and found to range from 0 . 05–0 . 26 ( Table 2 ) , indicating that the genes are evolving predominantly by purifying selection . Using eBURST , 4 clonal complexes ( CC ) were identified ( CC27 , CC29 , CC13 and CC10 ) , as shown in the population snapshot of 89 strains in Figure 1 . Clonal complexes were defined as sets of related STs that descended from the same founding genotype . Using a stringent definition of 6/7 shared alleles , CC27 contained ST 25 , 26 and 24 as single locus variants ( SLVs ) and ST37 as a double locus variant ( DLV; as an SLV of ST 25 ) . Expanding the group definition to 5/7 loci in common resulted in the inclusion of four extra STs ( 35 , 36 , 38 , 23 ) into CC27 ( not shown ) . CC29 contained 3 SLVs ( STs 30 , 28 and ST31 ) , CC13 contained 3 SLVs ( STs 4 , 14 , 15 ) and 1 DLV ( ST5 ) , and CC10 contained 2 SLVs ( ST9 , 11 ) . In addition there were four unconnected doublets at the 6/7 threshold , and 21 singletons . These links were entirely consistent with those defined using goeBURST , a recently developed optimized implementation of the BURST algorithm [19] . To understand the extent to which recombination has contributed to the diversification of this population compared with mutation , we estimated the ratio of recombination to mutation ( r/m ) at both the allelic and nucleotide level within clonal complexes by comparing the sequences of the non-identical alleles in all SLVs with their assigned clonal founders [20] , [21] . Recombination is assumed to be the cause of multiple nucleotide changes ( >1 ) , while de novo mutation is assumed to be the cause if there is only a single nucleotide difference and if the resulting allele is not found elsewhere in the database . Of the 11 SLVs in 4 clonal complexes available for examination in our strain collection , only one genetic event was consistent with a point mutation by these criteria . The recombination to mutation ratio ( r/m ) per allele site is calculated from the number of alleles that were different in SLVs , and the per-site recombination to mutation was calculated from the overall number of nucleotide differences found in SLVs compare to the putative ancestral ST . The upper-bound ratio of recombination to mutation for O . tsutsugamushi in this population of 89 strains was estimated as 10∶1 at the allele level and 60∶1 at the nucleotide site level ( r/m ) ( Table 3 ) . These estimates are comparable to the freely recombining human pathogens Neisseria meningitidis and Streptococcus pneumoniae [22] . However , this estimate is based on only 11 SLVs , and a larger dataset is required in order to compute a more reliable estimate . Nevertheless , it is striking that 5/11 of the variant alleles in SLVs in the current data differ from the corresponding alleles in the founder for at least 8 nucleotide sites , ( >1 . 5% sequence divergence ) . Thus even if many of the alleles differing by 2–4 nucleotides have emerged through point mutation and were misclassified as recombination events , the high diversity between these allelic comparisons points to a strong role for recombination . In order to find further evidence for recombination we used the RDP suite of programs . Six tests for recombination were employed on the concatenated sequences: Geneconv , Bootscan , Max Chi , Chimaera , SiScan and 3Seq . Together , these tests detected 85 recombination signals corresponding to 16 unique events . Eight of these recombination events were supported by at least 3 tests ( P<0 . 05 ) ( Table 4 ) . Approximately half of the recombination breakpoints detected by these tests corresponded to gene borders , which suggests a role for both intra- and inter-genic recombination . We visually inspected the sequence trace of breakpoints detected by these tests , which confirmed striking mosaicism , and two examples ( recombination events 5 and 8 ) are shown in Figure 2 . We examined the phylogeny of 49 STs including 47 STs from this study and the 2 sequenced strains Boryong and Ikeda from GenBank ( accession no . AM494475 and AP008981 , respectively ) , by using MEGA v 4 . 1 to construct a neighbour-joining tree ( Figure 3 ) . Although the boostrap values were generally very poor ( not shown ) , the tree was broadly consistent with the clusters delineated by eBURST based on a group definition of 5/7 alleles in common . However , there were exceptions; STs 35 and 36 were excluded from CC27 by the tree ( indicating recombination events between diverged parents ) , whereas ST32 was included in this group by the tree but excluded by eBURST at 5/7 loci ( indicating mutational events at multiple loci ) . We compared the discrimination provided by the 56-kDa gene sequence to the MLST data by comparing data for a set of 22 isolates using both methods . The 56-kDa gene sequence resolved the 22 isolates into 3 putative antigenic types ( Gilliam , Karp , and TA716 ) , based on comparisons to relevant reference sequences ( Table 5 ) . The 56-kDa gene sequence data were generally poorly congruent to the MLST data at both the ST level ( 31 . 4% ) and the concatenated sequence level ( 18 . 1% ) . The MLST data resolved 15 STs , corresponding to a Simpson's Index of Diversity of 0 . 95 ( 0 . 91–0 . 99 ) compared to 0 . 48 ( 0 . 30–0 . 66 ) for the 56-kDa data . This data indicated that MLST has higher discrimination power than 56-kDa typing . We repeatedly observed double peaks in the chromatograms from a number of DNA samples extracted directly from blood . For example , the 542 bp gpsA PCR product from strain no . 37 demonstrated a multiple ( C/A ) peak at position 223 and strain no . 70 demonstrated a multiple ( T/C ) peak at position 261 . In order to check whether this resulted from a mixed infection we digested the PCR product with DdeI which was predicted to cut one of the two putative PCR products at a single polymorphic site [Dde I site ( C223▾TNAG ) ] . Electrophoresis post digestion revealed 3 bands , two of the predicted size following Dde I digestion ( at 319 , 223 bp ) , and one representing an undigested product ( at 542 bp ) ( Figure 4 ) . Although this was consistent with the presence of two bacterial genotypes in the original patient blood sample , it is also possible that the three bands simply reflected partial digestion . We therefore cloned and sequenced gpsA amplicons from these samples to further evaluate the basis of the double peaks . The sequence of cloned amplicons resolved the double peaks by demonstrating the presence of either one or other nucleotide at these sites , confirming the presence of more than one PCR product in the original blood sample ( Figure 5 A–F ) . We infer from this that the results are consistent with mixed infection in 25% of human samples tested , which can either be explained by mixed strains in mites or multiple bites of a single human by mono-infected mites . We also note that in many cases where multiple strains were recovered from a single blood sample , that strains tended to be more similar to each other than to other strains in the study . This may reflect the adaptation of particular bacterial genotypes to specific mite genotypes , although a more extensive dataset is needed from both human and mite hosts to examine this possibility more thoroughly .
Here we describe a new MLST scheme that was developed for O . tsutsugamushi and discuss evidence concerning the rates of recombination and mixed infection in the human host . Shotgun cloning and sequencing of a Thai O . tsutsugamushi isolate ( UT 76 strain ) greatly facilitated gene choice and the design of primers , and the genes have been confirmed to be ubiquitous within the O . tsutsugamushi population and are likely to be predominantly under neutral selection . We therefore argue that the MLST genes proposed here fulfill all the criteria suggested for large-scale typing [7] and form a representative sample of the core O . tsutsugamushi genome . We noted a large number of STs and high allelic diversity at all loci within the 89 O . tsutsugamushi strains characterized by MLST . The population of O . tsutsugamushi is thus very diverse ( Simpson's index 0 . 95 ) , with a high number of STs per strain ( 49 STs in 89 strains , 0 . 55 STs per strain ) . Estimation of the relative contributions of recombination and mutation to the emergence of variant alleles provides insight into the way a bacterial population is diversifying . This disease-causing O . tsutsugamushi population showed high r/m ratios at both the allelic ( 10∶1 ) and nucleotide level ( 60∶1 ) , suggesting that the diversification of natural populations of O . tsutsugamushi is predominantly characterized by recombination rather than mutation and is comparable with other human pathogens known to recombine freely: Neisseria meningitidis ( 3 . 6∶1 and 100∶1 ) ; Streptococcus pneumoniae ( 8 . 9∶1 and 61∶1 ) and Helicobacter pylori ( 6 . 7∶1 and 76∶1 ) . Our estimated r/m ratio does not take account of the patient population who were putatively infected by more than one strain of O . tsutsugamushi . It is not possible to resolve the STs in these cases but we have no reason to think that inclusion of these data would lead to a reduction in this ratio . The genome sequence of O . tsutsugamushi shows characteristics that are consistent with high rates of recombination [3] , [23] . Sixty percent of functional genes have been reported to be involved in replication , recombination and repair processes [24] . In addition , the Boryong sequence strain has a massive proliferation of mobile elements and repeat sequences . Horizontal gene transfer probably occurs more readily due to the high number of mobile elements . The constant shuffling of DNA may in turn have ecological implications , such as facilitating host-adaptation . Comparison of MLST to a single locus typing method ( based on the gene for the immunodominant surface expressed 56-kDa protein ) showed low congruence between these two methods . Simpson's index , which is used to assess the discriminative ability of typing methods , was higher for MLST ( 0 . 95 ) than for the single locus typing method ( 0 . 48 ) . However , the number of isolates used in this assessment was low ( n = 22 ) , and further investigation is needed to accurately assess the relative abilities of the two methods . In general , typing that relies on antigenic gene variation , which is subject to diversifying selection from the immune response , is less able to reveal the underlying population genetic structure , although such approaches may be useful for characterising local outbreaks . The use of DNA extracted from patient blood enabled us to detect the presence of multiple infecting genotypes in a single patient sample . The finding that approximately 25% of patients had multiple MLST genotypes in their blood suggests that either the patient had been bitten by multiple mites harboring different strains , or that several strains of O . tsutsugamushi coexist in single mites . This second hypothesis is supported by the detection of multiple antigenic strains of O . tsutsugamushi in both naturally infected and laboratory-reared chigger mites ( Leptotrombidium spp . ) [25] . This implies that different strains of O . tsutsugamushi may commonly coexist in the same place at the same time , providing an opportunity for genetic exchange to occur and variation to arise . Recombination between different strains of O . tsutsugamushi could either occur in the mite , or in the rodent reservoir which may become infected on multiple , independent occasions . Further studies are now needed to investigate the molecular epidemiology of O . tsutsugamushi harboured by mites and rodents . | Scrub typhus , the rickettsial infectious disease caused by the obligate intracellular bacterium Orientia tsutsugamushi , is endemic across the Asia Pacific region . The bacterium is transmitted by the bite of larval stages of trombiculid mites ( “chiggers”; Leptotrombidium spp . ) , which more typically feed on small rodents . Clinical features include fever , headache , myalgia , lymphadenopathy and an eschar at the site of the bite . Despite the importance of this pathogen , little is known of the population diversity or the role of homologous recombination in driving the microevolution of this species . Here , we describe the development and application of a multilocus sequence typing ( MLST ) scheme that can be applied directly to blood samples , and that was applied to 108 O . tsutsugamushi isolates . We found that this organism demonstrated a high rate of homologous recombination , a surprising finding given the intracellular life-style of this species . We also found that 25% of patients in our study were simultaneously infected with multiple sequence types , suggesting multiple infection caused by either multiple mite bites , or multiple strains co-existing within individual mites . | [
"Abstract",
"Introduction",
"Materials",
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"Results",
"Discussion"
] | [
"infectious",
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] | 2010 | High Rates of Homologous Recombination in the Mite Endosymbiont and Opportunistic Human Pathogen Orientia tsutsugamushi |
When planning a series of actions , it is usually infeasible to consider all potential future sequences; instead , one must prune the decision tree . Provably optimal pruning is , however , still computationally ruinous and the specific approximations humans employ remain unknown . We designed a new sequential reinforcement-based task and showed that human subjects adopted a simple pruning strategy: during mental evaluation of a sequence of choices , they curtailed any further evaluation of a sequence as soon as they encountered a large loss . This pruning strategy was Pavlovian: it was reflexively evoked by large losses and persisted even when overwhelmingly counterproductive . It was also evident above and beyond loss aversion . We found that the tendency towards Pavlovian pruning was selectively predicted by the degree to which subjects exhibited sub-clinical mood disturbance , in accordance with theories that ascribe Pavlovian behavioural inhibition , via serotonin , a role in mood disorders . We conclude that Pavlovian behavioural inhibition shapes highly flexible , goal-directed choices in a manner that may be important for theories of decision-making in mood disorders .
Most planning problems faced by humans cannot be solved by evaluating all potential sequences of choices explicitly , because the number of possible sequences from which to choose grows exponentially with the sequence length . Consider chess: for each of the thirty-odd moves available to you , your opponent chooses among an equal number . Looking moves ahead demands consideration of sequences . Ostensibly trivial everyday tasks , ranging from planning a route to preparing a meal , present the same fundamental computational dilemma . Their computational cost defeats brute force approaches . These problems have to be solved by pruning the underlying decision tree , i . e . by excising poor decision sub-trees from consideration and spending limited cognitive resources evaluating which of the good options will prove the best , not which of the bad ones are the worst . There exist algorithmic solutions that ignore branches of a decision tree that are guaranteed to be worse than those already evaluated [1]–[3] . However , these approaches are still computationally costly and rely on information rarely available . Everyday problems such as navigation or cooking may therefore force precision to be traded for speed; and the algorithmic guarantees to be replaced with powerful––but approximate and potentially suboptimal––heuristics . Consider the decision tree in Figure 1A , involving a sequence of three binary choices . Optimal choice involves evaluating sequences . The simple heuristic of curtailing evaluation of all sequences every time a large loss ( ) is encountered excises the left-hand sub-tree , nearly halving the computational load ( Figure 1B ) . We term this heuristic pruning a “Pavlovian” response because it is invoked , as an immediate consequence of encountering the large loss , when searching the tree in one's mind . It is a reflexive response evoked by a valence , here negative , in a manner akin to that in which stimuli predicting aversive events can suppress unrelated ongoing motor activity [4] , [5] . A further characteristic feature of responding under Pavlovian control is that such responding persists despite being suboptimal [6]: pigeons , for instance , continue pecking a light that predicts food , even when the food is omitted on every trial on which they peck the light [7] , [8] . While rewards tend to evoke approach , punishments appear particularly efficient at evoking behavioral inhibition [9] , [10] , possibly via a serotonergic mechanism [11]–[15] . Here , we will ascertain whether pruning decision trees when encountering losses may be one instance of Pavlovian behavioural inhibition . We will do so by leveraging the insensitivity of Pavlovian responses to their ultimate consequences . We developed a sequential , goal-directed decision-making task in which subjects were asked to plan ahead ( c . f . [16] ) . On each trial , subjects started from a random state and generated a sequence of 2–8 choices to maximize their net income ( Figure 2A , B ) . In the first of three experimental groups the heuristic of pruning sub-trees when encountering large punishments incurred no extra cost ( Figure 2C ) . Subjects here pruned extensively: they tended to ignore subtrees lying beyond large losses . This alleviated the computational load they faced , but did not incur any costs in terms of outcomes because there was always an equally good sequence which avoided large losses ( see Figure 2C ) . In contrast , in the second and third experimental groups subjects incurred increasingly large costs for this pruning strategy ( Figure 2D , E ) ; yet , they continued to deploy it . That is , the tendency to excise subtrees lying below punishments persisted even when counterproductive in terms of outcomes . This persistence suggests that pruning was reflexively evoked in response to punishments and relatively insensitive to the ultimate outcomes . Computational models which accounted for close to 90% of choices verified that the nature of pruning corresponded to the Pavlovian reflexive account in detail . These results reveal a novel type of interaction between computationally separate decision making systems , with the Pavlovian behavioural inhibition system working as a crutch for the powerful , yet computationally challenged , goal-directed system . Furthermore , the extent to which subjects pruned correlated with sub-clinical depressive symptoms . We interpret this in the light of a theoretical model [17] on the involvement of serotonin in both behavioural inhibition [14] , [15] and depression .
The first model ‘Look-ahead’ embodied full tree evaluation , without pruning . It assumed that , at each stage , subjects evaluated the decision tree all the way to the end . That is , for an episode of length , subjects would consider all possible sequences , and choose among them with probabilities associated monotonically with their values . This model ascribed the higher action value to the subjects' actual choices a total of 77% of the time ( fraction of choices predicted ) , which is significantly better than chance ( fixed effect binomial ) . The gray lines in Figure 4A separate this by group and sequence length . They show that subjects in all three groups chose the action identified by the full look-ahead model more often than chance , even for some very deep searches . Figure 4B shows the predictive probability , i . e . the probability afforded to choices by the model . This is influenced by both the fraction of choices predicted correctly and the certainty with which they were predicted and took on the value 0 . 71 , again different from chance ( fixed effect binomial ) . These results , particularly when considered with the fact that on half the trials subjects were forced to choose the entire sequence before making any move in the tree , indicate that they both understood the task structure and used it in a goal-directed manner by searching the decision tree . In order to directly test hypotheses pertaining to pruning of decision trees , we fitted two additional models to the data . Model ‘Discount’ attempted to capture subjects' likely reluctance to look ahead fully and evaluate all sequences ( up to ) . Rather , tree search was assumed to terminate with probability at each depth , substituting the value for the remaining subtree . In essence , this parameter models subjects' general tendency not to plan ahead . Figure 4B shows that this model predicted choices better . However , since an improved fit is expected from a more complex model , we performed Bayesian model comparison , integrating out all individual-level parameters , and penalizing more complex models at the group level ( see Methods ) . Figure 4C shows that fitting this extra parameter resulted in a more parsimonious model . Note that this goal-directed model also vastly outperformed a habitual model of choice ( SARSA; [19] ) in which subjects are assumed to update action propensities in a model-free , iterative manner ( improvement of 314 ) . The third model , ‘Pruning’ , is central to the hypothesis we seek to test here . This model separated subjects' global tendency to curtail the tree search ( captured by the parameter of model ‘discount’ ) into two separate quantities captured by independent parameters: a general pruning parameter , and a specific pruning parameter . The latter applied to transitions immediately after large punishments ( red ‘−X’ in Figure 2B ) , while the former applied to all other transitions . If subjects were indeed more likely to terminate their tree search after transitions resulting in large punishments , then a model that separates discounting into two separate pruning parameters should provide a better account of the data . Again , we applied Bayesian model comparison and found strong evidence for such a separation ( Figure 4C ) . The fourth model added an immediate Pavlovian influence on choice . The need for this can be seen by comparing the observed and predicted transition ( action ) probabilities at a key stage in the task . Figure 4D shows the probability that subjects moved from state 6 to state 1 when they had two or more choices left . Through this move , subjects would have the opportunity to reap the large reward of ( see Figure 2B ) , by first suffering the small loss of −20 . Subjects duly chose to move to state 1 on 90% of these occasions in all three groups . This was well matched by the model ‘Pruning’ . However , when subjects only had a single choice left in state 6 , it would no longer be optimal to move to state 1 , since there would be no opportunity to gain the large reward afterwards . Instead , the optimal choice would be to move to state 3 , at a gain of 20 . Despite this , on about 40% of such trials , subjects were attracted to state 1 ( Figure 4E ) . This was not predicted by the pruning model: paired t-tests showed significant differences between empirical and predicted choice probabilities for each of the three groups: , ; , ; and , , for groups −70 , −100 and −140 respectively . Three subjects in group −70 and one subject in group −100 were never exposed to depth 1 sequences in state 6 . To accommodate this characteristic of the behavior , we added a further , ‘Learned Pavlovian’ component to the model , accounting for the conditioned attraction ( or repulsion ) to states that accrues with experience . This captured an immediate attraction towards future states that , on average ( but ignoring the remaining sequence length on a particular trial ) , were experienced as rewarding; and repulsion from states that were , on average , associated with more punishment ( see Methods for details ) . Figure 4B , C show that this model ( Pruning and Learned ) provided the most parsimonious account of the data despite two additional parameters , and Figures 4D–E show that the addition of the Learned parameters allowed the model to capture more faithfully the transition probabilities out of state 6 . The blue bars in Figure 4A display the probability that this model chose the same action as subjects ( correctly predicting 91% of choices ) . The model's predicted transition probabilities were highly correlated with the empirical choice probabilities in every single state ( all ) . Further , we considered the possibility that the Learned Pavlovian values might play the additional role of substituting for the utilities of parts of a search tree that had been truncated by general or specific pruning . However , this did not improve parsimony . We have so far neglected any possible differences between the groups with different large losses . Figures 3D–F might suggest more pruning in group −140 than in the other two groups ( as the probability of choosing optimal full lookahead sequences containing a large loss is minimal in group −140 ) . We therefore fitted separate models to the three groups . Figure 4B shows that the increase in the model flexibility due to separate prior parameters for each group ( ‘Pruning & Learned ( separate ) ’ ) failed to improve the predictive probability , increased the score ( Figure 4C ) , and hence represents a loss of parsimony . Returning to Figure 3D–F , we plotted the predictions of model ‘Pruning & Learned’ for each of the three groups , and found that this model was able to capture the very extensive avoidance of optimal full lookahead sequences including large losses in group −140 , and yet show a gradual decline in the other two groups . The qualitative difference between group −140 and the two other groups in Figure 3D–F is also important because it speaks to the ‘goal-directed’ nature of pruning . Pruning is only counterproductive in groups −70 and −100 . The apparent reduction in pruning suggested by the reduced avoidance of optimal sequences involving large losses in groups −70 and −100 ( Figure 3E , F ) could suggest that the extent of pruning depends on how adaptive it is , which would argue against a reflexive , Pavlovian mechanism . It is thus important that model ‘Pruning & Learned’ could capture these qualitative differences without recurrence to such a goal-directed , clever , pruning . It shows that these differences were instead due to the different reward structures ( −70 is not as aversive as −140 ) . Finally , we return to the decision tree in Figure 3B . This would prima facie seem inconsistent with the notion of pruning , as subjects happily transition through a large loss at the very beginning of the decision sequence . Figure 4F shows a different facet of this . Starting from the state 3 again , subjects in group −70 choose the optimal path that goes through the large loss straight away even though there is an optimal alternative in which they do not have to transition through the large loss so early . In fact , in the model , the relative impact of general and specific pruning factors interacts with the precise reinforcement sequence , and hence with the depth at which each reinforcement is obtained . More specifically , let us neglect the entire tree other than the two optimal ( yellow ) sequences the subjects actually took , and let . The value of the left sequence then equals . A similar , third-order polynomial in combinations of and describes the value of the right path , and indeed their difference . The blue line in Figure 4G shows , for each value of , what value of would result in the left and right sequences having the same value . The combinations of and for which the chosen left path ( with the early transition through the large loss ) has a higher total value turn out to lie below this blue line . In addition , pruning will only be more pronounced after large losses if is larger than . The overlap between these two requirements is shown in green , and the group means for and are shown by the red dot . Thus , because the effects of general and specific pruning interact with depth , the reflexive , but probabilistic , pruning in the model can lead to the pattern seen in Figure 4G , whereby subjects transition through large losses close to the root of the decision tree , but avoid doing so deeper in the tree . Put simply , fixed , reflexive Pavlovian pruning in these particular sequences of reinforcements has differential effects deep in the tree . In these cases , it matches the intuition that it is the exploding computational demands which mandate approximations . However , this is not a necessary consequence of the model formulation and would not hold for all sequences . An alternative to the pruning account is the notion of loss aversion , whereby a loss of a given amount is more aversive than the gain of an equal amount is appetitive . Consider the following sequence of returns: with an overall return of . The pruning account above would assign it a low value because the large terminal gain is neglected . An alternative manner by which subjects may assign this sequence a low value is to increase how aversive a view they take of large losses . In this latter account , subjects would sum over the entire sequence , but overweigh large losses , resulting in an equally low value for the entire sequence . To distinguish loss aversion from pruning , we fit several additional models . Model ‘Loss’ is equal to model ‘Look-ahead’ in that it assumes that subjects evaluate the entire tree . It differs , in that it infers , for every subject , what effective weight they assigned each reinforcement . In the above example , for the overall sequence to be as subjectively bad as if the reinforcement behind it had been neglected , the −100 reinforcement could be increased to an effective value of −240 . By itself , this did not provide a parsimonious account of the data , as model ‘Loss’ performed poorly ( Figure 5A ) . We augmented model ‘Loss’ in the same manner as the original model by allowing for discounting and for specific pruning . There was evidence for pruning even when reinforcement sensitivities were allowed to vary separately , i . e . even after accounting for any loss aversion ( cf . models ‘Discount & Loss’ and ‘Pruning & Loss’ , Figure 5A ) . Furthermore , adding loss aversion to the previous best model did not improve parsimony ( cf . models ‘Pruning & Learned’ vs ‘Loss & Pruning & Learned’ ) . Finally , the Pavlovian conditioned approach also provided a more parsimonious account than loss aversion ( cf ‘Pruning & Learned’ vs ‘Pruning & Loss’ ) . Replacing the four separate parameters in the ‘Loss’ model with two slope parameters to reduce the disadvantage incurred due to the higher number of parameters does not alter these conclusions ( data not shown ) . Finally , the screen subjects saw ( Figure 2A ) only showed four symbols: ++ , + , − and − − . It is thus conceivable that subjects treated a ++ as twice as valuable as a + , and similarly for losses . A model that forced reinforcements to obey these relationships did not improve parsimony ( data not shown ) . The inferred reinforcement sensitivities from model ‘Pruning & Loss’ are shown in Figure 5B . Comparing the inferred sensitivities to the largest rewards and punishments showed that subjects did overvalue punishments ( treating them approximately 1 . 4 times as aversive as an equal-sized reward was appetitive; Figure 5C ) , consistent with previous studies [20] . In conclusion , there is decisive evidence for specific Pavlovian pruning of decision trees above and beyond any contribution of loss aversion . We next examined the parameter estimates from the most parsimonious model ( ‘Pruning & Learned’ ) . If subjects were indeed more likely to terminate the tree search after large punishments , and thus forfeit any rewards lurking behind them , then the specific pruning probability should exceed the general pruning probability . Figure 6A shows the specific and general pruning parameters and for every subject . To test for the difference we modified the parametrization of the model . Rather than inferring specific and general pruning separately , we inferred the general pruning parameter and an additional ‘specific pruning boost’ , which is equivalent to inferring the difference between specific and general pruning . This difference is plotted in Figure 6B for the groups separately , though the reader is reminded that the model comparisons above did not reveal group differences ( Figure 4C ) . The posterior probability of no difference between and was . The parsimony of separate priors was tested earlier ( see Figure 4C ) , showing that specific pruning did not differ between groups . This is in spite of the fact that pruning in the groups −70 and −100 is costly , but not in the −140 group ( Figure 2C ) . The fact that pruning continues even when disadvantageous is evidence for a simple and inflexible pruning strategy which neglects events occurring after large losses when computational demands are high . Figure 6C shows the cost of pruning in terms of the loss of income during episodes when the optimal choice sequence would have involved a transition through a large punishment . These results suggest that pruning is a Pavlovian response in the sense that it is not goal-directed and not adaptive to the task demands , but is rather an inflexible strategy reflexively applied upon encountering punishments . We next tested two a priori predictions that relate the model parameters to psychometric measurements . Based on prior modelling work [17] , we hypothesized that the tendency to employ the simple pruning strategy should correlate with psychometric measures related to depression and anxiety , i . e . with the BDI score and NEO neuroticism . We also expected to replicate prior findings whereby the reward sensitivity parameter should be negatively correlated with BDI and NEO neuroticism [21]–[24] . Because parameters for different subjects were estimated with varying degrees of accuracy ( see individual gray error bars in Figure 6 ) , our primary analysis was a multiple regression model in which the influence of each subject's data was weighted according to how accurately their parameters were estimated ( see Methods ) . We found that BDI was positively correlated with the specific pruning parameter ( ) . Furthermore , this effect was specific in that there was no such correlation with general pruning . There was also a negative correlation between BDI score and reward sensitivity , although this did not survive correction for multiple comparisons ( ) . The regression coefficients for the BDI score are shown in Figure 7A . Notably , these correlations arose after correcting for age , gender , verbal IQ , working memory performance and all other NEO measures of personality . Thus , as predicted , subjects with more subclinical features of depression were more likely to curtail their search specifically after large punishment . However , against our hypothesis , we did not identify any significant correlations with NEO neuroticism . Finally , we examined correlations between all parameters and all questionnaire measures in the same framework . We found a positive correlation between NEO agreeableness and the weight of the ‘Learned Pavlovian’ influence which survived full correction for 60 comparisons .
Our work was inspired by a previous modelling paper [17] , which used the concept of behavioural inhibition to unify two divergent and contradictory findings on the relationship between serotonin and depression . On the one hand , drugs that putatively increase serotonin by inhibiting the serotonin reuptake mechanism are effective for both acutely treating [28] , and preventing relapse of [29] , depression . On the other hand , a genetic polymorphism that downregulates the very same serotonin reuptake transporter , thus acting in the same direction as the drugs , has the opposite effect on mood , predisposing towards depression and other related mood disorders ( [30]; though see also [31] for a discussion of replication failures ) . Dayan and Huys [17] explained this paradox by suggesting that people who experienced high levels of serotonin and thus exaggerated Pavlovian behavioural inhibition during early development [32] would be most sensitive to the effects of any interference with this inhibition in adulthood secondary to a drop in serotonin levels [33] , [34] . Thus , the inhibitory consequences of serotonin could account for both its predisposing qualities on a developmental time-scale , and more acute relief during depressive episodes . The hypothesis in [17] relates to two facets of the current study . First , if serotonin indeed mediates behavioural inhibition in the face of punishments [10] , [12]–[14] then it is a strong prediction that the pruning parameter , which mediates the inhibition of iterative thought processes , should be related to , and modulated by , serotonergic activity . We plan to test this directly in future studies . There is already some , though far from conclusive , evidence pointing towards such an influence of serotonin on higher-level cognition . First , serotonergic neurons project strongly to areas involved in goal-directed , affective choices including the medial prefrontal cortex [35] . Genetic variation in the serotonin transporter allele modulates functional coupling between amygdala and rostral cingulate cortex [36] . Next , orbitofrontal serotonin depletion impacts cognitive flexibility , or the adaptive ability to switch between contingencies , by impairing inhibitory control [37] in monkeys . Third , learned helplessness , which can be interpreted in goal-directed terms [17] , depends critically on pre- and infralimbic cortex in rats [38] , and is known to be mediated by serotonin [39] . Contrary to this , there is a recent report that mood manipulation , but not acute tryptophan depletion , impairs processing on the one-touch Tower of London ( OTT ) task [40] , which should certainly engage goal-directed processing . One possible explanation for this apparent discrepancy is that although the OTT requires sequences of moves to be evaluated , there is no obvious aversive point at which Pavlovian pruning might be invoked . Further , although OTT is explicitly framed as a ‘cold’ task , i . e . one which does not involve affective choices , there is also supporting evidence ( see below ) . The second facet of our theoretical model [17] concerns depression . The model suggested that subjects prone to depression exhibit decision making that is more reliant on serotonergic function , expressed as excess pruning , but that the depressed state itself is characterised by a low serotonin state and thus a loss of pruning . The stronger dependence on serotonin in at-risk subjects would explain why only they are sensitive to the mood effects of tryptophan depletion [34] , and why individuals with a polymorphism in the serotonin transporter gene that reduces serotonin uptake are more liable to develop mood disturbance , especially following serotonin depletion [41] , [42] . That is , this theory predicts excessive pruning to occur in subjects at risk for depression , and reduced pruning to occur during a depressive episode . The data presented here ( a positive correlation between mildly raised BDI scores and the tendency to prune when encountering a large loss; Figure 7 ) would be consistent with this theoretical account if mildly raised BDI scores in otherwise healthy subjects ( we screened for criteria for a major depressive episode; and 94% of our participants had BDI scores , rendering depression unlikely [43] ) could be interpreted as a vulnerability or proneness to depression . The mildly raised BDI scores do reveal a latent level of dysphoric symptoms amongst healthy participants [55] . This might be in line with findings that levels of dysphoric symptoms correlate with levels of dysfunctional thinking , and that a cyclical interaction between the two could , in the presence of certain environmental events , crescendo into a depressive episode proper [45] , [46] . However , we are not aware of any direct evidence that mildly raised BDI scores measure vulnerability , and maybe more critically , we did not observe correlations with NEO neuroticism , which is an established risk factor for depression [47] . The strong prediction that serotonergic function and behavioural inhibition in the face of losses should be reduced during a major depressive episode remains to be tested . However , there is already some evidence in favour of this conclusion . People actively suffering from depression are impaired on the OTT [48] , [49] . The impairment relative to controls grows with the difficulty of the problem; and depressed subjects also spend increasing amounts of time thinking about the harder problems , without showing improved choices [50] . This suggests that people who are suffering from depression have more difficulty searching a deep tree effectively ( possibly also captured by more general , superficial autobiographical recollections; [51] ) . However , given the finding by [40] , we note that it is at present not possible to interpret this conclusively in terms of pruning . Finally , the same group has also reported catastrophic breakdown in OTT performance in depressed subjects after negative feedback [52] . We used a novel sequential decision-making task in conjunction with a sophisticated computational analysis that fitted a high proportion of healthy subjects' choices . This allowed us to unpack a central facet of effective computation , pruning . Importantly , most subjects were unable to resist pruning even when it was disadvantageous , supporting our hypothesis that this process occurs by simple , Pavlovian , behavioural inhibition of ongoing thoughts in the face of punishments [17] . Provocatively , consistent with this model , we found a relationship between the propensity to prune and sub-clinical mood disturbance , and this suggests it would be opportune to examine in detail the model's predictions that pruning should be impaired in clinically depressed individuals and following serotonin depletion .
Fourty-six volunteers ( 23 female , mean age 23 . 84 years ) were recruited from the University College London ( UCL ) Psychology subject pool . Each gave written informed consent and received monetary , partially performance-dependent compensation for participating in a 1 . 5-hour session . The study was conducted in accord with the Helsinki declaration and approved by the UCL Graduate School Ethics Committee . Exclusion criteria were: known psychiatric or neurological disorder; medical disorder likely to lead to cognitive impairment; intelligence quotient ( IQ ) ; recent illicit substance use and not having English as first language . The absence of axis-I psychopathology and alcohol- or substance abuse/dependence was confirmed with the Mini International Neuropsychiatric Inventory [53] . Personality , mood , and cognitive measures were assessed with the State-Trait Anxiety Inventory [54] , the Beck Depression Inventory ( BDI; [55] ) , the NEO Personality Inventory [56] , the Wechsler Test of Adult Reading ( WTAR; [57] ) , and Digit Span [58] . Subjects who were assigned to the different groups , were matched for age , IQ and sex ( all , one-way ANOVA ) . Fifteen subjects were assigned to group −70 , 16 to group −100 and 15 to group −140 . Mean age ( 1 st . dev . ) was , and years respectively; mean digit span scores were , and ; mean IQ scores ( computed from WTAR ) were , and . There were 5 ( 33% ) , 8 ( 50% ) and 10 ( 66% ) men in each of the three groups . One subjects' age information , and one subject's STAI information were lost . These subjects were excluded from the psychometric correlation analyses . Participants first underwent extensive training to learn the transition matrix ( Figure 2A , B; [16] ) . During the training , subjects were repeatedly placed in a random starting state and told to reach a random target state in a specified number of moves ( up to 4 ) . After 40 practice trials , training continued until the participant reached the target in 9 out of 10 trials . Most subjects passed the training criterion in three attempts . Reaching training criterion was mandatory to move on to the main task . After training , each transition was associated with a deterministic reward ( Figure 2B ) . Subjects completed two blocks of of 24 choice episodes; each episode included 2 to 8 trials . The first block of 24 episodes was discarded as part of training the reward matrix , and the second block of 24 episodes was analysed . At the beginning of each episode , subjects were placed randomly in one of the states ( highlighted in white ) and told how many moves they would have to make ( i . e . , 2 to 8 ) . Their goal was to devise a sequence of that particular length of moves to maximize their total reward over the entire sequence of moves . To help the subjects remember the reward or punishment possible from each state , the appropriate “+” or “-” were always displayed beneath each box . Regardless of the state the subject finished in on a given episode , they would be placed in a random new state at the beginning of the next episode . Thus , each episode was an independent test of the subject's ability to sequentially think through the transition matrix and infer the best action sequence . After each transition , the new state was highlighted in white and the outcome displayed . On half of the trials , subjects were asked to plan ahead their last 2–4 moves together and enter them in one step without any intermittent feedback . The reward matrix was designed to assess subjects' pruning strategy; and whether this strategy changed in an adaptive , goal-directed way . All subjects experienced the same transition matrix , but the red transitions in Figure 2C led to different losses in the three groups , of −70 , −100 or −140 pence respectively . This had the effect of making pruning counterproductive in groups −70 and −100 , but not −140 ( Figures 2C–E ) . At the end of the task , subjects were awarded a monetary amount based on their performance , with a maximum of £20 . They were also compensated £10 for time and travel expenses . In the look-ahead model , the -value of each action in the present state is derived by i ) searching through all possible future choices; ii ) always choosing the optimal option available in the future after a particular choice; and iii ) assigning the two actions at the present state the values of the immediate reward plus the best possible future earnings over the entire episode . More concisely , the look-ahead ( ) model is a standard tree search model , in which the value of a particular action is given by the sum of the immediate reward and the value of the optimal action from the next state ( 1 ) where is the deterministic transition function . This equation is iterated until the end of the tree has been reached [59] . For notational clarity , we omit dependence of values on the depth of the tree . To make the gradients tractable , we implement the operator with a steep softmax . An explicit search all the way to the end of the tree is unlikely for any depths , given the large computational demands . The model ‘Discount’ ( ) thus allowed , at each depth , a biased coin to be flipped to determine whether the tree search should proceed further , or whether it should terminate at that depth , and assume zero further earnings . Let the probability of stopping be . The expected outcome from a choice in a particular state , the values , is now an average over all possible prunings of the tree , weighted by how likely that particular number of prunings is to occur: ( 2 ) where is the full lookahead value of action in state for the cut tree . Importantly , the number is immense . If the number of branches of a binary tree is , then there are possible ways of choosing up to branches of the tree to cut . Although this overestimates the problem because branches off branches that have already been cut off should no longer be considered , the problem remains overly large . We therefore use a mean-field approximation , resulting in values: ( 3 ) where , at each step , the future is weighted by the probability that it be encountered . This means that outcomes steps ahead are discounted by a factor . We note , however , that Equation 3 solves a different Markov decision problem exactly . Next , the ‘Pruning’ ( ) model encompassed the possibility that subjects were more likely to stop after a large punishment had been encountered . It did this by separating the stopping probability into two independent factors , resulting in: ( 4 ) ( 5 ) where is the specific pruning parameter that denotes the probability with which the subject stops evaluation of the tree at any state-action pair associated with the large negative reward . Here , we used binary pruning rather than the graded form of [17] , since there is only one extreme negative outcome . The second parameter was the probability of curtailing the tree search at any other transition ( −20 , +20 , +140 ) and is exactly analogous to the of the Discount model . To account for ‘Learned Pavlovian’ ( ) attraction or repulsion , i . e . the approach to , or avoidance of , states that are typically associated with future rewards on those trials on which these future rewards are not available ( e . g . a terminal transition from state 6 to state 1 ) , we modified the ‘Pruning’ model by adding a second state-action value which depends on the long-term experienced average value of the states: ( 6 ) The value is learned by standard temporal difference learning: ( 7 ) where is set to zero if it is the terminal transition . This model , which we term ‘Learned + Pavlovian’ , is based on [8] and the parameter is fit to the data . So far , when search terminates , a zero value for the rest of the decision tree was entered . An alternative to the Learned Pavlovian model is to additionally include the value as terminal value , i . e . : ( 8 ) with as in the Pruning model , and with evolving as in equation 7 . Note that we this model also incorporated the direct learned Pavlovian effect ( Equation 6 ) . To account for loss aversion , we fitted models in which we inferred all reinforcement sensitivities separately . Thus , these models relaxed the assumption of the above models that subjects treated a reward of 140 as exactly cancelling out a loss of −140 . In fact , these models in principle allowed subjects to be attracted to a loss and repelled from a reward . We used such a free formulation to attempt to soak up as much variance as possible . If pruning is visible above and beyond this , then differential sensitivities to rewards and punishments by themselves cannot account for the pruning effects in the above models . This formulation does have the drawback that the large number of free parameters may potentially exert a prohibitive effect on the scores . Although we saw no indication of that , we fitted a further , restricted loss aversion model with two slopes , i . e . where the rewards took on values and , and the losses and . The restricted models led to the same conclusions as the full loss aversion models and we thus do not report those results . Finally , in the habitual SARSA model , choice propensities were calculated in a model-free manner to capture habitual choices [18] , [19]: ( 9 ) Given the values , the probability of subjects' choices was computed as ( 10 ) where we emphasize that the value of each choice depends on how many choices are left after , but not on the choices preceding it . The parameter was set to unity for all loss models . We note that this probability is predictive in that it depends only on past rewards and choices , but not in the machine learning sense , whereby it predicts data not used to fit the parameters . We have previously described our Bayesian model fitting and comparison approach [60] , but repeat the description here for completeness . For each subject , each model specifies a vector of parameters . We find the maximum a posteriori estimate of each parameter for each subject: where are all actions by the subject . We assume that actions are independent ( given the stimuli , which we omit for notational clarity ) , and thus factorize over trials . The prior distribution on the parameters mainly serves to regularise the inference and prevent parameters that are not well-constrained from taking on extreme values . We set the parameters of the prior distribution to the maximum likelihood given all the data by all the subjects:where . This maximisation is achieved by Expectation-Maximisation [61] . We use a Laplacian approximation for the E-step at the iteration:where denotes a normal distribution and is the second moment around , which approximates the variance , and thus the inverse of the certainty with which the parameter can be estimated . Finally , the hyperparameters are estimated by setting the mean and the ( factorized ) variance of the normal prior distribution to:All parameters are transformed before inference to enforce constraints ( , ) . As we have no prior on the models themselves ( testing only models we believe are equally likely a priori ) , we instead examine the model log likelihood directly . This quantity can be approximated in two steps . First , at the group level [62]:where is the total number of choices made by all subjects , and is the number of prior parameters fitted ( mean and variance for each parameter ) . Importantly , however , is not the sum of individual likelihoods , but the sum of integrals over the individual parameters ( hence the subscript “int” to the Baysian Information Criterion ( BIC ) ) :The second approximation involves replacing the integral by a sum over samples from the empirical prior . This ensures that we compare not just how well a particular model fits the data when its parameters are optimized , but how well the model fits the data when we only use information about where the group parameters lie on average . | Planning is tricky because choices we make now affect future choices , and future choices and outcomes should guide current choices . Because there are exponentially many combinations of future choices and actions , brute-force approaches that consider all possible combinations work only for trivially small problems . Here , we describe how humans use a simple Pavlovian strategy to cut an expanding decision tree down to a computationally manageable size . We find that humans use this strategy even when it is disadvantageous , and that the tendency to use it is related to mild depressive symptoms . The findings , we suggest , can be interpreted within a theoretical framework which relates Pavlovian behavioural inhibition to serotonin and mood disorders . | [
"Abstract",
"Introduction",
"Results",
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] | [
"cognitive",
"neuroscience",
"biology",
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] | 2012 | Bonsai Trees in Your Head: How the Pavlovian System Sculpts Goal-Directed Choices by Pruning Decision Trees |
Since the first recorded epidemic of syphilis in 1495 , controversy has surrounded the origins of the bacterium Treponema pallidum subsp . pallidum and its relationship to the pathogens responsible for the other treponemal diseases: yaws , endemic syphilis , and pinta . Some researchers have argued that the syphilis-causing bacterium , or its progenitor , was brought from the New World to Europe by Christopher Columbus and his men , while others maintain that the treponematoses , including syphilis , have a much longer history on the European continent . We applied phylogenetics to this problem , using data from 21 genetic regions examined in 26 geographically disparate strains of pathogenic Treponema . Of all the strains examined , the venereal syphilis-causing strains originated most recently and were more closely related to yaws-causing strains from South America than to other non-venereal strains . Old World yaws-causing strains occupied a basal position on the tree , indicating that they arose first in human history , and a simian strain of T . pallidum was found to be indistinguishable from them . Our results lend support to the Columbian theory of syphilis's origin while suggesting that the non-sexually transmitted subspecies arose earlier in the Old World . This study represents the first attempt to address the problem of the origin of syphilis using molecular genetics , as well as the first source of information regarding the genetic make-up of non-venereal strains from the Western hemisphere .
As Naples fell before the invading army of Charles the VIII in 1495 , a plague broke out among the French leader's troops [1] . When the army disbanded shortly after the campaign , the troops , composed largely of mercenaries , returned to their homes and disseminated the disease across Europe [2] , [3] . Today , it is generally agreed that this outbreak was the first recorded epidemic of syphilis . Although its death toll remains controversial , there is no question that the infection devastated the continent [4] . Because the epidemic followed quickly upon the return of Columbus and his men from the New World , some speculated that the disease originated in the Americas [2] . Indeed , reports surfaced that indigenous peoples of the New World suffered from a similar malady of great antiquity [5] and that symptoms of this disease had been observed in members of Columbus's crew [3] . In the twentieth century , criticisms of the Columbian hypothesis arose , with some hypothesizing that Europeans had simply not distinguished between syphilis and other diseases such as leprosy prior to 1495 [6] . It was soon recognized that different varieties of treponemal disease exist . Unlike syphilis , which is caused by the spirochete T . pallidum subspecies pallidum , the other types normally strike during childhood and are transmitted through skin-to-skin or oral contact . All are quite similar with regard to symptoms and progression [7] , but endemic syphilis , or bejel , caused by subsp . endemicum , has historically affected people living in hot , arid climates and yaws , caused by subsp . pertenue , is limited to hot and humid areas . Pinta , caused by Treponema carateum , is the most distinct member of this family of diseases . Once found in Central and South America , this mild disease is characterized solely by alterations in skin color . Today , the debate over the origin of treponemal disease encompasses arguments about whether the four infections are caused by distinct but related pathogens [8] or one protean bacterium with many manifestations [9] . Paleopathologists have played a pivotal role in addressing the question surrounding the origin of syphilis . The treponemal diseases , with the exception of pinta , leave distinct marks upon the skeleton and can thus be studied in past civilizations . Paleopathological studies of populations in the pre-Columbian New World show that treponemal disease was prevalent , with cases dating back 7 , 000 years and increasing over time [10] . In contrast , paleopathological studies of large pre-Columbian populations in Europe and Africa have yielded no evidence of treponemal disease [11]–[13] . However , isolated cases of pre-Columbian treponemal disease from other Old World excavation sites have been reported sporadically [14] . Although these cases have often been met with criticism regarding diagnosis , dating , and epidemiological context , they have convinced some that treponemal disease did exist in the pre-Columbian Old World [15] , [16] . The T . pallidum genome is small ( roughly 1 , 000 kilobases ) and was sequenced in 1998 [17] . However , comparative genetic studies of T . pallidum [18]–[23] have been rare and relatively small in scope . One reason for this is the difficulty in obtaining non-venereal strains for study . Today only five known laboratory strains of subsp . pertenue , two strains of subsp . endemicum , and no strains or samples of T . carateum survive . Furthermore , it is uncertain whether the disease pinta still exists . No cases have been reported to the World Health Organization from the former endemic countries Mexico or Colombia since 1979 [24] . Similarly , endemic syphilis was eradicated some time ago in its European focus , Bosnia [25] . In Turkey , only one infected family has been reported in the last forty years [26] , and a large survey in the United Arab Emirates revealed only non-active cases of endemic syphilis in the elderly [27] . In the Old World , yaws is still reported but appears limited to a few isolated foci in the Republic of Congo and the Democratic Republic of Congo [28] , [29] , as well as Indonesia and Timor-Leste , where roughly 5 , 000 cases are reported annually [30] . In the New World , yaws appears to be constrained to an ever-constricting area of Guyana's interior [31] . Because of the paucity of samples available for experiments , most comparative studies have included very few non-venereal strains . Another limitation on comparative studies has been the small amount of variation present in the T . pallidum genome . Variability is sufficiently low that the discovery of a single nucleotide polymorphism ( SNP ) has warranted publication in the past [18] , [20] , [21] . One study has suggested that most variation between the subsp . pallidum and pertenue genomes lies within the tpr gene family [32] , a family of 12 genes with sequence similarity that make up roughly 2% of the genome . A recent examination of this large gene family demonstrated more extensive variation between strains than had previous studies but also documented an unusually high frequency of intra-gene conversion events [23] . Thus , it is possible that most polymorphism in the T . pallidum genome may be concentrated in genes with limited phylogenetic informativeness . Our goal in this study was to identify variable sites in the T . pallidum genome and characterize them in as many non-venereal strains as possible , in order to test the hypothesis that syphilis emerged in humankind's recent past , from New World-derived strains of T . pallidum . Recombination can result in a phylogeny different from the true one [33] , [34] , and it has been shown that gene conversion is an important evolutionary mechanism in one large T . pallidum gene family [23] . For this reason , after sequencing many sites from around the genome , we performed rigorous tests for recombination , then built a phylogeny from the non-recombining SNPs and insertions/deletions ( indels ) identified . These results , paired with geographic analysis of strains , provide novel information on the history of T . pallidum .
Twenty-two human Treponema pallidum strains , one T . pallidum strain collected from a wild baboon , and three T . paraluiscuniculi strains , which are responsible for venereal syphilis in rabbits , were used in this study ( Table 1 ) . This included all laboratory strains of subsp . pertenue ( n = 5 ) and subsp . endemicum ( n = 2 ) . Guyana is the only known active site of yaws infection in the western hemisphere . In order to provide representation of non-venereal strains from the Americas , subsp . pertenue strains ( n = 2 ) were obtained from indigenous children with clinical evidence of non-venereal treponemal disease during a humanitarian medical mission to protected native reserves deep within the Guyanese interior . These samples were collected from a population that has had very little contact with the outside world , both due to the remoteness of their forest location and to legal restrictions on outsider interference . Ethical approval for the sample collection protocol was obtained from the Ethics Board at Lakeridge Health Centre ( Oshawa , Canada ) and included obtaining informed consent from patients . Scrapings were taken from active yaws lesions and were immediately deposited in either ethanol or saline . They were kept as cool as possible , but not frozen , for the duration of the two-week medical trip . Upon return to the laboratory , they were kept frozen until DNA was extracted . Additional subsp . pertenue samples ( n = 4 ) came from a Dutch collection of strains destroyed years ago during a freezer breakdown . Although these strains were non-viable , the organisms were kept and a small amount of each was made available for this project . Eleven syphilis strains were chosen for analysis based on geographic and chronological span . This included two strains of uncertain subspecies and origin , Haiti B and Madras , which were originally labeled as subsp . pertenue strains , but appear to be subsp . pallidum strains based on both genetic studies and clinical manifestations in a rabbit model [18] , [20]–[22] , [35] . DNA was obtained from Treponema organisms grown in rabbit tissue or from clinical specimens . Laboratory isolates of T . pallidum were grown in New Zealand white rabbits , consistent with guidelines set out by the Institutional Animal Care and Use Committee at the U . S . Center for Disease Control and Prevention ( CDC ) . DNA was isolated using the QIAamp mini kit ( Qiagen , Valencia , CA ) according to the manufacturer's instructions for tissue or fluid preparations , depending on the type of sample . Whole genome amplification ( REPLI-g Midi Kit , Qiagen ) was performed on the strains for which only a limited amount of DNA was available: the four subsp . pertenue strains from the Dutch collection and a pallidum strain obtained from a South African clinical specimen . The whole genome amplification product was used as a template for subsequent polymerase chain reactions ( PCRs ) . Unfortunately , amplification of the whole genome of the two strains collected in Guyana could not be performed , due to DNA degradation . Twenty-one genetic regions ( Table 2 ) were sequenced in all strains except for the two clinical samples from Guyana . These regions were scattered around the genome ( Fig . 1 ) , and were chosen based on previously demonstrated polymorphism [18] , [20]–[22] , [36] , implication in pathogenesis , or because they harbored repetitive sequences . Because a very limited amount of considerably degraded DNA was available from the clinical specimens collected in Guyana , only seven polymorphic sites encompassing 17 SNPs could be sequenced in these strains . The sites were chosen based on 1 ) which appeared to be the most phylogenetically informative at the time of sequencing; and 2 ) which involved small molecular weight products , easily amplified from damaged DNA . They included IGR ( fliG-tp0027 ) , deoD , gpd , tp0618 , tprI , cfpA , and tpF-1 . Primers ( Table S1 ) were designed using the programs MacVector ( Accelrys , Burlington , MA ) and Primer3 [37] and the subsp . pallidum genomic DNA sequences posted on the Los Alamos National Laboratory Bioscience Division's STD Sequence Databases webpage ( http://www . stdgen . northwestern . edu ) . PCR amplifications were performed in 50 µL reactions containing 0 . 5 µM primers ( Invitrogen , Carlsbad , CA ) , 200 µM GeneAmp dNTPs ( Applied Biosystems , Foster City , CA ) , and 2 . 5 U AmpliTaq Gold polymerase with Gold Buffer and 3 . 0 mM MgCl2 ( Applied Biosystems ) . PCR conditions were as follows: One cycle of 94°C for 5 minutes; 35 cycles of 94°C for 30 seconds , primer annealing at the appropriate temperature for 30 seconds ( Table S1 ) , and 72°C for 1 minute and 30 seconds; followed by a final extension for seven minutes at 72°C . Standard precautions to avoid DNA contamination were employed , including the use of negative controls , aerosol resistant pipette-tips , and a three-station PCR set-up protocol . In addition , sequences that were especially important in the phylogenetic analysis were confirmed through independent amplifications . Amplicons were purified using a gel extraction kit ( Qiagen , Valencia , CA ) and sequenced using either an ABI 3100/3700 Automated Capillary DNA Sequencer and the Big Dye Sequencing kit ( Applied Biosystems ) or the CEQ 8000 Genetic Analysis System and the DTCS Quick Start Kit ( Beckman-Coulter , Fullerton , CA ) . Sequencing was performed at the CDC , SeqWright/Fisher Sequencing Services , or Oregon Health Sciences University's Core Laboratory . Sequences were deposited in GenBank under the accession numbers listed ( Table 2 ) . Open reading frames containing polymorphisms were translated into protein sequences to examine the resulting amino acid changes . The average number of nucleotide differences between groups , as well as the average pairwise difference for each polymorphic gene , was calculated using DnaSP [38] . All nucleotide substitutions occurring in genic areas were deemed either synonymous or non-synonymous ( Table S2 ) . The amino acid substitutions resulting from non-synonymous changes were scored according to three criteria . Amino acids were categorized according to charge , polarity and volume , and Grantham's distance [39] , [40] . In the first two cases , a substitution resulting in a change from one category to another was considered a radical substitution , while others were considered conservative . In the latter case , substitutions resulting in distances greater than 100 , according to Grantham's index , were categorized as radical , others conservative . Substitutions considered radical in at least two of the three categories were scored as radical ( Table S2 ) . Secondary DNA structure in the polymorphic region of IGR ( fliG-tp0027 ) was predicted using the program Mfold [41] . In order to rule out recombination in the areas analyzed , each gene was tested against all of the paralogs present in the sequenced T . pallidum subsp . pallidum Nichols strain genome , using the program RDP2 [42] . This type of search would identify recombination events between the strains sequenced in this study and the paralogs present in the sequenced genome , as well as recombination between the genes in the strains sequenced . In addition , when small stretches of extremely polymorphic DNA were identified , a BLAST search was performed in order to identify possible donor regions involved in intragenomic conversion events ( http://www . ncbi . nlm . nih . gov/BLAST/ ) . Because the complete genome sequence was only available for one strain , it is possible that some recombination events , between paralogs or donor regions not present in the sequenced genome , would not be detected . For this reason , the frequency of synonymous and non-synonymous substitutions was examined in highly polymorphic stretches; regions with a high number of synonymous substitutions and multiple substitutions per codon were considered to be possible results of recombination and were excluded from the analysis . The sequence of these regions can be found in Table S3 . The complete alignments , constructed as described below and encompassing all polymorphism in which within-gene recombination had been ruled out , were also analyzed using RDP2 , in order to rule out large-scale genomic recombination . In order to construct phylogenetic trees incorporating all variation , an alignment of the concatenated SNPs and indels was created using ClustalX version 1 . 83 [43] . The order of the regions in the concatenation corresponds to their position in the genome . Modeltest [44] was used to choose the appropriate model of nucleotide substitution , Kimura's two parameter , and phylogenetic trees were built in *PAUP 4 . 0 [45] . Both maximum likelihood and maximum parsimony methods were employed , to glean the most information from both parsimony-uninformative traits and indels , respectively . T . paraluiscuniculi was used as an outgroup ( i . e . a taxon known to lie outside of the T . pallidum grouping ) with which to root the trees and determine the directionality of substitutions . In the alignment , indels were trimmed to one basepair , in order to prevent their greater length from dominating the analysis . One thousand replicates were run to obtain bootstrap support at each node , with starting trees obtained through random , step-wise addition . Tree bisection and reconnection was used for the branch-swapping algorithm . The maximum likelihood tree was chosen for display , with bootstrap support from both methods displayed at nodes . Trees in which trimmed indels were weighted 1/5 the value of substitutions were also created , in light of the faster mutation rate of repeat regions . Because many substitutions were contained in one gene , tp92 , trees in which polymorphism in tp92 was not considered were built . In addition , trees built only from synonymous SNPS or non-synonymous SNPs were created using the methods described above .
Of the twenty-one genes sequenced in this study , data from T . paraluiscuniculi was obtained for all but one gene , tprI , which had previously been shown to be absent from the genome of this species [46] . The tp0618 gene could be amplified from T . paraluiscuniculi Strain A , but not from Strains H and M . It is possible that the gene is missing from the genome of the latter two strains or that the sequence in the priming regions has diverged sufficiently to prevent amplification . No areas likely to result from recombination were identified in formal tests performed in RDP2 . However , polymorphic regions in two genes , tp0618 and tp92 , contained small tracts of highly variable DNA with an elevated frequency of synonymous nucleotide substitutions and multiple substitutions per codon . No possible donor regions could be identified for these tracts using BLAST searches on the T . pallidum genome . However , because such a high frequency of synonymous substitutions could be explained by recombination , and it was thus not clear how many mutational events were responsible for the polymorphism observed , the polymorphism in tp0618 and four polymorphic regions from tp92 were excluded from the phylogenetic analysis . The tp92 substitutions included in the analysis can be found in polymorphism Table S2 , while all substitutions can be found in polymorphism Table S3 . In the remaining 7 kilobases of the T . pallidum genome examined , which were sequenced from 20 scattered regions , a total of 70 SNPs and 12 indels were identified . Most of them are described here for the first time . Twenty-six substitutions occurred between T . pallidum strains ( Table 3 ) , amounting to about one substitution per 275 basepairs . This value is likely to significantly overestimate the amount of polymorphism typical of the genome , however . A number of regions were sequenced either because of their previously demonstrated polymorphism or because they were thought likely to contain variation . This may have weighted the regions sampled towards exceptionally polymorphic areas . The region sequenced in tp92 , for example , contained 7 of the 24 substitutions observed between T . pallidum strains ( Table 3 ) . Roughly two-thirds of the total substitutions observed represented fixed differences between T . pallidum and the outgroup , T . paraluiscuniculi ( Table 4 ) . Singletons were rare , accounting for only 7 of the 70 observed substitutions ( Table S2 ) . Three of these singletons were found in the Pariaman strain , which was geographically distinct from other subsp . pertenue strains , and no singletons were observed in the other whole genome amplified strains . Thus , it appears that whole genome amplification did not introduce spurious substitutions . A few polymorphic regions analyzed here are of special interest in light of past studies and the paucity of genetic variation described to date . Polymorphism between subsp . pertenue strains occurring in the first 200 basepairs of the tprI gene , demonstrated here , had not been described in previous studies of this gene [23] , [47] . In the small region of tprI sequenced here , all 3 substitutions documented in T . pallidum strains were non-synonymous and in close proximity ( Table 3 ) . Two resulted in radical amino acid substitutions ( Table S2 ) . Variation in the cfpA gene had previously been reported in a study comparing the sequence of this gene in the Nichols and Haiti B subsp . pallidum strains [36] . We observed only one of the substitutions reported in this article , a polymorphism occurring among subsp . pallidum strains at position 92 ( Fig . 2 ) . However , we discovered one fixed difference between the non-venereal subspecies and subsp . pallidum , at residue 303 ( Fig . 2 ) . Much of the polymorphism in tp92 was not analyzed in this study , because it fell within hyper-variable regions that were difficult to align and in which recombination could not be ruled out . Even so , in the regions included , 7 non-synonymous and no synonymous substitutions occurred among T . pallidum strains ( Table 3 ) . Four of these substitutions resulted in radical amino acid changes ( Table S2 ) . Finally , in IGR ( fliG-tp0027 ) the presence of a SNP followed by a long homonucleotide repeat region ( Fig . S1 ) was documented in subsp . pallidum , but not in the other subspecies . The net polymorphism in the former strains was predicted , on the basis of conformational stability , to form a stem-loop structure in the intergenic region between the oppositely transcribed genes fliG and tp0027 . This structure , located between the predicted promoter and transcriptional start site of the operon containing genes tp0027 and tp0028 ( Fig . S1 ) , could attenuate transcript levels of these genes . Both of these genes are homologous to tlyC , which is believed to either code for a hemolysin or for a protein that regulates hemolysin production [48] . A phylogenetic tree ( Fig . 3 ) , constructed using maximum likelihood and parsimony methods , demonstrated that all T . pallidum strains fell within a single clade . Within this larger clade , several T . pallidum clades with bootstrap support greater than 90% were identified . Subsp . pallidum and subsp . endemicum strains formed groupings distinct from subsp . pertenue strains . In addition , subsp . pertenue strains CDC-1 , CDC-2575 , and Ghana formed a clade distinct from the other strains of this subspecies , including those gathered nearby . Several clades with lower bootstrap support were also identified within the pallidum and pertenue subspecies . The subsp . pertenue strains occupy a basal location on the tree , indicating an ancestral position for them in the T . pallidum family . This basal position is supported by the average number of nucleotide differences from the T . paraluiscunili clade , which was lower for the subsp . pertenue strains ( 50 . 13 ) then for the subsp . endemicum and pallidum clade ( 55 . 15 ) , or for the individual subsp . endemicum ( 51 . 33 ) and subsp . pallidum ( 56 . 00 ) clades . The genetic distance between subsp . pertenue and endemicum strains was small compared to the distance between these non-venereal subspecies and subsp . pallidum . The terminal position of the subsp . pallidum clade on the tree indicates that it diverged most recently . The results obtained from this phylogenetic analysis were robust . Trees in which trimmed indels were weighted one-fifth the value of substitutions were found to be qualitatively indistinguishable from unweighted trees . And because many of the substitutions analyzed here came from the portion of tp92 sequenced , a tree built without this gene was constructed ( tree not shown ) . The major groupings described above were observed . However , without tp92 , groupings that appeared in Fig . 3 with low bootstrap support , such as the clade containing the Mexico A and subsp . pallidum strain from South Africa , were not identified . Trees built using only synonymous or non-synonymous substitutions demonstrated that while the subspecies clades could be obtained by analyzing only synonymous substitutions , non-synonymous substitutions were responsible for increased phylogenetic resolution ( trees not shown ) . That is , identification of all within-subspecies clades was dependent on non-synonymous substitutions . Genetic analysis of the two subsp . pertenue strains collected from indigenous groups in Guyana revealed that they were the closest relatives of modern subsp . pallidum strains identified in this study . Only a subset of seven genetic regions could be analyzed in these strains: IGR ( fliG-tp0027 ) , deoD , gpd , tp0618 , tprI , cfpA , and tpF-1 . These regions contained 17 SNPs at which Old World non-venereal strains differed from subsp . pallidum strains ( Fig . 2 ) . At 4 of the 17 SNPs examined , the New World subsp . pertenue strains were found to be identical to subsp . pallidum strains . These 4 SNPs occurred in 2 loci on separate sides of the genome , tprI and gpd . At the remaining 13 sites , the New World pertenue strains were identical to Old World non-venereal strains . A network path was constructed , in which these SNPs were considered in their geographic context ( Fig . 4 ) . In addition , polarity of substitutions was determined using data from the previously constructed phylogeny ( Fig . 3 ) . The tree indicated that the ancestral state of the gpd gene could be found in Old World subsp . pertenue and endemicum strains . In addition , polymorphism in the tprI gene could be used to divide T . pallidum into four groups: subsp . pallidum , subsp . endemicum , and two smaller groups of Old World subsp . pertenue . The phylogeny indicated that of the two Old World subsp . pertenue groups , the more recently diverged was the CDC-1/CDC-2575/Ghana one , while the other subsp . pertenue group was ancestral . Thus , the 4 SNP sequence present in the majority of Old World subsp . pertenue strains appears to have arisen first . The network path suggests that a series of substitutions led from this first group of Old-World pertenue strains to a second group of African subsp . pertenue strains and to subsp . endemicum strains ( Fig . 4 ) . The pattern of substitutions suggest that a hypothetical intermediate strain , arising from either the group II subsp . pertenue strains or endemicum strains , once existed and was a progenitor to both New World subsp . pertenue and to all subsp . pallidum strains . This data also suggests that the New World subsp . pertenue strains belong to a group distinct from the Old World subsp . pertenue strains , occupying a phylogenetic position somewhere between Old World non-venereal strains and modern subsp . pallidum strains .
In the past , a number of different hypotheses regarding the origin of T . pallidum subsp . pallidum , the causative agent of syphilis , have been put forth . Using new data collected in this study , we assess a number of these hypotheses . The phylogenetic tree created in this study sheds light on the relative order in which the T . pallidum subspecies emerged . Subsp . pertenue strains gathered from central Africa and the South Pacific occupy basal positions on the tree , indicating that they most closely resemble the ancestral pathogen in humans ( Fig . 3 ) . The early emergence of these strains in human history is also supported by their low average nucleotide differences from T . paraluiscuniculi and their similarity to the simian strain of T . pallidum , which infects wild baboons . Although the simian strain could not be distinguished from subsp . pertenue strains using the polymorphic data in this study ( Fig . 3 ) , another study focusing on the tpr genes found that it was distinct from human T . pallidum strains examined [47] . This evidence is consistent with the hypothesis that yaws is an heirloom disease in humans , one caused by a pathogen that infected our anthropoid ancestors and has evolved with our species [49] . The presence of yaws in wild populations of our closest relatives , gorillas and chimpanzees [50] , [51] , further supports this theory . However , a more recent cross-species transfer event between humans and non-human primates cannot be ruled out using the available genetic data . It has been shown that inoculation with the simian strain can cause a yaws-like infection in humans [52] , and it is known that infection rates are high in both humans and wild baboons in yaws-endemic areas of West Africa [53] , [54] . Thus , it is possible that non-human primates serve as a source of human disease , or vice versa . Therefore , in the future it would be desirable to collect T . pallidum strains from various wild , non-human primate species and to sequence them at additional loci . Such information is likely to provide important information concerning the antiquity of yaws in humans . Subsp . endemicum strains , gathered from the Middle East and the Balkans , diverged from subsp . pertenue strains at some later date , and subsp . pallidum strains diverged most recently , indicating that they emerged relatively recently in human history ( Fig . 3 ) . The topology of the tree is consistent with the long-held belief that treponemal disease is very old and has traveled with humans during their migrations , evolving from ancestral subsp . pertenue , in hot , humid regions , into subsp . endemicum as people settled in cooler and dryer areas , and finally into subsp . pallidum [9] , [55] . Examination of additional variable sites in non-venereal strains from Africa and Asia may aid in pinpointing the trajectory of this pathogen family in the Old World . The study of two yaws-causing strains from the Americas provides additional clues to understanding the history of syphilis . The genetic analysis of the two subsp . pertenue strains gathered in Guyana demonstrates that they are the closest relatives of venereal syphilis-causing strains identified in this study ( Fig . 4 ) . These strains are genetically distinct from Old World subsp . pertenue strains , having diverged more recently than Old-World non-venereal strains . The geographic analysis of strains , paired with the phylogeny , suggests a three-stage model for T . pallidum's dissemination and evolution . First , T . pallidum arose in the Old World , in the form of non-venereal infection , before spreading with humans to the Middle East/Eastern Europe , in the form of endemic syphilis , and then to the Americas , in the form of New World yaws . Second , a T . pallidum strain from the Americas was introduced back into the Old World , probably as a result of the European exploration of the Americas , becoming the progenitor of modern syphilis-causing strains . Third , modern subsp . pallidum strains disseminated from Europe to the rest of the world . Descriptions of the clinical presentation of yaws in indigenous Guyanese patients [31] support the “transitional” position of South American subsp . pertenue strains , between Old World non-venereal and subsp . pallidum strains , indicated by the genetic comparison in this study . These yaws patients , who are inhabitants of Guyana's interior , typically present with a chancre ( a chronic , painless ulcer with raised margins ) similar to the type seen in venereal syphilis , though found in children and in extra-genital locations . This clinical presentation is quite different from the textbook “frambesiform” lesion characteristic of yaws in Africa and Asia . It is possible that this distinctive lesion results from differences in the pathogen genome , although host differences , both cultural and genetic , may also play a role . Though modern travel increases the probability that a strain present in one place may have been recently introduced from another , the isolation of the aboriginal population from which these strains were collected , as well as their unique genetic and clinical characteristics , makes such an event unlikely . It is possible that these strains possess some of the characteristics of subsp . pallidum strains while still retaining many of the features of subsp . pertenue strains , including a non-sexual transmission mode . The condition of the two samples precluded a complete comparative study involving these strains , but the distinct genetic make-up indicated by the available data makes further study of South American subsp . pertenue strains desirable , if additional samples can be gathered in the future . Although the data suggest that venereal syphilis-causing strains arose recently , from a New World progenitor , the transmission mode of the ancestral bacterium remains unclear . The closest relatives of syphilis-causing strains in this study were non-venereally transmitted . However , because of the disappearance of endemic treponemal disease from South America it was only possible to study two indigenous strains from this continent , gathered in close proximity . It is possible that a strain even more closely related to subsp . pallidum once existed in pre-Columbian South America and was transmitted venereally . Paleopathologists have assessed the age distribution of treponemal infection in pre-Columbian Native American populations , in hopes of determining the mode of transmission in these civilizations . However , because permanent bone remodeling due to treponemal infection usually occurs in tertiary-stage disease , which can take many years to develop , it is difficult to determine at which age individuals typically contracted the infection in past populations , even when finds are abundant [56] . Several possible cases of congenital syphilis , believed by many to occur only in venereal infection , have been reported from pre-Columbian America , but the diagnoses remain tentative [56] . Some researchers have attempted to determine the nature of treponemal infection in past populations through statistical comparison of specific pathologies in the skeletal record . Based on this method , they assert that the treponemal infection present in the Dominican Republic at the time of Columbus's landing was more akin to venereal syphilis than yaws or endemic syphilis [57] . However , this method remains controversial , because of the limited samples from which specific pathology rates have been determined for each disease [56] . Therefore , it is not clear whether venereal syphilis existed in the New World prior to Columbus's arrival . While it is possible that Columbus and his crew imported venereal syphilis from the New World to Europe , it is also possible that the explorers imported a non-venereal progenitor that rapidly evolved into the pathogen we know today only after it was introduced into the Old World . Indeed , analysis of the changing descriptions of venereal syphilis following its appearance in Europe have led many to believe that the pathogen did evolve rapidly after its initial introduction [16] . Given the limitations of the available data , the question of whether the progenitor of modern syphilis-causing strains was venereal or non-venereal may remain unresolved . The results of this molecular study clarify some findings of skeletal biology while obfuscating others . The virtual absence of syphilitic lesions from Pre-Columbian Old World skeletons can be explained simply in the context of this data; syphilis did not exist in these areas until the Renaissance . On the other hand , the absence of lesions typical of yaws or endemic syphilis in these areas is puzzling in light of the genetic data . If the non-venereal treponematoses arose in the Old World long before syphilis , then why isn't there more evidence of their presence ? If yaws was the first form of treponemal disease , as indicated by our study , and was limited to hot , humid areas , we would expect preservation of ancient , affected remains to be poor . This may contribute to the paucity of skeletal finds . In light of the hypotheses regarding the rapid evolution of subsp . pallidum in Renaissance Europe and the lack of knowledge concerning the genetic basis for the different clinical manifestations of the treponematoses , any evidence of positive selection or functional change in the T . pallidum genome would be of great interest . A comparison of trees constructed with only synonymous or non-synonymous substitutions emphasized the role of non-synonymous substitutions in differentiating T . pallidum strains . Ten of the 14 non-synonymous substitutions observed between T . pallidum strains occurred in just 2 genes: tprI and tp92 . Similarly , 6 of 9 radical amino acid substitutions observed between T . pallidum strains occurred in these 2 genes ( Table S2 ) . Given the evidence for the role of the TprI and Tp92 proteins in pathogenicity [22] , [58] , [59] , the substitutions clustered in the regions of these genes that were sequenced may hint at positive selection . Sequencing the entirety of these genes in many strains of T . pallidum may be worthwhile , in order to better assess this possibility . Similarly , since tp0027 and tp0028 are both homologous to tlyC , a gene that either encodes a hemolysin or regulates a cryptic one in E . coli [48] , a difference in transcript level between subspecies could affect pathogenesis . To this end , a regulatory function for the predicted stem-loop structure in IGR ( fliG-tp0027 ) in subsp . pallidum could be tested . Transcript levels of tp0027 could be characterized in the different subspecies; in addition , the two IGR variants discovered in this study could be placed upstream of a reporter gene in a genetically tractable bacterium , such as E . coli , in order to directly examine the effect of the stem-loop structure on transcript levels . Our conclusions regarding the history of T . pallidum differ from those drawn in a recent comparative study of the tpr gene family [23] , in which it was asserted that the times of emergence for the pathogens that cause endemic syphilis , yaws , and syphilis were similar and dated to sometime later than the emergence of modern humans but earlier than the Renaissance . We propose several reasons for why the conclusions arrived at in the two studies diverge . The strains collected from Guyana in this study played an integral role in our analysis , because they were most closely related to syphilis-causing strains and helped establish the geographic trajectory of T . pallidum evolution . In addition , in the genetic regions we examined , recombination was much less common than in the majority of the tpr genes , if it was present at all . Our data suggest that the T . pallidum genome is evolving in a largely clonal manner amenable to phylogenetic analysis , with the exception of frequent intra-gene conversion events confined within the members of the tpr gene family [23] . Finally , because T . paraluiscuniculi was extremely similar to T . pallidum at all but one of the sequences examined ( tprI is missing in the former species ) , it was easy to create alignments and to assess the directionality of mutations in this study . The evolutionary pathway between the tpr genes of T . paraluiscuniculi and T . pallidum is more complicated and much harder to interpret . This study has some obvious limitations . The level of polymorphism found between T . pallidum strains is quite low , as suggested by previous studies . For this reason , the level of resolution in the phylogenetic tree is relatively poor . It is likely that polymorphism data from the rest of the genome will clarify the topology of this tree , including the relationship between subsp . endemicum and subsp . pallidum strains , which are grouped together in this study with low bootstrap support , and the position of subsp . pertenue strains , which occupy a basal position on the tree but group together only by default . Similarly , the close relationship between subsp . pallidum strains and New World subsp . pertenue strains described in this paper hinges on the analysis of only 4 SNPs . Three of these SNPs were found in a single gene , tprI . The close proximity of these non-synonymous SNPs within the gene , the non-linear relationships of the strains indicated by the substitutions ( Fig . 4 ) , and the evidence that the TprI protein may be involved in pathogenesis , suggest that tprI may not be evolving neutrally . For this reason , it is unlikely that these SNPs have accumulated in a clockwork manner . Instead , the information that can be gleaned from this gene is limited to the relative order in which evolutionary events occurred . The large-scale comparative genetic studies possible on the pathogens that cause diseases such as malaria and anthrax will never be possible in T . pallidum , because of the disappearance of the non-venereal treponematoses and the strains that cause them [60] , [61] . The prevalence of yaws in Guyana , the last country in South America in which yaws has been documented in recent years , has decreased annually , and surveys carried out by our group in 2006 and 2007 in endemic yaws territory demonstrated no active cases of the disease . Analysis of South American strains is necessary in order to assess the relationship between subsp . pallidum and non-venereal treponemal strains . Because it is not clear whether an opportunity to examine such strains will arise again , the results presented in this paper are of special importance in the debate over the origins of treponemal disease . In conclusion , in this study we found that syphilis-causing strains evolved relatively recently in human history and that the closest relatives of subsp . pallidum were yaws-causing strains from the New World . When this genetic data is combined with extensive documentary evidence that syphilis appeared in Europe for the first time around 1495 [12] and the apparent absence of skeletal signs of syphilis in pre-Columbian Europe and North Africa , the Columbian hypothesis for syphilis's origin gains new strength . | For 500 years , controversy has raged around the origin of T . pallidum subsp . pallidum , the bacterium responsible for syphilis . Did Christopher Columbus and his men introduce this pathogen into Renaissance Europe , after contracting it during their voyage to the New World ? Or does syphilis have a much older history in the Old World ? This paper represents the first attempt to use a phylogenetic approach to solve this question . In addition , it clarifies the evolutionary relationships between the pathogen that causes syphilis and the other T . pallidum subspecies , which cause the neglected tropical diseases yaws and endemic syphilis . Using a collection of pathogenic Treponema strains that is unprecedented in size , we show that yaws appears to be an ancient infection in humans while venereal syphilis arose relatively recently in human history . In addition , the closest relatives of syphilis-causing strains identified in this study were found in South America , providing support for the Columbian theory of syphilis's origin . | [
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... | 2008 | On the Origin of the Treponematoses: A Phylogenetic Approach |
Anthelmintic resistance is a major problem for the control of parasitic nematodes of livestock and of growing concern for human parasite control . However , there is little understanding of how resistance arises and spreads or of the “genetic signature” of selection for this group of important pathogens . We have investigated these questions in the system for which anthelmintic resistance is most advanced; benzimidazole resistance in the sheep parasites Haemonchus contortus and Teladorsagia circumcincta . Population genetic analysis with neutral microsatellite markers reveals that T . circumcincta has higher genetic diversity but lower genetic differentiation between farms than H . contortus in the UK . We propose that this is due to epidemiological differences between the two parasites resulting in greater seasonal bottlenecking of H . contortus . There is a remarkably high level of resistance haplotype diversity in both parasites compared with drug resistance studies in other eukaryotic systems . Our analysis suggests a minimum of four independent origins of resistance mutations on just seven farms for H . contortus , and even more for T . circumincta . Both hard and soft selective sweeps have occurred with striking differences between individual farms . The sweeps are generally softer for T . circumcincta than H . contortus , consistent with its higher level of genetic diversity and consequent greater availability of new mutations . We propose a model in which multiple independent resistance mutations recurrently arise and spread by migration to explain the widespread occurrence of resistance in these parasites . Finally , in spite of the complex haplotypic diversity , we show that selection can be detected at the target locus using simple measures of genetic diversity and departures from neutrality . This work has important implications for the application of genome-wide approaches to identify new anthelmintic resistance loci and the likelihood of anthelmintic resistance emerging as selection pressure is increased in human soil-transmitted nematodes by community wide treatment programs .
Understanding the processes that affect the emergence of drug resistance in eukaryotic pathogens is an important goal . Anthelmintic resistance in parasitic nematodes is a threat to sustainable livestock production worldwide and a growing concern for the control of human parasites in the developing world [1] . Our limited understanding of how resistance mutations arise and spread in parasitic nematode populations limits our ability to develop evidence-based mitigation strategies . Moreover , there is little information on the changes that occur in the genome as anthelmintic resistance mutations increase in frequency in parasite populations; the so called “genetic signature” of selection . Such knowledge is critical if we are to apply genome-wide population genomic approaches to identify new anthelmintic resistance mutations . Haemonchus contortus and Teladorsagia circumcincta are two closely related parasitic nematodes of sheep for which anthelmintic resistance is widespread . In these parasites , resistance has occurred against all broad spectrum anthelmintic classes used in their control [1] . Resistance to benzimidazoles is particularly common due to their intensive use since the 1970s and is at least partially understood at the molecular level . Several mutations in the isotype-1 β-tubulin gene , which encodes the drug target , have been shown to be associated with resistance . Substitutions of a phenylalanine for a tyrosine at codon positions 167 and 200 ( F167Y and F200Y ) of the isotype-1 β-tubulin polypeptide have been reported in both species and a substitution of glutamic acid for alanine at position 198 ( E198A ) has also been described in H . contortus [2–9] . All three of these substitutions have been shown to be associated with the resistance phenotype and the F200Y substitution has been subject to extensive functional analysis [6 , 10] . Consequently , benzimidazole resistance in these two parasite species is currently the best system in which to study the population genetics of anthelmintic resistance in parasitic nematodes . In this study , we have investigated the population structure of T . circumcincta and H . contortus and studied the genetics of benzimidazole resistance on seven commercial sheep farms in the UK . The two species were sampled from the same animals on each farm to allow a direct comparison without any confounding differences in environment or management . The aim was to investigate how anthelmintic resistance mutations emerge and spread in response to selection . In the classical model of adaptation , a beneficial mutation arises once and increases in frequency under the influence of selection . This is known as a “hard” selective sweep and is characterised by a single haplotype increasing in frequency . There is a dramatic loss of marker polymorphism around the selected locus which is known as the “hitchhiking effect” [11] . However , it is also possible for multiple adaptive alleles to sweep through a population , particularly when there is rapid adaptation , and this leads to a more complex genetic signature of selection [12] . This is known as a “soft” selective sweep in which multiple haplotypes originate either from the standing genetic variation or arise independently by recurrent de novo mutations . In this study , we report a remarkably high diversity of benzimidazole resistance haplotypes with both hard and soft selective sweeps occurring in different parasite populations . We propose a model for anthelmintic resistance in which multiple independent resistance mutations recurrently arise in parasite populations and are then spread by migration . This explains why anthelmintic resistance is so common in livestock parasites and suggests that the emergence of resistance is likely whenever parasitic nematodes with large population sizes are exposed to intensive drug selection .
In order to enable a direct comparison of the population genetics of T . circumcincta and H . contortus , farms with high levels of infection of both species first had to be identified . As part of a previously published survey of 91 UK sheep farms sampled in 2008 , we had identified 45 farms on which both T . circumcincta and H . contortus worms were present in ewes at lambing [13] . Eggs were extracted from a pool of faeces from 20 ewes per farm and allowed to hatch into L1 larvae . DNA templates were made from approximately 1000 pooled L1 larvae from each of the 45 farms and then screened with three species-specific microsatellite markers ( H . contortus; Hcms25 , Hcms53265 and Hcms22193; T . circumcincta loci , mtg67 , mtg68 and mtg73 ) to identify farms on which both species were present at high frequency ( based on the identification of numerous alleles ) . The prevalence of the two species was then more accurately established on 20 chosen farms using single worm rDNA ITS-2 species-specific PCR assays on 90 individual L1 per farm ( S1 Fig . ) . Seven farms with a high prevalence of both H . contortus and T . circumcincta infection were chosen for detailed population genetic analysis: farm 37 ( Gloucester ) , farm 54 ( Devon ) , farm 86 ( Middlesex ) , farm 95 ( North Yorkshire ) , farm 101 ( East Sussex ) , farm 102 ( Inverness ) and farm 110 ( Kent ) ( Fig . 1A ) . The prevalence of the other major ovine gastrointestinal nematode species on these seven study farms was determined by applying rDNA ITS-2 species-specific PCR assays to 90 L1 individual larvae for H . contortus [14] , Trichostrongylus axei , Trichostrongylus vitrinus , [15] T . circumcincta , Trichostrongylus colubriformis , Cooperia curticei [13] , Chabertia ovina [15] and Oesophagostomum venulosum ( assay developed in this study , S1 Table ) . These data , along with the location of the farms are shown in Fig . 1A . 30 individual L1 DNA lysates for T . circumcincta and H . contortus were then used for the detailed population analysis undertaken in this study . Bulk DNA lysates were made using previously described techniques [14] and 1l of a 1:40 dilution of neat lysate was used as PCR template . Dilutions of several aliquots of lysate buffer , made in parallel , were included as negative controls for all PCR amplifications . Conditions for the rDNA ITS-2 species-specific PCR assays were 94°C for 2 min followed by 35 cycles of 92°C for 30s ( the exception being H . contortus with 40 cycles ) ; annealing temperature for 30s and 72°C for 30s with a single final extension cycle at 72°C for 10 min . PCR primers and annealing temperatures are given in S1 Table . A number of microsatellite loci have previously been characterised for H . contortus [14 , 16 , 17] and Teladorsagia circumcincta [18] . In order to identify additional loci for this study , Tandem Repeats Finder [19] was used to search the 2006 H . contortus and the 2009 T . circumcincta supercontig databases ( ftp . sanger . ac . uk/pub/pathogens/ ) . Loci chosen on the basis of repeat purity and sufficient flanking sequence were tested for consistency of amplification and allelic polymorphism using a range of genetically divergent isolates ( H . contortus: MHco3 ( ISE ) , Hco4 ( WRS ) , Hco10 ( CAVR ) and a 2007 UK ovine field isolate designated Hco ( UK-12/10/07 ) [14]; T . circumcincta: MTci5 , NzWS , FrGa and ScSo210 [20] ) . A summary of the screening results is shown in S2 Table and the primer sequences , repeat motifs and allele ranges of the new loci are given in S3 Table . A final panel of ten microsatellite loci were chosen as population markers for this study for each species: T . circumcincta ( previously described loci: mtg15 , mtg67 , mtg68 , mtg73; new loci: Tc22274 , Tc7989 , Tc4504 , Tc2066 , Tc2467 , Tc13604 ) and a panel of ten H . contortus loci ( previously described loci: Hcms25 , Hcms36 , Hcms40 , Hcms22co3; new loci: Hc53265 , Hc2884 , Hc3086 , Hc22193 , Hc12850 , Hc13507 ) . The PCR conditions for microsatellite amplification have been previously described [14] [18] . The forward primer of each microsatellite primer pair was 5’-end labelled with FAM , HEX , or NED fluorescent dyes and amplicons were electrophoresed , with a GeneScan ROX 400 ( Applied Biosystems ) internal size standard , on an ABI Prism 3100 Genetic Analyzer ( Applied Biosystems ) . Individual chromatograms were analyzed using Genemapper Software Version 4 . 0 ( Applied Biosystems ) . From multi-locus microsatellite genotype data , heterozygosity ( He and Ho ) , allele richness and estimates of FIS for each locus were calculated by Arlequin 3 . 1 [21] . Guo & Thompson’s ( 1992 ) Exact Test was used to statistically evaluate deviations from Hardy—Weinberg equilibrium for all populations [22] . Significance levels were adjusted using the sequential method of Bonferroni for multiple comparisons in the same dataset [23] . Linkage disequilibrium analysis was carried out by GENEPOP version 3 . 3 [24] using a log likelihood ratio test statistic ( G-test ) . Partition of microsatellite diversity between and within farm populations was estimated through an analysis of molecular variance AMOVA [25] . Data were defined as ‘standard’ rather than ‘microsatellite’ , as loci did not necessarily adhere to the stepwise mutation model . Pairwise FST values were calculated and significance testing was undertaken by random permutation in Arlequin 3 . 11 . Tests for the presence and influence of null alleles were conducted using FreeNA [26] , a software program that is able to produce FST estimates before and after a correction for null alleles and utilizes bootstrapping ( with 10 , 000 replicates ) to determine significance . Non-synonymous single nucleotide polymorphisms ( SNPs ) in the isotype-1 β-tubulin gene have been previously associated with benzimidazole resistance in H . contortus at codons P167 , P198 , and P200 [4–7 , 27] and at codons P167 and P200 for T . circumcincta [2 , 3 , 7–9] . Pyrosequencing assays designed to target the F167Y ( TTC>TAC ) , F200Y ( TTC>TAC ) and E198A ( GAA>GCA ) SNPs of the isotype-1 β-tubulin genes of H . contortus and T . circumcincta were used to estimate the prevalence of each resistant mutation on each farm . Genotypes were generated for 30 single worms per farm population . These assays have been described elsewhere: T . circumcincta [9] and H . contortus [28] . A fragment encompassing the F167Y ( TTC>TAC ) , F200Y ( TTC>TAC ) and E198A ( GAA>GCA ) SNPs in the isotype-1 β-tubulin gene was PCR amplified from the same individual worms that were genotyped . Specific primers anchored in exon 3 and 7 ( Tc37F: GCTGAGCTTGTTGACAACG Tc37R AGATAGCGTCCGTGGCGAG; Hc37F GCCGAGCTAGTTGATAACG Hc37R AGATAACGTCCATGGCGAG ) were used to amplify a 922bp fragment for H . contortus and a 940bp fragment for T . circumcincta . The PCR conditions were the same for both species ( 95°C for 5min followed by 45 cycles of 94°C for 15s; 60°C for 1min and 72°C for 1min with a single final extension cycle at 72°C for 20min ) . HotStar HiFidelity Polymerase ( Qiagen ) was used with the proprietary Q-solution to minimize the number of polymerase-induced errors . Amplicons from the 30 individual worms per farm population were pooled together and cloned , using the Zero Blunt cloning vector , Invitrogen ( 7 cloning reactions per species ) . A minimum of 20 clones were selected and sequenced per cloning reaction to provide adequate representation of the most common haplotypes on each farm . Each clone was sequenced in both orientations to create a consensus sequence for each haplotype . The error rate ( R ) for the PCR amplification of the isotype-1 β-tubulin was experimentally determined . A 922bp region of the H . contortus and a 940bp region of the T . circumcincta isotype-1 β-tubulin target was independently PCR amplified on 10 separate occasions from a single worm of each species that was heterozygous for the P200 polymorphism . Each amplicon was cloned and a single clone of each allele sequenced in both orientations . This approach yielded 10 sequences each corresponding to one of the two different alleles present in a single diploid worm for each species . The error rate ( R ) per bp per cycle was calculated by dividing the total number of sequence polymorphisms by the total length of sequence , L ( when L = lcn and l = length of amplicon , c = number of cycles , n = number of PCR reactions ) . For example , from the 10 sequences of the resistant ( P200Y ) H . contortus allele a total of 3 polymorphisms were identified . This equated to an error rate of 7 . 23*10–6 ( 3/ 922*45*10 ) . The error rates calculated for H . contortus and T . circumcincta using this method were similar to each other ( T . circumcinta: 7 . 09–9 . 46*10–6 , H . contortus: 7 . 23–9 . 64*10–6 ) . The error rate ( R ) can then be used to estimate the fraction of PCR-induced mismatches ( F ) with the formula 1-e-lRc [29] . For H . contortus and T . circumcincta the fraction of PCR-induced mismatches ( F ) proved to be identical to 3 decimal places , ( F = 0 . 259–0 . 330 , 1-e-922* ( 7 . 23*10–6 ) *45 = 0 . 259 and 1-e-922* ( 9 . 64*10–6 ) *45 = 0 . 330 ) , suggesting that approximately one in every three or four , isotype-1 β-tubulin sequences would contain a PCR-induced mutation . SNPs appearing more than once in a sequence data set of the size analyzed in this work ( 140 sequences for each species ) are highly likely to be real polymorphisms , whereas SNPs that only occur once are possible artifacts due to polymerase induced errors . In order to filter our dataset , the frequency distribution of the SNPs was plotted along the isotype-1 β-tubulin gene model ( S2 Fig . ) . The SNPs were classified into two groups: those that occurred only once and those that occurred more than once in the 140 sequence dataset . The distribution patterns for these two SNP categories strongly support the hypothesis that most , if not all , “unique” SNPs were PCR-induced mutations whereas those appearing more than once were genuine polymorphisms . “Unique” SNPs were evenly distributed across introns and exons and there was no bias for synonymous versus non-synonymous mutations for the exonic SNPs ( S2 Fig . ) . In contrast , SNPs appearing more than once in the dataset were clustered within the introns and , for the exonic SNPs , there was bias to synonymous changes . The only non-synonymous SNPs appearing more than once in the dataset occurred at the resistance associated positions P167 , P198 and P200 ( S2 Fig . ) . In order to take a conservative approach and ensure only real polymorphisms were considered in our analyses , all SNPs occurring only once in the dataset were discarded . This resulted in 15 different H . contortus and 43 different T . circumcincta haplotypes represented in this final “filtered” sequence data used for all subsequent analysis . Haplotype networks were generated from the isotype-1 β-tubulin sequence data based on genetic distance with the SplitsTree4 program [30] . Initially a distance matrix based on the proportion of positions at which any two isotype-1 β-tubulin sequences differed , was created ( Uncorrected-P option ) . Circular ( equal angle ) split networks were generated from the distance matrices with the neighbour-net method . Neighbour-net networks were also constructed from microsatellite data from each farm . Each split in a network between any two individuals or farms is displayed by parallel edges whose length is proportional to the weight of the associated split . Networks were also generated with the software TCS [31] . This is based on the concept of statistical parsimony for which all pairs of haplotypes are compared and the connections or “edges” between the haplotypes are scored according to their “probability of parsimony” . Only edges with a probability of parsimony of greater than 95% are used for the construction of the haplotype network . Analysis was conducted on a multiple alignment of “collapsed” sequences where allele frequency information had been removed . Gaps in sequences were classified as “missing” data . The analysis was able to infer the most probable “ancestral” haplotypes . Each branch represents a single nucleotide mutation and empty nodes are assumed haplotypes . Statistical support was generated through the calculation of consistency indicies ( CI ) in Winclada [32] . In addition , Median Joining networks were generated in Network 4 . 6 . 1 ( Fluxus Technology Ltd . ) . Sequences were initially aligned in ClustalX and prepared for import using DNA Alignment software ( Fluxus Technology Ltd ) . A full median network containing all possible shortest trees was generated by setting the epsilon parameter equal to the greatest weighted distance ( epsilon = 10 ) . All unnecessary median vectors and links were removed with the MP ( Maximum Parsimony ) option [33] . Small black dots represent median vectors and the number of mutations separating adjacent sequence nodes and/or median vectors are indicated along connecting branches . The most probable ancestral node was determined by rooting the network to a closely related outgroup; a T . circumcincta sequence was used to root the H . contortus network and vice versa . The following diversity indices were calculated using DnaSP 5 . 10 [34]: nucleotide diversity ( π ) , Gene diversity ( Hd ) , the mean number of pairwise differences ( k ) , the number of segregating sites ( S ) ; and the Mutation parameter based on an infinite site equilibrium model and the number of segregating sites ( θS ) . Tests for selective neutrality were analysed with the program DnaSP 5 . 10 to determine whether the observed frequency distribution of nucleotide polymorphism departs from neutral expectations . Neutrality tests were conducted included Tajima’s D [35] , and Fay and Wu’s H [36] . These were performed with the “unfiltered” as well as the filtered dataset . The analysis of both datasets were very similar indicating that the removal the SNPs considered likely to be polymerase -induced artifacts was valid and did not influence the results of the neutrality tests . The confidence limits and p-values were obtained by coalescent simulations ( 10 000 replicates ) without recombination . The number of unique recombination events and the cross-over positions were estimated according to the methods ( RDP , GenConv , Chimera , MaxChi , Bootscan , Siscan , 3Seq , LARD ) implemented in the software package RDP3 [37] .
In order to examine and compare the population genetic structure of H . contortus and T . circumcincta on the same farms , seven UK farms were chosen on which there was a high prevalence of both species present ( see Materials and Methods ) ( Fig . 1A ) . Thirty individual L1 larvae were genotyped for H . contortus and T . circumcincta at each of ten microsatellite loci for each farm population . There were no major departures from linkage equilibrium for all pairwise combinations of loci in any population , indicating that alleles at these loci were randomly associating and not genetically linked . All farm populations were polymorphic at all loci for both species , with the number of alleles per locus ranging from 2 to 17 for H . contortus and from 3 to 19 for T . circumcincta ( S4 and S5 Tables ) . Both parasites showed a high level of genetic diversity in all populations but T . circumcincta showed a higher level of overall diversity then H . contortus . The mean allele richness was 7 . 51 ±0 . 25 alleles per locus for H . contortus compared to 11 . 11 ±0 . 23 for T . circumcincta ( Table 1 ) . The expected heterozygosity ( He ) was 0 . 670 for H . contortus ( range: 0 . 631–0 . 694 ) compared to 0 . 823 for T . circumcincta ( range: 0 . 797–0 . 840 ) with very little difference in overall genetic diversity between any of the seven farms ( Table 1 ) . There was a significant departure from Hardy-Weinberg equilibrium , even after Bonferroni correction , in addition to relatively high inbreeding coefficient values ( FIS ) for 43 out of the 70 loci/farm combinations for H . contortus and 52 out of the 70 loci/farm combinations for T . circumcincta ( S4 and S5 Tables ) . The presence of null alleles for microsatellite loci has been previously reported for these two parasite species and is the likely reason for departures from Hardy-Weinberg Equilibrium [14 , 20 , 38 , 39] . This was confirmed for two loci ( Hc22c03 and Hcms40 ) where sequencing flanking sequence revealed deleted sequence encompassing primer sites in the ‘suspected’ null alleles . AMOVA analysis estimated that the percentage of variation that partitioned between farm populations was 10-fold less for T . circumcincta ( 0 . 24% ) compared to H . contortus ( 2 . 84% ) suggesting greater geographical sub-structuring of H . contortus than T . circumcincta . This was reflected by the pairwise FST estimates calculated between each of the seven populations ( Fig . 1B ) . Only two out of the possible 21 pairwise comparisons between farms showed statistically significant but low differentiation for T . circumcincta ( Fig . 1B ) . These were between farms 101 ( E . Sussex ) and 110 ( Kent ) ( FST = 0 . 0269 ) and between farms 101 ( E . Sussex ) and 54 ( Devon ) ( FST = 0 . 0340 ) . In contrast , 10 out of 21 possible pairwise comparisons showed significant differentiation for H . contortus ( Fig . 1B ) with FST values ranging from 0 . 0198 ( between farms 37 ( Gloucester ) and 86 ( Middlesex ) ) to 0 . 0757 ( between farms 54 ( Devon ) and 110 ( Kent ) ) . However , no single farm population was significantly genetically differentiated from all of the other six populations . Estimates of FST after null allele correction were similar to uncorrected estimates ( Fig . 1B ) . The SplitsTree4 network confirms the lack of genetic differentiation between populations for T . circumcincta but suggests moderate genetic differentiation between some of the H . contortus populations ( Fig . 1C ) . The same H . contortus and T . circumcincta larvae that were genotyped for the microsatellite loci were individually genotyped for the three currently known benzimidazole resistance associated polymorphisms in the isotype-1 β-tubulin gene ( F167Y , E198A and F200Y ) using pyrosequence genotyping . The F167Y ( TTC>TAC ) and F200Y ( TTC>TAC ) resistance polymorphisms were detected in both species and , although the substitution of glutamic acid ( E ) for alanine ( A ) at position 198 ( GAA>GCA ) was not identified in either species , a change in codon 198 from GAA ( or GAG ) to TTA resulting in the substitution of glutamic acid ( E ) for leucine ( L ) was identified in T . circumcincta . ( Fig . 2 ) . In the case of H . contortus , the F200Y resistance polymorphism was present on all seven farms and , with the exception of farm 95 ( N . Yorkshire ) , it was the most common resistance polymorphism in H . contortus ( 36 . 3% overall ) . The P167Y polymorphism was also common , being present in six out of seven farms , in some cases at relatively high frequency ( Fig . 2 ) . Farm 102 ( Inverness ) was the only farm on which the majority of H . contortus had susceptible genotypes at all three resistance associated positions . In the case of T . circumcincta , the F200Y resistance polymorphism was also extremely common , with a prevalence of >46% in five out of the seven farms . It was at fixation on farm 37 ( Gloucester ) and close to fixation on two other farms , farm 86 ( Middlesex ) and farm 110 ( Kent ) . However , unlike the situation in H . contortus , the F167Y resistance polymorphism was found at very low frequency ( 1 . 7% overall ) on just one farm , 110 ( Kent ) ( Fig . 2 ) . In contrast , the E198L polymorphism was present on four out of the seven farms . Although it was at relatively low frequency in three of these ( 2 . 0–8 . 8% ) , it was very high frequency on farm 95 ( N . Yorkshire ) , being close to fixation ( 91 . 7% ) . As with H . contortus , the only farm on which the majority of T . circumcincta ( 93 . 3% ) had susceptible variants at all three positions was farm 102 ( Inverness ) ( Fig . 2 ) . No individual worms of either species were homozygous for more than one resistance polymorphism . In order to sample the diversity of the isotype-1 β-tubulin locus and look for evidence of selection , twenty cloned copies of the region encompassing the P167 , P198 and P200 positions ( 922bp sequence for H . contortus and 940bp sequence for T . circumcincta ) , were sequenced for each parasite species from each of the seven farms ( a total of 140 sequences for each species ) . Polymorphisms likely to be due to PCR induced mutations were removed from the dataset to give a conservative estimate of allelic diversity ( see Materials and Methods ) . The isotype-1 β-tubulin genetic diversity was high in both species but was greater in T . circumcincta with higher numbers of polymorphic sites , total number of haplotypes , pairwise differences and higher values for nucleotide and gene diversity . The distribution of the resistant haplotypes is shown in Fig . 3 and the full data for both resistant and susceptible haplotypes is shown in ( Table 1 and S3 Fig . ) . The GenBank accession numbers for the sequences are given in S6 Table . There was a total of fifteen different isotype-1 β-tubulin haplotypes for H . contortus and forty three different haplotypes for T . circumcincta across the seven farms . Five out of the fifteen H . contortus isotype-1 β-tubulin haplotypes ( 33 . 3% ) encoded either a P167Y or P200Y resistance polymorphism ( resistance alleles ) . Twenty eight out of forty three T . circumcincta haplotypes ( 65 . 1% ) encoded either a P198L or P200Y resistance polymorphism ( resistance alleles ) ( Table 1 and S3 Fig . ) . There was clear evidence of selection at the isotype-1 β-tubulin locus in both species when resistance and susceptible haplotypes were compared for the full dataset overall . Pairwise FST values estimated from pyrosequence genotypes at the benzimidazole resistance-associated SNPs revealed considerable genetic differentiation between the populations: 18 of 21 and 13 of 21 pairwise comparisons were statistically significant for H . contortus and T . circumcincta respectively ( S4 Fig . ) . This was compared to the extremely low levels of genetic differentiation identified using the neutral loci ( Fig . 1B ) . In addition , for H . contortus , there was an overall reduction in gene diversity for resistant ( 0 . 506 ) relative to susceptible ( 0 . 743 ) isotype-1 β-tubulin haplotypes ( Table 1 ) . Moreover , there was a statistically significant departure from neutrality for both Tajima’s D ( -2 . 14 , p-value<0 . 005 ) and Fay and Wu’s H statistic ( -23 . 34 , p<0 . 001 ) for H . contortus resistant haplotypes but not for susceptible haplotypes ( 2 . 06 , p>0 . 05 and -7 . 09 , p>0 . 05 respectively ) suggesting selection at this locus ( Table 1 ) . The statistically significant positive estimate of Tajima’s D ( 2 . 06 , p<0 . 01 ) for the susceptible haplotypes is consistent with the moderate degree of sub-structure identified by the neutral microsatellite marker analysis ( Fig . 1 ) . For T . circumcincta , some evidence of selection was still apparent in spite of the high level of haplotype diversity . Although gene diversity estimates of T . circumcincta resistant and susceptible haplotypes were similar ( 0 . 878 , Resistant; 0 . 926 , Susceptible ) and Tajimas’s D was not statistically significant , the estimates of Fay and Wu’s H statistic showed statistically significant departure from neutrality for resistant haplotypes ( -28 . 069 , p<0 . 05 ) but not for susceptible haplotypes ( -14 . 87 , p>0 . 05 ) ( Table 1 ) . Genetic diversity was also assessed for the two parasite species at the individual farm level ( Table 1 , Fig . 3 , S3 Fig . ) . There was a significant departure of the H statistic from neutrality for farms 37 ( Gloucester ) and farm 101 ( E . Sussex ) . Farm 86 ( Middlesex ) was close to significance ( p = 0 . 054 ) and a single haplotype was at fixation on farm 95 ( N . Yorkshire ) . In contrast , higher gene diversity was observed on farms where there was a low frequency of resistance mutations ( farms 102 ( Inverness ) and 110 ( Kent ) ) . Hence , for the four out of the five farms which had a high frequency of resistance mutations , there was clear evidence of selection at the isotype-1 β-tubulin for H . contortus . In the case of T . circumcincta , in spite of the extremely high overall level of sequence diversity , evidence of selection at the isotype-1 β-tubulin locus was also detectable on some of the farms with a high frequency of resistance mutations . For farms 86 ( Middlesex ) and 95 ( N . Yorkshire ) , a significant H statistic and lower gene diversity estimates clearly indicate selection ( Table 1 ) . In the case of farm 37 ( Gloucester ) , even though 10 different resistance haplotypes were present , producing a high estimate of gene diversity ( 0 . 916 ) , a signature of selection was still detectable by the H statistic ( 19 . 38 , p = 0 . 025 ) . The only farm of the seven that had a predominantly susceptible T . circumcincta population , farm 102 ( Inverness ) , had the highest level of haplotype diversity and the least evidence of selection on the neutrality tests ( Table 1 ) . SplitsTree4 networks were produced to examine the phylogenetic relationship between isotype-1 β -tubulin haplotypes ( Fig . 4 ) . For both species , the resistance haplotypes are polyphyletic , being distributed across the entire network consistent with a hypothesis that resistance mutations have appeared on different ancestral susceptible haplotypes ( Fig . 4 ) . For H . contortus , resistance haplotypes are present in three separate branches of the SplitsTree4 network ( Fig . 4A ) ; group 1 ( Hr1/Hr2/Hr4 ) , group 2 ( Hr3 ) and Group 3 ( Hr5 ) . Group 1 ( Hr1/Hr2/Hr4 ) includes haplotypes with either the P200Y or P167Y mutations . While the network shows the frequency of the resistance and susceptible haplotypes across all farms in the study ( Fig . 4A ) , Fig . 3 shows the frequency of resistance haplotypes on each farm . The resistance haplotypes with the highest frequency for H . contortus are Hr1 ( P200Y ) and Hr4 ( P167Y ) and both of these are identical to the susceptible haplotype Hs1 except for the resistant-associated SNPs . Hr1 was the most frequent haplotype on five out of seven farms ( farm 37 ( Gloucester ) , farm 54 ( Devon ) , farm 86 ( Middlesex ) , farm 101 ( E . Sussex ) and farm 110 ( Kent ) ) and Hr4 was identified on five farms ( farm 95 ( N . Yorkshire ) , farm 37 ( Gloucester ) , farm 54 ( Devon ) , Farm 86 ( Middlesex ) and farm 101 ( E . Sussex ) ) ( Fig . 3 ) . In contrast , the putative ancestral susceptible haplotype Hs1 was present on just 2 farms ( farms 102 ( Inverness ) and 110 ( Kent ) ) ( Fig . 3 ) . Farm 37 ( Gloucester ) has the most diversity of resistance haplotype for H . contortus with all five resistant haplotypes being present ( Fig . 3 ) . Conversely , farm 95 ( N . Yorkshire ) has the least diversity of resistance haplotypes with Hr4 being almost at fixation . There are just two farms that possess a high proportion of H . contortus susceptible haplotypes: farm 110 ( Kent , 0 . 67 ) and farm 102 ( Inverness , 0 . 98 ) . The susceptible haplotypes on both these farms appear highly diverse ( S3 Fig . ) and distantly-related to each other ( Fig . 4A ) . For T . circumcincta , the extremely high level of haplotype diversity leads to a more complicated set of phylogenetic relationships ( Fig . 4B ) . Nevertheless , the overall pattern is similar to that described for H . contortus with resistance haplotypes occurring all across the SplitsTree4 network ( Fig . 4B ) . This again suggests independent origins of multiple resistance mutations . The most prevalent resistance haplotype for T . circumcincta , Tr1 was represented on five of the seven farms but not always at the highest frequency ( Fig . 3 ) . The exceptions being farm 110 ( Kent ) and farm 95 ( N . Yorkshire ) , where the Tr1 haplotype was not identified despite a high level of resistance haplotypes ( 95% and 100% respectively ) . The resistance haplotype , Tr25 ( carrying the P198L polymorphism ) was found on two farms with 94% of all Tr25 haplotypes being identified on farm 95 ( N . Yorkshire ) . The two farms on which there are relatively high frequencies of susceptible haplotypes for T . circumcincta , farm 54 ( Devon , frequency 0 . 5 ) and farm 102 ( Inverness , frequency 0 . 93 ) , the susceptible haplotypes are highly diverse ( S3 Fig . ) and distantly-related to each other ( Fig . 4B ) . Many other methods for network estimation including neighbor-net ( SplitsTree4 ) networks have been reviewed by Posada and Crandall [40] and evaluated in Woolley et al . , 2008 [41] who states “when the phylogenetic relationship of any set of sequences is being inferred , it is important that several methods be used and their inferences inspected and compared for discrepancies . ” Consequently in addition to a method based on genetic distance ( Neighbor-net , as implemented in SplitsTree4 ) two other methods were also chosen for this study: one character-based ( Median-Joining in Network 4 . 6 . 1 , see S5 Fig . ) and one based on Statistical Parsimony ( TCS , see S6 Fig . ) . Similar results were seen in these networks ( Median-Joining and Statistical Parsimony ) as was seen with the Neighbor-net networks for both species . We investigated the extent to which genetic recombination has played a role in generating the haplotypic diversity . For H . contortus , just one haplotype ( Hs10 ) of the total number of isotype-1 β-tubulin haplotypes ( 15 ) was identified as a recombinant ( S7A Fig . ) . Although recombination was detected , it does not account for the majority of the haplotypic diversity for H . contortus . In contrast , for T . circumcincta 22 of the total number of isotype-1 β-tubulin haplotypes ( 43 ) were identified as possible recombinants resulting from just 5 unique recombination events ( S7B Fig . ) . It is notable that three of the four hapotypes carrying the P185L polymorphism are clearly related by recombination on the basis of this analysis . Both re-current mutation and recombination contribute to the haplotypic diversity present in T . circumcincta .
The first major study of the population genetics of H . contortus and T . circumcincta used mitochondrial DNA analysis of samples obtained from several locations in North America [44] . This study detected no population sub-structuring between locations and concluded that high levels of gene flow and a larger overall effective population size ( Ne ) were responsible . However , more recent microsatellite marker studies have suggested that the situation is more complex . Although H . contortus and T . circumcincta are phylogenetically close , and their genetics similar , they appear to differ in population structure [14 , 20] . Whilst a lack of genetic sub-structuring has been confirmed for T . circumcincta in both the UK and France [20 , 39 , 45] , some genetic sub-structuring of H . contortus has been reported in France and Sweden [39 , 46] . Most notably , a microsatellite marker study on goat farms in France detected between-farm population divergence for H . contortus but not for T . circumcincta [39] . In the French study , farms were specifically chosen that had been closed to animal movement for at least 15 years in order to remove the effects of parasite migration . In the study reported here , we have sampled UK farms that are largely open to animal movement , a situation that is more typical of ruminant livestock systems worldwide . In addition , we have sampled the two parasite species from the same individual animals on each of seven farms in order to compare the two species without any confounding environmental differences such as climate , farm management or individual host effects . We have found that , even in the presence of animal movement , H . contortus has significantly lower genetic diversity and more population sub-structuring than T . circumcincta , both at neutral microsatellite markers and at the isotype-1 β-tubulin locus . Given our study design , we propose these genetic differences must be related to differences in parasite life-history traits . The evolutionary origins of H . contortus are in sub-Saharan Africa from where it has been transported around the globe by livestock movement over the last several hundred years [47 , 48] . Consequently , its larval stages are poorly adapted for winter pasture survival in temperate regions and so persistence during the colder months is largely confined to the parasitic stages inside the host [49] . This is likely to lead to population bottlenecks , particularly in the UK , where anthelmintic drug treatments are commonly given to all animals , including in the early spring when few parasites are present on the pasture . In contrast , T . circumcincta can potentially maintain a larger population size since it is indigenous to temperate regions and so its free-living stages are better adapted for winter pasture survival . This difference in the evolutionary history of the two parasites is clearly reflected in their epidemiological patterns in the UK . H . contortus is generally only detected at low levels on UK farms but its high fecundity can result in rapid expansion of numbers causing acute clinical disease outbreaks during the summer [13 , 47] . In contrast , T . circumcincta is consistently present in high numbers from year to year on all UK farms and this is typical of other temperate regions [13 , 49–51] . Three different single nucleotide polymorphisms ( SNP ) in the isotype-1 β-tubulin gene have been previously associated with benzimidazole resistance in these parasite species . TTC to TAC transversions at codons 200 and 167 resulting in substitution of phenylalanine with tyrosine ( F200Y and F167Y ) has been reported in both H . contortus [6 , 7 , 52 , 53] and T . circumcincta [2 , 3 , 7–9 , 39] . In addition , a GAA to GCA transversion at codon 198 leading to a substitution of glutamic acid with alanine ( E198A ) has been reported in H . contortus [4 , 27] . The F200Y and F167Y substitutions were identified in this study for both H . contortus and T . circumcincta . The finding that the F200Y is the most common substitution in both species ( 36 . 32% and 60 . 31% overall frequencies respectively ) is consistent with a variety of previous studies across the world [28 , 38 , 39 , 52 , 54] . However , the observation that F167Y was also very frequent in H . contortus ( 28 . 8% overall and almost at fixation on farm 95 , N . Yorkshire ) differs from previous reports where the F167Y has generally been only found at low frequency [39 , 52] . The identification of a substitution of glutamic acid ( GAA or GAG ) with leucine ( TTA ) at position 198 in T . circumcincta on four out of the seven farms ( 15 . 8% overall ) was of particular interest . To our knowledge this particular substitution has been identified only once previously , in an isolate from New Zealand [55] . This polymorphism was found at very high frequency on a single haplotype on farm 95 ( N . Yorkshire ) with strong evidence of selection ( based on neutrality tests ) consistent with a role in benzimidazole resistance . Understanding the nature of adaptive changes occurring in response to anthelmintic selection in parasite populations is key to understanding the origin and spread of resistance mutations . Rapid adaptation in response to selection results in so-called “selective sweeps” at the loci under selection . There are essentially two different types of selective sweep [12 , 56 , 54] . The classic “hard selective” sweep is characterised by a single resistance haplotype in the populations ( s ) resulting from a single mutation arising and sweeping through the population ( s ) eventually reaching fixation . Soft selective sweeps are characterised by the presence of multiple resistance haplotypes in the population ( s ) and can arise in two ways . First , from recurrent de novo mutations appearing on different susceptible haplotype backgrounds after the onset of the selection . Second , from polymorphisms already present in the standing genetic variation before the onset of selection . It has recently , been suggested that soft selective sweeps may be more common in eukaryotes than previously recognized , particularly for organisms with large census population sizes [12] . For example , resistance to organophosphate insecticides in Drosophila melanogaster and Lucilia cuprina involve multiple independent resistance alleles at the acetylcholinesterase and esterase loci respectively [57 , 58 , 56] . In reality , the difference between hard and soft selective sweeps is not absolute with selective sweeps differing in number and relative frequency of the different resistance haplotypes in a population [12] . The high diversity of resistance haplotypes described in this study is truly remarkable when compared to studies of drug resistance in other eukaryotic systems . Five and twenty eight resistance haplotypes were found for H . contortus and T . circumcincta respectively on just seven farms . This is much higher than previously reported even for insecticide resistance in insects such as D . melanogaster or Anopheles mosquitos [57 , 59–61] . For H . contortus , although both hard and soft selective sweeps are present there is a predominance of hard sweeps ( Fig . 3A and S3 Fig . ) . On farms 95 ( N . Yorkshire ) and 110 ( Kent ) a single resistant haplotype was detected characteristic of a classical “hard” selective sweep ( Fig . 3A and S3 Fig . ) . On farms 37 ( Gloucester ) , 86 ( Middlesex ) and 101 ( E . Sussex ) , although multiple resistance haplotypes are present , one of these predominates at high frequency in each case . However , in contrast , on farm 54 ( Devon ) there are two discrete resistance haplotypes at almost equal frequency characteristic of a “soft” selective sweep . For T . circumcincta , the selective sweeps are generally much softer than for H . contortus . The most extreme example is farm 37 ( Gloucester ) on which there are a remarkable ten different resistance haplotypes present at similar frequency . This is consistent with the overall higher level of genetic diversity of T . circumcincta providing a greater supply of resistance mutations . Even so , a predominantly hard selective sweep is present on farm 95 ( N . Yorkshire ) a single haplotype predominates at high frequency . In soft selective sweeps , it can be difficult to distinguish between adaptive mutations derived from the standing genetic variation versus those derived from recurrent de novo mutation occurring after the onset selection [12] . “Ancient” polymorphisms are present on many haplotype backgrounds due to historical recombination and so , if selected , would lead to a high level of haplotypic diversity and a soft selective sweep . Hence , the presence of a soft selective sweep doesn’t , in itself , prove that mutations are recurrently appearing in parasite populations subsequent to the onset of selection . However , the striking differences we see in the selective sweeps between farms is consistent with the hypothesis that recurrent mutations occur . This is most obvious for T . circumcincta; since there is little genetic sub-structuring of this parasite between farms ( based on neutral microsatellite markers ) , the standing genetic variation should be similar on each farm . Consequently , if the standing genetic variation were the only source of resistance mutations , one would predict that broadly similar selective sweeps and resistance haplotypes would be present on each farm . However , this is clearly not the case ( Fig . 3B and S3 Fig . ) . For example , T . circumcincta populations on farms 37 and 95 have little genetic differentiation based on neutral markers ( FST; -0 . 0023 ) but have dramatically different selective sweeps; ten different resistance haplotypes for farm 37 and a four different resistance haplotypes on farm 95 without a single haplotype being shared . Likewise for H . contortus , for farms 37 and 95 there are completely different resistance haplotypes present in spite of very little genetic differentiation for neutral markers ( FST 0 . 0087 ) . The phylogenetic relationship of the resistance haplotypes is also consistent with a model of multiple independent origins of benzimidazole resistance mutations across the UK . In the case of H . contortus , there are at least 4 independent origins of the resistance haplotypes present on seven UK farms and an even larger number for T . circumcincta . In both cases , with the exception of the P198L substitution , our analysis detects few recent recombination events consistent with mutations appearing on divergent susceptible haplotypes . Given the large population sizes of trichostrongylid nematodes , we should not be surprised that independent resistance mutations can repeatedly arise and become selected in these organisms . The population parameter = 2Ne describes the rate at which new mutations arise in a population where Ne = effective population size and = the per site mutations rate [62] . It has been suggested that if is greater than 0 . 1 , then soft selective sweeps are likely to occur [12 , 56] . A population size of 107 would be predicted to be sufficient for recurrent mutations to repeatedly arise and produce soft selective sweeps if the mutation rate is similar to that of C . elegans ( 10–8 mutations per base per generation ) . We , and others , have previously pointed out that a single flock of a few hundred sheep can potentially contaminate pastures with billions of eggs every week [9 , 43 , 47] . Although , the majority of these larvae on the pasture never make it into the host , it is very likely that Ne for these organisms is more than adequate for soft selective sweeps to occur even at the individual farm level . There is a large amount of movement of sheep between farms in the UK and so it is likely that migration of resistance haplotypes plays an important role in the spread of resistance . However , the presence of the same resistance haplotype on two or more farms cannot , in itself , be taken as conclusive evidence of spread . It is quite possible that a resistance mutation could independently appear on the same susceptible haplotype background at different locations particularly given the low level of population sub-structuring of parasite populations between farms . However there are a number of pieces of evidence to suggest that the migration of anthelmintic resistance mutations between farms is an important factor in the spread of anthelmintic resistance in the UK . Firstly , although there are major differences in the selective sweeps and haplotypes found on the seven different UK farms ( as described above ) , it is notable that the same H . contortus resistance haplotype ( Hr1 ) predominates on five of the seven farms . This contrasts with the results of a previous study of ten goat farms in France that were closed to animal movement for 15 to 30 years [7 , 8 , 39] . In that case , a different H . contortus resistant haplotype generally predominated on each farm . Hence , it seems much more likely that animal movement has contributed to the spread of the Hr1 haplotype on UK farms rather than it appearing independently so many times . Secondly , in the French study , only one , or at most two , resistance haplotypes were found on each farm whereas there was a much larger number of haplotypes present on most of the UK farms in this study . Although , the number of sequences examined per farm varied for the French study , thirty T . circumcincta alleles were sequenced on two of the closed goat farms and yet only a single resistance haplotype was detected in each case ( a different haplotype was present on each farm ) [39] . This may suggest that the high diversity of haplotypes seen on UK farms is at least partly due to the migration of resistance haplotypes due to animal movement . Thirdly , it seems unlikely that the E198L substitution has appeared independently on four out of the seven farms in this study given that it has never been previously reported and that two different SNP substitutions of the susceptible codon ( P198E; GAA or GAG ) are required to produce the putative resistance codon ( P198L; TTA ) . It seems more likely that this P198L substitution has appeared once and spread between farms by migration . This hypothesis is supported by the observation that three of the four P198L haplotypes are related by recombination ( S7B Fig . ) . The application of genome-wide approaches to identify regions of the genome under selection is increasingly feasible for parasitic nematodes due to the recent improvements of sequencing technologies and reference genomes . However , such approaches need a good understanding of the signature of selection . Hard selective sweeps are the simplest to detect as they are characterised by a dramatic loss of polymorphism around the locus under selection due to the “hitchhiking effect” [11 , 63] . Hence , simple measures of genetic diversity and neutrality such as Hd , Tajima’s D and Fay and Wu’s H should reliably detect hard selective sweeps . Soft selective sweeps are more difficult to detect since loss of polymorphism around the selected locus is often subtle or even absent although linkage disequilibrium can be strong [43 , 42 , 56 , 64] . However , our results suggest that , in spite of the overall complexity of the selective sweeps associated with benzimidazole resistance on UK farms , evidence of selection can still be detected in many cases . For H . contortus , where the selective sweeps on most of the individual farms was effectively hard , selection could be detected by significant departures from neutrality by Fay and Wu’s H statistic on 4 out of the 5 farms which had a high frequency of resistance mutations [farms 37 ( Gloucester ) , 86 ( Middlesex ) , 95 ( N . Yorkshire ) and 101 ( E . Sussex ) ] . For farm 54 ( Devon ) where two resistance haplotypes are present at almost equal frequency , although D and H statistics do not depart from neutrality , selection is still reflected by a loss in overall diversity . Even for T . circumcincta , where the selective sweep is generally much softer , selection could be detected on two of the farms , 95 ( N . Yorkshire ) and 86 ( Devon ) . As a final point , it is important to acknowledge that , negative values for Tajima’s D can sometimes result from population expansion following an historical population bottleneck . However , there are a number of reasons why this is unlikely to have occurred in this case . First , the use of the H statistic helps distinguish between the effects of population dynamics and selection since it is less sensitive to the effects of population expansion [36] . Second , and more importantly , it is difficult to explain the dramatic allele frequency differences , and departures from neutrality , at the isotype-1 β-tubulin locus when all 10 microsatellites show little or no between farm differences in allele frequency ( since all loci will have experienced the same demographic effects ) . Thirdly , the population biology of these organisms makes it unlikely that severe population bottlenecks have occurred . Although some bottlenecking could occur with Haemonchus contortus this would not be expected to be severe . This is particularly the case for T . circumcincta in the UK where large numbers of larvae survive on the pasture year round and so many genotypes would persist at the farm level even if drug treatments in the host were highly effective . Hence , there is a strong balance of evidence for selection at the isotype-1 β-tubulin locus in both parasite species . The seven farms we have examined are typical of those in many parts of the world in terms of animal movement , husbandry and parasite control practices . In this system , our data supports a model of parallel adaptation in response to selection at separate locations with subsequent mixing of independently derived resistance mutations due to parasite migration between farms . In the case of H . contortus , we identified resistance mutations from at least four independent origins and almost certainly more for T . circumcincta . The most common haplotypes were identified on several of the seven farms selected from different regions of the UK and so these haplotypes are likely to be amongst the most common present in the UK parasite community . Nevertheless , it seems likely that additional independently derived haplotypes would be identified if more farms were sampled . Our model , in which recurrent resistance mutations commonly arise , suggests that for this group of parasites anthelmintic resistance will be a likely consequence of intensive drug use . This explains the increasingly widespread resistance in strongylid nematodes of domestic animals and , although there are important differences for the potential spread of resistance in human populations , has implications for the potential consequences of intensive drug use for human parasite control . | Parasitic nematodes ( roundworms ) are major causes of disease in both domestic animals and humans . Strategic treatments with anthelmintic drugs have been used to control livestock parasites for several decades resulting in widespread drug resistance . Drug treatments have , until recently , been applied at a relatively low level to control human parasites . However , in recent years community wide treatment programs have been massively increased for the 1–2 billion people infected with roundworms . Hence , for both human and animal health , there is an urgent need to understand how resistance emerges and spreads and how we can detect resistance mutations in this important group of pathogens . In this study , we investigated how drug resistance mutations appear and spread in the two livestock parasites for which resistance is most widespread . We have found that resistance appears repeatedly and frequently in parasite populations , and propose a model to explain the high capacity of these pathogens to develop drug resistance . Our work suggests that anthelmintic resistance is likely to occur when repeated drug treatment is relied upon to control this group of pathogens . Our results also suggest that resistance mutations should be detectable when modern genome-wide approaches are used to scan the genomes of resistant parasites . | [
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] | [] | 2015 | The Emergence of Resistance to the Benzimidazole Anthlemintics in Parasitic Nematodes of Livestock Is Characterised by Multiple Independent Hard and Soft Selective Sweeps |
African trypanosomes are mammalian pathogens that must regularly change their protein coat to survive in the host bloodstream . Chronic trypanosome infections are potentiated by their ability to access a deep genomic repertoire of Variant Surface Glycoprotein ( VSG ) genes and switch from the expression of one VSG to another . Switching VSG expression is largely based in DNA recombination events that result in chromosome translocations between an acceptor site , which houses the actively transcribed VSG , and a donor gene , drawn from an archive of more than 2 , 000 silent VSGs . One element implicated in these duplicative gene conversion events is a DNA repeat of approximately 70 bp that is found in long regions within each BES and short iterations proximal to VSGs within the silent archive . Early observations showing that 70-bp repeats can be recombination boundaries during VSG switching led to the prediction that VSG-proximal 70-bp repeats provide recombinatorial homology . Yet , this long held assumption had not been tested and no specific function for the conserved 70-bp repeats had been demonstrated . In the present study , the 70-bp repeats were genetically manipulated under conditions that induce gene conversion . In this manner , we demonstrated that 70-bp repeats promote access to archival VSGs . Synthetic repeat DNA sequences were then employed to identify the length , sequence , and directionality of repeat regions required for this activity . In addition , manipulation of the 70-bp repeats allowed us to observe a link between VSG switching and the cell cycle that had not been appreciated . Together these data provide definitive support for the long-standing hypothesis that 70-bp repeats provide recombinatorial homology during switching . Yet , the fact that silent archival VSGs are selected under these conditions suggests the 70-bp repeats also direct DNA pairing and recombination machinery away from the closest homologs ( silent BESs ) and toward the rest of the archive .
African trypanosomes are protozoan parasites that have dedicated more than 20% of their coding capacity [1 , 2] and 10% total cellular protein content [3] to a single biological function . To survive in the challenging environmental niche of the mammalian bloodstream , subspecies of Trypanosoma brucei must regularly change their antigenic glycoprotein coat . In this manner , they are able to escape the antibody-mediated immune response of their host to cause a chronic infection of the bloodstream that results in death of both humans ( African sleeping sickness ) and livestock ( nagana ) if left untreated [4] . Each parasite’s coat is composed of a densely packed single member of a large family of Variant Surface Glycoproteins ( VSG ) [5] , which are thought to share a conserved membrane-bound structure but are encoded by highly divergent genes [2] . The T . brucei genome encodes more than 2000 VSG genes and VSG pseudogenes within a genome consisting of 11 megabase chromosomes ( MBC ) , a variable number ( usually 5–10 ) of intermediate chromosomes , and about 100 minichromosomes ( MC ) [2 , 6] . Yet , only one VSG is expressed at a given time from one of ~15 possible Bloodstream Expression Sites ( BES ) located at the subtelomeres of MBCs [7] . BESs share a similar sequence and organization , including an RNA polymerase I promoter , a series of Expression Site Associated Genes ( ESAGs ) , a large region of repetitive DNA ( 70-bp repeats ) that precede VSG gene , which is located a short distance upstream of telomere [7] . While minichromosomal VSGs are also subtelomeric , the majority of the VSG archive is located in VSG arrays on the arms of the MBCs [1] . Survival of T . brucei in the bloodstream requires the regular activation of silent VSGs from the genomic archive . Switching from the expression of one VSG coat to the next predominantly occurs by three genetic mechanisms . A change in the BES being transcribed , resulting in the expression of its subtelomeric VSG , is termed In Situ ( IS ) switching [8] . Telomeric Exchange ( TE ) is homologous recombination between subtelomeres that results in the exchange of a silent VSG with one in the active BES , retaining both VSG genes [9] . In contrast , duplicative Gene Conversion ( GC ) , as the name implies , results in the duplication of a silent VSG donor into the active BES and simultaneous deletion of the previously expressed VSG gene [10] . Unlike IS and TE , which activate silent VSGs already located at subtelomeric sites , GC is the mechanism of VSG switching that permits access to the entire VSG archive ( BES , MC , and MBC arrays ) . GC is thought to be the predominant mechanism during natural infections [11] and can be activated under laboratory conditions , where rates of switching are low ( ~1x10-5 ) , by increasing subtelomeric DNA breakage at the active BES [12–14] . Among all switching mechanisms there appears to be a semi-predictable hierarchy of VSG gene selection that begins with the selection of BES-encoded subtelomeric VSGs , followed by non-BES subtelomeric VSGs ( such as those on MCs ) , and finally those from non-telomeric sites in the genome ( loosely organized VSG arrays ) [15] . Selection of VSGs from other BESs is highly favored during early switch events and is the most common gene selection preference observed under laboratory conditions [12 , 15] . This is probably because BESs have very similar DNA sequences , including regions of near identity for many kilobases , which would provide ample homology for recombination during gene conversion [7] . Selection of BES-encoded VSGs alone , of which there are about 15 , would not be expected to support chronic T . brucei infection . DNA repeat expansions are a common source of genomic translocations ( like gene conversions ) and genomic instability among eukaryotic genomes , and can result in genetic disorders in humans ( reviewed in [16] ) . Thus , the discovery that the 5’ limit of translocation during VSG switching was a long region of repetitive DNA ( termed the 70-bp repeats based on their approximate length ) led to the predictions that these repeats are possible sites of the DNA lesions that initiate switching , or the source of DNA homology for VSG donor selection in recombination-based switching [17–19] . Often described as imperfect AT-rich 70-bp repeats , observations that this sequence also occurs proximal to VSGs within the genomic archive bolstered the VSG selection prediction [1] . Similarly , their predicted role in forming DNA lesions fell into disfavor when it was shown that gene conversion in trypanosomes grown in vitro , albeit at a very low frequency , does not require 70-bp repeats [20] , favoring the proposed role in providing homology for recombination . Yet , the proposed function of the 70-bp repeats was never experimentally tested . This was due , in part , to the inability to analyze these events due to low levels of switching that occur under laboratory conditions . Here , we artificially increase the rate of VSG switching to determine how the 70-bp repeats affect VSG donor selection during gene conversion . The data presented herein confirm that the 70-bp repeats can function to promote selection of VSGs from throughout the silent repertoire . In addition , an expanded analysis of the 70-bp repeat sequence enabled us to identify a minimal 70-bp repeat region that promotes archival VSG selection . In the course of this analysis we also discovered that the 70-bp repeats could have previously unreported affects on the frequency of VSG switching and cell cycle progression . Furthermore , our data showed that the 70-bp repeats can direct VSG selection away from other BESs , their closest homologs , and toward the genomic archive , which has mechanistic and physiological implications . Our findings suggest that the 70-bp repeat regions are required for the normal outcomes of VSG switching , and thus the ability of T . brucei to survive in its host during a chronic infection .
To investigate the putative functions of the 70-bp repeats we first subjected the two repeat regions of Lister427 BES1 ( Fig 1A—70 . I & 70 . II ) to fine mapping and the 42 identified repeat sequences were used to produce a consensus sequence logo ( Fig 1B ) . Similar to previous studies of more limited sample sizes , the repeats were an average of 76-bp ( usually running either 77-bp or 75-bp in length ) and were AT-rich ( 78% ) . For the sake of consistency within the literature , the 70-bp repeat nomenclature will be maintained [17–19] . These data support previous work suggesting that the 70-bp sequence is highly conserved [18] and identified two pronounced GC-rich regions ( Region1 and Region 2 ) . Expanding the analysis to include repeat regions of additional BESs , within both Lister427 [7] and TREU927 ( http://www . sanger . ac . uk/resources/downloads/protozoa/trypanosoma-brucei . html ) genomes , showed that this conservation is consistent among T . brucei BES regions ( S1 Fig and S1 Dataset ) . Thus , in the majority of BESs , a long region of conserved 70-bp sequence is maintained in close proximity to the sub-telomeric VSG gene . Aside from the BES sequences from these two genomes , direct comparison of the frequency and organization of the 70-bp repeat sequence within available African Trypanosome genomes is limited by the variable quality of each genomic assembly , especially near the subtelomeric regions ( http://tritrypdb . org/tritrypdb/ ) . Operating within these confines , we sought to determine the prevalence of the 70-bp regions by performing a BLAST analysis of the consensus sequence against each chromosome of the available genomes ( Fig 1C ) . While the 70-bp repeat sequence was not found in the genomes of South American trypanosome species , which do not undergo antigenic variation , it was abundant within the genomes of T . brucei TREU 927 , T . brucei Lister 427 , T . evansi , and T . brucei gambiense ( a human-infectious subspecies ) . The abundance of 70-bp repeats in T . evansi ( an emerging pathogen among livestock in the Middle East and Asia ) was anticipated as its genome has extensive similarity with that of T . brucei [21] . The observation that T . b . gambiense has fewer 70-bp repeats per chromosome than the other T . brucei subspecies is difficult to interpret as it could be an artifact resulting from the sequencing of its genome ( the genome of another human-infectious form , T . b . rhodesiense , has not been sequenced ) . In contrast , the absence of the 70-bp repeats from T . congolense and T . vivax could reflect real biological differences in antigenic variation between these very distinct species [22] . In addition to BESs and megabase chromosomes , VSG-containing contigs from T . brucei Lister 427 minichromosomes contained the 70-bp consensus sequence in the proximity of VSGs ( usually approximately 1 . 5 kb upstream ) ( S1 Table ) [2] . Thus , the conserved 70-bp repeat sequence identified here is widely distributed among the genomes of African trypanosomes with anticipated positioning in long tracts on BESs and shorter tracts on the megabase and minichromosome arms in the proximity of VSG genes . The genomic conservation and distribution of this sequence lends support to the hypothesis that the 70-bp repeats contribute to homologous pairing and VSG donor selection during GC [20] . To test this hypothesis , we sought to genetically manipulate the 70-bp repeats of the active BES and monitor the effects on switching , but were hindered by the naturally low frequency of in vitro switching ( ~1x10-6 ) in the Lister 427 strain . We therefore established cell lines in which DNA double-stranded breaks ( DSB ) could be induced in the actively expressed BES , to increase the depth of analysis by increasing the frequency of switching by GC [12 , 14] . An ISceI enzymatic cleavage site was introduced into BES1 proximal to a long region of repeats ( “70 . II-ISceI” , 39 repeat iterations ) , a short region of repeats ( “70 . I-ISceI” , 3 repeats ) and in a repeat deletion mutant ( “Δ70-ISceI” , no repeats ) ( Fig 2A; oligos used for constructs are in S1 Text ) . The veracity of the ISceI cleavage sites was confirmed by Southern blot analysis and the consistent expression of the ISCEI enzyme among lines confirmed ( Fig 1B and 1C ) . Five populations of each ISceI-bearing strain ( 70 . II-ISceI , 70 . I-ISceI , or Δ70-ISceI ) were grown for 3 days under normal ( - doxycycline ) or DSB-inducing ( + doxycycline ) conditions , and cells that had switched from their initial VSG ( 427–2 ) to an alternative VSG gene were isolated over magnetic cell-sorting ( MACS ) columns , as described [12 , 13] ( experimental pipeline details S2 Fig ) . The resulting VSG-switched cells were cloned by limiting dilution and the resulting clones were used to determine both the mechanism of switching ( using established genetic methods [13 , 23] ) and to identify the newly expressed VSG ( using traditional RT-PCR followed by sequence analysis and VSGnome BLAST alignment at http://tryps . rockefeller . edu ) for more than 100 clones from each line ( S2–S4 Tables ) . As anticipated , based on previous studies [12 , 13] , following DSB induction , all lines switched by GC and preferentially favored the selection of BES-encoded VSG donors ( Fig 2D ) . Notably , when the 70-bp repeat region proximal to ISceI was long ( 70 . II-ISceI ) , 48% of the selected VSGs arose from minichromosomal ( MC ) or undetermined sites ( UD ) as opposed to homologous BESs ( Fig 2D ) . In contrast , ISceI break formation proximal to a very small repeat region ( 70 . I-ISceI ) or after repeat deletion ( Δ70-ISceI ) resulted in the selection of BES encoded VSGs in 98% or 100% of clones , respectively ( Fig 2D ) . Thus , short or deleted 70-bp repeats appeared defective in selecting VSGs from the VSG genomic archive when compared with longer 70-bp repeat regions . Following a DSB , either naturally occurring or induced , single-stranded DNA is liberated initiating a homology search that is likely resolved by break-induced replication . Genetic analysis of individual switched clones can determine the extent of DNA transferred from the donor site into the active BES during GC . One of the most common switching events observed was between BES1 and BES7 , resulting in the expression of VSG427-3 . Using a BES7 probe upstream of the VSG , clones that have recombined VSG427-3 into BES1 will form a new band ( upon appropriate restriction digestion ) whose length indicates the region of BES7 transferred during GC . The resulting data indicate how GC affects 70-bp repeat maintenance or the recovery of defective repeat regions ( Fig 3 ) . Clones arising from a BES1 with normal 70-bp repeats ( Fig 3A—70 . II-ISceI ) showed a variety of outcomes that included the addition of no new repeats ( 5_H9 = 6 . 6 kb ) , partial addition of BES7 repeats ( 2_E5 ~8 kb & 5_F6 > 10 kb ) , or the translocation of full length BES7 repeats ( 2_B8 > 12 kb ) . In contrast , when BES1 harbors no 70-bp repeats ( Fig 3B—Δ70-ISceI ) the full region of BES7 repeats was consistently incorporated into BES1 during switching ( 1_A4 , 3_A2 , & 3_B10 > 12 kb ) . In one clone it appears that a region larger than the BES7 repeats was incorporated into BES1 ( 3_E9 ) ; similar long-range recombination events have been reported during GC switching in other studies [23] . It should be noted that the determination of the precise lengths of the regions transferred from BES7 to BES2 is hindered by the fact that the exact length of the repeats encoded in BES7 is unknown . Thus , we observe that the 70-bp repeat region in the active BES can be repopulated , maintained , or extended during GC-based recombination with another BES . The growth rate and frequency of VSG switching following DSB induction could affect the number of VSG donors selected . To verify that the VSG donor selection phenotypes reported in Fig 2 were dependent solely on the effect of the 70-bp repeat regions , cellular growth and VSG switching were monitored in these lines . Following doxycycline induction , all lines harboring an ISceI site in BES1 displayed a growth defect when compared to the parental line . For strains with intact 70-bp repeats ( Fig 4A—70 . II-ISceI [blue lines] ) the delay in growth was modest , yet DSB formation in the 70-bp deletion mutant ( Δ70-ISceI ) exacerbated a pronounced preexisting growth defect ( Fig 4A—Red lines ) . We predicted that the growth defect observed without doxycycline induction resulted from leaky expression of the ISCEI enzyme ( a known complication of expression from the rDNA spacer [24] ) , and tested this prediction using a 70-bp repeat deletion mutant that did not harbor the ISCEI enzyme ( Δ70-NO ISCEI ) . Deletion of the repeats from BES1 did not result in a growth defect in the absence of the ISCEI ( as anticipated from previous work on a similar construction [20 , 25] ) . Thus , the observed growth defects in ISCEI-expressing lines appear to result from DSB formation in the active BES , which was most pronounced when the 70-bp repeats were deleted . Because DSB formation can activate a cell cycle checkpoint ( reviewed in [26 , 27] ) and the Δ70-ISceI cell line has a growth defect , the effects of DSB formation on the cell cycle were examined in these lines . Cells harboring a DSB site near wild-type 70-bp repeat regions resulted in a minor cell cycle delay at 24 hours that was largely resolved by 48 hours ( Fig 4B—70-II-ISceI ) . In contrast , deletion of the BES1 70-bp repeats resulted in a severe cell cycle defect that was only partially resolved at 48 hours post-induction ( Fig 4B—Δ70-ISceI ) . To determine if the defect results from DSB formation , cell-cycle progression was monitored in the 70-bp repeat deletion mutant that does not harbor ISCEI ( Δ70-No ISCEI ) . These cells did not have the cell cycle defect observed in ISCEI-expressing lines at 24 hours , but did display minor accumulation of cells in S-phase at 48 hours post-induction ( this could result from naturally occurring breaks arising late in growth that are not resolved normally in this line ) . Together these data indicate that the growth delays observed in these cell lines are associated with cell cycle defects arising from DSB formation and suggest that the deletion of 70-bp repeats from the active site exacerbates these defects . Multiple studies have shown that induction of ISceI-induced breaks in the active BES results in increased VSG switching , but the precise amount of switching can vary depending on the location of DSB formation an the activity of the ISCEI enzyme [12 , 14] . To determine if the diversity of VSG gene selection ( reported in Fig 2 ) resulted from differences in switching dynamics , the VSG switching frequency was quantified for the ISceI-bearing cell lines and normalized to the number of population doublings ( Fig 4C , normalization derived from Fig 4A ) . DNA break formation proximal to the long repeat region ( Fig 4C—70 . II ) resulted in approximately 100-fold increase in switching , compared to wild-type cells , as previously observed [12] . DSB formation in the proximity of only three 70-bp repeats ( 70 . I ) resulted in a similar switching frequency , but a vastly different diversity in the selected VSGs ( 98% BES encoded VSGs selected compared with 52% in the 70 . II cell line [Fig 2] ) . This comparison underscores the role of the 70-bp repeat region in selection of VSGs from the genomic archive . However , deletion of the 70-bp repeats resulted in a switching frequency , upon DSB induction , that was 10–100 fold greater than strains harboring 70-bp repeats ( Fig 4C—Δ70-ISceI ) , such that 1 in 10 cells had switched ( a frequency observable by flow-cytometry alone [Fig 4D] ) . This was in contrast with the previous report of a similarly constructed strain [12] , probably because the slow growth phenotype had , in the previous study , led to the selection of a clone in which the ISceI site or enzymatic function was lost . The switching frequency calculated after DSB formation in the 70-ISceI line is likely affected by the observed growth and cell cycle defects , so is not directly comparable to the values calculated for isogenic lines containing repeats , where DSB-induction does not noticeably affect growth and cell-cycle progression . Nonetheless , the diminished capacity for VSG donor selection in the Δ70-ISceI line was definitely not the result of a reduction in switching frequency . Based on the observation that the 70 . I-ISceI has a normal switching frequency and a modest capacity for archival VSG donor selection , we predicted that a minimal 70-bp repeat region could recapitulate the phenotypes associated with the long , cognate 70-bp repeat regions . To test this prediction , the conserved 70-bp repeat sequence presented in Fig 1 was used to design synthetic 70-bp regions , which were introduced into the Δ70-ISceI landscape to produce stable cell lines and analyze their phenotypes . The resulting cell lines , which harbor discrete repeat regions proximal to the ISceI site , are as follows: “Monomer” , which bears a single 70-bp repeat; “Dimer” , consisting of two monomeric units separated by a cognate spacer ( ATAATA ) ; and “Dimer_Rv” , which harbors the Dimer sequence in the opposite orientation with respect to transcription ( Fig 5A , repeat insertion sequences shown in S1 Text ) . DSB induction in the Dimer cell line reduced the VSG switching frequency nearly 10-fold from Δ70-ISceI levels ( 2 . 3x10-2 compared with 1 . 9x10-1 , respectively ) , where strains harboring the 70-bp repeat Monomer or Dimer_Rv sequences were unchanged from the deletion mutant ( Fig 5B ) . Similarly , the growth and cell cycle defects observed in the absence of 70-bp repeats ( Fig 4—Δ 70-ISceI ) were significantly improved by the addition of the Dimer region , while this was not the case for Monomer or Dimer_Rv lines ( Fig 5C and S3 Fig ) . ( Phenotypes of an additional mutated repeat line “Mut_Dimer” did not suppress the Δ70-ISceI phenotypes [shown only in S3 Fig] ) . If VSG switching and cell growth phenotypes correlate with VSG donor selection ( as suggested by data in Figs 2 and 3 ) , we would expect the Dimer cell line to result in selection of VSGs from within the genomic archive . To determine the effect of the synthetic repeat sequences on VSG donor selection at an increased depth , RNA was extracted from DSB-induced post-MACS eluates from biological triplicates of these lines for VSG-seq analysis [28] . The VSG-Seq method is distinct from the clonal analysis of VSG donor selection presented in Fig 2 in that it permits the identification of VSG RNAs comprising as little as 0 . 01% of the population [28] . At this sensitivity , we observed that the line lacking repeats in the active BES ( Δ70-ISceI ) could occasionally select VSGs from sites other than BESs ( Fig 5D , supported by data in S5 Table ) , including two from metacyclic expression sites ( MES ) , one from a MC , and four from other undetermined ( UD ) loci . Introduction of the repeat Dimer resulted in a significant ( pval = 0 . 0026 ) increase in the number of VSGs selected when compared with the no-repeat line ( average of 18 VSGs in Δ70-ISceI and 35 VSGs in Dimer populations , SI 9 ) . This near doubling in the diversity of VSG selection was the result of a substantial increase in MC VSG selection ( pval = 0 . 005 , average Δ70-ISceI = 1 MC & Dimer = 11 MC ) and a more modest , but statistically significant , increase in the selection of VSGs arising from undetermined loci ( pval = 0 . 001 , average Δ70-ISceI = 4 UD & Dimer = 12 UD ) . In contrast , addition of the Dimer_Rv sequence did not result in a significant increase in the selected VSG repertoire ( pval = 0 . 205 ) , although some subtle differences between Δ70-ISceI and Dimer_Rv strains can be observed ( Fig 5D ) . These data have identified a minimal 70-bp repeat region able to partially suppress the collection phenotypes ( i . e . cell growth defect , cell cycle delay , increased VSG switching , and reduced VSG donor selection ) associated with DSB formation proximal to a 70-bp repeat deletion mutant and result in phenotypes similar to lines harboring cognate 70-bp repeats .
While unbalanced chromosomal translocations can fuel evolutionary change , they are generally deleterious to eukaryotic , especially mammalian , genomes . African trypanosomes are a useful model of chromosomal translocations because their essential pathogenic process , antigenic variation , depends on them . The early observation that genetic transposition of a new VSG into the active BES terminates within a tract of repetitive DNA inspired passionate functional speculation . Yet , the available sequence information and genetic tools of the time ( and of studies that followed in the 1990s ) restricted the scope of possible analyses . Thus , a viable hypothesis , that the repeats provide homology for recombination , became widely accepted [29 , 30] but was not tested . In the present study we applied a variety of recently available sequencing databases ( BESs , trypanosome genomes , and VSGnome ) , a next-generation sequencing method ( VSG-seq ) , genetic tools ( including ISceI DSB induction ) , and cell biology assays ( such as VSG switching frequency quantification ) to test this long-standing hypothesis . Classic sequencing approaches of the mid-1980s allowed three groups to determine the essential characteristics of the 70-bp repeats [17–19] and analysis of cosmid clones suggested that VSG genes and 70-bp repeats were widely distributed in the genome [31] . Completion of the first African trypanosome genome sequencing project ( TREU927 ) confirmed , in detail , that the 70-bp repeat sequence is not only found at the BES subtelomeres but also proximal to VSGs on the chromosome arms [1] . Yet , at that time , determining the degree of 70-bp repeat conservation within the genome was hindered by inherent challenges associated with assembling the sequences at the ends of chromosomes . Here , we utilized existing comprehensive BES sequence data ( ABI 3730 , with approximately 700-bp read length [7] ) to produce a 70-bp consensus sequence and confirm its degree conservation among numerous BESs . The length and conservation of this sequence corroborates some early findings [18] , but disagree somewhat with the often-asserted position that the 70-bp repeats are imperfect and have variable length [7 , 30 , 32 , 33] . While the length of the AT-rich regions between conserved repeating units can vary , as reported [19] , we would suggest that the data presented in this study highlight the significance of the conserved repeating unit presented in Fig 1 . It is important to note that the findings reported here do not address the putative function of the repetitive regions in DNA instability , the proposed function of the triplet repeats [19 , 34] . The order and conservation of the 70-bp repeats inspired us to revisit the question of function . Previous deletion of the BES1 70-bp repeat regions showed that the repeats themselves are not required for the low levels of gene conversion observed in vitro [20] . This finding was significant in that it challenged long-held speculation that the repeats function as specific endonuclease-cleavage sites . The recent availability of the Liste r427 VSGnome ( sequences of all VSG genes within the genomic archive ) [2] enabled testing of the second predicted function of the 70-bp repeats , namely providing homology in VSG donor selection . However , the amount of switching that occurs in vitro is too low ( 1x10-6 ) to permit a substantive analysis of VSG switching outcomes . This limitation was overcome through utilization of an artificial DNA breaking system that has been shown to increase the VSG switching frequency [12 , 14] , which occurs by gene conversion , in a similar manner to those that occur through more natural DNA break systems analyzed [13] . Use of the established ISceI endonuclease cleavage system for DSB formation enabled in-depth analysis of how different regions , and mutations , of 70-bp repeats affect VSG switching and its outcomes . The caveat , of course , is that ISceI is an artificial system and limits our interpretation of the implications for naturally occurring infections . Nonetheless , this genetic tool enabled the observation of genetic phenomena that would not have been detectable otherwise . Thus , individual clonal analysis of switched cells using the VSGnome resource allowed us to demonstrate that the BES encoded repetitive regions are required for selection of a normal repertoire of VSG genes , the first observed phenotype for the 70-bp repeats . The increase in switching frequency following DNA break formation in the BES1 constructions presented here also enabled us to observe unexpected outcomes of 70-bp repeat deletion and variations . Among these cell lines we observed that the 70-bp repeats have previously unappreciated and apparently connected effects on cell growth , cell cycle progression , and VSG switching following DSB formation . The observation that deletion of 70-bp repeats results in significant cell cycle delays following DSB formation could suggest that , in comparison to lines harboring wild-type repeats , this cell line is defective for DNA break repair . The fact that the same mutant cell line also switches much more frequently and results in increased cell death may suggest that cell lines harboring functional repeats process the DNA breaks more efficiently , as evidenced by the minimal cell cycle delay at 24 hours in 70 . II-ISceI . These effects appear to depend on ISceI-induced DSB formation , as shown by the Δ70-No ISCEI cell line , whose behavior was largely unaffected by the repeat deletion , as expected from the literature [20] . This collection of phenotypes was consistent among all cell lines that harbor “functional” ( 70 . II-ISceI , 70 . I-ISceI , and Dimer ) vs . “dysfunctional” ( Δ70-ISceI , Monomer , & Dimer_Rv ) 70-bp repeat regions . Alternatively , similar phenotypes might be observed if the 70-bp repeats affect the ISceI cutting efficiency . This could occur if there was steric hindrance at the cut site , which could result from binding proteins or DNA secondary structure . Further exploration of the phenotypic alterations associated with the 70-bp repeat variations could lead to new mechanistic understanding of the requirements for the chromosomal translocations that support T . brucei antigenic variation . Diverse pathogens utilize antigenic variation to escape the host immune system; among them T . brucei has the most extensive archive of surface antigen genes ( the VSGs ) [35] . Yet , the extensive repertoire of VSG genes would be useless if they could not be activated . Here we have shown that the 70-bp repeats are a key feature that permits access to the VSG archive . This result experimentally validates previous speculations and extends our understanding by highlighting the specific DNA element , sequence , and orientation required for selection , at a depth of analysis only recently made possible by VSG-seq [28] . It is unclear at this time if the 70-bp repeats influence the formation of new VSG gene variants through mosaicism , a known from of repertoire expansion mediated by recombination within VSG coding sequences . At the sensitivity of VSG-seq and the discriminatory ability of its cognate assembly component , genes identified as VSG-variants ( Fig 5D—“var” ) appear to share similarities with mosaic VSGs . BESs are essentially long homologous regions that not only contain the same organization , genes , and genetic elements , but are also nearly identical to one another at the sequence level for more than 50 kilobases [7] . As homology length generally determines the frequency of recombination [36] , it is not unexpected that homologous BESs ( harboring many kb of 70-bp repeats ) are primary genomic sites favored during VSG selection . What is surprising is the extent to which regions of wild-type repeats select sites other than BESs ( 48% of VSGs selected following induction of 70 . II-ISceI ) . While VSG donor selection could be based on homology alone , our findings raise the possibility that another external factor , acting on the 70-bp repeats , promotes selection of non-BES encoded VSGs . This effect could be in the form of repeat-specific DNA-binding proteins or be associated with subnuclear positioning during gene conversion , which is known to affect VSG expression [37] . Overall , this study demonstrates that , following DSB formation and subsequent liberation of ssDNA , the 70-bp repeats guide homologous pairing toward diverse genomic sites that harbor the VSG archive , a repertoire-expansion function that is crucial to the long-term survival of the parasite in its host . While the intricacies of VSG switching may be unique to African trypanosome parasites , the genetic processes described here have implications for chromosomal translocations that occur within other eukaryotic genomes .
The conserved repeat sequence was identified visually based on the BES sequences from T . brucei Lister 427 , and logos produced by http://weblogo . berkeley . edu/logo . cgi . The same approach was then applied to TREU927 BES sequences ( S1 Fig and S1 Dataset ) . The consensus sequence from the BES1 repeat logo was used to BLAST ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) the TREU927 genome and hits were called based on Max Score and Percent Identity . VSG proximity was called based on the TREU927 genome annotation . Contigs resulting from deep sequencing of the MC DNA fraction were used to determine repeat conservation and distance with respect to MC VSG genes [2] . The sequence of each of the 11 megabase chromosomes from TREU927 ( T . brucei brucei ) , Lister427 ( T . brucei brucei ) , DAL972 ( T . brucei gambiense ) , IL3000 ( T . congolense ) , STIB805 ( T . evansi ) , and Y486 ( T . vivax ) genomes were downloaded from http://tritrypdb . org/tritrypdb/ and BLASTed ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) against the 77 bp consensus sequence ( Fig 1A ) . Hits were counted as 70-bp repeats if their length was greater than 45 bp , had and e-value was greater than 40 , and their identity was greater than 70% . Cell lines were generated from Lister427 bloodstream-form trypanosomes derived from the “single marker” ( SM ) line [38] . “Wild-type” in this study was SM with a blasticidin-resistance gene inserted at the active BES1 promoter , which can be put under blasticidin selection to prevent BES transcriptional switching , as was done in the present study only to stabilize the population until the time of DSB induction . The parental line ( PA ) of all ISCEI introduction experiments is “SM-NLS-ISCEI-HA” [12] , which has a copy of a tetracycline-inducible ISCEI enzyme encoded in the rDNA spacer region and a hygromycin resistance marker incorporated at the BES1 promoter . The ISceI cut site and recombinatorial homology to specific locations of BES1 were added to a puromycin selection cassette by PCR ( oligos found in S1 Text ) , cloned into a pGEMT vector , and DNA fragments were liberated by digest prior to transfection using the AMAXA Nucleofector [39] . Sequences were confirmed and DNA fragments librated from the vector for AMAXA transfections . Transformants were selected in 10 μg/mL puromycin , screened for BES1 incorporation by PCR , and confirmed by Southern blot analysis . Semi-quantitative RT-PCR was performed using Superscript III for cDNA amplification as described ( thermofisher . com ) followed by 25 cycles of PCR amplification using taq polymerase . Cell lines were cultured in vitro in HMI-9 medium at 37°C [40] and ISCEI induced using 1μg/mL doxycycline ( dox ) . Strain growth was monitored by continuous passage by diluting daily to 1x105 cells and measuring additive growth over 5 days . Standard flow-cytometry approaches were used to measure cell death and cell cycle progression using propidium iodide [41] . DNA restriction fragments were separated by either standard agarose gel electrophoresis ( 1–12 kb ) or Field Inversion Gel Electrophoresis ( FIGE ) ( 1–25 kb ) using established methods . Southern blots were produced using capillary blotting and neutral transfer paper ( GE Scientific ) . DNA probes were made by PCR amplification , 32P-radiolabeled using Prime-It II Random Labeing Kit ( Stratagene ) , and purified over G-50 microcolumns . Blots were probe-hybridized , washed and visualized by phosphorimaging ( GE Healthcare ) . An experimental pipeline was established for the direct comparison of switching frequency , mechanism , and VSG donor selection ( S2 Fig ) . Cells were grown from 5 , 000 cells to 50 million cells in media with or without doxycycline . Approximately 50 million cells were harvested and depleted over magnetic-activated cell sorting columns ( MACS ) using anti-Lister427 VSG-2 antibody ( monoclonal antibody available for order through Memorial Sloan Kettering Cancer Center https://www . mskcc . org/research-advantage/core-facilities/monoclonal-antibody-core-facility ) as described previously [13] . Half of the resulting “switcher-enriched” cells were used to quantify switching by flow-cytometry ( measuring the number of switched cells as a proportion of the total population , as previously described [12] ) and the other half was plated to limiting dilution and single cell clones were recovered and replica-plated for genetic analysis ( similar to previous studies [13 , 23] ) , RNA extraction and VSG analysis , or long-term storage . Mechanisms of switching were determined by a combination of genetic tests and antibiotic sensitivity , as described [13 , 23] . RNA from clones was used to make cDNA , and the VSG was amplified by RT-PCR and sequenced directly from PCR products . The resulting sequence for each clone was aligned to the VSGnome database BLAST server ( http://129 . 85 . 245 . 250/index . html ) to identify the top VSG hit [2] . Cell lines bearing BES1 70-bp deletion or alterations were doxycycline induced for ISCEI DSB formation and the resulting switched cells isolated by MACS , as described above . RNA was extracted from three biological replicate populations of each induced strain and this material was used to prepare VSG-seq libraries [28] . A reference VSG database was created from VSG sequences assembled with the de novo assembler Trinity [42] . Trinity was first run on each library individually , and then run on libraries grouped by condition . All open reading frames ( ORFs ) were identified in each assembled contig , where an ORF is defined as a start codon to stop codon , a start codon to the end of a contig , or the beginning of a contig to a stop codon . BLASTn ( v2 . 2 . 28+ ) was used to identify VSG ORFs [43] and ORFS with an alignment to a 427 VSG sequence with an e-value of < 1e-10 were considered true VSG sequences . The sets of VSG sequences from all assemblies were then merged using cd-hit-est ( cd-hit v4 . 6 . 1 ) [44 , 45] , with the parameters -c 0 . 98 -n 8 -r 1 -G 1 -g 1 -b 20 -s 0 . 0 -aL 0 . 0 -aS 0 . 5 . Final assembled VSG sequences were all checked against NCBI’s nr/nt database using BLASTn . Once reference sequences were determined , quantification was performed as described previously [28] . Noise ( VSGs measured below the limit of detection , 0 . 01% ) and contamination ( the starting VSG , 427–2 ) were removed . The relative abundance of each remaining expressed VSG was then calculated using its measured FPKM ( fragments per kilobase of transcript per million mapped reads ) . To evaluate donor selection with respect to genomic position , each of these expressed VSGs was then compared to the Lister427 VSGnome database ( http://129 . 85 . 245 . 250/index . html ) . Assembled VSG sequences , when compared to the most similar 427 VSG , were identified as the 427 VSG when they had either 100% identity over >99% of the length of the assembled ORF or >99% identity over 100% of the assembled ORF . Otherwise , assembled VSGs were referred to as variants ( “var” in Fig 5 ) of the most similar Lister427 VSG . These data were then used to create a heatmap using heatmap . 2 from the gplots package in R ( https://cran . r-project . org/web/packages/gplots/gplots . pdf ) . ) . The VSG-seq data have been deposited in the SRA database under the project number SRP062141 . | Chromosomal translocations can fuel genetic change or cause catastrophic genomic damage . African trypanosomes , exemplified by Trypanosoma brucei sub-species , are unicellular parasites that can chronically infect their human and livestock hosts by using a strategy of antigenic variation by which they repeatedly change their protein coats . Switching the surface coat requires the accurate selection and translocation of a single silent coat gene , from a large genomic archive , into an actively transcribed site . How the coat genes from within this deep archive are selected and activated was unproven . Here we show that a specific repetitive DNA sequence is required to access coat genes from diverse sites within the genome . The likely outcome of restricting this process of coat gene selection in natural infections would be a reduction in the chronic nature of African trypanosomiasis . | [
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"... | 2016 | A Conserved DNA Repeat Promotes Selection of a Diverse Repertoire of Trypanosoma brucei Surface Antigens from the Genomic Archive |
T-killer cells of the immune system eliminate virus-infected and tumorous cells through direct cell–cell interactions . Reorientation of the killing apparatus inside the T cell to the T-cell interface with the target cell ensures specificity of the immune response . The killing apparatus can also oscillate next to the cell–cell interface . When two target cells are engaged by the T cell simultaneously , the killing apparatus can oscillate between the two interface areas . This oscillation is one of the most striking examples of cell movements that give the microscopist an unmechanistic impression of the cell's fidgety indecision . We have constructed a three-dimensional , numerical biomechanical model of the molecular-motor-driven microtubule cytoskeleton that positions the killing apparatus . The model demonstrates that the cortical pulling mechanism is indeed capable of orienting the killing apparatus into the functional position under a range of conditions . The model also predicts experimentally testable limitations of this commonly hypothesized mechanism of T-cell polarization . After the reorientation , the numerical solution exhibits complex , multidirectional , multiperiodic , and sustained oscillations in the absence of any external guidance or stochasticity . These computational results demonstrate that the strikingly animate wandering of aim in T-killer cells has a purely mechanical and deterministic explanation .
The high specificity of the immune response depends in large measure on direct cell-cell interactions . An example is the interaction of a T-killer lymphocyte with a tumor cell , or with a cell that has been infected and is producing new viral particles . It is generally accepted ( e . g . , ref . [1] ) that the T-killer cell patrols the tissue , comes in contact with the abnormal cell , recognizes the specific antigen on its surface , develops firm contact with the target cell , and releases toxic compounds in its direction . The directionality of the release , which makes the killing efficient and spares the bystander cells , is arguably as important as the precise molecular recognition of the antigen for the specificity of the immune response [2] , [3] . The killing apparatus in T cells is structurally assembled around the centrosome , the organelle in which the microtubule fibers of the cytoskeleton are anchored . Experiments suggest that the killing apparatus may be positioned next to the target cell by molecular motors . According to this hypothesis , dynein motors anchored at the T cell interface with the target “reel in” the centrosome by pulling on microtubules that pass over the interface [4] , [5] . Surprisingly , large fluctuations of the centrosome next to the cell-cell interface have been observed , as well as fluctuations between interfaces with simultaneously engaged targets [4] . Is the pulling mechanism biophysically plausible ? And what is the nature of the apparent wandering of aim in T-killer cells ? Here we show by means of biomechanical modeling that the pulling mechanism is indeed capable of bringing about the functional orientation of the centrosome in a range of conditions . Our analysis also predicts substantial and verifiable limitations of this mechanism . Our calculations show that the complex fluctuations are an intrinsic property of this mechanism and of the T-cell structure , in the absence of any stochasticity or external guidance , suggesting a deterministic mechanical explanation for one of the most “animate” cell behaviors .
From the experimental videos [4] we obtain the following idealizations to set up our numerical model ( refer to the diagram in Figure 1 ) . The cell outline consists of an unattached round part and of a flat part which is attached to the target cell ( called synapse , or synaptic plane ) . The large nucleus is coupled to the aster of microtubules converging near its surface , and the mobility of both is constrained by the cell outline . Microtubules slide along the cell outline in the areas of contact with the targets . This active sliding–specified in more detail below–drives all movements that are observed . The movements are opposed by microtubule bending elasticity and by viscous drag in the cytoplasm . This condition completes the physical specification of our model; for its exact numerical implementation , refer to the Methods section . The active microtubule sliding in the model is meant to represent the action of cortically anchored molecular motors . Idealizing what should happen when microtubules come in contact with the cell cortex on which motor molecules are anchored [4] , [5] , we assume that the unit length of the contacting part of the microtubule will experience a constant tangential force . The mechanical property of the synapse with respect to a microtubule is therefore characterized in our model by a one-dimensional force density ( units of force per length ) . It is additionally assumed that the force exerted on the microtubule is directed , along the local tangent to the microtubule , to the end of the microtubule that is free ( not attached to the centrosome ) . This end is commonly referred to as the plus end of the microtubule . The direction of the force so exerted on the microtubule is the intrinsic property of the dynein-type molecular motors that have been implicated in T cell polarization [5] . In the more commonly considered situation of vesicular transport , dynein motors ferry intracellular cargo to the so-called minus end of the microtubule ( the end that is anchored at the centrosome ) . Considering the action and reaction forces , when the intracellular vesicle is moved along the microtubule to the minus end , the force exerted by dynein on the microtubule is directed to the plus end . We assume that the force direction is the same also in the case where the dynein-type motor is anchored at the inner surface of the cell outline in the synapse area . The arrangement of motors on this surface can be envisioned as entirely random ( uniform and isotropic ) . This is the implication behind our cell-level model assumption that the direction of the force acting on a microtubule depends only on the direction in which the microtubule passes over the inner surface of the synapse . Indeed , one can envision motor molecules that can pivot on their cortical attachments and will therefore be aligned by their very interaction with a microtubule . Alternatively , motors may be randomly and stably oriented , and only the ones with a matching orientation will engage with the microtubule passing over the synapse in a certain direction . In both cases the pulling force density experienced by the microtubule will be a constant , and the resulting force will be tangential to the microtubule . The effectively isotropic arrangement of motors is considered here merely as the simplest possibility in the absence of empirical data on what an anisotropic arrangement could be like . The model assumption of the constant pulling force density also stipulates that , in molecular terms , there should always be a sufficient number of individual motor molecules in contact with the microtubule . Then the pulling on that microtubule can be processive ( continuous ) , whether the individual motors are processive or not: When some motor molecules disengage , others engage , and the average pulling force is continuously exerted . We would like to emphasize that all considerations regarding the motors are not part of our quantitative model per se but are plausible molecular interpretations of the actual model assumption of the constant density and tangentiality of the pulling force . Figure 2 and Video S1 show a simulation where the centrosome is initially oriented at 90° to the developing cell-cell interface . This orientation is the likeliest if the spherical T cell comes in contact with the target surface entirely at random . This is so for the following reasons . Centrosomes facing any point around a circumference on the T cell surface , which circumference is parallel to the forming synapse , will all have an identical angular separation from the synapse . Indeed , which way the centrosome is facing around the axis perpendicular to the synapse , is of no consequence for the magnitude of the reorientation that is required to bring the centrosome into the functional orientation toward the synapse . Such a circumference corresponding to the identical orientation with respect to the synapse will be the longest , when the angular separation of the centrosome from the synapse is 90° . Random orientation of the T cell cytoskeleton in three dimensions would mean that the centrosome is equally likely to point towards any small area on the spherical outline of the T cell body . The longest circumference then corresponds to the likeliest orientation with respect to the synapse , which is therefore 90° . Our model reproduces the observation that the centrosome becomes reoriented to the interface . Interestingly , stabilization of the centrosome orientation in the model is soon followed by development of pulse-like oscillations of the centrosome position ( Figure 2 ) . The oscillations are in agreement with the experimental observations [4] , and are analyzed in more detail below . An interesting prediction of the model is that the long-range reorientation also results in an arrangement of microtubules that is very asymmetrical . On the side of the microtubule aster that was leading during the reorientation movement ( i . e . , on the side next to which the synapse initially developed ) , a relatively tight “bundle” of microtubules is formed . The bundle is separated by a distinctive gap from the microtubules that were trailing . There exists a published three-dimensional experimental image of an early T cell-target cell conjugate ( Figure 6a in ref . [4] ) that may arguably show a similar gap . However , the gap formation has not been specifically investigated experimentally , and therefore remains a prediction to be verified . For the verification it will be important that in the model the gap is a transient feature seen after reorientation , not a static-equilibrium configuration . ( Available images of fully established T cell-target cell conjugates , e . g . , in ref . [4] , show only a comparatively symmetric structure . ) The induced asymmetry in the model aster should be responsible for the ratchet-like behavior of the microtubule cytoskeleton , which is predicted by our model when the T cell develops a second synapse . The centrosome readily reorients by another 90° in the same direction as it did the first time , but does not reorient in the opposite direction ( Figure 2 ) . In view of this , another testable prediction can be made regarding the experimentally observed oscillations of the centrosome between two synapses: The cortical-pulling mechanism does not permit reversible intersynaptic oscillations in cases where the centrosome undergoes a large reorientation to the synapse that is the first to develop . Before embarking on the analysis of the mechanical conditions that do permit the intersynaptic oscillations , the capacity of the pulling mechanism for achieving the functional polarity of the T-cell cytoskeleton needs to be outlined more systematically . The orientation of the centrosome is described here using an angular measure . The rounded outline of the T cell makes the angular measures and the terms “orientation” and “reorientation” convenient . It also makes the centrosome trajectory during the long-range reorientation look at least partly like an arc . To show as much of this movement as two-dimensional representation can convey , we chose throughout our paper to show reorientation in figures and videos from such an angle that the line of sight is directed along the axis of the arc . From any other angle , the same movement would appear only less arc-like , and more “vectorial” . In this sense , we feel that our model is compatible with the vectorial description of translocation in experiments [4] . The movements in our model are , strictly speaking , a superposition of the movements caused by pulling and of movements caused by the deformation of the cell outline in the beginning of each simulation . Simulations in which pulling force density was set to zero ( Figure S1 ) show , however , that the “passive” component is small , usually not exceeding several degrees of centrosome rotation . Thus , in the framework of the present model , achieving any specific centrosome position , such as next to the synapse ( or at the rear in a migrating T cell ) , requires the active pulling force . Given the quasi-exponential kinetics of the reorientation to the target , i . e . , one characterized by a rapid beginning followed by a slow stabilization at the final position ( Figure 2 ) , it is appropriate to measure the rapidity of the reorientation by the time it takes to reorient by one-half of the angle that separated the initial and the functional orientations of the centrosome . This is analogous , for example , to the widely used half-recovery time in photobleaching experiments . The half-reorientation time achieved by the dynein-pulling mechanism in our model is plotted in Figure 3A vs . the initial misorientation of the centrosome , i . e . , vs . the angular separation of the initial centrosome orientation and the middle of the forming synapse . This plot is essentially the structural challenge – kinetic response curve for the T cell polarization driven by the cortical dynein . It shows that for the comparatively small required reorientations , up to about 70° , the rise of the response time is nonlinear: the movement induced is actually the slower the larger reorientation is needed . This can be attributed to the spatial separation of the microtubules diverging from the centrosome . As a result , a synapse of the given size that is formed farther away will be contacted by fewer microtubules , and the integral force exerted on the microtubule cytoskeleton by such a synapse will be smaller . Figure 3A further shows that this dependence breaks down for even larger “challenges”: Between about 70 and 110° of the initial separation of the synapse from the centrosome , the half-reorientation time actually goes down with the increasing reorientation range . This can be attributed to the advantages of the tighter contact of microtubules with the pulling surface . The microtubules can therefore experience a larger pulling force . This apparently becomes the overriding factor in this range of initial misorientations . ( Notice in the initial , mechanically relaxed cell structure shown in the first inset in Figure 2 that the more distal parts of the microtubules are straighter and potentially better aligned with a synapse that can form next to them than the highly curved proximal parts can be . ) The challenge-response plot in Figure 3A shows further that for initial misorientations that are larger still , the half-reorientation time displays a tendency to rise and fall once more , but the kinetics becomes much more dependent on the microtubule length , and the half-reorientation may not then be achieved at all . The complexity of outcomes reveals the limitations imposed by the basic cell structure on the functional capacity of the pulling mechanism . The chart of the simulation outcomes ( Figure 3B ) shows that the functionally required reorientation up to about 100° can be completed by the cell with microtubules of any plausible length ( Figure 3B , region 1 ) . However , as the initial separation of the centrosome and the synapse increases , the microtubule cytoskeleton is predicted to become jammed at certain positions without reaching the fully functional orientation ( regions 2 and 3 ) . This is apparently due to limits to the movement of the microtubule aster in the space between the nucleus and the outline of the cell . For certain microtubule lengths and initial orientations ( region 4 ) , the microtubules are simply too short to contact the synapse and initiate any movement . Interestingly , examination of the boundary between regions 1 ( “success” ) and 2 ( “jammed” ) shows that making microtubules longer can actually create impediments to complete reorientation , when the movement could otherwise commence . The most interesting in this regard are the predictions for the largest initial misorientations of the centrosome with respect to the forming synapse , such as near 180° , which is commonly hypothesized to be the case in vivo ( e . g . , ref . [1] ) . If the microtubules are long enough to reach such a synapse , they will also likely to be long enough to overlap there in the anti-parallel fashion . The model shows that in this case , the pulling will lock the microtubule system in place ( with microtubules wound tightly around the nucleus ) , rather than reorient it . This can happen even if the synapse is quite far from being symmetrically opposite the centrosome , provided only that the microtubules are long enough to overlap at the synapse ( Figure 3B , region 7 ) . However , for certain microtubule lengths and initial orientations ( regions 5 and 6 ) , the locking , although it may initially appear stable , is resolved through a catastrophic loss of stability , and reorientation can then commence . Interestingly , the comparatively violent loss of stability may make possible final reorientation that is complete , even though this region in the parameter space ( region 5 ) is beyond the zone where functional orientation was already impossible in the absence of any locking ( region 3 ) . The predicted variability of the dynein-driven cytoskeleton polarization in T cells , depending on the exact initial orientation and individual cell structure , appears very life-like and demands experimental testing . Additional simulations where dynamic instability [6] , [7] was included show that the jamming may be overcome if the microtubule length is not constant but undergoes stochastic fluctuations . Our model predicts that due to the very stochastic nature of dynamic instability , the jamming may be overcome in some cells and not in others ( Figure 3C ) . Statistically , therefore , dynamic instability of microtubules has the capacity to facilitate reorientation driven by pulling . The mechanically dead-locked state with the non-functional orientation of the centrosome has not been experimentally documented . This suggests three possibilities: ( 1 ) the specific initial conditions that lead to it in the model ( region 7 in Figure 3B ) are not encountered in reality; ( 2 ) the pulling mechanism is not the correct mechanism , or should be translated substantially differently into quantitative model assumptions; ( 3 ) the pulling mechanism is complemented by other mechanisms in reality . The first possibility is likely because the locking is predicted only in a small fraction of the feasible parameter space ( region 7 in Figure 3B ) . The second possibility is less likely , because the other predictions reproduce a number of striking experimental observations . The third possibility is highly likely; in particular , our simulations suggest that dynamic instability of microtubules is one such additional mechanism that has the capacity to resolve the locking . Disintegration of microtubules under load is another possibility in this regard that our present model does not consider . It is however made less likely by the fact that excessive bending is not seen in our simulations . The axial stress induced by the pulling force in our simulations is likely to be withstood . Measurements suggested that microtubules have mechanical properties resembling Plexiglas [8] . From this , M . W . Berns and colleagues [9] estimated that , although the yield strength of a microtubule is not known , it can be similar to that of polymethylmethacrylate , 40–70 MPa . Considering the cross-section of microtubules 25 nm in diameter , we conclude that microtubules should be able to bear the tensile loads encountered in our model ( up to ∼100 pN ) without structural disintegration . Returning to the analysis of the purely deterministic effects of pulling ( without incorporating the dynamic instability of microtubule length in the model ) , we analyzed further the mechanism of the deterministic mechanical instability of the centrosome position that followed the long-range reorientation . Video S2 shows a generic case of oscillations developing after the functional position of the centrosome next to the synapse is reached . It was found that oscillations develop in the model even if the synapse is formed next to the initial location of the centrosome . An otherwise insignificant tilt of the synapse ( such as 2° ) will determine the initial phase of the oscillations in our deterministic model . Engagement of the microtubules with the pulling surface causes the model centrosome to greatly “overshoot” and to continue moving beyond the center point of the interface . It eventually stops and begins the reverse motion , again approaching the center point and again overshooting ( Figure 4A ) . The oscillations may persist without noticeable systematic changes over at least 1 h of simulated physical time . Typically it appears that there are overlapping and interfering periodic motions ( Figure 5B ) . Also , oscillatory movements that are mostly tangential to the model cell-cell interface occur simultaneously with oscillatory movements that are orthogonal to it ( Figure 5B ) . Gyrations ( looping motions parallel to the interface ) can also be discerned in the complex trajectory of the model centrosome ( Figure 5A ) . To determine the impact dynamic instability of microtubules [6] , [7] and ring-shaped distribution of pulling motors [5] might have on the deterministic oscillations , we performed additional simulations that incorporated these structural and kinetic details . The dynamic instability was modeled as in the simulations described above ( Figure 3C ) . The ring-shaped distribution of dynein was modeled by assuming that only the annulus between 0 . 225R and 0 . 775R , where R is the synapse radius , could exert pulling force on the microtubules . ( The annulus is shown in Video S3 . ) Results show that incorporation of these kinetic and structural details does not dramatically affect the oscillations predicted by the simple deterministic model ( Figure 5C and Video S3 ) . Overall our results suggest that although dynamic instability of microtubules and ring-shaped distribution of dynein may influence the exact trajectory of the centrosome in living cells , they need not be the root cause of the oscillations , nor do they necessarily have a large impact on the oscillation pattern . It is interesting that , as can be seen in Figure 4B and Video S4 , when the pulling surface is assumed to be a disk , the area actually contacted by the microtubules is nonetheless a ring , due to how the microtubules bend against the synapse . This may explain the absence of a significant effect of the assumption of the shape of the pulling area ( ring or disk ) on the oscillations dynamics . Also , the movement of the centrosome to the edge of the synaptic area in the model is restricted by bending of microtubules against the sides of the cell , as discussed above . A similar effect restricting the centrosome movement is predicted to arise , in the case of the ring-shaped pulling area , from the reversion of polarity of microtubules contacting the pulling annulus as the centrosome crosses it . It is tempting to speculate that real T cells [4] , [5] may arrange their cortical motors in the ring-shaped areas not to waste any in areas not contacted by microtubules . In the rest of our analysis we refer only to the case of purely deterministic and structurally simplified modeling that does not incorporate the dynamic instability or the ring-shaped distribution of dynein . As regards the origin of the deterministic oscillations and of the repeated overshooting which are exhibited by the centrosome , it is important to point out that inertia plays no role in intracellular movements due to the prevailing near-zero Reynolds number conditions . In fact , like in models for comparable types of intracellular movements ( e . g . , refs . [10]–[12] ) , there is no mass in our mechanical model . Also , the model is strictly deterministic , and therefore the deflections from the middle position of the centrosome are not due to molecular stochasticity . Close inspection of the model reveals that when the centrosome passes the middle point during oscillations , the microtubule aster shows significant asymmetry . This asymmetry is reversed when the centrosome passes the middle point the next time ( Figure 4B and Video S4 ) . Moreover , the microtubules are engaged with the pulling surface more to one side of the centrosome than to the other . The other side of the aster becomes engaged during the reverse swing ( Figure 4B and Video S4 ) . Similarly to a model for pronucleus oscillations in worm eggs [12] , it can be observed that the distal ( “plus” ) ends of microtubules hardly move during the oscillation cycle . This should be attributed to the cytoplasm viscosity dampening propagation of the elastic perturbation along the microtubules from their proximal parts , which may be pulled and which are coupled to the moving centrosome . As a result , when microtubules on one side are pulled and the centrosome shifts , the proximal parts of microtubules on the opposite side will be lifted off the synaptic surface ( Figure 4A and 4B and Video S4 ) . This makes the tug of war nonlinear: whenever one side is winning , this weakens the opposing side . We ascribe to this effect the fact that our model tends to swing through the middle position . At the same time the movement appears to be limited by the deformation of the microtubules on the winning side . Their distal parts are bent against the side of the cell , and therefore the zone where they can contact the pulling surface cannot extend very close to the edge of the flat synaptic zone . Movement toward the edge therefore diminishes the pulling force . This gives the elastic relaxation of the trailing microtubules time to catch up and to bring their proximal parts in apposition with the pulling surface . At this point the microtubules that trailed are lying relatively flat on the synapse . They are therefore experiencing a pulling force that is greater than the force exerted on the microtubules which led and which are now contacting the synapse only with their highly curved parts . Movement in the reverse direction ensues ( Figure 4A and Video S4 ) . It is important to point out that while microtubule elasticity orchestrates the movement , the continued oscillations are ultimately powered by the pulling forces , which work ultimately against the energy-dissipating forces of viscous drag . The source of energy is part of the present model only by implication: it is ATP hydrolysis coupled to the working cycle of the dynein motors that are behind the pulling force in the model . Simulations with different pulling force densities show that the basic frequency of the oscillations is fairly insensitive to this parameter , although the overall pattern of oscillations changes abruptly when a certain value of it is crossed ( Figure 6 ) . Below approximately 140 pN/µm , the oscillations appear multiperiodic and continuous ( Figure 6A ) . Above approximately 150 pN/µm , the oscillations are pulse-like ( Figure 6C ) . In the relatively narrow range of pulling force densities between approximately 140 and 150 pN/µm , the oscillations are continuous and pure , i . e . , they exhibit a single frequency and amplitude . Only in this narrow intermediate range does the distance of the centrosome to the synaptic plane not oscillate ( Figure 6B ) . Based on the experimental estimate of the force that can be exerted by a single cytoplasmic dynein molecule interacting with a microtubule , 2 . 6 pN [13] , we limit the range of the pulling force densities that are of analytical interest to between 20 and 200 pN/µm . Below this range , there will be only a few molecular motors pulling on a given microtubule , giving rise to stochasticity that our deterministic approach cannot reflect . Above this range the number will reach into the hundreds , which may not be realistic . The present model shows that within the entire range of 20–200 pN/µm , the period of oscillations parallel to the synapse remains near 15–20 s ( Figure 6D ) . This is close to the typical frequency seen in the experimental videos [4] . This intrinsic frequency of oscillations parallel to the synapse ( x direction ) is seen in its pure form when the orthogonal ( z direction ) oscillations are absent between 140 and 150 pN/µm ( Figure 6B ) . In the other two regimes ( Figure 6A and 6C ) , however , measurements show that the x-frequency is approximately the same ( Figure 6D ) . The period of the z-oscillations is also mostly insensitive to the force density , except that it is much longer for all values above 150 pN/µm than it is below 140 pN/µm ( Figure 6D ) . In the intervening range , the z-oscillations are not sustained ( Figure 6B ) , and their period , therefore , not defined . The range of the distance ( z ) of the centrosome from the synapse exhibits a similar step-like dependence on the pulling force density , collapsing fully in the narrow transition zone ( Figure 6E ) . It can be observed that the farther away from the synapse the centrosome is at any given time , the smaller the amplitude of the movement parallel to the synapse will be . Figure 6F shows that this dependence is essentially independent of the force density and is quasi-linear . The exception to its linearity appears related to the natural limit of zero amplitude . When this limit is reached ( this can happen only at high force densities ) , the amplitude-distance relationship exhibits a breakpoint at the axis intercept ( Figure 6F ) . The zero amplitude of motion parallel to the synapse is observed during the intervals between the pulses , such as shown in Figure 6C . Notably , the breakpoint of the x-amplitude vs . z-position curve ( Figure 6F ) is near the centrosome-synapse distance of 1 µm , same as the breakpoint in the dependence of the z-position on the force density ( Figure 6E ) . Close inspection shows that this transition corresponds in individual trajectories to complete but temporary loss of contact between the microtubule system as a whole and the synapse . Explanation of this phenomenon proved challenging , although it appears to arise from the viscous drag-induced “liftoff” of the microtubules that was discussed above and illustrated in Figure 4A . During particularly vigorous movement that can occur at the higher force densities , not just one side but the entire microtubule system may lose contact with the synapse ( apparently due to the lift force ) . In the absence of the active driving force it will take the motile system considerable time to relax and contact the synapse again . These periods of time correspond to the long , high arcs of the z-trajectory and no x-movement , as seen in Figure 6C . Intuition does not appear to keep up with the complexity of the movement . It is satisfying that complexity exhibited in the simulations compares favorably with the multi-periodic and variable-amplitude movement seen in the experiments [4] . However , the mechanistic explanation of it offered by the model will be difficult to test with the existing live-cell imaging techniques , because it would depend on resolving optically the small distances around the predicted breakpoint ( ∼1 µm , Figure 6E and 6F ) . Capacity to explain oscillations of the centrosome within a synapse is a stringent test of a mechanism proposed for centrosome polarization , and our computer simulation results indicate that the empirical hypothesis of cortical dynein pulling [4] , [5] passes this test . The immunological function of the oscillations within a synapse is however unclear . ( One can speculate that they might facilitate extrusion of the toxic granules . ) In contrast to this uncertainty , oscillations between two synapses appear to be part of how a T cell engages two targets simultaneously [4] , no matter how illogical this may seem from a “design” standpoint . We have therefore tested the ability of the cortical pulling mechanism to produce oscillations between two synapses as well . Numerical solution shows that after simultaneous development of two synaptic areas on two sides of the initial centrosome position , the model centrosome goes to one of them . Which one it goes to first in our deterministic model can be decided by an otherwise insignificant deviation of the initial centrosome orientation from the middle , such as by 2° . What is important is that after pausing at the first synapse , which pause can last for a significant period of time , the model centrosome spontaneously moves to the other synapse ( Figure 7A and Video S5 ) . The cycle of movement , pause , and movement to the other synapse appears to continue indefinitely with a rather well-defined periodicity . The characteristic delay before the reverse motion is as seen in the experiments [4] . The model predicts that for the delay to take place , the angle between the two synaptic planes must be narrower than 150° ( Figure 7B ) . The angle was indeed sharp in the experiment [4] . By only crudely adjusting the pulling force density and effective cytoplasm viscosity ( to 40 pN/µm and 2 pN s/µm2 , respectively ) , it is easy to reproduce with remarkable precision both the duration of the pause and the duration of the movement phase ( Figure 7B ) . Whereas the match of the absolute model time scale to the experiment is a matter of ( crude manual ) data-fitting and therefore not particularly significant , the fact that the computed phase of pause and the computed phase of migration can have the same relative duration as seen in the experiment is very remarkable . The same viscosity was used in all other simulations shown , including those that , as was discussed above , reproduced closely the characteristic period of intra-synaptic oscillations independently of the pulling force density . This indicates that the deterministic mechanics of the cortical pulling mechanism may indeed account for the relevant features of centrosome motility in the T cell . In the light of the model , the pause of the centrosome and of the associated killing apparatus next to each of the engaged targets appears to arise from the delayed relaxation of microtubules that were trailing during the last period of centrosome migration . This can be discerned by close examination of Figure 7A , and it is the same factor that leads , in the extreme , to the irreversible , ratchet-like behavior of the model cytoskeleton following very large reorientations ( Figure 2 ) . In comparison , the migration between the two synapses is medium-range , and it therefore can be reversible . Comparing it on the other hand with the relatively small-amplitude oscillations within a synapse ( Figures 4 and 5 ) , the migration of the centrosome between the synapses winds up the trailing microtubules much more around the nucleus , and it takes them longer to relax and contact the other synaptic area after the movement was limited by the deformation of the previously leading microtubules . Irrespective of these mechanistic details that are suggested by the model , it is notable that the time which the killing apparatus spends next to the given target may be determined so directly by mere elasticity of the cytoskeleton . It is equally notable that , as the model suggests , the movement of the killing apparatus to the other synapse is a direct mechanical consequence of its previous movement to the synapse where it is presently found . Simulations in which the pulling force density at the two synapses is unequal show that the centrosome can be retained at the synapse which is the stronger , even if it visits the weaker synapse first ( Figure S2 ) . This result suggests that the preferential orientation of the centrosome and associated organelles to the stronger synapse , which was observed experimentally [14] , [15] may be a limiting case of the inter-synaptic oscillations . In summary , a purely deterministic , biomechanical model is capable of exhibiting complex , life-like centrosome movements in a conceptually simple , three-dimensional computer simulation of the dynein-pulling mechanism . Our computational results demonstrate that the origin of the strikingly animate wandering of aim in T-killer cells need not be sought necessarily in stochastic dynamics of individual molecules , or in indecision that might be exhibited by complex information processing in the T cell , or in indeterminate changes in the signaling input from the target cells . Instead , the rigorous numerical demonstration that a purely deterministic mechanical explanation exists for one of the most animate behaviors exhibited by cells suggests that similar explanations and supporting experimental evidence can be sought for other types of cell behavior that appear strikingly far from mechanistic . | Beyond the more widely known molecular recognition of antigen , specificity of the cellular immune response relies on the precise orientation of immune cells toward infected and tumorous cells . We studied the mechanics of the structural orientation of T-killer cells ( a type of immune cells ) to their immunological targets . One of the most remarkable features of this process as seen under the microscope is the apparent “wandering of aim”: instead of pointing steadily at the intended target , the killing apparatus inside the T-killer cell can wave around . When two targets are engaged simultaneously , the killing apparatus in the T cell can repeatedly oscillate between the two . It might appear that the origin of this strikingly animate behavior should lie in stochasticity of the underlying mechanism . Our numerical model , however , was able to reproduce the complex , continuing motion in spite of the fact that the model was purely deterministic . This result suggests that deterministic quantitative explanations and supporting experimental evidence can be sought in the other cases of extremely complex cell motility that give the microscopist an acute sense that the object is alive . | [
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] | 2009 | Deterministic Mechanical Model of T-Killer Cell Polarization
Reproduces the Wandering of Aim between Simultaneously Engaged Targets |
In chronic infections , pathogens are often in the presence of other microbial species . For example , Pseudomonas aeruginosa is a common and detrimental lung pathogen in individuals with cystic fibrosis ( CF ) and co-infections with Candida albicans are common . Here , we show that P . aeruginosa biofilm formation and phenazine production were strongly influenced by ethanol produced by the fungus C . albicans . Ethanol stimulated phenotypes that are indicative of increased levels of cyclic-di-GMP ( c-di-GMP ) , and levels of c-di-GMP were 2-fold higher in the presence of ethanol . Through a genetic screen , we found that the diguanylate cyclase WspR was required for ethanol stimulation of c-di-GMP . Multiple lines of evidence indicate that ethanol stimulates WspR signaling through its cognate sensor WspA , and promotes WspR-dependent activation of Pel exopolysaccharide production , which contributes to biofilm maturation . We also found that ethanol stimulation of WspR promoted P . aeruginosa colonization of CF airway epithelial cells . P . aeruginosa production of phenazines occurs both in the CF lung and in culture , and phenazines enhance ethanol production by C . albicans . Using a C . albicans adh1/adh1 mutant with decreased ethanol production , we found that fungal ethanol strongly altered the spectrum of P . aeruginosa phenazines in favor of those that are most effective against fungi . Thus , a feedback cycle comprised of ethanol and phenazines drives this polymicrobial interaction , and these relationships may provide insight into why co-infection with both P . aeruginosa and C . albicans has been associated with worse outcomes in cystic fibrosis .
Pseudomonas aeruginosa is an opportunistic pathogen capable of causing severe nosocomial infections and infections in immunocompromised patients . P . aeruginosa is a common pathogen of individuals with cystic fibrosis ( CF ) , a genetic disease that is caused by a mutation in the gene coding for the CFTR ion transporter and strongly associated with chronic , recalcitrant lung infections . Altered CFTR function leads to a fluid imbalance that results in thick , sticky mucus in the lungs that is difficult to clear , thus creating a hospitable environment for microbial growth , biofilm formation , and persistence . While P . aeruginosa is a common microbe in the CF lung , it is rarely the only microbe present [1]–[5] . Co-infections of P . aeruginosa with other bacterial and fungal species are common , and there is a need to understand how these complex multi-species infections impact disease course and treatability . For example , the presence of the fungus Candida albicans correlates with more frequent exacerbations and a more rapid loss of lung function in CF patients [6] , [7] . Additional studies are needed to determine if the presence of the fungus contributes to more severe disease . Published reports strongly suggest that in the CF lung , P . aeruginosa forms biofilms [8] , described as hearty aggregations of cells in a sessile group lifestyle that includes extracellular matrix comprised of proteins , membrane vesicles , DNA , and exopolysaccharides . A biofilm existence provides many advantages to P . aeruginosa including increased antibiotic tolerance [9] , [10] . As with many Gram-negative species , P . aeruginosa biofilm formation is positively regulated by the secondary signaling molecule cyclic-di-GMP ( c-di-GMP ) [11] . C-di-GMP is formed from two molecules of GTP by diguanylate cyclases ( DGCs ) and its levels inversely correlate to motility . High levels of c-di-GMP promote biofilm formation in a number of ways including via increased matrix production and decreased flagellar motility [12]–[14] . P . aeruginosa also produces a class of redox-active virulence factors called phenazines . In CF sputum , the phenazines pyocyanin ( PYO ) and phenazine-1-carboxylate ( PCA ) are found in micromolar ( 5–80 µM ) concentrations , and their levels are inversely correlated with lung function [15] . Phenazines play a role in the relationships between P . aeruginosa and eukaryotic cells . Several studies have shown how phenazines can negatively affect mammalian physiology [16] , [17] . In addition , phenazines impact different fungi , including C . albicans . At high concentrations , phenazines are toxic to C . albicans , and lower concentrations of phenazines reduce fungal respiration and impair growth as hyphae [18] . Phenazines figure prominently in shaping the chemical ecology within mixed-species communities . For example , when exposed to low concentrations of phenazines , C . albicans increases the production of fermentation products such as ethanol by 3 to 5 fold [18] . Furthermore , P . aeruginosa-C . albicans co-cultures form red derivatives of 5-methyl-phenazine-1-carboxylic acid ( 5MPCA ) that accumulate within fungal cells [19] . In the present study , we show that ethanol produced by C . albicans stimulated P . aeruginosa biofilm formation and altered phenazine production . Ethanol caused a decrease in surface motility in both strains PA14 and PAO1 concomitant with a stimulation in levels of c-di-GMP , a second messenger nucleotide that promotes biofilm formation . Through a genetic screen , we found that the diguanylate cyclase WspR , a response regulator of the Wsp chemosensory system , was required for this response . Elements upstream and downstream of WspR signaling were required for the ethanol response . Ethanol no longer stimulated biofilm formation in a mutant lacking WspA , the membrane-localized sensor methyl-accepting chemotaxis protein ( MCP ) that is involved in the activation of WspR [20] . In addition , an intact Pel exopolysaccharide biosynthesis pathway , known to be stimulated by c-di-GMP derived from the Wsp pathway [21] , [22] , was also required for ethanol stimulation of biofilm formation . The effects were observed on both abiotic surfaces and a cell culture model for P . aeruginosa and P . aeruginosa-C . albicans airway colonization . We found that both exogenous and fungally-produced ethanol enhanced the production of two phenazine derivatives known for their antifungal activity [19] , [23] , 5MPCA and phenazine-1-carboxamide ( PCN ) , through a Wsp-independent pathway and independent of ethanol catabolism . Because phenazines stimulate fungal ethanol production [18] , we present evidence for a signaling cycle that helps drive this polymicrobial interaction .
Our previously reported findings show that P . aeruginosa produces higher levels of two phenazines , PYO and 5MPCA [24] , [25] , when cultured with C . albicans and that phenazines stimulate C . albicans ethanol production [18] . Thus , we sought to determine how fungally-derived ethanol affects P . aeruginosa . A concentration of 1% ethanol ( v/v ) was chosen for these studies based on the detection of comparable levels of ethanol in C . albicans supernatants from cultures grown with phenazines [18] . The presence of 1% ethanol in the culture medium did not affect P . aeruginosa growth in minimal M63 medium with glucose ( Fig . S1 ) or LB ( doubling time of 36±2 min in LB versus 39±2 min in LB with ethanol ) , or on solid LB medium ( Fig . S1 inset ) except that the final culture yield in M63 was slightly higher in cultures amended with ethanol ( Fig . S1 ) . When we performed a microscopic analysis of the effects of ethanol on P . aeruginosa strain PAO1 , we observed a significant increase in attachment of cells to the bottom of a titer dish well within 1 h ( 15±5 cells per field in vehicle treated compared to 31±6 cells per field in cultures with ethanol , p<0 . 01 ) and development of microcolonies was strongly enhanced ( Fig . 1A ) . Ethanol also promoted an increase in the number of attached cells and microcolonies in cultures of another P . aeruginosa strain , PA14 ( Fig . 1B ) . Using two assays that assess biofilm-related phenotypes ( swarming motility and twitching motility ) , we sought to gain additional insight into how ethanol impacted biofilm formation . Our initial studies focused on strain PA14 . We found that ethanol repressed swarming motility , a behavior that is inversely correlated with biofilm formation ( Fig . 1C ) . Ethanol did not affect type IV-pili-dependent twitching motility , a form of movement that is required for microcolony formation in biofilms on plastic ( Fig . S2B ) [26] . Because P . aeruginosa can catabolize ethanol [27] , we sought to determine if ethanol consumption contributed to the repression of swarming motility . P . aeruginosa first oxidizes ethanol to acetaldehyde by an ethanol dehydrogenase , ExaA , which requires the cofactor PQQ ( pyrroloquinoline quinone ) [27] . Acetaldehyde is further oxidized to acetate by an NAD+ dependent acetaldehyde dehydrogenase ( ExaC ) , and the acetate is subsequently oxidized to acetyl-CoA by AcsA [28] . We retrieved the exaA::TnM , pqqB::TnM , and acsA::TnM mutants predicted to be defective in ethanol catabolism from the P . aeruginosa strain PA14 NR transposon library [29] and confirmed the transposon insertion sites by PCR ( see Materials and Methods for more detail ) . As predicted , none of these mutants grew with ethanol as the sole carbon source , and growth on glucose was unaffected ( Fig . S3A ) . When we used these mutants in the swarm assay , we found no difference in the effects of ethanol on these three ethanol catabolism mutants in comparison to the wild-type parental strain ( Fig . S3B ) indicating that ethanol catabolism was not required for the ethanol response . Furthermore , other carbon sources such as glycerol , another fungal fermentation product , or choline , another two-carbon alcohol degraded by a PQQ-dependent enzyme , did not inhibit swarming motility ( Fig . S4 ) . Ethanol stimulated attachment and biofilm formation on plastic and inhibited swarming motility ( Fig . 1 ) . These two phenotypes are positively and negatively regulated by levels of the second messenger molecule c-di-GMP [30] . Thus , we measured intracellular levels of this dinucleotide in P . aeruginosa strain PA14 cells grown on swarm plates with or without 1% ethanol for 16 . 5 h as described previously . We found a 2 . 4-fold increase in c-di-GMP levels in cells exposed to ethanol ( Fig . 2 ) . To identify the enzyme ( s ) responsible for this increase , we screened a collection of 31 P . aeruginosa strain PA14 mutants [31] defective in different genes predicted to encode proteins that may modulate c-di-GMP levels based on the detection of a DGC and/or an EAL domain [22] , [31] . We found that one mutant , ΔwspR , was strikingly resistant to the repression of swarming by ethanol ( Fig . 3 ) . As expected , this mutant also had a slight hyperswarming phenotype when compared to the wild type in control conditions [31] , and both phenotypes were complemented by the wild-type wspR allele on an arabinose-inducible plasmid when grown in the presence of 0 . 02% arabinose ( Fig . 3 ) . The empty vector ( EV ) control exhibited a swarming pattern comparable to that of the ΔwspR mutant . WspR is a response regulator with a GGDEF domain [32] , which is associated with diguanylate cyclase activity [20] . Consistent with the observation that ΔwspR continued to swarm on medium with ethanol , c-di-GMP levels were not different between cultures with and without ethanol in the ΔwspR background ( Fig . 2 ) . These data suggest that WspR activity , and thus c-di-GMP levels , are enhanced by ethanol . WspR is known to regulate the production of the Pel polysaccharide [21] , [22] , and production of Pel is associated with colony wrinkling and biofilm formation [33] . After 72 hours on swarm plates , we also observed that ethanol strongly promoted colony wrinkling while the addition of equivalent amounts of other carbon sources , such as glycerol or choline , did not have this effect . Furthermore , the colony wrinkling induced by ethanol was less apparent in a ΔwspR strain ( not shown ) and completely absent in a strain lacking pelA , an enzyme required for Pel biosynthesis ( Fig . 4 ) . The ΔpelA mutant , like the ΔwspR mutant , continued to swarm in the presence of ethanol ( Fig . 4 ) suggesting that the repression of swarming in the presence of ethanol was , at least in part , due to increased Pel production . As we found that ethanol stimulated biofilm formation in P . aeruginosa wild-type strains PA14 and PAO1 ( Fig . 1 ) and that WspR mediated the ethanol effect in strain PA14 , we also examined the role of WspR in the ethanol response in strain PAO1 . As shown above , PAO1 wild-type cells had increased early attachment and subsequent microcolony formation on plastic when ethanol was added to the medium ( Fig . 5 ) . Consistent with our model that ethanol is acting through WspR , ethanol did not stimulate surface colonization in the PAO1 ΔwspR mutant ( Fig . 5 ) . We also examined the ethanol-responsive phenotype for P . aeruginosa strain PAO1 ΔwspA , which lacks the membrane bound receptor that is the most upstream element described in the Wsp system [20] . Like ΔwspR , ΔwspA did not show increased attachment to plastic upon the addition of ethanol ( Fig . 5 ) suggesting that both the MCP sensor and the WspR response regulator were required for the response to ethanol . Ethanol also promoted colony wrinkling in strain PAO1 , as was observed in strain PA14 , consistent with the prediction that increased WspR activity would lead to increased matrix production . Enhanced wrinkling with ethanol was shown most clearly for both strains in non-motile ( flgK ) mutants which formed colonies of similar size regardless of the presence of ethanol ( Fig . S5 ) . Because strain PAO1 WT does not swarm robustly in control conditions , the effects of ethanol on swarming in strain PAO1 were not quantified . Previous studies have shown that the fluorescently-tagged WspR protein forms intracellular clusters when in its active phosphorylated form upon incubation of cells on an agar surface , and cluster formation is positively correlated with WspR activity [20] . To complement the mutant analyses , we determined if ethanol also promoted WspR-YFP clustering , and if known components of the WspR activation system were required for WspR stimulation by ethanol . To facilitate these analyses , we used the WspR variant WspRE253A-YFP , which forms larger clusters that are more easily visualized [34] . In these studies , we observed a two fold increase in WspR clustering in the presence of ethanol ( Fig . 5C ) . To determine if WspF , a methylesterase that negatively regulates WspR activity [21] , was involved in the regulation of WspR in response to ethanol , we also assessed WspR clustering in a ΔwspF background where WspR is constitutively active . In ΔwspF , WspR clustering was higher than in the wspF+ reference strain , and WspR clustering was not further stimulated by ethanol , lending support for the model that ethanol was acting through the Wsp system and not through an independent pathway for WspR activation . To understand the effects of ethanol on P . aeruginosa in a well-established CF-relevant disease model , we studied the effects of ethanol on P . aeruginosa strain PAO1 in the context of bronchial epithelial cells with the most common CF genotype ( homozygous CFTRΔF508 ) [35] , [36] . We cultured P . aeruginosa strain PAO1 with the epithelial cells in medium without and with 1% ethanol , and observed an obvious enhancement in the size of biofilm microcolonies ( Fig . 6A ) and a 2 . 2-fold increase in colony forming units ( CFUs ) on the airway cells with ethanol ( Fig . 6B ) . When the same experiment was performed with the ΔwspR or ΔwspA mutants , no stimulation by ethanol was observed . Ethanol alone did not impact epithelial cell viability as measured by an LDH release assay ( 9 . 44%±0 . 98 LDH release for control and 10 . 47%±1 . 2 LDH release with ethanol , N = 3 ) and other studies have also found these concentrations of ethanol to be well below those that cause overt toxicity to epithelial cells or disruption of epithelial barrier integrity [37] , [38] . When P . aeruginosa PAO1 and C . albicans were co-inoculated into epithelial cell co-cultures , 4 . 7-fold more P . aeruginosa CFUs were found to be associated with the monolayer after 6 h ( Fig . 7 ) . To determine if C . albicans-derived ethanol contributed to the enhanced colonization by P . aeruginosa in the presence of C . albicans , we used a C . albicans adh1/adh1 mutant that produced lower levels of ethanol . We constructed the adh1 null strain and its complemented derivative , and confirmed that the absence of ADH1 caused a reduction in ethanol by HPLC analysis of culture supernatants , a finding consistent with previously published work [39] . When P . aeruginosa was co-cultured with the C . albicans adh1/adh1 strain , there was a significant decrease in P . aeruginosa CFUs recovered , and this defect was corrected upon complementation with the ADH1 gene in trans . Furthermore , there was no significant difference in the stimulation of colonization by wild-type or adh1/adh1 mutant C . albicans in the ΔwspR or ΔwspA backgrounds ( Fig . S6 ) . Together , these data strongly suggest that C . albicans-produced ethanol promotes P . aeruginosa colonization of both abiotic and biotic surfaces through activation of the Wsp system , which likely exerts these effects through promoting Pel production . In part , these studies were instigated by the finding that P . aeruginosa phenazines strongly stimulate C . albicans ethanol production [18] . Thus , we were intrigued by the observation that colonies on ethanol-containing swarm plates , but not control plates , contained abundant emerald green crystals , similar to those formed by reduced phenazine-1-carboxamide ( PCN ) [40] ( Fig . 8A , Fig . 4 and Fig . S7A ) , which could indicate a reciprocal relationship between ethanol and phenazines . Phenazine concentrations were measured using HPLC in either extracts from P . aeruginosa strain PA14 colonies or extracts from the underlying agar . In extracts from wild type colonies , PCN and PCA concentrations were 24 . 2- and 5 . 8-fold higher , respectively , when ethanol was in the medium ( Fig . S7B ) ; much smaller differences in PCN and PCA concentrations were found in extracts of the underlying agar ( Fig . S7C ) . Because PCA is the precursor for all other phenazine derivatives , including PCN ( Fig . S7A ) , we further explored the effect of ethanol on PCA production . For this , we measured levels of PCA in a strain lacking all of the PCA modifying enzymes ( PhzH , PhzM , and PhzS; see Fig . S7A for pathways ) [41] . We found that ΔphzHMS colonies contained 1 . 7-fold more PCA ( Fig . S7D ) and released 1 . 3-fold more PCA into the agar ( Fig . S7E ) when grown in the presence of ethanol compared to control conditions . These data suggest that ethanol may cause a minor increase in PCA , and that it has greater effects on which species of phenazines are formed . The differences in phenazine levels or profiles did not appear to be responsible for ethanol effects on swarming as the Δphz mutant [42] , which lacks phzA1-G1 and phzA2-G2 , was like the wild type in that its swarming was repressed in the presence of ethanol , but it swarmed robustly in its absence ( Fig . S7F ) . To determine if there was a connection between ethanol effects on Wsp signaling and ethanol stimulation of PCN levels , we assessed PCN accumulation in mutants lacking wspR or pelA . We found that both strains responded like the wild type in terms of PCN crystal formation upon growth with ethanol ( Fig . S8A and Fig . 4 ) . Similarly , ethanol catabolic mutants still showed enhanced levels of PCN crystals upon ethanol exposure ( Fig . S8A ) . Having observed alterations in the phenazine profile induced by ethanol , we examined the impact of ethanol in the production of a fourth phenazine derivative , 5MPCA , which we have previously shown to be released by P . aeruginosa when in the presence of C . albicans [19] , [24] . Because P . aeruginosa-produced 5MPCA is converted into a red pigment within C . albicans cells , 5MPCA accumulation can be followed by observing the formation of a red color where P . aeruginosa and C . albicans are cultured together [19] , [24] . To examine the effects of ethanol production on the accumulation of red 5MCPA derivatives , we again used the C . albicans adh1/adh1 mutant and its complemented derivative . Strikingly , when P . aeruginosa was cultured on lawns of the C . albicans adh1/adh1 strain , a strong decrease in red pigmentation was observed ( Fig . 8B ) . When ADH1 was provided in trans to the adh1/adh1 mutant , accumulation of the red pigment was restored ( Fig . 8B ) . Neither ethanol catabolism nor WspR activity was required for the stimulation of levels of 5MPCA derivatives by P . aeruginosa on fungal lawns ( Fig . S8B ) . Together , our data suggest that ethanol only slightly increases total phenazine production ( Fig . S7D and E ) but more strongly affects the derivatization of phenazines in P . aeruginosa colonies ( Fig . S7B and C ) . Furthermore , C . albicans-produced ethanol stimulated P . aeruginosa 5MPCA production , and in turn , phenazines , including 5MPCA analogs , promote ethanol production [18] . Thus , it appears that P . aeruginosa-C . albicans interactions include a positive feedback loop that promotes fungal ethanol production and P . aeruginosa Wsp-dependent biofilm formation when the two species are cultured together .
This paper reports new effects of ethanol on P . aeruginosa virulence-related traits , and illustrates that these effects occur through multiple pathways ( Fig . 9 ) . We found that ethanol: i ) promoted attachment to and colonization of plastic and airway epithelial cells , ii ) decreased swarming , but not twitching motility , iii ) increased Pel-dependent colony wrinkling , and iv ) increased c-di-GMP levels . All of these responses to ethanol required the diguanylate cyclase WspR . WspR is part of the Wsp chemosensory system , which is a member of the “alternative cellular function” ( ACF ) chemotaxis family [20] , [21] , [43] . The Wsp chemosensory system is different from the chemotaxis systems in P . aeruginosa in terms of its localization and response to environmental signals [44] . The membrane-bound receptor WspA and the CheA homologue WspE are necessary for the Wsp system to function , and WspE activates WspR via phosphorylation [44] . Consistent with our hypothesis that the entire Wsp system is required for the response to ethanol , we found that a wspA mutant was also insensitive to the effects of ethanol on biofilm formation ( Fig . 5 ) . The activation of WspR was independent of ethanol catabolism and independent of phenazine production . Ethanol and other alcohols can increase the rigidity of cell membranes by promoting an altered composition of fatty acids [45] , and future studies will determine if the Wsp system , particularly the membrane localized WspA , can be activated by changes in the lipid composition or changes in the physical properties of P . aeruginosa membranes . Because the Wsp system is also activated upon contact with a surface [20] , it is intriguing to consider how these stimuli might be similar . Ethanol had mild , if any , effects , on biofilm formation at the air-liquid interface in a commonly used 96-well microtiter dish assay in either strain ( Fig . S9 ) suggesting that in this environment , different Wsp activating cues were not additive . C . albicans and other Candida spp . are commonly detected in the sputum of CF patients , and clinical studies suggest that the presence of both P . aeruginosa and C . albicans results in a worse prognosis for CF patients [6] , [46] . In vivo ethanol production by other fungi has been documented [47] , [48] , but a link between Candida spp . and ethanol production in the lung has not yet been made . It is important to note , however , that ethanol was one of two metabolites in exhaled breath condensate that differentiated CF from non-CF individuals [49] . Thus , regardless of the source of ethanol , be it fungal or bacterial , the effect of ethanol on pathogens such as P . aeruginosa is likely of biological and clinical relevance . We tested this interaction in the context of CF , but this polymicrobial interaction likely occurs in other contexts as well . As shown above , ethanol promoted biofilm formation and likely concomitant increases in drug tolerance . In the airway epithelial cell system , P . aeruginosa CFU recovery was increased 3-fold by addition of ethanol ( Fig . 6B ) and 4 . 7-fold by co-culture with C . albicans ( Fig . 7 ) . A two-fold difference is comparable to the differences in colonization between wild-type P . aeruginosa strains and mutants lacking genes known to play a role in virulence in animal models . For example , a ΔplcHR mutant lacking hemolytic phospholipase C or a Δanr strain defective in a global regulator have 1 . 3- to 2 . 6-fold fewer CFUs recovered from airway cells compared to wild type , and notable differences in animal models [50] , [51] . Hence the presence of ethanol may result in increased virulence of P . aeruginosa in the host . Ethanol has also been shown to promote P . aeruginosa conversion to a mucoid state [52] , in which the exopolysaccharide alginate is overproduced; mucoidy is common in CF isolates and is correlated with a decline in lung function [53] , [54] . Ethanol has been shown to enhance virulence and biofilm formation by other lung pathogens such as Staphylococcus aureus [55] and Acinetobacter baumanii [56]–[59] via mechanisms that have not yet been described . Like in P . aeruginosa ( Fig . S1A ) , ethanol caused a slight stimulation of growth in A . baumanii [58] . In addition to the effects of ethanol on P . aeruginosa , ethanol is an immunosuppressant that negatively influences the lung immune response [60]–[64] . In a mouse model , ethanol inhibits lung clearance of P . aeruginosa by inhibiting macrophage recruitment [65] . Together , these observations suggest that in mixed infections , P . aeruginosa may promote the production of ethanol by fungi , and that fungally-produced ethanol may in turn enhance the virulence and persistence of co-existing pathogens , and thus may directly impact the host . It is not yet known how ethanol influences the spectrum of P . aeruginosa phenazines produced . In a previous study , we found evidence for increased production and release of 5MPCA when P . aeruginosa is grown in co-culture with C . albicans , and that live C . albicans is required for this effect [19] . More recent studies show that C . albicans ethanol production increased in the presence of even very low concentrations of the 5MPCA analog phenazine methosulfate [18] , that the 5MPCA-like compounds were even more effective inhibitors of fungi than PCA and PYO , the two phenazines normally produced when P . aeruginosa is grown in mono-culture . Here , our findings suggest a feedback loop in which C . albicans-produced ethanol promoted the release of phenazines ( Fig . 7 ) that may promote further ethanol production [18] . It is also important to consider that some studies have reported that 5MPCA and PCN have enhanced antifungal activity when compared to PCA and PYO [19] , [23] , [24] . The ethanol-induced changes in PCA were not as dramatic when compared to the ethanol-induced changes in PCN and 5MPCA , suggesting that ethanol mainly affected the biosynthetic steps after the formation of PCA leading to its conversion to PCN , 5MPCA and PYO . In different settings , such as liquid cultures or in clinical isolates lacking activity of LasR , a transcriptional regulator for quorum sensing that controls phenazine production , the presence of C . albicans enhanced the production of 5MPCA and PYO [24] , [25] . Taken together , all these observations indicate that fungally-produced ethanol may enhance the conversion of PCA to end products such as PCN , 5MPCA and PYO . These studies indicate how microbial species can alter the behavior of one another and suggest that the nature of these dynamic interactions can change depending on the context . In the rhizosphere , where pseudomonad antagonism of fungi includes the colonization of fungal hyphae and phenazine production , the enhancement of fungally-produced ethanol by phenazines and stimulation of biofilm formation and phenazine production by ethanol may create a cycle that is relevant to biocontrol [23] , [66] , [67] . In chronic infections where these two species are found together , such as in chronic CF-associated lung disease , this molecular interplay may be synergistic and promote long-term colonization of both species in the host . These findings indicate that the treatment of colonizing fungi may be beneficial due to their effects on other pathogens even if the fungi themselves are not acting as overt agents of host damage .
Bacterial and fungal strains and plasmids used in this study are listed in Table S1 . Bacteria and fungi were maintained on LB [68] and YPD ( 2% peptone , 1% yeast extract , and 2% glucose ) media , respectively . When stated , ethanol ( 200-proof ) , choline chloride or glycerol was added to the medium ( liquid or molten agar ) to a final concentration of 1% . Control cultures received an equivalent volume of water . When ethanol was supplied as a sole carbon source , glucose and amino acids were omitted . Mutants from the PA14 Non-Redundant ( NR ) Library were grown on LB with 30 µg/mL gentamicin [29] . When strains for the NR library were used , the location of the transposon insertion was confirmed using sets of site-specific primers followed by sequencing of the amplicon . The primers are listed in Table S2 . For growth curves , overnight cultures were diluted into 5 ml fresh medium ( LB or M63 with 0 . 2% glucose [69] with or without ethanol ) to an OD600 nm of ∼0 . 05 and incubated at 37°C on a roller drum . Culture densities below 1 . 5 were measured directly in the culture tubes using a Spectronic 20 spectrophotometer . At higher cell densities , diluted culture aliquots were measured using a Genesys 6 spectrophotometer . To measure the attachment of cells to the plastic surface in 6-well or 12-well untreated polystyrene plates , wells were inoculated with a suspension of cells at an initial OD600 nm of 0 . 002 from overnight cultures . Every 90 minutes , the culture medium was removed and fresh medium was supplied . Pictures were taken using an inverted Zeiss Axiovert 200 microscope with a long distance 63× DIC objective at specified intervals . To quantify the number of cells or microcolonies in control cultures compared to cultures with ethanol , images were captured , randomized , and analyzed by a researcher who was blind to the identity of the sample at the time of analysis . In each experiment , more than 10 fields were counted for each strain . Microcolonies were defined as clusters of more than 5 cells in physical contact with one another . Biofilm formation on plastic microtiter dishes were performed and analyzed using the crystal violet assay as described in [55] and biofilm values were measured by quantification of dye as measured absorbance at 650 nm . The analysis of P . aeruginosa colonization of airway epithelial cells was performed using CFBE human bronchial epithelial cells ( CFBE410− ) with the CFTRΔF508/ΔF508 genotype [70] as described previously [35] , [36] . For imaging , cells were grown in 6-well glass bottom dishes ( MatTek ) . For quantification of attached cells , CFBEs were grown in 6 or 12 well plates . P . aeruginosa strain PAO1 cells were added at an MOI of 30∶1 , and the medium was exchanged every 1 . 5 hours . For experiments with C . albicans , PAO1 cells and C . albicans were added together to CFBE monolayers , where C . albicans was at an MOI of 10∶1 with respect to the epithelial cells . Pictures were taken using a Zeiss Axiovert 200 microscope with a 63× DIC objective at specified intervals . We performed multiple experiments with technical replicates ( between three and six ) on different days and analyzed the data with a one-way analysis of variance and Tukey's post hoc t-test using Graph Pad Prism 6 . We observed that cells from different passages had differences in the mean attachment across all samples from that day . Thus , we normalized values to the mean across all samples from each experiment . LDH release was measured after six hours using the Promega CytoTox96 Non-Radioactive Cytotoxicity kit as described in the manufacturer's instructions . Swarming motility was tested by inoculating 2 . 5 µL of overnight cultures on fresh M8 ( M8 salts without trace elements supplemented with 0 . 2% glucose , 0 . 5% casamino acids , and 1 µM MgSO4 ) containing 0 . 5% agar as described previously [71] . Plates were incubated face up at 37°C with 70–80% humidity in stacks of no more than 4 for 16 . 5 hrs . To quantify the degree of swarming , percent coverage of the plate was measured using ImageJ software [72] . Twitching motility was analyzed as described previously [26] . Cells were collected from swarm plates after incubation at 37°C for 16 . 5 h and placed in pre-weighed 1 . 5 mL Eppendorf tubes . Tubes were centrifuged at 5 , 000 rpm for 4 minutes . The pellets were then resuspended in 250 µL of extraction buffer by vigorous vortexing ( extraction buffer: MeOH/acetonitrile/dH2O 40∶40∶20+0 . 1 N formic acid stored at −20°C ) . The extractions were incubated at −20°C for 30 minutes in an upright position . The tubes were then centrifuged at 13 , 000 rpm for 5 minutes at 4°C . 200 µL of the extraction were recovered into new Eppendorf tubes and neutralized with 4 µL of 15% NH4HCO3 per 100 µL of sample . The tubes with cell debris were left to dry and reweighed for normalization of cell numbers from swarm plates . 150 µL of samples were sent to the RTSF Mass Spectrometry and Metabolomics Core at Michigan State University for LC-MS analysis . Sample preparation and microscopy were performed as previously described [20] , [34] . To analyze liquid-grown cells , cultures were grown at 37°C while shaking to an optical density at 600 nm ( OD600 ) of 0 . 3 in M9 medium ( 1× M9 salts pH 7 . 4 , 2 mM MgSO4 , 0 . 1 mM CaCl2 , 0 . 2% glycerol , 0 . 2% casamino acids and 10 µg/ml thiamine HCl ) . 1% arabinose was included for induction of wspR , and 1% ethanol was added when comparing its effect on WspR clustering . From each culture , 3 µl were spotted onto a 0 . 8% agarose PBS pad on a microscope slide and then covered with a coverslip . More than 100 cells were counted for each condition . Preformed lawns of C . albicans CAF2 and adh1/adh1 were prepared by spreading 700 µL of a YPD-grown overnight culture onto a YPD 1 . 5% agar plate followed by incubation at 30°C for 48 hr . Exponential phase P . aeruginosa liquid cultures were spotted ( 5–10 µL ) onto the C . albicans lawns , then incubated at 30°C for an additional 24 to 72 hours . Overnight cultures of P . aeruginosa PA14 wild-type and ΔphzHΔphzMΔphzS strains were grown in LB at 37°C ( shaken at 250 rpm ) . Ten microliters of each culture were spotted onto a track-etched membrane ( Whatman 110606; pore size 0 . 2 µm; diameter 2 . 5 cm ) that was placed on a 1 . 5% agar M8 medium supplemented with either vehicle ( water ) or 1% v/v ethanol . Plates contained 3 ml of medium in a 35×10 mm agar plate ( Falcon ) . The colonies were incubated at 37°C for 24 hours and then at room temperature for 72 hours , after which phenazines were extracted from the colonies and agar separately . Each track-etched membrane with a colony was lifted off the agar plates and nutated in 5 mL of 100% methanol overnight at room temperature . Similarly , the agar was nutated overnight in 5 mL of 100% methanol . Colony and agar extracts were filtered ( 0 . 2 µm pore ) and phenazines in the extraction volume ( 5 mL ) were quantified by high-performance liquid chromatography as previously described [41] at a flow rate of 0 . 4 mL/min . All data were analyzed using Graph Pad Prism 6 . The data represent the mean standard deviation of at least three independent experiments with multiple replicates unless stated otherwise . For normally distributed data , comparisons were tested with Student's t-test . | In many human infections , several species of microbes are often present . This is typically the case with the disease cystic fibrosis , characterized by thick mucus in the lungs that is colonized by bacteria and fungi . Here , we show evidence that interactions between the bacterium Pseudomonas aeruginosa and the fungus Candida albicans result in attributes of infection that are worse for the human host . We found that ethanol , such as that produced by C . albicans , causes increased levels of a signaling molecule in P . aeruginosa that promotes biofilm formation . Biofilm formation by P . aeruginosa is associated with infections that are more difficult to treat . Ethanol stimulated P . aeruginosa colonization of plastic surfaces and airway cells , and we identified components of this mechanism . Fungally-produced ethanol also changes the spectrum of phenazine toxins produced by P . aeruginosa , and phenazines are associated with worse lung function in people with cystic fibrosis . In light of the fact that phenazines interact with C . albicans to promote ethanol production , we propose a positive feedback loop between C . albicans and P . aeruginosa that contributes to worse disease . Our findings could have implications for the study and treatment of multi-species infections . | [
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] | 2014 | Candida albicans Ethanol Stimulates Pseudomonas aeruginosa WspR-Controlled Biofilm Formation as Part of a Cyclic Relationship Involving Phenazines |
The genetic dissection of the phenotypes associated with Williams-Beuren Syndrome ( WBS ) is advancing thanks to the study of individuals carrying typical or atypical structural rearrangements , as well as in vitro and animal studies . However , little is known about the global dysregulations caused by the WBS deletion . We profiled the transcriptomes of skin fibroblasts from WBS patients and compared them to matched controls . We identified 868 differentially expressed genes that were significantly enriched in extracellular matrix genes , major histocompatibility complex ( MHC ) genes , as well as genes in which the products localize to the postsynaptic membrane . We then used public expression datasets from human fibroblasts to establish transcription modules , sets of genes coexpressed in this cell type . We identified those sets in which the average gene expression was altered in WBS samples . Dysregulated modules are often interconnected and share multiple common genes , suggesting that intricate regulatory networks connected by a few central genes are disturbed in WBS . This modular approach increases the power to identify pathways dysregulated in WBS patients , thus providing a testable set of additional candidates for genes and their interactions that modulate the WBS phenotypes .
Williams-Beuren Syndrome ( WBS; OMIM #194050 ) is a de novo neurodevelopmental disorder occurring in approximately 1/10'000 births . WBS is characterized by mental retardation , with a unique cognitive and personality profile . Clinical features include supravalvular aortic stenosis ( SVAS ) , connective tissue anomalies , distinctive facial features ( elfin face ) , short stature , hypertension , infantile hypercalcemia , dental , kidney and thyroid abnormalities , premature ageing of the skin , elevated body fat percentage , impaired glucose tolerance and silent diabetes . The cognitive hallmark of the condition is a striking contrast between a relative strength in auditory memory and language abilities , and a profound impairment in visuospatial construction . WBS individuals are hypersensitive to sound , with strong emotional responses to music , either positive or negative , and some individuals display unusual musical skills . In addition to this hyperacusis , which is thought to be due to the absence of acoustic reflexes , WBS individuals may suffer from sensorineural hearing loss as they age . They are also very sociable , emphatic , loquacious and over-friendly , with a complete lack of fear towards strangers . Many present an attention deficit disorder with hyperactivity and anxiety [1]–[9] . The WBS is associated with a microdeletion within the 7q11 . 23 chromosomal band , which encompasses 28 genes [10]–[13] . It is flanked by specific low copy repeats that serve as substrate for non-allelic homologous recombination leading to the deletion [14] . These rearrangements are facilitated by the paracentric inversion of the region [14] , [15] , as well as the presence of a specific copy number variant [16] . The most common deletion , occurring in approximately 95% of cases , involves a 1 . 5 megabase ( Mb ) segment , while a larger 1 . 84 Mb deletion is observed in about 1 of 20 cases [14] , [17] . Larger and smaller atypical deletions have been reported in sporadic cases [18]–[31] . While the primary cause of WBS is well-understood , we still know little about the molecular basis of the phenotype . Only very recently , strains of mice were engineered to carry complementary half-deletions of the region syntenic to the WBS region , which replicate several features of WBS , including abnormal social interaction phenotypes [32] . Yet , so far the dissection of the phenotype relies mainly on evidence from other mouse models — e . g . single gene knock-out — and atypical deletions in humans . Findings from these studies suggest some correlations between hemizygosity of certain genes and specific phenotypic features seen in WBS individuals . For example , the SVAS phenotype was shown to be unequivocally associated with haploinsufficiency of the elastin gene [33]–[35] . Furthermore , mouse models hemizygote for some of the orthologs of the WBS deletion most telomerically-mapping genes suggested that these were linked to craniofacial abnormalities ( GTF2I and GTF2IRD1 genes ) [36] , tooth anomalies and visuospatial deficit ( GTF2I , GTF2IRD1 and GTF2IRD2 genes ) [22] , [37] , as well as deficits in motor coordination ( CLIP2 ) [38] . Likewise , the function of the carbohydrate response element-binding protein ( MLXIPL , a . k . a . ChREBP or WBSCR14 ) in the regulation of the expression of enzymes involved in glucose and lipid metabolism [39]-[43] suggests that its haploinsufficiency is associated with the higher relative body fat , silent diabetes and/or impaired glucose tolerance found in adult WBS individuals [2] . We showed in previous work that the vast majority of the genes hemizygous due to the 7q11 . 23 deletion are underexpressed in lymphoblastoid cell lines and fibroblasts derived from patients [44] , consistent with their possible role in some of the WBS phenotypes . Some of the genes that map to the flank of the microdeletion might also influence the WBS phenotype , as it was recently shown that structural rearrangements affect the relative expression levels of neighboring normal-copy genes ( [44]–[48] , reviewed in [49] , [50] ) . To identify which downstream pathways are perturbed in WBS by these two classes of human chromosome 7 ( HSA7 ) genes , we generated genome-wide transcription profiles for primary fibroblasts from eight individuals with WBS and nine sex- and age-matched controls . We first focus on differentially expressed genes and then on co-expressed gene sets to elucidate the genes and pathways that are dysregulated in WBS and how they may contribute to its clinical phenotypes .
Gene expression in fibroblasts can only provide a partial picture of the gene dysregulation that gives rise to the WBS clinical phenotypes . Thus , data from other cell types or tissues may provide additional clues as to dysregulated pathways , as well as confirm some of our findings in fibroblasts . Indeed , comparison with the recently published transcriptome of lymphoblastoid , i . e . EBV-transformed , cell lines from WBS patients [66] revealed a few commonly dysregulated genes . The expression of 11 common genes was altered with the same sign in both cell types , while for 29 others we observe opposite expression ( Table S11 ) . Eight of the 11 genes with consistently altered expression were part of 28 dysregulated M1 or M2 modules ( Table S11 ) . Out of the 72 M1 modules the average gene expression of which is altered in WBS fibroblasts , seven are also changed in the lymphoblastoid cell lines; four modules are altered in the same direction , three modules are opposite in the two studies . Moreover , 19 of the 23 dysregulated M2 modules are also perturbed in the lymphoblastoid samples , 18 in the same direction ( Table S11 ) , suggesting that the pathways identified in the fibroblasts are disrupted in multiple tissues . Furthermore , we can surmise that modules consistently regulated in both cell types may represent central pathways influenced by the WBS deletion , while the remaining modules may reflect cell-type specific alterations , which in turn might be important for tissue-specific phenotypes .
We have profiled the transcriptomes of skin fibroblasts from eight WBS patients and nine sex- and age-matched control individuals , and identified a number of transcription modules dysregulated in WBS patient cells . One caveat of this study lies in the use of isolated cells in vitro that may not reflect all the different tissue-dependent transcriptional changes in vivo that give rise to the complex WBS phenotypes , such as cognitive features or connective tissue anomalies . Moreover , the samples we consider only allow us to observe the downstream global effects of the primary cause , as opposed to the immediate effect on early development . However , these cell types are the most readily available samples , and the replication of a subset of the fibroblast dysregulations in lymphoblastoids supports the hypothesis that at least some of these changes appear in multiple cell types as a direct result of the 7q11 . 23 deletion and thus provide clues about pathways that may generally be perturbed in WBS . Our results reveal a transcriptional network which may contribute to the pathophysiology of WBS . We propose that many of the WBS phenotypes arise due to the dysregulation of a few key gene products , which influence ( possibly in concert ) “regulatory subnetworks” , leading to specific traits . Also , disturbances in a process due to one group of genes may trigger compensatory mechanisms in another set , either directly in the cell , or indirectly through intercellular or more systemic effects . Both our single-gene and modular analyses provide a resource to enable a deeper exploration of the pathophysiology of WBS , which may lead to the discovery of potential novel functional interactions between their products . Our study further exemplifies how integration of transcription data unrelated to the studied condition can be used to complement annotation-dependent analyses . Indeed , the modular approach reduces the complexity of the expression data , allowing a more targeted assignment of functional categories to specific sets of co-regulated genes . Consistently , Turcan et al . recently used a similar methodology to identify groups of genes coherently regulated during cochlear development , which allowed them to pinpoint candidate genes for further study [67] . It is important to underline that further investigations and more data are needed to distinguish between biologically relevant associations of differentially regulated modules and spurious co-expression signals . Nevertheless , we think that the information generated by our study ( and made available at http://www . unil . ch/cbg/ISA/Fibroblasts ) provides a testable set of candidate pathways dysregulated in WBS and possibly involved in mediating the wide range of associated phenotypes .
We have obtained the approval of the ethics committees of the University of Lausanne ( reference number Protocol 123/06 ) and of the “Hospices Civils de Lyon” for this project . All patients provided written informed consent for the collection of samples and subsequent analysis . Skin fibroblasts of 8 classical WBS and 9 control Caucasian female individuals aged between 3 and 8 years ( see Table S1 for details ) and similar numbers of passages were obtained from the cell culture collections of the Centre de Biotechnologie Cellulaire , CBC Biotec , CRB-Hospices Civils de Lyon , Lyon , France . The respective presence and absence , as well as the extent of the deletion were ascertained by SybrGreen real-time quantitative PCR as previously described [26] . Human skin fibroblasts were grown in HAM F-10 , supplemented with 10% fetal bovine serum and 1% antibiotics ( all Invitrogen ) . Total RNA was prepared using TriZOL Reagent ( Invitrogen ) and RNeasy Mini Columns ( Qiagen ) according to the manufacturers' instructions . The quality of all RNAs was assessed using an Agilent 2100 Bioanalyzer ( Agilent Technologies ) and used as a template for complementary DNA ( cDNA ) synthesis and biotinylated antisense cRNA preparation . The synthesis of cDNA and cRNA , labeling , hybridization and scanning of the samples were performed as described by Affymetrix ( www . affymetrix . com ) . The cRNA samples were hybridized to GeneChip Human Genome U133 Plus 2 . 0 arrays ( Affymetrix ) . The chips were washed , stained and scanned , according to the manufacturer's protocol . The data of the 17 expression arrays produced for this report have been deposited in NCBIs Gene Expression Omnibus ( GEO , http://www . ncbi . nlm . nih . gov/geo/ ) and are accessible through GEO Series accession number GSE16715 . Expression data analyses were performed using GNU R ( version 2 . 9 . 2 ) [68] and the Bioconductor package ( version 2 . 4 ) [69] . All R package versions are listed in Table S12 . Low-level analysis and normalization were done using GCRMA . For differential expression analysis we filtered the probesets and kept only those present in at least six samples , according to the Affymetrix Present/Absent calls calculated with the affy R package . To reduce noise , we also removed probesets that do not map to an Entrez gene . 18 , 429 probesets , mapping to 10 , 570 genes were tested for differential expression , using the moderated t-statistics , as implemented in the limma R package . In addition to the significant p-value , we required a minimum of 50% change for declaring a gene differentially expressed . 1 , 114 probesets , corresponding to 868 genes were found differentially expressed at the 5% FDR level , 367 probesets , mapping to 306 genes at the 1% FDR level . The FDR was controlled using the Benjamini-Hochberg correction [51] . Gene set enrichment analysis of the WBS hemizygous genes was performed by comparing the mean t-statistics of these genes , for the WBS patients and the control individuals; the reference distribution for this was established by permuting the phenotype labels 10 , 000 times [70] . Gene Ontology and KEGG Pathway enrichment was calculated via a hypergeometric test , using the eisa and GOstats Bioconductor packages . The enrichment P-values were corrected using the Benjamini-Hochberg method for the number of categories tested . A transcription module comprises a subset of genes that are co-expressed in a subset of conditions [56] . The Iterative Signature Algorithm ( ISA ) [71] is an unsupervised method to identify such modules . It starts from many random initial sets of genes ( seeds ) that typically converge to a set of potentially overlapping transcription modules . The ISA assigns a signed score to every gene of the module and every sample of the module ( zero scores imply that the gene or sample is not included in the module ) . The further the gene/sample score is from zero , the stronger the association between the gene/sample and the rest of the module . Co-expressed genes of a module have the same sign , whereas opposite signs signal opposite expression . The scores of the samples are exactly the same as the weighted averages of the expression of the module genes , the weights being the scores of the genes . Sample scores can be extended to the samples that are not included in the module , by calculating the same weighted average of the module genes for them . These samples have ( in absolute value ) lower scores than the module samples , by definition . The extended sample scores can be used to test whether the genes of a module are differentially regulated in some samples . The aim is to identify dysregulated transcription modules containing genes that are differentially expressed in the cases compared to the control samples . In the first ISA run , we used skin fibroblast samples from seven experiments from public repositories , as well as collaborators of the AnEUploidy consortium ( the latter can be obtained by contacting the consortium at http://www . aneuploidy . eu/ ) ( Table S4 ) . For each dataset we downloaded the raw data and normalized them separately with the GCRMA method . The non-common probesets were omitted and the normalized expression data were merged; the data set included 22 , 277 probesets and 96 samples . To reduce noise we removed probesets that were called “Present” in less than ten samples , using the standard Affymetrix Present/Absent calls . We also removed probesets that were not associated with any Entrez gene . In order to avoid a bias towards genes with multiple probesets we only kept the single probeset with the highest variance for those genes . The final dataset included 9 , 329 probesets . We applied the ComBat batch correction algorithm [72] to minimize non-biological variation; we used the “disease status” of the samples as an additional covariate for the correction ( column “disease status” in Table S4 ) . The additional covariate ensures that we do not remove the signal associated with the different syndromes in the data sets , only the systematic experimental variation . We ran ISA as implemented in the eisa R package [73] , with gene thresholds 2 , 2 . 2 , … , 4 and sample thresholds 1 , 1 . 2 , … , 2 . The ISA identified 1 , 094 transcription modules . For the identification of the dysregulated modules , we used the GCRMA normalized WBS data set . Probesets that were called “Present” in less than six samples were omitted from the analysis . We only considered the 7 , 447 probesets that were included both in this filtered WBS data set and the modular study . 732 modules that contained at least ten genes were tested for dysregulation . For the dysregulation test we standardized the WBS expression data for every gene separately . Standardization is an important step , since the test for dysregulation involves the average expression of the module genes . Specifically , to test a module , we calculated the weighted average expression of its genes , separately for each WBS sample . The weights were defined by the gene scores of the module . Then a t-test with unequal variance was performed for the WBS cases against controls . The t-test P-values were corrected with the Benjamini-Hochberg method . At the 5% FDR level 72 dysregulated modules were found . To check the significance of finding 72 dysregulated modules , we permuted the WBS case/control labels 1 , 000 times and tested for dysregulation as before . These permutations serve as a null-model to estimate how many dysregulated modules could have resulted by chance . Only 14 permutations yielded at least one dysregulated module . Within these 14 cases , the mean number of dysregulated modules was 12 . 1 , the median 1 . 5 . The highest number of dysregulated modules found for a permutation was 58 . We note that the three permutations that yielded multiple ( false positive ) WBS dysregulated modules had almost correct WBS case/control labels: only one pair was swapped . Hypergeometric tests were used to calculate the functional enrichment of the 72 dysregulated modules , with Benjamini-Hochberg correction for the number of categories and the number of modules tested . The significance threshold was chosen as 0 . 05 . The second modular study ( M2 ) was performed almost identically , but this time the WBS samples were also included in the data set . The ISA was run on 9 , 460 probesets and 113 samples , using gene thresholds 2 , 2 . 2 , … , 4 and sample thresholds 1 , 1 . 2 , … , 2 . The ISA found 1 , 035 modules , of which 290 contain at least ten genes and one sample from our study . These were tested for dysregulation using t-tests for the sample scores of the WBS cases vs . controls , identifying 23 modules that are differentially expressed . As an additional validation , we permutated the labels of the WBS samples 1 , 000 times; no permutation showed any dysregulated modules . Enrichment calculation for the dysregulated modules was done the same way as for the M1 modules , using Benjamini-Hochberg multiple testing correction for the number of categories and the number of modules tested , and a significance threshold of 0 . 05 . We used version 8 . 3 of the STRING database to interrogate the genes that frequently appear in the dysregulated modules . All network measures were calculated using the igraph R package [74] . We fitted hierarchical models [60] to the subnetwork of frequent module genes , and also to 1 , 000 randomized networks . For fitting the hierarchical models , we only considered the largest connected component of the network , consisting of 90 proteins and 203 connections among them . The randomized networks had the same degree sequence as the original network , and they were produced using Monte-Carlo methods [75] . The enrichment calculations for the extracellular region genes ( Figure S1 ) were done using hypergeometric tests and the eisa and GOstats R packages . Only the second level terms in the “Biological process” and “Molecular function” ontologies were tested . To identify genes commonly dysregulated in cells from WBS patients identified in this study and in [66] , which uses two-color arrays ( GEO accession number GSE18188 ) , we tested the lymphoblastoid samples for differentially expressed genes . We used the moderated t-statistics and a fold-change threshold of 1 . 5 and applied the Benjamini-Hochberg multiple testing correction method to identify 574 differentially expressed genes . Forty of these are common with the 868 differentially expressed genes we found in the fibroblast samples . To test the dysregulation of the fibroblast dysregulated modules in the lymphoblastoid samples , we calculated the weighted mean log fold change of the module genes for each lymphoblastoid array , where the gene scores of the modules were used as weights . Then we used a t-test to check whether the mean log fold change is significantly above or below zero , followed by the Benjamini-Hochberg multiple testing correction method . The modules and related details are available at http://www . unil . ch/cbg/ISA/Fibroblasts . These web pages contain the summary of all M1 and M2 transcription modules and their GO/KEGG enrichment statistics . An interactive version of Figure 3 is also included; this allows the exploration and annotation of the dysregulated modules , using various criteria . It is also possible to query the modules that contain a specific gene , or a list of genes . See the help page of the supplementary material for details . Additionally , the modules can be visualized interactively with the online version of ExpressionView [76] . The expression array annotation data were taken from the hgu133a2 . db ( version 2 . 2 . 11 ) and hgu133plus2 . db ( version 2 . 2 . 11 ) Bioconductor packages . The GO . db package ( version 2 . 2 . 11 ) was used for the Gene Ontology and the KEGG . db package ( version 2 . 2 . 11 ) for the KEGG pathway data . Software packages are listed in Table S12 . | A fundamental question in current biomedical research is to establish a link between genomic variation and phenotypic differences , which encompasses both the seemingly neutral diversity , as well as the pathological variation that causes or predisposes to disease . Once the primary genetic cause ( s ) of a disease or phenotype has been identified , we need to understand the biochemical consequences of such variants that eventually lead to increased disease risk . Such phenotypic effects of genetic differences are supposedly brought about by changes in expression levels , either of the genes affected by the genetic change or indirectly through position effects . Thus , transcriptome analyses seem appropriate proxies to study the consequences of structural variation , such as the 7q11 . 23 deletion present in individuals with Williams-Beuren syndrome ( WBS ) . Here , we present an approach that takes experimental data into account instead of relying solely on functional annotation , following the rationale that coherently regulated genes are likely to play a role in the same biological process . While our algorithm can be applied to expression data from any source , our study provides a resource for the identification of additional candidate genes and pathways to explain the WBS phenotype , as well as a basis for uncovering novel functional interactions between sets of genes . | [
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... | 2011 | Using Transcription Modules to Identify Expression Clusters Perturbed in Williams-Beuren Syndrome |
Enterohemorrhagic Escherichia coli ( EHEC ) , particularly serotype O157:H7 , causes hemorrhagic colitis , hemolytic uremic syndrome , and even death . In vitro studies showed that Shiga toxin 2 ( Stx2 ) , the primary virulence factor expressed by EDL933 ( an O157:H7 strain ) , is encoded by the 933W prophage . And the bacterial subpopulation in which the 933W prophage is induced is the producer of Stx2 . Using the germ-free mouse , we show the essential role 933W induction plays in the virulence of EDL933 infection . An EDL933 derivative with a single mutation in its 933W prophage , resulting specifically in that phage being uninducible , colonizes the intestines , but fails to cause any of the pathological changes seen with the parent strain . Hence , induction of the 933W prophage is the primary event leading to disease from EDL933 infection . We constructed a derivative of EDL933 , SIVET , with a biosensor that specifically measures induction of the 933W prophage . Using this biosensor to measure 933W induction in germ-free mice , we found an increase three logs greater than was expected from in vitro results . Since the induced population produces and releases Stx2 , this result indicates that an activity in the intestine increases Stx2 production .
Enterohemorrhagic E . coli ( EHEC ) has emerged as a serious health threat with numerous outbreaks most commonly due to contaminated beef , but also to contaminated vegetables and water [1] . Although EHEC strains [2] , and another recently identified pathogenic E . coli [3] , encode a number of virulence factors , the most serious sequelae of infection by these strains are due to the acquisition and expression of genes encoding Shiga toxins ( Stx ) . In many EHEC strains these toxins are encoded in the genomes of prophages of the λ family ( referred to as lambdoid phage ) [4] . Two major classes of Shiga toxins , Stx1 and Stx2 , have been identified in EHEC strains [5] . Although sharing the same activity , they differ somewhat in sequence and Stx2 is associated with the more severe sequelae in humans [6] and is the cause of disease in animal models [7] . These members of the AB5 class of toxins bind eukaryotic cells by attachment of the pentameric structure of the B subunit to a glycoprotein receptor on the eukaryotic cell [8] . Retrograde transit through the endosomic pathway to the cytosol results in the A subunit , a glycosidase , reaching the ribosomal RNA [9] . There , a specific adenine residue in the large ribosomal subunit is cleaved , resulting in arrested protein synthesis that leads to cellular intoxication [10] . EHEC strains commonly isolated in outbreaks are those of the O157:H7 serotype [6] . Members of the lambdoid family of temperate phages share a common genome organization with prototypical λ . Genes at the same relative position on their respective genomes may differ in sequence , but for the most part they share the same activity [11] . For example , the repressors and operators may differ in sequence and specificity , but the different lambdoid phages have a common structure and location for these genetic elements on their genomes [12] . Moreover , the lambdoid phages are mosaics with each phage sharing a number of different genes with different members of the family [11] , [13] . These conserved structure-function relationships allowed for the relatively rapid determination of the role of the phage in Stx expression [14] . When present , the stxA and B genes are located downstream of PR′ , the late phage promoter [15] , [16] , and upstream of the phage lysis genes ( Fig . 1 ) [14] , [17] . In vitro and in vivo studies with the O157:H7 strain 1∶361 and its resident stx2-phage , φ361 , showed that transcription from PR′ is required for Stx2 production [18] . In vitro studies with the E . coli strain K9675 ( a derivative of the nonpathogenic strain K37 lysogenized with the stx2-phage 933W ) showed that Stx2 expression requires prophage induction [19] . Hence , Stx2 expression , at least under these in vitro conditions , depends on the phage induction cascade . Prophage induction explains why patient treatment with antibiotics that can act as inducing agents , such as the quinolones , lead to higher Stx levels [20] and exacerbate the disease [21] . The lambdoid phage regulatory cascade which leads to phage production and cell lysis has been the subject of years of study with λ and to a lesser extent with other members of this family of phages [22] . Induction , which results in the initiation of the regulatory cascade , is set in motion when the bacterium containing the prophage ( lysogen ) sustains DNA damage and responds by activation of the LexA regulon , leading to a cellular change in gene expression termed the SOS response [23] , [24] . One member of this regulon , RecA , increases in quantity and assumes an activated form , RecA* , by interacting with single-stranded DNA generated by DNA damage [25] . RecA* , through its co-protease activity , facilitates the autocleavage of phage repressor [25] , allowing initiation of transcription from the early PL and PR promoters ( Fig . 1 ) . Transcription from PL results in expression of N protein , which acts to modify RNA polymerase initiating specifically at PL and PR to a form resistant to downstream terminators [26] . N-modified transcription from PR transcends downstream terminators resulting in Q expression . Q in turn modifies transcription initiating at the late PR′ promoter to a termination-resistant form allowing expression of downstream genes [27] , including stx A and B in stx-phages [14] , [17] , [18] , [28] . A λ prophage fails to induce if the repressor gene ( cI ) has a mutation that inhibits autocleavage [29] , [30] . These mutations , called ind , change amino acid residues within the repressor that participate in a serine protease activity that catalyzes autocleavage [25] . We have previously suggested that the induced subpopulation is responsible for Stx production and release [14] . Lysogens with most lambdoid prophages are stable with only an extremely small fraction of the population , in the absence of an external inducing agent , sustaining sufficient DNA damage to be induced , a stochastic process referred to as “spontaneous induction” [25] . It has been suggested that collapse of the replisome in normally growing bacteria caused by single-stranded breaks or noncoding lesions may be an internal event responsible for spontaneous induction [31] . DNA damage-inducing agents change induction from a stochastic to a deterministic process that activates RecA and , in turn , repressor cleavage [32] . Although recA mutants have been used to study conditions where the prophage fails to be induced and Stx is not expressed [33] , such an experimental approach suffers from the disadvantage of the pleiotropic effects on bacterial physiology due to loss of RecA activity [34] , [35] . Using a phage with an ind mutation avoids this problem by limiting the failure of SOS control only to the prophage with the ind mutation . Linkage of Stx expression to prophage induction raises the question as to whether the intestinal environment increases Stx levels by causing prophage induction . One way this could occur would be by increasing DNA damage in the bacterium . In vitro experiments showed a modest increase in Stx production by an O157:H7 strain when bacteria were cultured with neutrophils [36] , which produce H2O2 that can cause DNA damage leading to an SOS response and prophage induction . Here , we report experiments with the O157:H7 strain EDL933 and derivatives of EDL933 that carry a 933W prophage with a cI ind mutation . Using a germ-free mouse model of disease , we show that whereas the parent EDL933 with wild-type 933W prophage produces high levels of Stx in vivo and causes severe disease that can lead to death , a derivative isogenic except for a cI ind mutation in the 933W prophage produces extremely low levels of Stx2 and does not cause any observable disease . These results provide compelling evidence that induction of the 933W prophage is a major factor in pathogenesis of EDL933 and prophage induction may play a role in the severity of infection by other O157:H7 strains . Using an EDL933 SIVET reporter strain , which survives induction but undergoes a change in antibiotic resistances following induction , we show that the intestinal environment contributes to induction of the 933W prophage in EDL933 .
Induction of lambdoid prophages , including that of 933W , occurs when the repressor protein autocleaves in the presence of activated RecA protein [25] . Mutations , ind , in the cI gene result in a noncleavable repressor and thus an uninducible prophage [29] . The strategy used to obtain the ind mutation in the cI gene of the 933W prophage in EDL933 , a change of Lys codon 178 [suggested by John Little [37]] , was based in part on the procedure previously employed by our laboratory to construct an identical point mutation in the cI gene of the 933W prophage in strain K9675 [19] . Sequencing confirmed that the cI gene in EDL933 with the mutant 933W had only the designed nucleotide substitution at codon 178 . The mutation , named ind1 , is a change of the Lys codon to an Asn codon ( K178N ) . This change interferes with the autocatalytic serine protease activity of the CI repressor [38] , rendering the prophage uninducible . We will refer to the derivative of EDL933 carrying the 933W prophage with the cIind1 mutation as EDL933cIind1 ( Table 1 ) . This strain carries the stx2 genes and differs from EDL933 only by the 933W cI mutation . To assess the effectiveness of the ind1 mutation on prophage induction , we treated EDL933 and EDL933cIind1 with mitomycin C [39] . At an appropriate concentration , this DNA damaging agent activates the SOS response of most of the population sufficiently to induce the prophage [40] . Treatment of the EDL933 parent with 2 µg/ml of mitomycin C led to full induction of the culture; i . e . , lysis was nearly complete ( Fig . 2 ) . Identical treatment of EDL933cIind1 failed to cause lysis ( Fig . 2 ) . This result confirms that the ind1 mutation blocks induction of 933W . Additionally , it shows that the inducing agent does not cause any of the large number of defective prophage in EDL933 [41] to express lytic activity . This finding provides direct evidence that induction of 933W is not only responsible for Stx2 production , as shown below , but also for the lysis that releases Stx2 from the bacterium . We used an ELISA to assess Stx2A levels; comparing levels in EDL933 with those in the EDL933cIind1 derivative and the nonpathogenic 933W lysogen K9675 . In the absence of an inducing agent , the parent EDL933 expresses ∼40 times the level of Stx2 expressed by EDL933 cIind1 mutation ( Fig . 3 ) . This result provides compelling evidence that in culture a significant fraction of Stx2 production derives from the subpopulation of EDL933 in which the 933W prophage is induced . These results are only partially consistent with our previous findings with strain K9675 [19] . In that study we found that in the absence of an external inducing agent , the level of Stx2A produced by K9675 was ∼10 fold lower than the level produced under these conditions by EDL933 with its wild-type 933W prophage . In the current study , we confirmed these findings , showing that in the absence of an external inducer ( spontaneous induction ) EDL933 produces ∼10 times more Stx2A than K9675 ( Fig . 3 ) . To rule out the possibility that the low Stx2A levels in the nonpathogenic strain resulted from alteration of the prophage or the host , we measured Stx2A production from another nonpathogenic K12 related strain , MC1000 [42] , with a 933W prophage . As above , we observed the lower level of Stx2A expression in the non-pathogenic strain compared to EDL933 ( data not shown ) . Although comparison of spontaneous induction shows that EDL933 produces ∼10 fold higher levels of Stx2A than K9675 , the source of Stx2A for each is primarily that fraction of the population in which the prophage is induced ( this study and Tyler et al . [19] . To specifically assess the role of induction of the 933W prophage in Stx2 production , we determined Stx2 levels following treatment with mitomycin C ( 2 µg/ml ) . As shown in Fig . 3 , mitomycin C treatment resulted in a 100 to 200-fold increase in Stx2 production by EDL933 . Although K9675 produced significantly less Stx2 than EDL933 in the absence of an inducing agent , it produced about the same levels of Stx2 following treatment with mitomycin C as similarly treated EDL933 . The EDL933cIind1culture treated with mitomycin C produced 5- to 10-fold more Stx2 than the untreated culture . Although two orders of magnitude lower than the Stx2 production reached by EDL933 treated with mitomycin C , the increased levels we observed with the treated EDL933cIind1 were reproducible . The increase in Stx2 following mitomycin C treatment is consistent with the observation of measurable levels of Stx2 produced by EDL933cIind in the absence of an inducing agent . Either the mutant repressor retains some ability to autocleave ( leaky mutant ) in the EDL933 environment or there is an alternative route to Stx2 expression . However , in either event the production of Stx2 is extremely low in the presence of the cIind1 mutation . Results of clinical studies of children with EHEC infection show that phage induction likely plays an important role in the disease; e . g . , those treated with antibiotics that elicit an SOS response may experience more severe outcomes [21] . In mice , treatment with ciprofloxacin ( an antibiotic that elicits the SOS response ) also results in greater in vivo expression of Stx , likely via prophage induction [43] . Although suggestive , these findings are far from definitive . As discussed , the SOS response has pleiotropic effects on bacterial gene expression and does far more to affect cell physiology than induce prophage [34] , [35] . Relevant to our studies , treatment of EDL933 with the DNA damaging agent norfloxacin results in changes in the expression of a number of prophage and non-prophage genes in EDL933 [44] . Because the only effect of the ind1 mutation is to interfere with induction of the 933W prophage , experiments with EDL933cIind1 allowed us to ask specifically how significant induction of the 933W prophage is in causing the pathology associated with EDL933 infection . The germ-free mouse has proven an effective and practical animal model for studying the pathology of EHEC infection [7] . We found that germ free mice infected with O157:H7 strains such as EDL933 develop acute renal tubular necrosis and renal glomerular thrombosis leading to renal failure and death . In the same study , we also reported that a similar infection with a derivative of EDL933 isogenic except for a deletion of the stx2 genes does not result in any of the pathogical changes seen with the wild-type parent strain . Hence , in this animal model , all of the described pathological changes result from the action of Stx2 . For these reasons , we chose the germ-free mouse to assess the role in the disease process of induction specifically of the 933W prophage carried by EDL933 . Groups of 6 ( 3 female and 3 male ) germ-free Swiss-Webster mice were used in the experiments . They were infected with one of three bacteria , EDL933 or either of two isogenic strains that differed by having the Δstx::cat deletion substitution or the cIind1 point mutation . For all strains tested , each animal was challenged with 106 cfu administered orally . All three groups of mice were equally colonized over the seven days of the experiment in which bacteria in the feces were measured ( ∼1010 cfu/g ) . As expected from our previous work , all 6 mice infected with the wild-type EDL933 parent strain became moribund prior to the scheduled time mice were euthanized at three weeks . All mice infected with the Δstx::cat deletion derivative showed no signs of disease . Like the latter group , mice infected with EDL933cIind1 showed no signs of disease ( Fig . 4 ) . Figure 4A shows a Kaplan-Meier survival curve of mice inoculated with the three strains . All 6 mice given EDL933 became moribund or died prior to 21 days after inoculation . At necropsy , these mice were dehydrated and thin , and their ceca were distended with fluid contents . Mice in this group had moderate-severe acute renal tubular necrosis ( Fig . 4B ) , failed to gain weight as indicated by significantly lower body weights at necropsy ( Fig . 4C ) , and all and dilute urine ( Fig . 4D ) , indicating renal failure . Histologically , renal disease was characterized by necrosis of renal tubules and occasional glomerular fibrin thrombi ( Fig . 4E ) . Mice in the other two groups did not show any signs of disease , and had normal renal morphology Fig . 4F ) . As noted above , cecal colonization was similar in all three groups of mice ruling out poor colonization as an explanation for the failure of EDL933cIind1 to cause disease . As discussed , in vitro EDL933cIind1produces measurable levels of Stx2 , raising the question of whether it produces measurable levels of Stx2 in the infected mouse . Although there was wide variation , we found low but measurable levels of Stx2 in the feces of some of the mice infected with EDL933cIind1 , 0–300 ng/ml of feces . Much higher levels of Stx2 , with considerable variation , were found in the feces of mice infected with EDL933 , 6529±4432 ng/ml of feces ( P = 0 . 0039 ) . Based on the RIVET ( recombinase based in vivo expression technology ) [45] , [46] , we developed SIVET ( selectable in vivo expression technology ) , with the aim of determining if there is any effect on prophage induction when bacteria are in the intestine . Studies with the first generation SIVET , constructed in the nonpathogenic E . coli strain MC1000 , established this reporter system as a valid method for measuring prophage induction [47] . Here we report construction of a second generation SIVET through modification of EDL933 ( see Materials and Methods for details ) . Figure 1-II outlines the essential features of the SIVET system . Briefly , the 933W and 933V prophages in EDL933 were genetically altered so that functions lethal to the bacterial host [48] are not expressed upon induction and the bacterium therefore survives challenge with an inducing agent . The tnpR gene from the γδ transposon [49] was cloned downstream of the 933W early PR promoter distal to the cro gene . Thus , following induction of the 933W prophage transcription initiating at the phage promoter PR results in production of the TnpR resolvase that , in turn , acts at another site on the bacterial chromosome to excise a kanR cassette that interrupts a cat gene . This recombination serves two purposes , establishes a functional cat gene and removes the kanR cassette , conferring CamR . Hence , upon induction of the altered 933W prophage there is an irreversible and inheritable change of the host bacterium from KanR/CamS to KanS/CamR . The fraction of the total bacterial count that is CamR provides a measurement of the number of bacteria in which the prophage was induced . That this change is due to prophage induction is shown by the results of the following experiments . First , treatment of the SIVET strain with mitomycin C , known to cause prophage induction [39] , results in an increase of ∼1000 fold in CamR colonies and a reduction of ∼1000 fold of KanR colonies ( Fig . 5 ) . Second , treatment of a cIind1 mutant derivative of the SIVET strain ( K11607 ) under exactly the same conditions used with the SIVET parent failed to cause any measurable change in the levels of KanR or CamR bacteria ( Fig . 5 ) . In the following in vitro and in vivo experiments , the ratio of CamR/KanR SIVET was standardized to simplify the presentation using what will be referred to as the “Induction Index” . This function is calculated as the log10 of ( CamR/KanR output ) / ( CamR/KanR input ) ( for details see Materials and Methods ) . Because of the way the Induction Index is calculated , the starting point in the graphs , the input , is equal to log10 ( 1 ) or 0 . This allows changes in induction to be monitored by observing movement of the Index away from 0 . The only way we see the ratio deviate , beyond expected scatter , from 0 on the Induction Index , is if one of the two populations increases more than the other either by a growth advantage or by addition of newly generated derivatives . To rule out alteration in the induction index due to a growth advantage of one or the other marked strain , we used two SIVET derivatives; one , K11607 , locked in the KanR form by virtue of the cIind1mutation and the other , K11608 , a derivative of K11607 which is isogenic except for the excision of the KanR cassette and thus is locked in the CamR form . The CamR/KanR ratio ( calculated employing the formula used to generate the Induction Index ) following coinfection with the locked in CamR and KanR derivatives hovers around 0 ( Fig . 6A ) . Since there is no growth advantage to either form , any positive increase in the CamR/KanR Induction Index of the parental SIVET would have to be explained as addition by conversion from the KanR population to the CamR population , a direct consequence of induction of the 933W prophage in the KanR bacteria . As discussed above , a small fraction of a population of lysogens growing in the absence of an added inducing agent undergo induction , a process called spontaneous induction [25] . To determine whether spontaneous induction of the SIVET prophage adds to the population of CamR bacteria , we measured the CamR/KanR ratio , determined as the Induction Index , over the course of a large number of doublings in vitro in two different ways ( Fig . 6 ) . In both approaches , the SIVET strain was serially passaged in vitro for a number of generations in LB medium and the CamR and KanR populations periodically measured by viable counts . In one set of experiments , the SIVET bacteria were grown to stationary phase and diluted 10-fold for the next passage ( Fig . 6B ) while in the other , the bacteria were kept in log phase and diluted from an OD600 of ∼1 . 0 to an OD600 of 0 . 1 for the next passage ( Fig . 6C ) . Both protocols yielded similar experimental results; the Induction Index remained relatively constant over many doublings , hovering around 0 . These results lead us to conclude that spontaneous induction does not significantly affect the CamR/KanR ratio . We consider these results further in the Discussion . To determine if the intestine environment contributes to prophage induction and thus Stx production , we employed the ELD933 SIVET strain using the infection protocol as described above . Each mouse was orally infected with ∼106 SIVET bacterium . Because the 933W prophage was mutationally disarmed ( see Materials and Methods for details ) and thus does not produce Stx2 , as expected , mice infected with EDL933 SIVET did not show signs of disease . Feces were isolated each day for seven days and bacterial counts were determined by plating on LB agar plates containing kanamycin or chloramphenicol . The total EDL933 SIVET count remained relatively constant over the course of the experiment , ∼108 CFU/g of feces , although slightly decreasing by the seventh day ( data not shown ) . The Induction Indexes over the 7 days presented in Fig . 7 were compiled from results of three independent experiments , each comprised of five mice . By day seven the Induction Index has increased by over three logs . The study was terminated at day 7 , when the onset of severe disease caused by EDL933 usually occurs [7] . To determine if the change in the CamR/KanR Induction Index during in vivo growth of SIVET reflects a difference in viability of the two forms of the SIVET , we employed the SIVET pair K11607 and K11608 . These derivatives , as discussed above , are locked in either the KanR or CamR form . Mice were co-infected with K11607 and K11608 and followed essentially as described for the in vivo SIVET study outlined above . Examination of fecal samples showed that the ratio of CamR/KanR ( calculated using Induction Index formula ) did not significantly change over the course of 7 days ( Fig . 7 ) ; i . e . , neither form of SIVET has a growth advantage during in vivo growth . Hence , the null hypothesis stands and we conclude that the increase of the CamR/KanR Induction Index observed during growth in the mouse intestine results from prophage induction . Based on this collection of data , we conclude that there is significant induction of the 933W prophage in the germ free mouse intestine . Since Stx2 production is directly linked to 933W induction , it follows that the intestine , through action of a yet to be identified factor ( s ) , stimulates Stx2 production through induction of the 933W prophage .
With the information gained from sequencing numerous bacterial genomes , it has become apparent that virulence factors are commonly located in genomes of prophage [50] , [51] . Introduction of a new function , such as a virulence factor , to a bacterium by a prophage is referred to as lysogenic conversion . Although Stx2 is an example of a phage-encoded toxin whose expression is controlled by the phage regulatory cascade , many other phage-encoded toxins are expressed independently of prophage regulatory functions . This is true for the classic toxin of Corynebacterium diphtheriae [52] and of cholerae toxin ( CTX ) , which is encoded in the genome of the CTXΦ prophage [53] . Expression of CTX is controlled by a complex circuitry of proteins encoded by regulatory genes located outside of the prophage genome [54] . Observations like these led to the idea that phage , like other mobile elements , serve as agents that can transfer genetic information from one bacterium to another [55] . However , at least in the case of stx-phages , the phage serves a wider role , not only being the source of transfer , but also the regulator of expression from the transferred virulence gene [16] . The construction of a derivative of EDL933 with the ind1 mutation in the 933W prophage coupled with an animal model that mimics , to a large degree , the human disease , has allowed us to specifically assess the contribution of induction of the 933W prophage to the disease process . Like its EDL933 parent , EDL933cIind1 effectively colonizes the host intestines . However , unlike the parental strain , the ind1 strain fails to elicit any of the hallmarks of an EHEC infection; e . g . , physical signs of illness , renal disease , and death . That EDL933cIind1 colonizes the host intestine is consistent with our previously reported findings showing that a derivative of EDL933 with a deletion-substitution of the stx2 genes colonized as well as the parent strain with a functional stx2 gene [7] . This observation is contrary to the findings of Robinson et al . [56] , who reported that colonization was reduced if the O157:H7 strain did not express Stx2 . As we have suggested previously [7] , this difference may reflect our use of germ-free mice , while Robinson et al . used mice with normal microbiota . Our results provide evidence that the major pathogenic effect of EDL933 results from induction of the 933W prophage . Hence , the phage regulatory cascade plays a central role in the pathogenesis of this O157:H7 strain and likely many others . Since repressor auto-cleavage requires activated RecA protein , which , in turn , is a product of the SOS response , it is primarily that subpopulation of bacteria , with a sufficiently vigorous SOS response that induces the 933W prophage and results in the production and release of Stx2 . Our observation that Stx2 production and disease in the mouse are directly related to induction of the 933W prophage raises the question as to whether there is a factor ( s ) in the intestines that increases the SOS response resulting in increased prophage induction beyond that expected from results of in vitro experiments . Such a role was found for a factor in human pharyngeal cells that induces a group A Streptococcus prophage [57] . And a small but significant level of induction of Stx was observed when an EHEC strain was co-cultured with human neutrophils [36] . In a similar manner , a factor ( s ) in the intestines that induces an SOS response might increase the levels of Stx produced by a population of infecting EHEC . Such a factor ( s ) could be a product of the host ( e . g . , neutrophils ) . Not considered here is the possible importance of the interaction between the microbiota and the mammalian intestine in the SOS response and resulting Stx2 production [58] . Our studies with germ free mice show that even in the absence of the normal microbiota there is sufficient prophage induction to produce and release levels of Stx capable of causing renal disease and death . Constructed regulatory networks as biosensors have wide biological applications [59] . The studies reported here demonstrate the utility of the comparatively simple SIVET regulatory network as a tool for identifying conditions where prophage induction is enhanced . First , treatment in vitro of SIVET with the inducing agent mitomycin C results in overwhelming conversion of KanR to CamR ( Fig . 5 ) , confirming that SIVET responds to inducing agents as designed . Second , the experiments with the 933W cIind SIVET derivatives showed that the increase in CamR relative to KanR colonies observed during in vitro and in vivo growth is not due to a growth advantage of the CamR variants ( Figs . 6 and 7 ) . Third , no significant change in the ratio of CamR to KanR was observed over a large number of doublings during continuous in vitro growth of SIVET in the absence of an inducing agent ( Fig . 6 ) . This observation held true whether cultures prior to dilution were allowed to grow to stationary phase or were maintained in log phase . In each case dilutions were at a sufficiently high level to ensure that CamR bacteria were carried over during each dilution . It might be expected that CamR bacteria contributed de novo by induction should add to the growing population , resulting in an increase in the CamR/KanR ratio . However , the in vitro experiments failed to show an increase in the Induction Index over a large number of doublings ( we discuss this apparent paradoxical finding in detail below ) . Based on these results , we conclude that spontaneous induction ( induction in the absence of a known inducing agent ) of the 933W prophage fails to lead to a measurable increase in conversion of SIVET from KanR to CamR . Hence , SIVET is not sufficiently sensitive to distinguish no induction from low levels of induction . By eliminating obvious alternative explanations and showing that mitomycin C treatment results in an increase in the SIVET CamR/KanR ratio , these results confirm that SIVET can be used to identify the presence of inducing agents . Moreover , the failure to observe changes in the Induction Index over many rounds of doubling during in vitro growth , in the absence of an extrinsic inducing agent , indicates that even small measurable increases in the Induction Index should provide evidence of an extrinsic inducing agent . In the light of this background information , the >3 log increase in the Induction Index observed in SIVET isolated from feces ( Fig . 7 ) over the seven days following the initial infection provides evidence for action of an inducing factor in the mouse intestinal tract . We suggest three alternative , but not mutually exclusive , scenarios to explain this increase in the rate of induction: 1 ) a substantial portion of the bacteria reach a section of the intestinal tract that contains resident inducing activity; 2 ) the infection causes an increase in the amount and/or activity of a resident inducing activity; or 3 ) infection attracts an inducing activity or a cell ( e . g . , neutrophils ) producing an activity . Since Stx2 production , in large measure , is directly related to phage induction ( Fig . 3 ) , the intestinal environment likely contributes to the severity of the EHEC infection . Although we failed to observe any significant change in the Induction Index over many generations of in vitro growth , an increase in the Induction Index over time might be expected because spontaneous prophage induction [25] should result in TnpR expression and , at some level , conversion of KanR to CamR bacteria . This , in turn , would add to the total of CamR population over the number produced by replication of preexisting CamR population resulting in an increase in the CamR/KanR ratio . We used mathematical modeling to gain a quantitative understanding of what the expected Induction Index over time would look like if all of the spontaneously induced KanR bacteria were able to contribute immediately to the CamR bacterial population . Based on a starting Induction Index of 0 , the model adds the newly produced CamR bacterium at each division to the growing preexisting CamR population , predicting an increase in the Induction Index over time as shown in Fig . S1 . If we assume a doubling every hour over the seven days of in vivo growth , the model predicts the Induction Index would increase a little over one log and , even assuming a doubling time of 20 minutes , the Index would increase by slightly over two logs , both substantially less than the over three logs observed in the in vivo SIVET experiment . The counterbalancing actions that we see as potentially reducing the contribution of spontaneous induction might make to the CamR population , include: 1 ) as discussed above , SIVET may not be sufficiently sensitive to distinguish no induction from low induction; 2 ) there may be a delay in initiation of growth following recovery from the consequences of DNA damage that caused the induction [60] , [61]; i . e . , a phenotypic lag ( graphed in Fig . S1 ) ; 3 ) removal of the KanR cassette may occur in only one of the multiple bacterial chromosomes [62] resulting in segregation of both CamR and KanR derivatives from a single induced KanR bacterium and thus resulting in no change in the CamR/KanR ratio ; and 4 ) there may be sufficient DNA damage in some of the bacteria to block further growth , compromising survival of those bacteria . This subpopulation would be part of the induced pool that although theoretically adding to the CamR population would not be alive to do so . Although collectively these actions could explain our results , we are far from having a definitive answer as to how the Induction Index maintains this steady state . Nor can we explain how the ratio of CamR/KanR colonies reaches a steady state that is maintained for many generations . However , failure of SIVET to identify low level induction ( spontaneous ) , but identify high level induction , as with mitomycin C , indicates measurements by SIVET are likely to be an under representation .
All animal protocols were approved by the University Committee on Use and Care of Animals at the University of Michigan Medical School . The University of Michigan is fully accredited by the Association for Assessment and Accreditation of Laboratory Animal Care , International ( AAALAC , Intl ) and the animal care and use program conforms to the standards of “The Guide for the Care and Use of Laboratory Animals” ( published by the NRC ) . See Table 1 . See Table 2 . LB , 10 g tryptone , 5 g yeast extract , 5 g NaCl/liter of H2O . For LB plates 10 g of agar was included . LB sucrose plates are LB plates without NaCl and made 10% in sucrose . Antibiotics were added at the following concentrations; spectinomycin 80 µg/ml , ampicillin 100 µg/ml ( plasmids ) 25 µg/ml ( chromosomal ) , kanamycin 30 µg/ml , hygromycin 200 µg/ml , and chloramphenicol 9–10 µg/ml . TB plates , 10 g tryptone , 2 . 5 g NaCl , and 10 g agar/liter of H2O . All of our constructs were engineered using the λ Red recombination system , colloquially referred to as recombineering [63] . The λ Red functions were supplied in either of two ways: transiently by a heat pulse freeing a λ promoter on a truncated λ prophage from control by a Ts repressor ( cI857 ) so that the downstream red genes could be transcribed , using DY378 [64] or from pKD46 and derivatives of that plasmid carrying cloned λ red genes by adding arabinose to the growth medium to activate an Ara-regulated promoter [65] . Single-stranded oligonucleotides or double-stranded PCR products of varying lengths having ∼40 nucleotides of flanking sequences with homologies to the target regions were introduced by electroporation into bacteria expressing λ Red functions . The expressed Red functions recombine the introduced DNAs with the target site . In the absence of a selectable marker , a two-step procedure was used: a cat-sacB ( CSB ) cassette [64] was inserted by recombineering and the recombinant selected by resistance to chloramphenicol . This cassette was then exchanged by recombineering with the designed DNA product using as selection resistance to sucrose and confirming by screening for CamS . DNA sequencing by the University of Michigan Sequencing Core Facility confirmed structure of constructs . Recombineering was used to cross the designed mutation from a single stranded oligonucleotide to the chromosome of strain K10985 . The oligonucleotide contained a single nucleotide change that resulted in a replacement of Lys codon 178 ( AAG ) with an Asn codon ( AAC ) . The following is the sequence of the DNA oligonucleotide ( oligo #2 ) with the mutant nucleotide capitalized: 5′-ccgggtgatgaggtgtttgtcagaaccgttgaaggacacaacatgattaaCgttcttggctatgacagagatggagaataccaatttacaagcattaacca-3′ . The pairing of the oligonucleotide with its complementary chromosomal DNA strand forms a C-C mismatch at the position of the nucleotide change . This mispairing is not repaired by the mismatch repair system [66] . In the absence of mismatch repair there is a significant increase in isolation of bacteria with the designed nucleotide change [67] . K10985 , an EDL933 derivative with the pKD46 plasmid [65] , was prepared for electroporation essentially as described by Murphy and Campellone [68] . Following electroporation , bacteria were resuspended in 10 ml of LB broth and grown at 37° . After ∼5 hrs of growth , dilutions of the bacteria were placed on LB plates and incubated overnight at 37° . The following day colonies were picked and stabbed to an LB plate and a TB plate that was layered with a lawn of K37 , a strain that supports growth of 933W . Plates were incubated at 37° for two hours and the seeded plate was UV irradiated ( 1 . 6 Joules/M2/S for 30 seconds ) . Following overnight incubation at 37° , a zone of lysis in the lawn showed phage had been synthesized by an induced prophage . Two clones out of 160 tested showed no zones of lysis . These derivatives failed to lyse following treatment with mitomycin C and subsequent DNA sequencing showed that although they both had the cIind mutation , only one , EDL933cIind1 , had no other changes and was selected for further study . A similar strategy was used to construct an EDL933 SIVET ind mutant , K11607 , that was KanR . A CamR derivative , K11608 , isogenic except for the loss of the KanR cassette and thus converted to CamR , was constructed from K11607 using a plasmid , pJLTnpRhygro , which supplied the TnpR resolvase . Overnight cultures were diluted and grown to early log phase in LB . The cultures were divided into two aliquots; one grown untreated and the other treated with 2 µg/ml mitomycin . Cultures were grown for 3–4 hours , based on time of lysis for the mitomycin C treated culture . Uninduced cultures were diluted every 30 minutes to maintain them in logarithmic growth . Cultures were sonicated 3× for 10 seconds at amplitude of 30% to obtain total cell lysis . Lysates were passed through 0 . 22 µm filter and concentrated using Amicon Ultra-4 ( Millipore ) . Stx2A levels in supernatants were measured using an enzyme-linked immunoabsorbent assay ( ELISA ) following a previously published procedure [69] using anti-Stx2A monoclonal and anti-Stx2 polyclonal antisera . Results were determined as ng Stx2A/µg total protein . Germ-free Swiss-Webster mice of both sexes were raised in the University of Michigan Laboratory of Animal Medicine germ free colony , housed in soft-sided bubble isolators , and fed autoclaved water and laboratory chow ad libitum . Inoculations , monitoring of animals , and sample collections were performed as previously described [7] . In brief , mice were inoculated orally with ∼106 cfu of LB-cultured bacteria . Each group of inoculated animals contained 3 male and 3 female mice between 5 and 6 weeks of age . Throughout the experiment and at necropsy , feces or cecal contents were collected for quantitative EHEC culture . Gram stain and aerobic and anaerobic culture were used to demonstrate the absence of microorganisms other than EHEC . Mice remained sterile ( except for the infecting EHEC strain ) throughout the course of the experiment . Mice inoculated with EDL933Δstx:cat or EDL933cIind1 showed no signs of disease and were euthanized 3 weeks after inoculation . All of the mice inoculated with EDL933 became moribund prior to the scheduled necropsy date , and these mice were necropsied when they became moribund , between 10 and 18 days after inoculation ( see Results ) . All animal experiments were conducted with the approval of the University of Michigan Animal Care and Use Committee . At necropsy , cecal contents were cultured to determine bacterial colonization density . Quantitative counts were determined using LB agar plates containing appropriate antibiotics . Stx concentration in cecal contents was measured using a commercial kit ( Premier ) as previously described [7] . For histologic examination , right and left kidney were immersion-fixed in formalin , embedded in paraffin , cut in 5 micron sections , and stained with hematoxylin and eosin ( Fig . 4 ) . Kidney sections were scored by a single pathologist without knowledge of the source of the section . For quantitation , a midline section of the right renal cortex was examined in its entirety , and the number of 200× fields with tubular or glomerular lesions was recorded . Acute tubular necrosis was subjectively scored as mild , moderate , or severe . For the SIVET experiment , animals were similarly infected with ∼106 cfu of LB-cultured bacteria . Because of the deletion-substitutions in the 933W prophage , the SIVET strain does not express significant levels of Stx2 . Details of the experiment procedure have been discussed above . In these experiments colony counts were obtained using LB plates containing either kanamycin ( 30 µg/ml ) or cloramphenicol ( 9 µg/ml ) . Statistics: Quantitative data were analyzed by Mann-Whitney U test . Multiple groups were compared by ANOVA and Fisher's Least Significant Difference . The design of SIVET [47] is based on Camilli and colleague's “Recombinase-based Reporter of Transcription ( RIVET ) system” [45] , [46] . However , SIVET differs from RIVET in providing a selection for cells in which the assayed transcription occurred ( Fig . 1-II ) . The first generation of EDL933 SIVET , was constructed similarly to the original K12 SIVET strain [47] , [70] using recombineering [63] , with a SpcR ( this laboratory ) derivative of pKD46 [65] supplying the λ Red functions . The 933W prophage was inactivated by elimination of genes controlling two critical components of phage growth , transcription and replication . The N gene , encoding a transcription regulator , was deleted and replaced with a KanR cassette . The O and P genes , encoding proteins involved in initiation of DNA replication [48] , were replaced with the tnpR gene and ampR cassette . This was accomplished using a PCR product containing the ampR cassette and the sequence encoding the 168 variation of the γδ resolvase , tnpR-168 , [46] with flanking sequences having homology to the 933W cro and ren genes ( Fig . 1-I ) . These changes generated strain K11084 that , even though having a defective 933W prophage , is unable to survive treatment with an inducing concentration of mitomycin C . The cryptic prophage CP933V in EDL933 , although defective , has nearly a complete lambdoid phage genome [41] , leading us to suspect that its induction might be responsible for this sensitivity to mitomycin C . Therefore , we deleted the control region of CP933V rendering that prophage uninducible; the deletion included the putative repressor ( cI ) gene with immediate surrounding putative promoters , operators , genes , and relevant associated genetic material in a two-step process . A cat-sacB ( CSB ) cassette [71] with flanking ends having appropriate homologies to CP933V ( primers 3 and 4 , template K9685 ) was recombined into the targeted region , extending from N to cII ( Fig . 1-I ) in CP933V , generating strain K11114 . The CSB inserted in CP933V was then replaced with a single-stranded DNA oligomer ( oligo #1 ) [71] , generating strain K11115 . This strain survives the inducing levels of mitomycin C used in our studies . Addition of the reporter cassette in a two-step procedure completed the construction of the EDL933 SIVET strain . First , K11161 was constructed by crossing a cat cassette ( primers: 9 and 10 , K10373 template ) into the lacZ gene of K11115 providing homology for the next step . Second , K11173 was constructed by crossing the cat::resC-tetR-resC::cat cassette ( primers7 and 8 , K10449 template ) into the inserted cat gene in K11161 with selection for tetracycline resistance . This first EDL933 SIVET construct had to be modified because its constitutive expression of TetR made the bacteria sensitive to the in vivo environment . We therefore made the following changes using recombineering , λ Red functions were supplied by a hygromycin resistant derivative of pKD46 ( pKD46hygR ) . The KanR cassette in the N gene was replaced by a spcR cassette and the selective tetR cassette in the cat::resC::tet::resC::cat reporter was replaced by a kanR cassette yielding the cat::resC-kan-resC::cat reporter . To complete the process , the strain was cured of pKD46hygR yielding K11604 , the SIVET strain used in the experiments reported here . The method used to obtain the results shown in figure 5 was essentially those outlined in Livny and Friedman [47] . Briefly , SIVET strain was grown ∼108/ml in LB , made 2 µg/ml in mitomycin C , grown for 2 hrs , washed and resuspended in LB , grown for 4 hours , and dilutions of bacteria were plated on selective media . This metric provides a log10 scale readout that allows for a simplified comparison of results of different SIVET experiments . The calculations compare the ratio of CamR/KanR colonies at any given time relative to the starting ratio of CamR/KanR colonies . It is calculated as log10 [ ( CamR titer/KanR titer at any time after start of experiment ) / ( CamR titer/KanR titer at start of experiment ) ] . It follows that the Induction Index at the start would obviously be 0; i . e . , log10 1 ( starting ratio/starting ratio ) . Overnight cultures of O157:H7 and the cIind1 derivative grown in LB broth were diluted 1∶100 in LB and grown to early log phase . Each were divided into two aliquots , one untreated and the other treated with 2 µg/ml of mitomycin C . Samples , 200 µl , were placed in a 96 well plate and grown at 37° with OD600 read at 30 minute intervals in the SpectraMax 250 ( Micro Devices ) . | Infection with Enterohemorrhagic E . coli ( EHEC ) , and more recently with the Enteroaggregative E . coli strain O104:H4 , is a significant health risk , causing bloody diarrhea , kidney failure , and even death . The virulence factor in these bacteria responsible for the severe outcomes is Shiga toxin ( Stx ) . Genes encoding Stx are in the genome of bacterial viruses ( prophages ) on the pathogenic E . coli chromosomes . The prophage remains quiescent until damage to the bacterial chromosome occurs causing prophage gene expression ( called induction ) , which leads to production of bacteriophages that are released into the environment . Because stx expression is controlled by the phage regulatory system , prophage induction leads additionally to production and release of Stx . This study provides conclusive evidence that in a mouse model of EHEC infection , induction of the prophage carrying the stx genes is specifically required for EHEC to cause disease and that the intestinal environment adds to the induction and therefore to the production of Stx . Similar events likely regulate Stx production and release by the Stx encoding phage in the O104:H4 strain . Controlling prophage induction offers a means to control EHEC infection . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biology",
"microbiology"
] | 2013 | Prophage Induction Is Enhanced and Required for Renal Disease and Lethality in an EHEC Mouse Model |
Epigenetic changes are widely considered to play an important role in aging , but experimental evidence to support this hypothesis has been scarce . We have used array-based analysis to determine genome-scale DNA methylation patterns from human skin samples and to investigate the effects of aging , chronic sun exposure , and tissue variation . Our results reveal a high degree of tissue specificity in the methylation patterns and also showed very little interindividual variation within tissues . Data stratification by age revealed that DNA from older individuals was characterized by a specific hypermethylation pattern affecting less than 1% of the markers analyzed . Interestingly , stratification by sun exposure produced a fundamentally different pattern with a significant trend towards hypomethylation . Our results thus identify defined age-related DNA methylation changes and suggest that these alterations might contribute to the phenotypic changes associated with skin aging .
Aging is defined by characteristic phenotypic changes , but there appear to be few corresponding changes in the genotype . As such , aging represents a fundamental epigenetic phenomenon [1] . Epigenetic mechanisms regulate the interpretation of genetic information and thus have the ability to produce different phenotypes from a single genotype [2] . The corresponding regulatory mechanisms are based on two independent modification systems , the covalent modification of histones and the methylation of cytosine residues in DNA [3] . Over the past few years , numerous histone modifications have been described and it has been suggested that complex histone modification patterns encode detailed information about the regulation of associated genes and promoters [4] . Similarly , the methylation status of a gene promoter can have an important effect on the activity status of the corresponding gene and hypermethylation-associated silencing of tumor suppressor genes has been shown to play a prominent role in human cancers [5] . Epigenetic mechanisms are generally considered to represent a regulatory interface between environmental cues and the genome [6] . More specifically , it has also been suggested that age-associated epigenetic changes might restrict the phenotypic plasticity for a successful adaptation to environmental changes [7] . Due to methodological limitations , however , experimental evidence supporting these notions has remained scarce . Based on the analysis of DNA methylation patterns in peripheral blood samples from monozygotic twins it has been suggested that widespread epigenetic variations arise during the lifetime of human individuals [8] . A later study also described profound methylation changes when unfractionated peripheral blood samples were compared that had been sampled from the same individual 11–16 years apart [9] . However , it was also noted that variations in the cellular composition of tissues are prevalent in older individuals and that the observed epigenetic variations might be a consequence of the tissue heterogeneity [10] . In agreement with this notion , an independent study concluded that tissue-specific interindividual DNA methylation differences are very small and that major variations could only be observed when different tissues were compared [11] . As such , the magnitude of age-related epigenetic changes has remained unresolved . Human skin represents an organ with several advantages for studying age-associated and environmentally-induced epigenetic changes . The skin is directly exposed to many environmental factors and shows an invariant age-dependent phenotype characterized by changes in the vascular network , reduction in the number of melanocytes and Langerhans cells , decreased thickness of the epidermis , lower levels of specific collagen types and other factors [12] , [13] . Skin samples can be obtained from healthy individuals , either as suction blisters or via punch biopsies . Importantly , these samples contain defined tissue layers with a very high degree of cellular homogeneity , with epidermal tissues consisting mainly of keratinocytes and dermal tissues consisting mainly of fibroblasts . We have now used a recently developed array technology [14] for the analysis of complex DNA methylation patterns in skin samples obtained from healthy human volunteers . Our results identify a comparably small , but consistent and statistically significant trend towards DNA hypermethylation in aged samples and consistent hypomethylation of a small set of markers in sun-exposed samples .
The recent development of robust arrays for comprehensive DNA methylation analysis provided a sensitive tool for the identification of limited DNA methylation changes in the human genome . We investigated DNA methylation patterns in human skin samples , because the tissue is known to undergo well-defined age-related and sun exposure-related phenotypic changes . Skin samples were obtained as suction blisters ( epidermis ) or as punch biopsies ( epidermis and dermis , see Figure 1A ) . Punch biopsies were taken both from the outer forearm ( sun-exposed area ) and the inner arm ( sun-protected area ) and were separated into epidermal and dermal tissues by dispase II treatment . Samples were obtained from healthy male and female donors and from two distinct age groups to analyze the influence of various intrinsic and extrinsic factors on the genomic DNA methylation patterns ( Figure 1B , see Table S1 for details ) . We used Illumina Infinium arrays to determine the methylation status of 27 , 578 CpG dinucleotides in the human genome . The analysis of 50 samples generated 50 million data points for further analysis ( see Materials and Methods for details ) . The robustness of array-predicted methylation patterns was characterized by an initial bisulfite sequencing experiment . Three genes with methylation scores ( beta values ) of 0 ( unmethylated ) , 0 . 5 ( partially methylated ) and 1 ( completely methylated ) were arbitrarily chosen from a pilot array . The analysis of bisulfite sequencing results revealed a very good correlation between the array data and the bisulfite sequencing results for 2 genes ( Figure S1 ) . For the partially methylated gene ( DIRAS3 , Figure S1 ) , bisulfite sequencing indicated a higher methylation level than the array , which can probably be explained by strand-specific PCR bias in the bisulfite sequencing reaction [15] . Indeed , bisulfite sequencing of a second partially methylated gene ( ZIM2 , Figure S1 ) showed a very good correlation between the array data and the bisulfite sequencing results . It should be noted that our complete bisulfite sequencing validation set consisted of 10 PCR amplicons ( see below in Results ) and that bisulfite sequencing confirmed the array-predicted results in 9 out of 10 cases . This strongly suggests that the array produces reliable methylation results . In an initial step of our analysis , we analyzed methylation profiles from suction blister samples obtained from 5 healthy male donors aged 26–35 years . The methylation profiles ( see Figure 1C for an example ) showed that the majority ( 86% ) of CpG island associated markers had a methylation score of 0 . 2 or less and were therefore considered unmethylated . Consistent with other published reports [16] , [17] , 5% of the CpG island associated markers showed a methylation score of 0 . 8 or more and were therefore considered methylated . The fraction of methylated markers was substantially higher ( 29% ) in regions not associated with CpG islands , which is again consistent with the general patterns of human DNA methylation reported in other studies [16] , [18] . Comparisons between individual methylation profiles also showed a very high degree of similarity between samples ( Figure 1D ) , with correlation coefficients ranging from 0 . 97–0 . 98 . This substantial similarity of methylation patterns was important for our overall study design , because it permitted the identification of statistically significant changes in a comparably low number of samples . The methylation pattern observed in the pilot sample set obtained from 5 male volunteers aged 26–35 years was largely confirmed in an independent set of epidermis samples obtained from punch biopsies of 5 healthy female individuals aged 19–24 years . However , we also noted a prominent group of markers that had a substantially higher methylation score in the second sample set ( Figure 1E ) . Notably , the vast majority ( 547 out of 595 , Figure 1E ) of these markers were derived from the X-chromosome , which is known to be dosage compensated by DNA methylation in females [16] , [17] , [19] . The observed methylation differences therefore reflect gender differences in the donor groups ( Table S1 ) and further illustrate the sensitivity of the methylation array . We also analyzed the methylation profiles from all dermis samples ( n = 20; outer forearm and inner arm ) of 10 healthy female individuals aged 18–72 years ( Table S1 ) . The results again revealed substantial interindividual similarities , with correlation coefficients between 0 . 95 and 0 . 98 . However , when the dermal methylation profiles were compared to the matched epidermal methylation profiles of the punch biopsy samples ( n = 20 ) , both tissues showed remarkable differences ( Figure 2A ) , with 742 markers being considerably more ( Δ ( beta ) ≥0 . 2 ) methylated in the epidermis and 1034 markers being more methylated in the dermis . Genes associated with differentially methylated markers were strongly enriched in functional categories associated with the molecular and cellular characteristics of ( skin ) tissue development ( Figure 2B ) . These findings are consistent with a role of DNA methylation in the regulation of cell type-specific gene expression patterns and illustrate the differences in the cellular composition of epidermis ( keratinocytes ) and dermis ( fibroblasts ) . In this context , several keratin genes provided a notable example for differential methylation ( Figure 2C ) , which was validated by bisulfite sequencing of the KRT5 gene ( Figure 2D ) . The results showed low levels of KRT5 methylation in the epidermis ( 0% in the promoter region , 9% in the downstream CpG island ) and substantially higher methylation levels in the dermis ( 38% in the promoter region , 71% in the downstream CpG island ) . In agreement with a role of DNA methylation in the silencing of cell type-specific genes , KRT5 has been shown to be highly expressed in the epidermis , but not in the dermis [20]–[24] . We then extended our analysis to the comparison of methylation patterns of the two distinct age groups . Again , the comparison of suction blister methylation patterns from 5 healthy male individuals aged 65–71 years revealed a very high degree of similarity ( Figure 3A ) , with correlation coefficients between 0 . 91 and 0 . 98 . The establishment of average methylation values for young and old epidermis samples subsequently allowed the comparison of these data sets . To identify markers with relevant methylation changes , we applied a stringent cutoff at a beta value change of 0 . 2 or more , based on previously published data [14] . The results indicated that 104 markers ( 0 . 37% ) showed a beta value increase by 0 . 2 or more in old epidermis , while only 8 markers ( 0 . 03% ) showed a beta value decrease by 0 . 2 or more ( Figure 3B ) . Out of the 104 hypermethylated markers , 90 were associated with CpG islands , which may be related both to the overall low methylation of CpG islands ( see above ) and to the overrepresentation of CpG island-associated probes on the array [14] . Together , these data suggested that skin aging is associated with predominant hypermethylation in a comparably small set of markers . Age-related hypermethylation was also confirmed by an independent statistical analysis ( see Materials and Methods for details ) and by inclusion of the methylation profiles from the epidermal and dermal punch biopsy samples ( outer forearm and inner arm ) . The results showed a strong and unambiguous trend towards hypermethylation in epidermis and dermis samples from the old donor group ( Figure 3C and 3D ) . Age-related hypermethylation was stronger in the epidermis than in the dermis ( Figure 3C and 3D ) , which is conceivably due to the more immediate environmental exposure of the epidermis . Out of the 61 markers found to be substantially ( Δ ( beta ) ≥0 . 2 , with a Benjamini-Hochberg adjusted P-value P ( BH ) <0 . 01 ) hypermethylated in epidermal punch biopsies from old female individuals , 43 were also found to be hypermethylated in suction blister samples from old male donors , that were obtained with a completely independent sampling protocol ( Figure 3E ) . This result provided further confirmation for a distinct age-associated epigenetic shift in human skin . To further demonstrate the specificity of the observed methylation shift , we also stratified the punch biopsy sample sets according to chronic sun exposure ( outer forearm vs . inner arm ) . Remarkably , this comparison revealed a fundamentally different methylation change: out of the 27 , 578 markers analyzed , none showed a beta value increase by 0 . 2 or more in sun-exposed epidermis , while 14 ( 0 . 05% ) showed a beta value decrease by 0 . 2 or more ( Figure 4A ) . Statistical analysis confirmed the trend towards hypomethylation and also indicated that sun exposure-related hypomethylation was less pronounced than age-related hypermethylation ( Figure 4B and 4C ) . Again , the effects appeared stronger in the epidermis than in the dermis ( Figure 4B and 4C ) , which may be related to the more direct sun exposure of the epidermis . Importantly , global age-related and sun exposure-related DNA methylation shifts were highly significant when compared to Gaussian distributions of randomly generated methylation differences and after a global Benjamini-Hochberg adjustment of the P-values ( Figure 4D ) . This finding further confirms our notion that aging and sun exposure cause distinct global DNA methylation changes in human skin . Finally , to confirm the observed methylation changes on the level of individual genes we analyzed the gene-specific methylation patterns by bisulfite sequencing . Because previous reports have suggested that UV-induced mutations preferentially occur at methylated CpG dinucleotides [25] , a sequencing approach also allowed us to control for the possibility that the methylation changes identified by the array might actually represent genetic polymorphisms or mutations . In a first experiment , we therefore focused on the KRT75 promoter region , which was found among the 16 markers that were substantially ( Δ ( beta ) ≤−0 . 2 , P ( BH ) <0 . 01 ) hypomethylated in the sun-exposed epidermis samples . KRT75 encodes a Keratin protein that is normally not expressed in the epidermis , but can affect the intermediate filament architecture of epithelial cells [26] , [27] . Results from deep sequencing of pooled samples ( epidermis DNA from a sun-protected area and a sun-exposed area from 10 healthy female donors ) did not reveal any genetic polymorphisms or mutations in the KRT75 5′ upstream region that could have contributed to the array result ( Figure S2 ) . Nevertheless , methylation analysis by deep bisulfite sequencing of the same region confirmed the demethylation of the CpG dinucleotide represented on the array ( 61% methylation in sun-protected samples , 33% in sun-exposed samples , Figure 5A ) . Similarly , the data also revealed demethylation for the CpG dinucleotide located immediately distal ( 81% methylation in sun-protected samples , 48% methylation in sun-exposed samples , Figure 5A ) . Together , these results validate the demethylation of sun-exposed epidermis samples at the KRT75 promoter region . Lastly , we also sought to validate the age-associated hypermethylation shift by deep bisulfite sequencing . A probe from the SEC31L2 gene was identified among the 43 markers substantially ( Δ ( beta ) ≥0 . 2 , P ( BH ) <0 . 01 ) hypermethylated both in epidermal punch biopsies and in suction blister samples from old individuals . SEC31 proteins have been shown to be important for collagen secretion in primary human fibroblasts [28] and hypermethylation-associated silencing of SEC31L2 could thus play an important role in the age-associated skin phenotype . To experimentally validate age-associated hypermethylation , we pooled equal amounts of epidermis DNA samples obtained from 5 young male ( 26–35 years ) and 5 young female donors ( 19–24 years ) and from 5 older male ( 65–71 years ) and 5 older female ( 67–72 years ) individuals , respectively , and used deep bisulfite sequencing to determine the methylation pattern of 19 CpG dinucleotides in the SEC31L2 5′ region ( Figure 5B ) . Sequence analysis showed that SEC31L2 was mostly unmethylated in the young sample pool , but became distinctly methylated in the old sample pool ( Figure 5B ) . Two additional , arbitrarily chosen loci that showed age-related hypermethylation on the array , DDAH2 , an epigenetic marker associated with cellular differentiation [29] , [30] , and TET2 , a putative tumor suppressor gene [31] , also were substantially hypermethylated in old epidermis samples ( Figure 5C and 5D ) . These results provide direct confirmation for the array-based findings and again illustrate that aging and sun exposure of human skin are characterized by distinct epigenetic variations .
The molecular pathways contributing to human aging are currently being investigated in many experimental contexts . It is widely assumed that epigenetic changes play a fundamental role in establishing gene expression patterns specific for aged cells and tissues [1] . However , the experimental evidence to support this notion has been limited . For example , it was shown that global DNA methylation levels decrease during in vitro fibroblast cultivation [32] , suggesting that DNA hypomethylation might be a molecular marker of aging . More recently , evidence has been provided for age-associated hypermethylation of specific loci in various model systems [1] . However , there are only few published reports that have directly analyzed this question on a genome-scale level . A distinct age-related phenotype and a high level of cellular homogeneity establish human skin as an excellent model system to study age-related epigenetic alterations . Our results show that defined skin tissue layers ( epidermis and dermis ) are characterized by specific DNA methylation patterns that are highly similar between individual samples , with correlation coefficients that are commonly achieved for biological and technical replicates . Our results further show that skin aging is associated with DNA hypermethylation in less than 1% of the markers analyzed . The availability of array-based technologies for methylation patterns analysis also allows the identification of defined epigenetic candidate biomarkers for human aging . For example , a recent study has used Illumina GoldenGate methylation arrays to interrogate the methylation status of 1505 CpG dinucleotides representing 803 cancer-associated genes [33] . While this study investigated age-related methylation changes in various primary human tissues , the analysis was restricted to cancer-associated genes and did not account for the cellular heterogeneity of the tissues analyzed . In addition , the methylation changes appeared rather minor and were only analyzed at single cytosine residues . Our data provides evidence for directed methylation shifts that were validated by bisulfite sequencing of PCR fragments containing several CpG dinucleotides beyond the cytosine residue queried by the array . Notably , the methylation changes observed by bisulfite sequencing of SEC31L2 , DDAH2 and TET2 show a distinct similarity to epigenetic mutations described in human cancers [7] ( see below ) . The molecular steps leading to the establishment of altered methylation patterns are presently unclear . Because DNA methyltransferase mRNA levels did not show any significant differences between old and young and between sun-exposed and non-exposed samples ( Figure S3 ) , the observed DNA methylation changes do not seem to involve altered expression of DNA methyltransferase genes . An association between DNA methylation and sunlight-mediated mutagenesis has been suggested previously , based on the observation that UV-induced mutations preferentially occurred at methylated CpG dinucleotides [25] . However , when we analyzed the mutational status of KRT75 by deep sequencing , we did not detect any evidence for local genetic mutations . It is possible that the observed epigenetic changes may be influenced by more distant genetic mutations or by mutations in trans-acting epigenetic modifiers . These details will have to be investigated in future studies . Recently , specific age-related hypermethylation has also been described at a subset of developmentally regulated genes in various human tissues [34] , [35] . While the methylation differences observed in these studies were highly significant , they were also comparably small and might thus have escaped detection in our analysis . In addition , age-related hypermethylation has also been observed in the human intestine [36] , [37] , and has been interpreted as a preneoplastic epigenetic lesion . Age is the most significant risk factor for human cancer [38] and gene-specific hypermethylation represents one of the most consistent markers of tumorigenesis [5] . Our bisulfite sequencing results showed pronounced age-related hypermethylation of DDAH2 , which encodes a key enzyme in the nitric oxide pathway . Nitric oxide plays an important role in the regulation of keratinocyte proliferation and in the development of skin cancer [39] . Similarly , our results also demonstrated age-related hypermethylation of TET2 , a putative tumor suppressor gene that has recently been shown to be genetically mutated in myeloproliferative disorders [31] . These findings lend further support to the notion that age-related epigenetic changes provide a molecular link between aging and tumorigenesis .
The recommendations of the current version of the Declaration of Helsinki and the guideline of the International Conference on Harmonization Good Clinical Practice ( ICH GCP ) were observed as applicable to a non-drug study . All donors provided written , informed consent . Punch biopsies were obtained through a clinical study approved by the Ethics Committee of the Medical Association of Hamburg ( PV 3107 ) . All volunteers provided written , informed consent . Suction blisters were obtained from the volar forearms of 10 healthy male volunteers , as described previously [40] . Suction blister roofs were taken and immediately stored at −20°C . Full-thickness skin samples ( diameters: 6 mm or 4 mm ) were obtained from 20 female volunteers by Sciderm GmbH ( Hamburg , Germany ) . Biopsies were isolated from the outer forearm ( sun-exposed area ) and inner arm ( sun-protected area ) , respectively . Immediately after removal , punch biopsies were transferred into DMEM medium ( Gibco BRL ) and stored on ice for up to 5 h . After dispase II treatment ( 2 U/ml , Roche ) for 2 h at 37°C , epidermis and dermis were separated and stored at −80°C . Suction blister and punch biopsy samples ( epidermis or dermis ) were washed in DPBS ( Cambrex ) and homogenized using a TissueLyser ( Retsch ) . DNA from suction blister samples was isolated using the QIAamp DNA Investigator Kit ( Qiagen ) according to the manufacturer's instructions . RNA and DNA from punch biopsy samples were processed with the Qiagen AllPrep DNA/RNA/Protein Mini Kit ( Qiagen ) as recommended by the supplier . The concentrations and purities of isolated DNA and RNA were assessed spectrophotometrically using a NanoDrop ND-1000 ( Peqlab ) . The HumanMethylation27 BeadChip has been described previously [14] . A single BeadChip utilizes more than 1 , 000 , 000 beads per sample and generates 27 , 578 DNA methylation measurements . The CpGs under investigation are located in more than 13 , 500 promoters of well-annotated genes . Genomic DNA ( 500 ng ) from each sample was bisulfite converted using the EZ-96 DNA Methylation Kit ( Zymo Research Corporation ) according to the manufacturer's recommendations . After bisulfite conversion , each sample was whole-genome amplified , enzymatically fragmented , and about 200 ng of DNA was applied to the arrays . During hybridization , the DNA molecules anneal to locus-specific DNA oligomers linked to individual bead types . After extension , the array was fluorescently stained , scanned , and the intensities of the non-methylated and methylated bead types were measured . DNA methylation values , described as beta values , were recorded for each locus in each sample and analyzed by BeadStudio ( Illumina ) . Microarray data are available in the ArrayExpress database ( www . ebi . ac . uk/arrayexpress ) under accession number E-MTAB-202 . Because the raw data from the green and the red channel of the array did not show the same shape of distribution , both channels were quantile normalized [41] separately . Subsequently , all beads of one probeID from all samples of a group were aggregated to calculate the mean values , standard deviations , detection P-values and beta values of the given probeID in the group . The detection P-value is defined as the minimum of the 2 separate P-values of the 2 variables ( grn . A/B or red . A/B ) , where each P-value is the result of testing ( Mann-Whitney-U-test ) against the negative beads of each channel . Because 2 variables were measured to calculate the beta value we applied the Benjamini-Hochberg correction [42] before choosing the minimum P-value . Less than 0 . 1% of the probeIDs showed detection P-values >0 . 05 and the corresponding data were excluded from further analysis . For differential methylation analysis , the difference of beta values was calculated by using the mean beta values of the two groups in question and the two-sample Wilcoxon test ( Mann-Whitney-U-test ) was used to calculate P-values . For global statements about overall methylation effects all P-values were adjusted using the Benjamini-Hochberg [42] correction . To visualize the differential methylation trend between two groups the data points were plotted as an RS-plot ( ratio over sum plot ) , which is a variation of the MA-plot [43] . Each point was calculated as follows: Calculate the difference ( Δ ( beta ) ) between the beta values of two groups and sort the data according to the absolute difference in decreasing order . The N-th data point for the graph is then calculated as: and on the y-Axis and on the x-Axis . Assuming a normal distribution of Δ ( beta ) ( P ( BH ) <0 . 01 ) values , the t-test was used to test the distributions of the experimental data ( sd<0 . 1 ) against a random normal distribution ( mean = 0 . 0 , sd = 0 . 1 ) . This provided P-values for the significance of the observed global methylation shifts . Genes were randomly chosen from array-predicted groups . For bisulfite treatment of DNA , the EpiTect Bisulfite Kit ( Qiagen ) was used according to the manufacturer's instructions . Up to 300 ng DNA was used . Deaminated DNA was amplified by PCR using the following primers: Krt5_1_for GTTGTTTGGAAAAGTGTAAGAGTAGATTAT , Krt5_1_rev TACCTTACAACACTAATCTCTTAACAACAA , Krt5_2_for AGTGTTTGGTTTTTTTGTTTTATTAGG , Krt5_2_rev CCCCCAAATTATAAAAACTCC . PCR conditions were as follows: 95°C for 3 min followed by 40 cycles at 95°C for 30 sec , annealing temperature for 40 sec and 72°C for 45 sec . At last , the reaction was incubated at 72°C for 3 min . PCR products were gel-extracted using the QIAquick gel extraction Kit ( Qiagen ) and cloned using the TOPO TA cloning Kit for sequencing ( Invitrogen ) , according to the manufacturer's instructions . Equal amounts of DNA from each skin sample were pooled according to age and sun exposure , respectively . The DNA pools were bisulfite treated by using the EpiTect Bisulfite Kit ( Qiagen ) according to the manufacturer's instructions . KRT75 , DDAH2 , TET2 and SEC31L2 fragments were amplified by PCR , using the primers KRT75_for GGTTTGTATTAATATAAGATGTTTGGATAG , KRT75_rev AACCACTAACTAATTCCCTAACACC , DDHA2_ for TAGGGTAGAAGTTAGGAATTAAGAAGG , DDAH2_rev CCAAACCCACCCAAATCTAA , TET2_for GAGAAATTTATTTTAATTTGTGAGA , TET2_rev TAAAAACCTATATTTTTAAAAACCC , SEC31L2_for GTTTGGGGTTTTTGGTAGTAGAGA , SEC31L2_rev CAACAATAAACAAAAAAACCCTCAT . PCR conditions were as follows: 95°C for 3 min followed by 40 cycles at 95°C for 30 sec , annealing temperature for 40 sec and 72°C for 45 sec , followed by a 3 min incubation at 72°C . PCR products were subsequently purified using the QIAquick gel extraction Kit ( Qiagen ) . For sequencing , equimolar amounts of all amplicons were combined in a single tube . Ligation of adaptor sequences and pool-specific tags and Roche 454 sequencing was provided by GATC ( Konstanz , Germany ) and LGC Genomics ( Berlin , Germany ) . Sequencing data were processed using DNAstar Lasergene 8 . 0 software . | Although a role of epigenetic mechanisms in aging and in the adaptation to environmental exposures has been widely assumed , research in this area has been hampered by major methodological challenges . We have now used a novel platform for genome-scale methylation analysis to determine the methylation patterns of human skin samples . Skin represents a particularly suitable model for this study because of its well-known phenotype changes associated with aging and sun exposure , and because skin samples are characterized by a very high degree of cellular homogeneity . By examining 50 samples , and analyzing 50 million data points , we show that aging and sun exposure are associated with comparably small , but significant changes in the DNA methylation patterns of human epidermis and dermis samples . Interestingly , aging was not associated with a general variation in DNA methylation patterns , but rather with a directed DNA hypermethylation shift . Importantly , our results also suggest that epigenetic mechanisms may be functionally important for the phenotypic changes associated with aging and chronic sun exposure . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genetics",
"and",
"genomics/epigenetics",
"dermatology/photodermatology",
"and",
"skin",
"aging",
"developmental",
"biology/aging",
"molecular",
"biology/dna",
"methylation"
] | 2010 | Aging and Chronic Sun Exposure Cause Distinct Epigenetic Changes in Human Skin |
Leptospirosis is a major cause of febrile illness in Africa but little is known about risk factors for human infection . We conducted a cross-sectional study to investigate risk factors for acute leptospirosis and Leptospira seropositivity among patients with fever attending referral hospitals in northern Tanzania . We enrolled patients with fever from two referral hospitals in Moshi , Tanzania , 2012–2014 , and performed Leptospira microscopic agglutination testing on acute and convalescent serum . Cases of acute leptospirosis were participants with a four-fold rise in antibody titers , or a single reciprocal titer ≥800 . Seropositive participants required a single titer ≥100 , and controls had titers <100 in both acute and convalescent samples . We administered a questionnaire to assess risk behaviors over the preceding 30 days . We created cumulative scales of exposure to livestock urine , rodents , and surface water , and calculated odds ratios ( OR ) for individual behaviors and for cumulative exposure variables . We identified 24 acute cases , 252 seropositive participants , and 592 controls . Rice farming ( OR 14 . 6 ) , cleaning cattle waste ( OR 4 . 3 ) , feeding cattle ( OR 3 . 9 ) , farm work ( OR 3 . 3 ) , and an increasing cattle urine exposure score ( OR 1 . 2 per point ) were associated with acute leptospirosis . In our population , exposure to cattle and rice farming were risk factors for acute leptospirosis . Although further data is needed , these results suggest that cattle may be an important source of human leptospirosis . Further investigation is needed to explore the potential for control of livestock Leptospira infection to reduce human disease .
Leptospirosis is a zoonotic bacterial infection and is increasingly recognized as an important cause of fever in Africa [1] . Leptospirosis was a leading cause of severe febrile illness in a study conducted in northern Tanzania during 2007–8 , where it was diagnosed in 8 . 8% of participants [2] . The annual incidence of severe acute leptospirosis in northern Tanzania is high , but has fluctuated during surveillance over two time periods: from 75–102 cases per 100 , 000 people in 2007–08 to 11–18 cases per 100 , 000 people in 2012–14 , suggesting dynamic transmission patterns [3] . An understanding of major animal reservoirs , sources , and modes of transmission to humans is required to inform leptospirosis control . Animals infected by Leptospira may become carriers and excrete Leptospira in urine leading to environmental contamination . Humans can be infected following direct exposure to the urine of infected animals or through contact with contaminated surface water or moist soil [5] . Portals of entry include mucous membranes and broken skin [5] . While the major reservoirs , sources of human infection , and modes of transmission of infection are established on a global scale , there is substantial variation by location reflecting the diverse ecology of Leptospira . In many tropical countries , rodent species are considered the most important animal reservoir for human infection [4] . As such , dominant risk factors for leptospirosis in many tropical countries include activities that expose individuals to rodent urine , such as living in urban slums , proximity to sewers , and exposure to flood waters [4 , 6 , 7] . In Tanzania and most other African countries , the risks factors for human infection are not well characterized [1 , 4] , and there is some evidence that the risk factors may differ from other tropical countries . In northern Tanzania there is evidence that leptospirosis is more common in rural areas where both livestock and rodents could be important sources of human infection [8] , and previous Leptospira exposure studies have identified livestock farmers as a high-risk group for Leptospira seropositivity [9] . Serogroup reactivity patterns of acute human leptospirosis infections have also suggested that livestock may be reservoirs for human cases [8] , and studies of livestock have found high proportions that were seropositive or with leptospiruria [10–12] . To inform leptospirosis control in Tanzania , we aimed to identify risk factors for acute leptospirosis and Leptospira seropositivity , and identify sources of human Leptospira infection .
We conducted a cross-sectional study at Kilimanjaro Christian Medical Centre ( KCMC ) , a 450-bed zonal referral hospital and , Mawenzi Regional Referral Hospital ( MRRH ) a 300-bed regional referral hospital , both in Moshi . Moshi ( population ~180 , 000 ) is the administrative capital of the Kilimanjaro Region ( population ~1 . 6 million ) of Tanzania . Moshi is situated at approximately 890 meters above sea level and has a tropical climate with rainy seasons from October through December , and March through May . Agriculture in northern Tanzania includes smallholder systems involving mixed crop and livestock farming , as well as pastoralism . We enrolled pediatric and adult patients presenting to KCMC and MRRH from February 2012 through May 2014 . From Monday through Friday , we screened all patients in the adult medical ward at KCMC and the adult and pediatric medical wards at MRRH within 24 hours of admission , as well as patients presenting to the outpatient department at MRRH . We enrolled consecutive eligible inpatients and every second eligible outpatient . Patients were eligible to participate if they had an axillary temperature of >37 . 5°C or a tympanic , oral , or rectal temperature of ≥38 . 0°C at presentation . Inpatients were also eligible if they reported a history of fever within the past 72 hours . After obtaining informed consent , a trained study team member completed standardized clinical history and risk factor questionnaires . The risk factor questionnaire included questions on socio-demographic characteristics , participant living environment , and daily activities performed over the past 30 days , focusing specifically on animal-related activities , exposure to surface water and to rodents ( S1 Text ) . The questionnaire was designed to include established risk factors for leptospirosis from studies done in other settings [4 , 6 , 7 , 13–15] , and was piloted prior to use . For participants who lived in the Kilimanjaro Region , study personnel visited participant households to record Global Positioning System ( GPS ) coordinates of participants’ dwellings . Clinician diagnoses were recorded . Participants were asked to return 4–6 weeks after enrollment for collection of a convalescent serum sample . Blood was allowed to clot for between 30 and 60 minutes . It was then centrifuged for 15 minutes at 1 , 126–1455 relative centrifugal force to separate serum . Serum was stored at -80°C . Serum specimens were batch shipped on dry ice from Moshi , Tanzania to Atlanta , GA , United States of America for testing . Serology for leptospirosis was performed at the US Centers for Disease Control and Prevention using the standard microscopic agglutination test ( MAT ) with a panel of 20 Leptospira serovars belonging to 17 serogroups [16] . These included: Australis ( represented by L . interrogans serovar Australis , L . interrogans serovar Bratislava ) , Autumnalis ( L . interrogans serovar Autumnalis ) , Ballum ( L . borgpetersenii serovar Ballum ) , Bataviae ( L . interrogans serovar Bataviae ) , Canicola ( L . interrogans serovar Canicola ) , Celledoni ( L . weilii serovar Celledoni ) , Cynopteri ( L . kirschneri serovar Cynopteri ) , Djasiman ( L . interrogans serovar Djasiman ) , Grippotyphosa ( L . interrogans serovar Grippotyphosa ) , Hebdomadis ( L . santarosai serovar Borincana ) , Icterohaemorrhagiae ( L . interrogans serovar Mankarso , L . interrogans Icterohaemorrhagiae ) , Javanica ( L . borgpetersenii serovar Javanica ) , Mini ( L . santarosai serovar Georgia ) , Pomona ( L . interrogans serovar Pomona ) , Pyrogenes ( L . interrogans serovar Pyrogenes , L . santarosai serovar Alexi ) , Sejroe ( L . interrogans serovar Wolffi ) , and Tarassovi ( L . borgpetersenii serovar Tarassovi ) . MAT was performed beginning at a dilution of 1:100 , with subsequent two-fold dilutions . Positive and negative controls were included with each run . We defined leptospirosis cases as participants with either a four-fold rise in agglutinating antibody titers between acute and convalescent serum , or a single reciprocal titer of ≥800 [17] . Seropositivity was defined as a single positive reciprocal titer of ≥100 from either sample . Controls were participants with negative titers on both acute and convalescent serum samples . The predominant reactive serogroup for cases and seropositive participants was defined as the serogroup containing the serovar with the highest titer . For each participant , village population density was calculated from the 2012 Tanzania Population and Housing Census [18] . For the purpose of analysis , a priori zone classifications were applied to each village [19] . Villages with a population density of 10 inhabitants/km2 were classified as urban; villages ≤15km distance from urban areas with a population density ≥3 and < 10 inhabitants/km2 were classified as peri-urban; and villages ≥15km distance from an urban area with a population density of <3 inhabitants/km2 [19] . Georeferenced mean annual rainfall and soil type data were obtained from the 2002 Kenya International Livestock Research Institute report [20] . Land use data were obtained from the 2010 National Geomatics Center of China report [21] . Daily rainfall data were obtained from the Tanzania Production Company ( TPC ) rainfall stations located near Moshi . Patient history , questionnaire , and MAT data were entered using the Cardiff Teleform system ( Cardiff , Inc . , Vista , CA , USA ) into an Access database ( Microsoft Corporation , Redmond , WA , USA ) . Geospatial data were managed using QGIS , version 2 . 8 . 3 ( Free Software Foundation , Boston , MA , USA ) . Spatial scan statistics were calculated using a Bernoulli model to assess evidence of spatial clustering of cases using SatScan version 9 . 0 ( www . satscan . org ) [22] . All other analyses were performed using Stata , version 13 . 1 ( StataCorp , College Station , TX , USA ) . This study was conducted in accordance with the Declaration of Helsinki . It was approved by the KCMC Research Ethics Committee ( #295 ) , the Tanzania National Institute for Medical Research National Ethics Coordinating Committee ( NIMR1HQ/R . 8cNo1 . 11/283 ) , Duke University Medical Center Institutional Review Board ( IRB#Pro00016134 ) , and the University of Otago Human Ethics Committee ( Health ) ( H15/055 ) . Written informed consent was obtained from all participants or their guardians .
Of 15 , 305 patients admitted and 30 , 413 presenting to the outpatient department , 2 , 962 met eligibility criteria and 1 , 416 ( 47 . 8% ) were enrolled . Of 1 , 293 participants who completed the risk factor questionnaire and had serum tested , 24 ( 1 . 9% ) met the study criteria for acute leptospirosis , 252 ( 19 . 5% ) were seropositive , and 592 ( 45 . 8% ) were classified as controls ( Fig 1 ) . The remaining 449 ( 34 . 7% ) were seronegative but provided only a single serum sample and so were excluded from analysis . The frequency with which participants were predominantly reactive to different serogroups is shown in Table 1 . Participant characteristics are shown in Table 2 . Clinicians did not diagnose leptospirosis in any study participant . Four ( 25 . 0% ) of 16 leptospirosis cases with discharge diagnoses recorded were diagnosed with malaria despite negative blood parasite examinations . Bivariable logistic regression of individual risk factors are included in S2 Table . There was a strong association between behaviors involving a single livestock species . For example having cleaned cattle waste was associated with having fed cattle with an OR 324 . 1 ( 95% confidence intervals 96 . 6–1087 . 0 ) . There was some association between behaviors involving different livestock species . For example having cleaned cattle waste was associated with having cleaned goat waste with an OR 28 . 8 , 95% confidence interval 12 . 0–69 . 1 . There was a small magnitude association between rodent contact variables and livestock related variables . For example owning cattle was not associated with seeing rodents frequently in the house , compound or fields , and had a low magnitude association with seeing rodents in the kitchen or food store ( OR 1 . 5 , 95 confidence intervals 1 . 1–2 . 1 ) . Results for the logistic regression analysis of individual behaviors are shown in Table 3 . On bivariable regression , variables associated with acute leptospirosis included working in rice fields ( OR 14 . 6 , 95% confidence intervals ( CI ) 2 . 9–59 . 5 ) ; cleaning up cattle waste ( OR 4 . 3 , CI 1 . 2–12 . 9 ) ; feeding cattle ( OR 3 . 9 , CI 1 . 3–10 . 3 ) and working as a farmer ( OR 3 . 3 , CI 1 . 3–8 . 2 ) . Nine ( 42 . 9% ) of 21 experts ( three livestock field officers , four veterinarians , and two zoonotic disease epidemiologists provided internally consistent multiple pairwise rankings of the relative exposure to livestock urine from the behaviors listed in Table 4 . Four ( 100 . 0% ) of four experts ( one water engineer , one water and sanitation epidemiologist , and two zoonotic disease epidemiologists ) provided consistent multiple pairwise rankings of the relative exposure to surface water . Three ( 75 . 0% ) of four experts ( one rodent ecologist , one veterinarian , and one zoonotic disease epidemiologist ) provided consistent multiple pairwise rankings of the relative exposure to rodent urine . The individual behaviors evaluated for each exposure scale and the geometric means of the weights assigned to each are listed in Table 4 . The results of pairwise comparisons , and calculated weights for each behavior are presented in S3 Table , S4 Table , and S5 Table . The distributions of participants’ exposure scores on each scale are shown in Fig 2 . Overall , 534 ( 69 . 3% ) of participants had no evidence of exposure to cattle urine , 563 ( 73 . 0% ) had no exposure to goat urine , 241 ( 31 . 2% ) had no exposure to rodent urine , and 262 ( 34 . 0% ) had no exposure to surface water . There was limited correlation between cattle urine exposure and both goat urine exposure ( r2 = 0 . 21 ) and pig urine exposure ( r2 = 0 . 04 ) . In addition there was little correlation between livestock urine exposure scores and rodent urine exposure ( for example , cattle urine exposure and rodent urine exposure , r2 = 0 . 04 ) , livestock exposure scores and surface water exposure ( for example cattle urine and surface water ( r2 = 0 . 02 ) , and between rodent urine exposure and surface water exposure ( r2 = 0 . 02 ) . All exposure scales had a linear relationship with log odds of acute leptospirosis Our bivariable logistic regression ( Table 5 ) found that increasing exposure to cattle urine ( OR 2 . 3 , CI 1 . 1–4 . 7 ) and exposure to rodents ( OR 1 . 7 , CI 1 . 1–2 . 8 ) were both associated with increased odds of acute leptospirosis . In multivariable logistic regression ( Table 5 ) , no exposure scale was independently associated with leptospirosis . As shown in S6 Table , there were no significant interactions . The largest variance inflation factor was 1 . 33 . GPS co-ordinates were available for houses of 649 ( 84 . 2% ) participants . No two or more participants lived at the same household . Land use designation could be determined from participant’s self-reported village of residence for an additional 79 ( 10 . 2% ) participants . There was no evidence of clustering in the spatial distribution of cases . Results of the bivariable logistic regression analysis of geo-referenced variables and rainfall , and acute leptospirosis are shown in Table 6 . There were no statistically significant associations . Results of the logistic regression of individual risk factors for Leptospira seropositivity are listed in Table 7 . Working in rice fields ( OR 3 . 6 , 95% CI 1 . 5–9 . 0 ) ; slaughtering goats ( OR 2 . 3 , 95% CI 1 . 0–4 . 8 ) , working as a farmer ( OR 1 . 8 , 95% CI 1 . 3–2 . 5 ) , and frequently seeing rodents in the kitchen ( OR 1 . 5 , 95% CI 1 . 1–2 . 1 ) were significant risk factors ( p < 0 . 05 ) on bivariable regression . We fitted an initial multivariable model using the risk factors shown in Table 8 . As shown in S6 Table , we did not identify any significant interactions between variables . In our final multivariable model , working as a farmer ( OR 1 . 6 , CI 1 . 1–2 . 3 ) , working in the rice fields ( OR 2 . 7 CI 1 . 0–7 . 2 ) , or seeing rodents in the kitchen ≥ once per week ( OR 1 . 5 , CI 1 . 0–2 . 1 ) were all independent risk factors for Leptospira seropositivity . Walking barefoot ( OR 0 . 7 , CI 0 . 5–0 . 9 ) and owning dogs ( OR 0 . 6 , CI 0 . 4–1 . 0 ) were associated with reduced odds of Leptospira seropositivity . The logistic regression models of the exposure scales and Leptospira seropositivity are shown in Table 9 . Increasing exposure to rodent urine ( OR1 . 2 , CI 1 . 0–1 . 5 ) was associated with Leptospira seropositivity on bivariable logistic regression , but not on multivariable regression . Results of the bivariable logistic regression analysis of rainfall and Leptospira seropositivity are shown in Table 10 . There was an inverse association with mean annual rainfall >1 , 600mm per year ( OR 0 . 56 , 95% CI 0 . 33–0 . 93 ) . We fitted an initial multivariable model using household elevation , mean annual rainfall , maximum daily rainfall in the preceding 30 days , and total rainfall in the preceding 30 days . The final model contained elevation ( OR 0 . 99 per 10m , CI 0 . 98–1 . 0 , p = 0 . 06 ) , and total rainfall in the preceding 30 days ( OR 1 . 2 per 100mm , CI 0 . 95–1 . 5 , p = 0 . 13 ) but neither association was statistically significant . An analysis of the risk factors for seropositivity against Leptospira serogroup Icterohaemorrhagiae is included as S6 Table .
We identified multiple associations between exposure to cattle and acute leptospirosis , suggesting that cattle may be important sources of human leptospirosis in northern Tanzania . We also identified work in rice fields as an important risk factor for human leptospirosis . These findings must be interpreted with caution , as they were based on a small number of cases , and were present in only bivariable regression . Despite this , our findings have implications for the control and prevention of leptospirosis in Tanzania . On bivariable regression , exposure to cattle was associated with acute human leptospirosis both when we evaluated individual behaviors and scales of cumulative exposure to cattle urine . These findings support other data from northern Tanzania that indicate that livestock may be an important source of human leptospirosis [31] . Among cattle slaughtered for meat in the Moshi area , 7 . 6% of cattle tested were carrying pathogenic Leptospira spp . in their kidneys [31] . Furthermore , seroreactivity against serogroups Australis and Sejroe , the two dominant serogroups among human cases in our study , was also frequently observed among cattle slaughtered for meat in the Moshi area in 2014 [12] . Our findings are also consistent with studies examining risk factors for Leptospira seropositivity in Africa . Leptospira seropositivity was common among abattoir workers in Kenya and Tanzania [11 , 27] . In rural Uganda , livestock skinning was reported as a risk factor for seroreactivity and human seropositivity to livestock-associated Leptospira serovars was common [28] . In a global context , cattle have also been identified as a key risk factor in other rural livestock-farming communities in Central America and South Asia [14 , 15] , suggesting that strategies to reduce either livestock leptospirosis or transmission of leptospirosis from livestock to humans may be important global public health interventions . Rodent exposure is an important risk factor for leptospirosis in the tropics , particularly in urban areas of Asia and South America [4 , 29 , 30] . In our study , an increasing score on the exposure to rodent urine scale was associated with acute leptospirosis in bivariable regression . However , the only individual component of the scale for which we found an association on bivariable regression was smallholder farming . Since smallholder farming may involve substantial exposure to both livestock and rodents , and other rodent related variables were not associated with leptospirosis the role of rodents in this association is uncertain . We also found that frequently sighting rodents in the kitchen or food store was associated with Leptospira seropositivity . Rodents could transmit leptospirosis to humans , or act as a reservoir that transmit Leptospira to livestock . However , recent work in the Kilimanjaro Region found no evidence of Leptospira urinary shedding , or renal infection among 393 wild rodents [31] Although practiced by few participants , we found an association between working in rice fields , and both acute leptospirosis and Leptospira seropositivity . In some areas of northern Tanzania rice farming is practiced intensively , and there are active efforts to increase irrigated , continuously flooded rice farming across Tanzania [32] . In Asia rice farming is an established risk factor for leptospirosis . In Asia humans are infected through prolonged contact with water that may be contaminated by infected animal hosts [4 , 29] . Further work is needed to evaluate possible sources of contamination of rice paddies in Tanzania and promote personal protective measures among rice farmers . We did not find associations between acute leptospirosis and rainfall , or environmental risk factors around the home . The small number of cases available for analysis , and the relative lack of resolution of geo-referenced data meant that this result must be interpreted with caution . The lack of association with heavy rainfall differs from findings of studies from other locations [33 , 34] . We found that seropositivity was associated with lower elevation and lower rainfall . While we did not have household level slope data , the topography of the study area includes steeply sloping terrain on the flanks of Mount Kilimanjaro that may not favor surface water accumulation . The lack of association between leptospirosis and home location may indicate that the workplace is an important site for infection [9 , 11] . Future studies should collect data regarding workplace location . Clinicians did not diagnose leptospirosis during the study period , and over-diagnosis of malaria was common . At the time of our study , there were no locally available , accurate diagnostic tests for leptospirosis . In addition , despite the high incidence in the region , clinician awareness of leptospirosis and other zoonotic diseases remains low [35] . This highlights the need for clinician education and evaluations in Africa of inexpensive point-of-care diagnostic tests . We found that risk factors and the pattern of predominant reactive serogroups among leptospirosis cases was markedly different from those in seropositive individuals , for whom the febrile illness concurrent with enrollment was unlikely to be leptospirosis . In particular , reactivity to serogroup Icterohaemorrhagiae was common among seropositive participants , but there were few acute cases associated with this serogroup . These results may indicate that a serovar from the Icterohaemorrhagiae serogroup was circulating in this region [36] , causing only mild disease not requiring tertiary medical care . Elsewhere , a difference in severity of disease has been linked to variability of infecting Leptospira species [37] , Alternatively , the presence of Icterohaemorrhagiae seropositivity but absence of acute cases could indicate historic circulation of this serogroup that has since declined . Other results suggest that leptospirosis has a dynamic epidemiology in this area with the emergence and decline of specific serovars over time [3] . Cross reactivity between serogroups , and non-specific reactivity are other possible explanations [38] . Our study had several limitations . First , the prevalence of acute leptospirosis was lower than anticipated [8] , potentially curtailing our ability to detect important associations . Conversely , associations of individual activities and leptospirosis identified by this study were sometimes based on only a few cases and should be interpreted with caution , especially given the multiple statistical tests . In addition , changes in leptospirosis incidence in the study area might also reflect changes in predominant sources and modes of transmission over time [3] . Second , the associations for acute leptospirosis were seen only on bivariable analysis , and these associations may be due to confounding from unobserved behaviors . Due to the complex interconnection between individual behaviours , we also consider that confounding may influence the multivariable logistic regression model of individual behaviours and Leptospira seropositivity . For example , the inverse association of walking barefoot and leptospirosis is puzzling , and we think it is likely to be influenced by an association with some protective factor , despite not identifying such an association among the behaviors we investigated . Diagnostic test limitations may have also introduced classification errors of participant cases or controls into our analysis . Leptospirosis is notoriously difficult to diagnose , particularly in the acute stages of illness and all currently available diagnostic tests for leptospirosis , including MAT [39] , are imperfect . The sensitivity of MAT on paired serum samples is approximately 80% and the specificity close to 100% [40] . Specifically , not all participants with leptospirosis will seroconvert [40] , and it is not possible to differentiate between historic and recent infection based on a single high titer [41] . We chose MAT for our case definitions since MAT on paired serum samples , while imperfect , remains the reference standard [40] . Furthermore , culture , nucleic acid amplification and point-of-care IgM serology lack sensitivity in our setting [12 , 42 , 43] , and reports from other settings have been mixed [39 , 44–46] . Our MAT panel comprising 20 serovars covered the major Leptospira serogroups that cause human disease , and all those within which African isolates are grouped [1] . We did not use locally isolated serovars and this may have influenced identification of cases . However , studies on the use of local isolates in MAT reference panels have shown that they do not necessarily perform better than other serovars from the same serogroup [47 , 48] . Our analysis of acute leptospirosis was limited to cases across all serogroups . We acknowledge that risk factors may vary by infecting serovar , and pan-serogroup analyses may mask important associations . We developed scales for use in our analyses for dimension reduction due to the unanticipated low number of cases . We suggest that cumulative exposure scales may have a future role in assessing sources of acute leptospirosis , as they allow assessment of cumulative exposure that may be important in assessing individual risk of disease . The analytic hierarchy process was an appropriate method of creating these scales , as it is an effective tool for quantifying multi-dimensional qualitative knowledge [24] . While we acknowledge that there is scope to improve our cumulative exposure scales , our scales that quantify expert opinion offer more biologically plausible groupings than statistical methods of dimension reduction . Key areas for future development of cumulative exposure scales are to validate them across multiple groups of experts , and to formally compare their effectiveness against purely statistical dimension reduction . Since our questionnaire sought exposures over a 30 day period , recall bias may have influenced our findings . Finally , we enrolled only 47 . 1% of eligible patients . We found no bias towards particular ethnic or occupational groups . However , we cannot rule out the possibility that the enrollment pattern influenced our results . Despite these limitations , the consistency of the association of the livestock related variables strengthens our confidence in the interpretation of their role in transmitting leptospirosis to people in our region . Our results have implications for control of leptospirosis . Transmission of leptospirosis within rice fields , and from livestock to people is amenable to control through personal protective equipment for those performing high risk activities [49] . In addition , Leptospira vaccines are available for use in livestock against some Leptospira serovars . In some countries such vaccines have contributed to successful control of leptospirosis [49] . However , before a vaccination program is considered it is essential to understand reservoir structure and predominant infecting serovars . Our study identifies associations between cattle contact and work in rice fields with acute leptospirosis . Our findings suggest that cattle may be a source of human leptospirosis in northern Tanzania . Further work is needed to determine if these findings are stable over time , and to investigate the link by isolating infecting serovars from humans and animal hosts . The development of local MAT capacity , or use of nucleic acid amplification or point-of-care IgM tests that have sufficiently high sensitivity would enable real-time diagnosis and allow testing of potential animal hosts living in proximity to humans with acute leptospirosis . Nonetheless , our findings suggest that control of Leptospira infection in livestock could play a role in preventing human leptospirosis in Africa . | Leptospirosis is an under-recognized but important cause of febrile illness and death in Africa . The bacteria that cause leptospirosis have their usual life cycle in animals; humans are infected as accidental hosts . There is considerable variation between countries as to which reservoir animals and human activities are important for transmission of leptospirosis to humans . In many tropical countries flooding and rodents are the dominant sources of human infection . However , in Africa it is unknown which sources of leptospirosis are most responsible for human infection and what behaviors put people at risk for infection We performed a prospective cross-sectional study , to identify risk factors for acute leptospirosis and sources of human infection . We identified contact with cattle and work in rice fields as risk factors for acute leptospirosis . Our findings indicate that cattle may be an important source for human leptospirosis , and therefore control of leptospirosis in livestock may help prevent leptospirosis in people . Further work is needed to isolate Leptospira from humans and livestock . Rice farming was an uncommon activity in our study , but strongly associated with acute leptospirosis . Efforts are warranted to prevent infection in rice farmers living in Africa . | [
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"bacte... | 2018 | Risk factors for human acute leptospirosis in northern Tanzania |
HIV-1 is dependent on the host cell for providing the metabolic resources for completion of its viral replication cycle . Thus , HIV-1 replicates efficiently only in activated CD4+ T cells . Barriers preventing HIV-1 replication in resting CD4+ T cells include a block that limits reverse transcription and also the lack of activity of several inducible transcription factors , such as NF-κB and NFAT . Because FOXO1 is a master regulator of T cell functions , we studied the effect of its inhibition on T cell/HIV-1 interactions . By using AS1842856 , a FOXO1 pharmacologic inhibitor , we observe that FOXO1 inhibition induces a metabolic activation of T cells with a G0/G1 transition in the absence of any stimulatory signal . One parallel outcome of this change is the inhibition of the activity of the HIV restriction factor SAMHD1 and the activation of the NFAT pathway . FOXO1 inhibition by AS1842856 makes resting T cells permissive to HIV-1 infection . In addition , we found that FOXO1 inhibition by either AS1842856 treatment or upon FOXO1 knockdown induces the reactivation of HIV-1 latent proviruses in T cells . We conclude that FOXO1 has a central role in the HIV-1/T cell interaction and that inhibiting FOXO1 with drugs such as AS1842856 may be a new therapeutic shock-and-kill strategy to eliminate the HIV-1 reservoir in human T cells .
As other viruses , HIV-1 is an obligate intracellular pathogen strictly dependent on a suitable host cell machinery for most of the steps of its life cycle , a machinery that is hijacked by the virus to generate its progeny . In the case of human CD4+ T lymphocytes , the permissiveness to HIV-1 infection depends on their cellular activation state . While activated and proliferating CD4+ T lymphocytes are highly susceptible to infection and support efficient HIV-1 replication , resting CD4+ T cells are mainly non-permissive because of their low level of transcriptional activity [1] . Host cell transcription factors such as NF-κB and NFAT , the activity of which is dependent on T cell stimulation , recognize specific target sites in the viral promoter contained in the long terminal repeats ( LTRs ) , and are therefore essential for expression of viral components and HIV-1 genome replication [2] . This transcriptional control is also instrumental for the generation of viral reservoirs , defined as cell types where the virus persists during therapy [3 , 4] . These reservoirs , established during the first days of infection , are responsible for the recurrence of a detectable level of viremia in treated patients upon interruption of combinatory antiretroviral therapy ( cART ) . The main reservoir resides in latently infected resting CD4+ memory T cells [5] . These cells carry stably integrated and transcriptionally silent but replication-competent proviruses . They do not produce virus particles when cells are in a resting state , but can give rise to infectious virions following activation by various stimuli , leading to viral rebound when cART is interrupted . In T cells , IL-7 is critical for the loss of quiescence [6 , 7] . In this context , primary naive T cells , typically not permissive to HIV , can be productively infected when pre-treated with IL-7 alone [8 , 9] . One molecular step that participates in this effect of IL-7 is the neutralization of SAMHD1 activity [10] . SAMHD1 is one of the cellular factors that have evolved to counteract HIV-1 replication . These so-called restriction factors constitute barriers present in the host cell to inhibit specific steps of the viral life cycle . SAMHD1 is a deoxynucleoside triphosphate triphosphohydrolase that regulates cell-cycle progression , and is a major viral restriction factor that blocks early reverse transcription of HIV-1 by depleting the intracellular dinucleotide triphosphate ( dNTP ) pool [11 , 12] . The function of SAMHD1 is regulated through the phosphorylation of threonine 592 by cyclin A2/Cdk1 , an event that is induced by IL-7 [13] . Previous work also showed that IL-7 induced NFAT activity is a supplementary mechanism through which IL-7 can affect HIV-1 infection in naïve T cells [8] . Thus , it is now well established that the mechanisms that control the state of quiescence of naïve T cells are essential for regulating their permissiveness to HIV infection [1] . FOXO1 is a transcription factor that actively maintains quiescence of human T lymphocytes in conditions where the PI3-kinase/Akt pathway is inactive [14] . Data showing an increase of viral replication kinetics after inhibition of FOXO1 in quiescent T cells treated with IL-7 now suggest that this molecule may be another molecular switch controlling HIV-1 infection and participating in the effects of this cytokine on the biology of HIV-1 in T cells [15] . In this study , we explored whether and how directly inhibiting FOXO1 activity with AS1842856 [16] , a specific pharmacological inhibitor of FOXO1 affects the permissiveness of naïve human T cells to HIV infection . We show that inhibition of FOXO1 alone was sufficient to trigger a G0→G1 transition of human T lymphocytes upstream of the R restriction point of the cell cycle . This transition is characterized by a parallel increase in cell size , metabolism and transcriptional activity . We also show that FOXO1 inhibition is accompanied by the inactivation of the SAMHD1 viral restriction factor together with permissiveness of resting human CD4+ T cells to lentiviral infection . We finally observe the reactivation of HIV-1 proviruses by the AS1842856 drug or after FOXO1 knowdown by RNA interference using different HIV-1 latency models of human T cells , and also of latent viral reservoirs present in CD4+ T cells from nonhuman primates under cART . Taken together , these results demonstrate that FOXO1 is a major player in T lymphocyte/HIV-1 interaction and that its pharmacological inhibition is a new potential clinical strategy to eradicate latent provirus reservoirs during HIV-1 infection .
We first determined whether FOXO1 inhibition by AS1842856 allowed the infection of resting T cells by HIV-1 , in the absence of any additional treatment . For this aim , peripheral blood human T cells ( PBT ) were cultured with AS1842856 or DMSO vehicle control only , and then brought into contact with a VSV-G non-replicative lentiviral vector expressing GFP under LTR control . Three days later , the percentage of GFP-positive cells was analyzed by flow cytometry . As a positive control , we also examined the infection of PBT stimulated with anti-CD3/CD28 beads . A view of the experimental schedule is given in Fig 1A . FACS dot plot analyses of the results obtained with a representative donor , as well as mean results from five independent donors , are illustrated in Fig 1B ( left and right panel , respectively ) . They show a marked increase of GFP positive cells after FOXO1 inhibition . Since the use of V-SVG envelope to infect resting T cells can introduce a bias in these experiments , we next checked using the same experimental set-up the capacity of AS1842856-treated PBT to be infected with a bona fide HIV-1 strain , NL4 . 3 . Three days after infection by NL4 . 3 , intracellular expression of the GAG precursor was measured by flow cytometry . As shown in Fig 1C , the number of GAG positive cells increases after AS1842856 treatment , and dose-dependently ( S1 Fig , upper panel ) . Similar results were obtained with the LAI HIV-1 strain , thereby demonstrating that the transactivation induced by AS1842856 was not restricted to NL4 . 3 viruses ( S1 Fig , lower panel ) . Thus inhibition of FOXO1 , in the absence of any other cell treatment , allows infection of resting T cells by HIV-1 . Retrovirus replication is highly dependent on the metabolic activity of the cellular host [17 , 18] . We therefore hypothesized that the susceptibility to HIV-1 infection of FOXO1-inhibited resting T cells could be due to an increased cell metabolism . Cell size variation is often linked to metabolic rate . As shown in Fig 2A , AS1842856 induces a substantial increase of T cell size , illustrated by FSC/SSC dot plot analyses . Time-course analyses showed a gradual and continuing increase , usually reaching a maximum after 7 days of culture ( S2A Fig ) and for drug concentrations around 500nM ( S2B Fig ) . Importantly , no associated toxicity of the drug was observed ( S2C Fig ) . These results led us to use this condition ( 500nM during a 7-day culture ) in all subsequent experiments . Parallel labelling of CD4+ and CD8+ T-cells and of their naïve ( CD45RA+ ) and memory ( CD45RA- ) sub-populations showed that this cell size increase was very similar in both CD4+ and CD8+ T-cell subsets ( S2D Fig ) . They also indicated that within these two subsets both naïve and memory T cells were similarly affected . As increased cell metabolism is often associated with glucose consumption , we analyzed the uptake of the fluorescent glucose analog 2-NBGD in T cells treated or not with AS1842856 . As shown in Fig 2B , FOXO1 inhibition induced a significant increase of 2-NBDG uptake . We also checked the consequences of AS1842856 treatment on mitochondrial respiration , another cell function associated with an increase in metabolism . Results obtained by high-resolution respirometry experiments of PBT treated with or without AS1842856 showed that respiration at the steady state was increased by AS1842856 ( Fig 2C ) . Using oligomycin , an inhibitor of ATP synthase which reduces respiration to the baseline leak level , followed by successive addition of CCCP ( carbonyl cyanide m-chlorophenyl hydrazone ) to stimulate respiration to the non-coupled state of the electron transfer capacity , we also observed that the maximum respiratory capacity was strongly increased by the drug . Finally , we investigated the effect of AS1842856 on the expression of the receptor of transferrin ( CD71 ) ( Fig 2D ) and the heavy chain of the system L amino-acid transporter ( CD98 ) ( Fig 2E ) . These cell-surface markers are known to be associated with an increased metabolic status in T lymphocytes [19–22] . Mirroring the glucose uptake and mitochondrial respiration results , we observed a significant increase of these two receptors on T cells treated with AS1842856 . Both CD4+ and CD8+ T-cells and their naïve and memory subsets were affected . To get an overall view of these changes in T-cell metabolism , gene expression microarray analysis of PBT cultured during 7 days in the presence or not of AS1842856 were performed . By comparing the results obtained from 3 individual donors , lists of mRNAs whose levels were down-regulated or up-regulated after AS1842856 treatment ( with a <-1 . 5 and >1 . 5-fold change cut-off and a P-val <0 . 01 ) were established . Each contains around 1000 differentially expressed genes ( S1 Table ) . These gene lists were analyzed using the functional annotation tool of the DAVID Bioinformatics Resources [23 , 24] and the KEGG database . Results identified FoxO signaling genes and genes involved in the negative regulation of the cell cycle as the most significantly inhibited by AS1842856 ( Fig 3A ) . In contrast , AS1842856-treated cells showed a strong increase in the expression of molecular networks involved in cell metabolic activity . Among them , and in accordance with the mitochondrial respiration results , the oxidative phosphorylation pathway was the most affected . We also verified at the protein level that some prototypic targets of FOXO1 in T cells whose expression is positively or negatively controlled by FOXO1 , such as CD62-L and IL7-R [25] or granzyme B [26 , 27] , respectively , were also down or up-regulated after AS1842856 treatment ( Fig 3B ) . Increase in cell size and number of organelles ( such as mitochondria ) , as well as accumulation of nutrients , are hallmarks of the transition from the G0 to the G1 phase of the cell cycle that are required to prepare the subsequent phases leading to mitosis [28] . Moreover , the transcriptome modification induced by AS1842856 treatment of PBT revealed that the cell cycle pathway was one of the most affected ( Fig 3A and S1 Table ) . We therefore directly investigated the cell cycle status of PBT treated with AS1842856 using acridine orange staining , an intercalating dye that labels both RNA and DNA . As a positive control of increase in both RNA and DNA cellular content , we used untreated T cells activated for 3 days with anti-CD3/CD28 beads . Results showed that AS1842856 markedly increased cellular RNA levels without any significant change of the DNA content , whereas CD3/CD28 beads increased both ( Fig 4A ) . This was confirmed by classical CFSE dilution assays ( S3 Fig ) . These results demonstrate that AS1842856-treated PBT show characteristic features of cells undergoing a G0→G1 cell cycle progression , but without any cell division . To further investigate this process , we checked whether typical molecular events involved in cell progression through the G1 phase of the cell cycle , such as Rb phosphorylation , p27 down-regulation or CDK2 up-regulation [29] , were changed after AS1842856 treatment . In parallel to an increase of Rb phosphorylation , we observed a decrease in p27 expression , paralleled by an up-regulation of CDK2 ( Fig 4B ) ( also found at the mRNA level , see S1 Table ) . As phosphorylation of the retroviral restriction factor SAMHD1 ( i . e . its inactivation ) by CDK2 is associated with the exit of the quiescent state and also because this molecular event controls T-cell susceptibility to HIV-1 infection [13] , we also measured pSAMHD1 levels after AS1842856 treatment of PBT . A clear phosphorylation was consistently found ( Fig 4B ) . This phosphorylation was less pronounced than after CD3/CD28 stimulation , which is known to strongly trigger SAMHD1 phosphorylation in T cells [10] ( S4 Fig ) . Additionally , in parallel experiments measuring the permissiveness of AS1842856 treated cells to HIV-1 infection , we also observed a relationship between SAMHD1 phosphorylation levels and GAG expression ( S5 Fig ) . Since HIV-1 replication in resting T cells is limited by the transcriptional activity of the viral LTR , we also investigated the consequences of FOXO1 inhibition on LTR activity ( i . e . at the post-integrative level ) . For this purpose , PBT were stimulated with anti-CD3/CD28 beads and then infected with the previously used VSV-G non-replicative lentiviral vector expressing GFP . Subsequently , the cells were incubated with or without AS1842856 for two days , and GFP expression levels measured by flow cytometry to see whether FOXO1 inhibition by the drug could activate the LTR integrated in the host cell genome . A representation of the experimental schedule is given in Fig 5A . Results showed that whereas the percentage of GFP-positive cells remained unchanged , there was a marked increase in GFP fluorescence intensities in the presence of AS1842856 in these cells , as compared to the control ( Fig 5B ) . We concluded that AS1842856 could increase LTR activity in the absence of any other viral proteins . In order to validate this result in a model where all viral proteins are present , we also used chronically HIV-1 infected T-lymphoid H9 cells , a clonal derivative of the Hut 78 lymphoma T-cell line . These cells were treated with AS1842856 for 3 days and GAG expression analyzed by flow cytometry . As shown in Fig 5C , as expected , a high fraction of these cells spontaneously expressed GAG . However , in this model also , AS1842856 treatment increased LTR activity , as illustrated by the clear shift in GAG expression . LTR activity is mainly controlled by NFAT and NF-κB , which transcriptional activities are dependent on T cell activation . We therefore measured the activity of these transcription factors in PBT after AS1842856 treatment . No activation of the NF-κB pathway by AS1842856 could be detected , given the absence of degradation of the NF-κB inhibitor IκBα ( S6 Fig ) . A short PMA plus iomycin stimulation was used here as a positive control , showing an almost complete loss of IκBα , also seen with cells that have been pretreated with AS1842856 . In contrast , we observed a clear nuclear translocation of NFAT1 in AS1842856-treated cells ( Fig 5D ) . In this experiment , we also observed that the drug potentiated the effect of the calcium ionophore ionomycin , initially used as a positive control to trigger NFAT1 activation by increasing intracellular calcium . In a consistent way , we found in parallel experiments that steady-state levels of intracellular calcium were higher in AS1842856-treated cells ( S7A Fig ) and that the drug could also potentiate the response to ionomycin ( S7B Fig ) . To further study the consequences of FOXO1 inhibition on LTR activity , and especially to explore the ability of AS1842856 to reactivate latent forms of HIV-1 , we next used the J-Lat cell line HIV-1 latency model system . J-Lat cells were derived from the leukemia T cell line Jurkat . They contain an integrated silent form of a minimal HIV-1 provirus encoding GFP that can be used as a fluorescent read-out of the reactivation of the latent provirus [30] . In various cell types , one main mechanism involved in FOXO1 inhibition by AS1842856 results from the direct inhibition by the drug of FOXO1 transcriptional activity [16] . Thus , we first checked whether the same mechanism held true in Jurkat cells . For this aim we used a dual-luciferase reporter assay system with a reporter plasmid controlled by the Forkhead responsive element [31] . Cells were co-transfected with vectors encoding either GFP or the constitutively active form of FOXO1 , mutated on the three phosphorylation sites by Akt , FOXO1TM GFP . As shown in S8A Fig , AS1842856 treatment strongly inhibits the transcriptional activity of FOXO1TM . An inhibition was also observed in cells transfected with the GFP control vector , suggesting an inhibition by the drug of the residual activity of the endogenous form of FOXO1 in this cell line . In agreement , we found that the strong expression of CD62-L triggered by FOXO1TM GFP in this T-cell line , was also markedly inhibited by AS1842856 dose-dependently ( S8B Fig ) . Again , this experiment suggested some residual activity of the endogenous form of FOXO1 to control CD62-L levels in Jurkat cells , as its expression was also decreased by AS1842856 in cells transfected with GFP alone . After having checked this , J-Lat cells ( clone A1 ) were incubated with different concentrations of AS1842856 . After a 3-day treatment we observed a strong dose-dependent increase of the percentage of GFP-positive cells , as well as an increase of GFP expression levels , indicative of a reactivation of the LTR ( Fig 6A ) . These results were confirmed with two other J-Lat cell clones ( S9 Fig ) . To strengthen these observations , we measured reactivation induced by a non-pharmacological approach by knocking-down FOXO1 expression in the J-Lat A1 clone using a FOXO1-specific shRNA construct . These cells showed an increase percentage of GFP-positive cells , as compared to cells in which a control shRNA had been used ( Fig 6B ) . We next investigated whether these findings could be extended to primary T cells . For this purpose , we set up an experimental model using PBT activated with anti-CD3/anti-CD28-coated beads , then infected with the previously used ( see Fig 1A ) VSV-G non-replicative lentiviral vector expressing GFP under LTR control . Cells were maintained in culture with interleukin 2 ( IL-2 ) for several weeks . As shown in Fig 6C ( left panel ) , the percentage of GFP-positive cells continuously decreased over time , due to a gradual silencing of LTR activity , as reported previously [32] . Cells were then treated with AS1842856 or anti-CD3/anti-CD28-coated beads as a positive control , and latency reversion was assessed by measuring GFP fluorescence after 3 days of reactivation . AS1842856 treatment was found to increase the number of GFP-positive cells ( Fig 6C , left panel ) . Repeating these experiments with 4 donors , we observed that , although lower than the reactivation induced by anti-CD3/anti-CD28 beads , a significant increase of virus reactivation was always found with AS1842856 ( Fig 6C , right panels ) . These results demonstrate that inhibiting FOXO1 with AS1842856 could reverse HIV-1 latency in human T lymphocytes . In order to confirm this result in a model more relevant to pathophysiology , we investigated whether AS1842856 could reactivate latent SIVmac in CD4+ T cells from non-human primates under cART treatment . For this aim , we used rhesus macaques that had been previously infected by SIV mac251 , and treated for 6 months with a triple antiretroviral therapy combining Tenofovir , Emtricitabine and Dolutegravir to induce latency . We first controlled that , as in human T cells , AS1842856 was able to induce the G0→G1 transition of T cells purified from the blood of healthy macaques ( Fig 7A ) . Next , CD4+ T cells from the blood of the infected macaques were purified and cultured with AS1842856 , anti-CD3-CD28 coated beads as a positive control , or vehicle only . Two days later , to amplify infectious viruses produced by CD4+ T cells , activated splenocytes from non-infected macaques were added . Nine days later genomic DNA was extracted and analyzed for the presence of viral GAG by quantitative PCR . A view of the experimental schedule is given in Fig 7B . As shown in Fig 7C , GAG was undetectable in cells treated with vehicle only . In contrast , inhibition of FOXO1 by AS1842856 led to latent proviruses recurrence in three out of four animals in a manner comparable to the positive control . The absence of reactivation in the presence of AS1842856 observed for the fourth animal was observed not only after AS1842856 treatment but also with anti-CD3/CD28 , suggesting an individual response defect . To evaluate virus production obtained in these conditions , ultracentrifugated supernatants were used to infect freshly activated splenocytes from non-infected macaques . As shown in Fig 7D , five days post infection , substantial infection levels were obtained with supernatants obtained from macaques under cART treatment having shown a viral reactivation after AS1842856 treatment . These results demonstrate that inhibiting FOXO1 with AS1842856 reverses in vivo-induced retroviral latency leading to the production of infectious retroviral particles .
In this report , we show that the FOXO1 inhibitor AS1842856 induces a significant increase of both the bioenergetics and transcriptional activity of human T cells , together with a significant increase in their size , without any cell division . These modifications are accompanied by a decrease of p27 expression , contrasting with an increase of CDK2 cellular levels and by the phosphorylation of Rb and SAMHD1 proteins . As these changes are known to be characteristic of cells undergoing a G0 to G1 progression [33–35] , we conclude that inhibition of FOXO1 by AS1842856 is sufficient to induce a profound reprogramming of human T lymphocytes , regulating their exit from quiescence . Mechanisms controlling the extent of quiescence are poorly understood , representing a currently underappreciated layer of complexity in growth control [35] . When cells emerge from quiescence , they remain in the G1 phase of the cell cycle up to the restriction point R , defined as the point after which further progression becomes independent of continued mitogenic stimulation [34–36] . In our experiments , we observed no concomitant synthesis of DNA in T cells treated with AS1842856 . This suggests that whereas FOXO1 inhibition allows T cells to progress into G1 , it does not allow the cells to cross this restriction point to enter into S phase . These results strengthen the concept that quiescence is not a default state , but an actively maintained state [35] . They also reveal the key role played by the transcriptional program induced by FOXO1 in maintenance of quiescence in human T lymphocytes . Upon FOXO1 inhibition , we observed an enhanced metabolic activity of T cells , affecting all T cell subsets , including naive T cells . This was illustrated by their higher expression of CD71 and CD98 metabolic markers , increased glucose uptake and greater mitochondrial respiration after AS1842856 treatment . The drug also induced a substantial cell size growth , including in naive T cells . Consistently , a comprehensive analysis of differential expression profiles of mRNA has revealed enrichment in the expression of various sets of genes involved in cell metabolism . Recent observations have shown that a hallmark of naive CD8 T cell differentiation into memory CD8 T cells is an increase of their intrinsic metabolic activity [26 , 37] . It is therefore tempting to speculate that inhibition of FOXO1 may not only induce G0 exit of naïve T cells , but also some important steps in their differentiation program into memory T cells . Thus , like quiescence , the naïve T cell state may also be actively maintained in part by FOXO1 . In this context , the fact that memory T cells express lower amounts of FOXO1 is probably not fortuitous [38] . This phenomenon may also contribute to their greater responsiveness to a new antigen challenge , in keeping with the now well-established anti-proliferative action of FOXO1 , in particular through specific targets of this transcription factor , such as the RhoA binding partner FAM65B [39] . One main conclusion of the present work is that reprogramming T cells after FOXO1 inhibition modifies the HIV-1/T cell interaction at several stages of the viral life cycle . We found that inhibition of FOXO1 by AS1842856 allows the efficient infection of resting T cells by HIV-1 . This result is in line with our observation that the restriction factor SAMHD1 is phosphorylated after AS1842856 treatment . Indeed , it is now clear that this post-translational modification inhibits SAMHD1 , the enzymatic activity of which reduces the availability of dNTP required for the viral reverse transcriptase [13] . SAMHD1 inactivation also clearly plays a role in IL-7-treated resting T cells , which are more susceptible to HIV-1 infection . IL-7 mediates signals triggering PI3-kinase activation in T cells , and an inhibition of FOXO1 in quiescent T lymphocytes after IL-7 treatment has been observed [40] . It is therefore possible that the effect of IL-7 in resting T cell infection relates to this inhibition of FOXO1 , but this requires further exploration . It is quite interesting to mention here that a recent report has shown that cellular metabolism , especially glucose metabolism , seems to be a major contributor to HIV-1 reservoir implementation in CD4+ T cells [41] . Thus , and to explain the effect of FOXO1 inhibition on HIV-1 at the pre-integrative level , several mechanisms are likely at work . This may involve not only regulation of a group of genes required for cell cycle exit and the maintenance of cell quiescence in human T cells , like SAMHD1 , but also of genes allowing a higher cell metabolism . This hypothesis is fitted very well with our transcriptomic data showing that major metabolic pathways are ranked at the top of enriched gene sets after FOXO1 inactivation by AS1842856 in PBT . A second conclusion drawn from our results is that inhibition of FOXO1 appears to orchestrate not only pre-integrative , but also post-integrative stages of the viral life cycle . Indeed , we found an increase of viral promoter activity after AS1842856 treatment . In this case , the reactivation of LTR activity cannot be interpreted just as a consequence of some T cell activation induced by AS1842856 , as this effect has been observed in TCR stimulated PBT and transformed J-Lat cells , two cellular models where cells are already very active metabolically . It has been shown that FOXO1 directly inhibits LTR activity in a TAT-dependent manner [42] . However , TAT is not expressed in the reporter system consisting of pseudotyped retrovirus encoding GFP under LTR control that we have used in PBT ( Fig 5B ) and in J-Lat cells ( Fig 6A ) . Thus , we have explored the possibility that FOXO1 inhibition stimulated LTR activity indirectly . The activity of LTRs in T cells is mainly regulated by NF-κB and NFAT transcription factors . Both are inactive in resting T cells and active upon T cell stimulation . FOXO1 inhibition by AS1842856 does not affect NF-kB activity . In contrast , we found a marked activation of NFAT1 . To explain this result , one explanation might be the increase basal calcium level observed in T cells after AS1842856 treatment . This finding was unexpected as no direct control of calcium homeostasis by FOXO1 has been reported to date . T-cell calcium responses to ionomycin were also strongly amplified . Interestingly , we found that Stim1 and ORAI3 , two proteins controlling the entry of calcium [43] , were induced at the mRNA level by AS1842856 ( see S1 Table ) . This suggests a relationship , which still needs to be explored , between FOXO1 and the mechanisms regulating calcium fluxes in T cells . Whatever it may be , the control by FOXO1 of the NFAT pathway could be another mechanism implemented by T cells to protect them from HIV-1 infection [8] . In this context , it is interesting to note that FOXO1 is inhibited upon HIV-1 infection [15] . This is in keeping with the numerous examples of strategies that have been developed by HIV-1 to counteract the various cellular processes capable of inhibiting its viral life cycle . Therefore , FOXO1 is a central player in the interplay between HIV-1 and its cellular host . One of the most remarkable achievements of modern biomedical research is the discovery and widespread use of cART for the treatment of HIV-1 infection . However , infected individuals who receive clinically effective antiretroviral therapy will have to continue this treatment for life . This is mainly due to the persistence of viral reservoirs , causing a plasma viral rebound observed in virtually all infected individuals who discontinue cART . The identification of compounds that can inhibit HIV-1 latency in resting CD4+ T cells is therefore a major challenge [5] . Our results demonstrate that AS1842856 can stimulate HIV-1 latent provirus reactivation . Therefore , AS1842856 can be considered as a LRA drug and as a new therapeutic candidate to reverse HIV-1 latency . Numerous studies reported the effects of FOXO1 inhibition by AS1842856 in vitro and also in vivo in mice [44–47] . In these studies , it appears that this drug is remarkably well tolerated , without any reported significant adverse effects , including at the immune system level . This is very encouraging with a view to its use as a therapeutic tool , alone or in combination with other pharmacological agents . Moreover , in such a shock and kill strategy , it is also possible that AS1842856 could induce some reprogramming of CD8 T-cell metabolism , thereby increasing their anti-HIV activity . The hope , behind , would be to have a treatment that will reverse latency but also capable of boosting anti-HIV immune responses , an increasingly recognized major challenge in treating this infectious disease [48] .
The previously established J-Lat model of HIV latency was kindly provided by Eric Verdin , Gladstone Institute of Virology and Immunology . Jurkat T antigen ( JTag ) and J-Lat as well as HEK293T ( ATCC-CRL-3216 ) cells were cultivated in complete RPMI medium . The H9 cell line , chronically infected with the LAI HIV-1 strain , was obtained from NIH AIDS Reagent Program . Human peripheral blood CD3 positive T lymphocytes ( PBT ) were purified from the blood of healthy donors as described [39] . Where indicated anti-CD3/anti-CD28-coated Dynabeads ( 1 beads for 5 cells , Invitrogen ) , IL-2 ( 20 U/ml , R&D Systems ) , or FOXO1 inhibitor AS1842856 ( EMD Millipore ) , were used . AS1842856 was dissolved in DMSO at a 10mM stock concentration and dilutions were performed in RPMI medium . The “vehicle” condition corresponds to the same concentration of diluted DMSO or to the highest DMSO concentration used with AS1842856 in a given assay . JTag cells ( 5x106 ) were co-transfected by electroporation ( 260V , 950μF ) with plasmids encoding Firefly luciferase under the control of Forkhead responsive element ( FRE ) ( 1 μg ) , CMV-Renilla luciferase ( 0 . 1 μg ) , and GFP ( 1μg ) or a constitutively active form of FOXO1 fused to GFP ( FOXO1TM-GFP ) ( 1μg ) . Cells were cultured in complete culture medium and 6 hours post-transfection AS1842856 or vehicle only were added . Luciferase activity was assayed 18 hours later using the Dual-Luciferase Reporter assay system ( Promega ) following the manufacturer’s instructions . Firefly/Renilla luciferase levels were then calculated . After validation of the RNA quality with Bioanalyzer 2100 ( using Agilent RNA6000 nano chip kit ) , 250 ng of total RNA was reverse transcribed following the GeneChip WT Plus Reagent Kit ( Affymetrix ) . Briefly , the resulting double strand cDNA was used for in vitro transcription with T7 RNA polymerase ( all these steps were included in the WT cDNA synthesis and amplification kit of Affymetrix ) . After purification according to Affymetrix protocol , 5 . 5 μg of Sens Target DNA were fragmented and biotin labelled . After control of fragmentation using Bioanalyzer 2100 , cDNA was then hybridized to GeneChip Clariom S Human ( Affymetrix ) at 45°C for 17 hours . After overnight hybridization , chips were washed on the fluidic station FS450 following specific protocols ( Affymetrix ) and scanned using the GCS3000 7G . The scanned images were then analyzed with Expression Console software ( Affymetrix ) to obtain raw data ( cel files ) and metrics for Quality Controls . Microarrays CEL files were directly analyzed using the Transcriptome Analysis Console software obtained from ThermoFisher Scientific . The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE125328 ( https://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE125328 ) . O2 concentration and consumption by T cells was measured with a high-resolution respirometer ( Oroboros Oxygraph-2k ) . Both electrodes were calibrated at 37°C and 100% oxygen before adding 2 . 5ml of cells ( 2x107 cells/ml ) to each chamber . After stabilization of the basal respiratory rate ( i . e . in the absence of any exogenous agent ) oligomycin ( 1μM final , Sigma Aldrich ) and then successive doses of Carbonylcyanure m-chlorophénylhydrazone ( CCCP , 1μM final , Sigma Aldrich ) at intervals of 300 sec were added to reach the optimal concentration causing a maximal uncoupled respiratory rate . Protein expression levels were analyzed by Western blot as described [39] . Blotting antibodies used were anti-FOXO1 ( C29H4 clone ) , anti-SAMHD1 , anti-SAMHD1P Thr592 , anti-RBP Ser807/811 ( Cell Signaling ) , anti-CDK2 and anti-IκBα ( Santa Cruz ) , anti-p27 ( BD Biosciences ) and anti-β-actin ( Sigma ) , followed by HRP-conjugated goat-anti-mouse or anti-rabbit antibodies ( Jackson ImmunoResearch ) and ECL revelation . The following antibodies were used for flow cytometric analysis: anti-CD4 APC , anti-CD8 APC , anti-CD25 PE-Cy7 , anti-CD127 APC and anti-Granzyme B PE ( clone GB11 ) were from BD Biosciences . Anti-CD62-L PercP ( MEL14 ) and anti-CD45RA FITC were from eBioscience . Biotinylated anti-CD71 and anti-CD98 were from Pharmingen and Miltenyi , respectively . Anti-GAG ( clone KC57 ) was from Beckman Coulter . For staining with Granzyme B , GAG and SAMHD1P , cells were first fixed with 4% paraformaldehyde ( PFA ) , then permeabilized in a buffer containing PBS , 1% BSA , 0 . 1% Triton X-100 . For acridine orange staining , 106 cells were washed with PBS-2% FCS at 4°C and labeled with 0 . 4ml of a Triton X100 0 . 1% , HCL 0 . 1 mM , NaCl 150 mM solution , followed by addition of 1 . 2 ml of a citric acid 0 . 1M , Na2HPO4 0 . 2M , NaCl 150mM , EDTA 1mM solution containing 1μg/ml of acridine orange ( Thermo Fischer ) and directly analyzed by flow cytometry . For glucose uptake measurements , PBT treated with or without AS1842856 ( 500nM ) for 7 days were washed twice with PBS and incubated for 45 min at 37°C with PBS , Hepes 10 mM . 2-NBDG ( 2- ( N- ( 7-Nitrobenz-2-oxa-1 , 3-diazol-4-yl ) Amino ) -2-Deoxyglucose; Sigma ) , a fluorescent glucose analog ( final concentration of 25μM ) , was then added and cells maintained for an additional incubation time of 30 min at 37°C . After two PBS washes , cell fluorescence was analyzed by FACS . Proliferation was assessed by dilution of CellTrace CFSE ( Thermo Fisher ) . After two washes in PBS , cells were resuspended at 106 cells/ml in a 5μM CellTrace CFSE solution and incubated at 37°C for 20 min . After loading , cells were washed with a volume of ice cold PBS 10% FCS corresponding to 5 times the loading volume . 48 hours later fluorescence was measured . Vehicle control cells stimulated with anti-CD3/CD28 coated-beads ( Dynabeads , Life technologies ) during 48 hours were used as a positive control of T-cell proliferation . For all experiments , fluorescence was measured on a BD FACS Calibur and analyzed using the FlowJo software . At the end of the culture period , cells were washed once in cold PBS and fixed for 20 minutes on ice in cytofix/cytoperm ( BD Biosciences ) solution . Cells were then stained with anti NFAT1 ( D43B1 ) ( Cell Signaling ) , and finally with anti-rabbit Alexa-488 ( Cell Signaling ) . DAPI ( Sigma D21490 ) ( 5nM ) was added to stain the nucleus immediately before analyses . Flow cytometry was performed on an ImageStreamX MKII high-speed imaging flow cytometer ( Amnis Corporation ) and analyzed with aIDEAS Analysis Software ( Amnis Corporation ) . To assess nuclear NFAT1 translocation , the corresponding nuclear ( DAPI ) image and NFAT1 ( Alexa-488 ) image of each cell was compared and a Similarity Score ( SS ) was assigned for individual cells . T cells were incubated for 20 min at 37°C with 1 . 5 μM Fura-2/AM ( Molecular Probes ) . Experiments were performed at 37°C in mammalian saline buffer ( 140 mM NaCl , 5 mM KCl , 1 mM CaCl2 , 1 mM MgCl2 , 20 mM HEPES , 11 mM glucose ) . Calcium measurements by spectrofluorimetry were performed as previously described [49] with a Cary Eclipse spectrofluorimeter ( Varian ) ( excitation: 340 and 380 nm; emission: 510 nm ) . For the production of GFP viral particles , HEK293T cells were transfected with psPAX2 lentiviral packaging plasmid along with the plasmid encoding VSV-G and HIV-1 LTR-GFP [30] . Oligonucleotides targeting firefly luciferase ( 5′-CGTACGCGGAATACTTCGA-3’ ) or FOXO1 ( 5-GCCGGAGTTTAGCCAGTCCAA-3’ ) were inserted down to H1 promoter in pSuper . Neo vector ( OligoEngine ) and H1-shRNA expression cassettes were introduced into the pTRIPΔU3-Gfp lentiviral vector where GFP sequence was replaced by human IL2Ralpha one . Lentiviral particles were produced and pseudotyped as previously described [50] . The titer of the virus stock was measured by flow cytometry analysis of GFP or CD25 expression , 3 days after infection of Jurkat or K562 human leukemia cells respectively . Replication-competent HIV-1 NL4 . 3 strains , were produced in HEK293T cells by cotransfection of the proviral plasmid in combination with pVSVg using the calcium phosphate precipitation technique as described previously [51] . The amounts of CAp24 produced were determined by enzyme-linked immunosorbent assay ( ELISA; Innogenetics ) . 106 primary cells were infected using 250 ng of CAp24 for 3 to 7 days . Four adult male cynomolgus macaques ( Macaca fascicularis from Mauritian origin ) chronically infected with SIVmac251 and treated for 60 to 75 weeks with ART were used . These macaques are part of the SIVART ANRS-IDMIT CO1 research program . Macaques were intravenously inoculated with 1 , 000 50% animal infectious doses ( AID50 ) of pathogenic cell-free SIVmac251 ( kindly provided by . A . M . Aubertin , Université Louis Pasteur , Strasbourg , France ) . 17 weeks post infection , cART regimens ( kindly provided by Gilead and ViiV ) were given daily at 1 ml kg-1 body weight by subcutaneous injections of Tenofovir disoproxyl fumarate ( 5 . 1 mg/kg ) , Emtricitabine ( 40 mg/kg ) and Dolutegravir ( 2 . 5 mg/kg ) [52] . Blood was periodically collected throughout the infection and the treatment for the monitoring of blood plasma viral loads , assessed as previously described [53] . Durable suppression of viremia to below the limit of quantification ( 37 vRNA copies/ml ) was achieved after 8 weeks of treatment and was maintained during all the monitoring period . For this study , animal blood was collected after 60 ( one animal ) , 69 ( two animals ) or 75 ( one animal ) weeks of cART treatment . Anonymized human blood samples from the Etablissement Français du Sang ( EFS , Paris , France ) were obtained from healthy donors with written informed consent according to the guidelines of the medical and ethical committees of EFS and Inserm ( protocol number E-2075 ) . Experiments using human blood were performed in full compliance with French law . All experiments on non-human primates were performed under the supervision of national veterinary inspectors in accordance with French national regulations ( CEA Permit Number D92-032-02 ) and with the Standards for Humane Care and Use of Laboratory Animals of the Office for Laboratory Animal Welfare ( OLAW , USA , agreement number #A5826-01 ) and with European guidelines for NHP care ( EU Directive N 63/2010 ) . The study and procedures were approved by ethics committee “Comité Régional d'Ethique pour l'Expérimentation Animale Ile-De-France Sud” with notification number 15–035 . Experimental procedures were performed while animals were under sedation with 10 mg/kg ( body weight ) of ketamine chlorhydrate and throughout the experiments all efforts were made to minimize suffering , including improved housing conditions with enrichment opportunities ( 12:12 light dark scheduling , provision of treats as biscuits and supplemented with fresh fruit , constant access to water supply in addition to regular play interaction with staff caregivers and research staff ) . Cells were first purified by Ficoll-Hypaque gradient centrifugation , then CD4+ T cells were isolated using a CD4+ isolation kit ( StemCell ) . After two days of culture with AS1842856 or anti-CD3/CD28 beads , or vehicle only , 3x106 CD4+ T cells were co-cultured with 106 activated heterologous simian splenocytes for nine days . SIV DNA quantifications were performed as in Ponte et al . [54] . Cells were lysed in Tween-20 ( 0 . 05% ) , Nonidet P-40 ( 0 . 05% ) , and proteinase K ( 100μg/ml ) for 30min at 56°C , followed by 15min at 98°C . Gag sequences were amplified together with the rhesus macaque CD3γ chain in triplicate using the “outer” 3′/5′ primer pairs by 15min of denaturation at 95°C , followed by 22 cycles of 30sec at 95°C , 30sec at 60°C , and 3min at 72°C . SIV-Gag and CD3γ were quantified within each of the PCR products in LightCycler experiments performed on 1/280th of the PCR products; “inner” 3′/5′ primer pairs and the LightCycler480 SYBR Green I Master Mix ( Roche Diagnostics , Meylan , France ) were used . The PCR cycling program consisted of 10min of initial denaturation at 95°C , 40 cycles of 10sec at 95°C , 6sec at 64°C , and 15sec at 72°C . Fluorescence measurements were performed at the end of the elongation steps . Plasmids containing one copy of both the CD3γ and SIV-Gag amplicons were used to generate standard curves . Quantifications were performed in independent experiments using the same first-round serial dilution standard curve . Quantifications were made in triplicate for all samples studied . The sequences of primers used were CD3-Out-5’: ACTGACATGGAACAGGGGAA , CD3-Out-3’: AGCTCTGAAGTAGGGAACATAT , SIV-Gag-Out-5’: CAACAAGGACAGCTTAGGGA , SIV-Gag -Out-3’: TTGACAGGCCGTCAGCATTT , CD3-In-5’: GGCTATCATTCTTCTTCAAGGTA , CD3-In-3’: TTCCTGGCCTATGCCCTTTT , SIV-Gag-In-5’: CCGTCAGGATCAGATATTGCA , SIV-Gag -In-3’: GAAACTATGCCAAAAACAAGT . The results were expressed as the absolute number of SIV copies per 105 cells . In these experiments , at the end of the culture , supernatants were collected and passed through 0 . 45-μm pore filters . Viral particles were then concentrated through a 25% sucrose cushion by ultracentrifuged at 150000 x g for 1 h . Concentrated viruses were then added to 106 heterologous splenocytes from non-infected monkey preactivated with anti-CD3/CD28 beads . After five days of culture , cells were harvested and infection levels were measured by Gag sequence quantification in genomic DNA as described above . Means +/- SE are shown when indicated . Statistically significant differences between groups were assessed with the Graph Prism software using Student’s t tests . ( *p < 0 . 05; **p < 0 . 01; ***p < 0 . 001 ) . | HIV-1 is controlled by host restriction factors that interfere with its life cycle . However , the virus has equipped itself to counter these strategies . We report a new interplay between HIV-1 and human T lymphocytes through the FOXO1 transcription factor . By using AS1842856 , a drug targeting FOXO1 , we found that FOXO1 inhibition triggers metabolic activation and G0/G1 transition of resting T cells and also by the inactivation of the SAMHD1 viral restriction factor . FOXO1 inhibition makes resting CD4+ T cells permissive to HIV-1 infection . We finally found that pharmacologic ( AS1842856 treatment ) or genetic ( shRNA ) silencing of FOXO1 reactivate HIV-1 latent proviruses . Thus FOXO1 appears as an important player of the HIV-1/T-cell relationship and a new potential therapeutic target for intervention during HIV-1 infection . | [
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"microbiolo... | 2019 | FOXO1 transcription factor plays a key role in T cell—HIV-1 interaction |
Genome-wide association studies ( GWAS ) have identified chromosomal loci that affect risk of coronary heart disease ( CHD ) independent of classical risk factors . One such association signal has been identified at 6q23 . 2 in both Caucasians and East Asians . The lead CHD-associated polymorphism in this region , rs12190287 , resides in the 3′ untranslated region ( 3′-UTR ) of TCF21 , a basic-helix-loop-helix transcription factor , and is predicted to alter the seed binding sequence for miR-224 . Allelic imbalance studies in circulating leukocytes and human coronary artery smooth muscle cells ( HCASMC ) showed significant imbalance of the TCF21 transcript that correlated with genotype at rs12190287 , consistent with this variant contributing to allele-specific expression differences . 3′ UTR reporter gene transfection studies in HCASMC showed that the disease-associated C allele has reduced expression compared to the protective G allele . Kinetic analyses in vitro revealed faster RNA-RNA complex formation and greater binding of miR-224 with the TCF21 C allelic transcript . In addition , in vitro probing with Pb2+ and RNase T1 revealed structural differences between the TCF21 variants in proximity of the rs12190287 variant , which are predicted to provide greater access to the C allele for miR-224 binding . miR-224 and TCF21 expression levels were anti-correlated in HCASMC , and miR-224 modulates the transcriptional response of TCF21 to transforming growth factor-β ( TGF-β ) and platelet derived growth factor ( PDGF ) signaling in an allele-specific manner . Lastly , miR-224 and TCF21 were localized in human coronary artery lesions and anti-correlated during atherosclerosis . Together , these data suggest that miR-224 interaction with the TCF21 transcript contributes to allelic imbalance of this gene , thus partly explaining the genetic risk for coronary heart disease associated at 6q23 . 2 . These studies implicating rs12190287 in the miRNA-dependent regulation of TCF21 , in conjunction with previous studies showing that this variant modulates transcriptional regulation through activator protein 1 ( AP-1 ) , suggests a unique bimodal level of complexity previously unreported for disease-associated variants .
Coronary heart disease ( CHD ) , involving atherosclerosis and myocardial infarction ( MI ) , is a genetically complex trait and represents the leading cause of mortality worldwide . Meta-analyses of genome-wide association studies ( GWAS ) for CHD have identified 46 replicated loci in subjects of European descent [1] . Of these loci , the region at 6q23 . 2 contains the lead variant , rs12190287 , which had the lowest P value among several SNPs that reached the genome-wide significance threshold in this locus [2] . rs12190287 is located within an exon of the basic-helix-loop-helix transcription factor TCF21 , and represents an expression quantitative trait locus ( eQTL ) for this gene by regulating TCF21 gene expression levels in omental adipose and liver tissues [2] . Importantly , the TCF21 locus association with CHD was recently confirmed in a meta-analysis of predominantly European subjects genotyped with the Cardio-Metabochip ( Illumina ) and in a three stage GWAS for CHD in individuals of Han Chinese descent [1] , [3] . The association of TCF21 with CHD is particularly compelling , given its association with fundamental cardiovascular embryonic events that might relate to subsequent responses to cardiovascular injury . Tcf21 has recently been shown to regulate cell-fate determination and stages of cell differentiation throughout coronary vascular development in mice . Tcf21 was shown to mark populations of mesodermal-derived cells in the proepicardial organ ( PEO ) at embryonic day 9 . 5 , and mesenchymal-derived cells in the developing pericardium at later time points [4]–[7] . Global knockout studies in mice have confirmed an important role for Tcf21 in the formation of coronary artery smooth muscle cells and cardiac fibroblasts [8] , [9] . Tcf21 deletion results in aberrant smooth muscle cell ( SMC ) differentiation and an absence of cardiac fibroblasts , as evidenced by increased epicardial SMC marker expression [8] . Together , these mouse studies suggest that loss of Tcf21 expression leads to SMC expansion while sustained expression is essential to cardiac fibroblast maturation , likely through regulation of multipotent precursor cell fate . Recent work in this laboratory has identified a cis-acting mechanism by which the protective TCF21 G allele at variant rs12190287 disrupts an activator protein 1 ( AP-1 ) -like enhancer element , to alter allele specific transcriptional control of TCF21 gene expression [10] . Interestingly , this cis-regulatory element modulates growth factor ( platelet-derived growth factor receptor beta-β ) and epicardial development ( Wilms tumor 1 ) signaling pathways in coronary artery SMC [10] . In complementary studies reported herein we provide evidence that the 3′-untranslated region ( 3′-UTR ) of TCF21 binds miR-224 to regulate expression of this gene , and that this regulation is obviated by the minor allele which confers a seed mismatch to disrupt miR-224 binding and accessibility of this region of the TCF21 3′-UTR . To our knowledge , these data provide the first example of miRNA binding disruption as a likely mechanism for a CHD risk gene association , and the first example of concurrent miRNA and transcriptional regulation at a single disease associated causal variant .
To better understand the mechanisms of disease risk at 6q23 . 2 , we set out to define causal variation among the CHD-associated SNPs by examining the allele specific expression ( ASE ) in heterozygous individuals for the transcript variant rs12190287 , which is located in the 3′-UTR of the TCF21 gene . By measuring the relative ASE within individuals , this approach has the ability to maximize detection of cis-regulatory variation on TCF21 gene expression , with each allele controlled by similar trans-acting and environmental influences . Based on TaqMan SNP genotyping assays of total white blood cell RNA and genomic DNA from 22 heterozygous individuals ( from GENEPAD cohort ) , we observed an approximate 1 . 3–2 . 0 fold ASE of the minor protective allele ( G ) over the major risk allele ( C ) in 18/22 samples , P = 1 . 1×10−8 ( Fig . 1A ) . Importantly we observed consistent allelic imbalance ( 1 . 8–2 . 5 fold ratio G/C ) in primary human coronary artery smooth muscle cells ( HCASMC ) maintained under normal conditions and detected using pyrosequencing assays ( Fig . 1B ) . Together these data suggest that the disease-associated risk allele , or other variants in tight LD , contribute to decreased TCF21 allele-specific expression . Intriguingly , these results contrast with published eQTL data at rs12190287 , which demonstrate the risk allele is associated with elevated TCF21 expression in omental adipose and liver tissues [2] , [11] . Also , our recent work elucidated a bi-directional mechanism involving both trans-activating AP-1 and trans-repressing ( Wilms tumor 1 ) WT1 transcription factor binding to a cis-regulatory element at rs12190287 resulting in altered allele-specific TCF21 expression levels [10] . Given this bi-directional mode of transcriptional regulation we explored alternative regulatory mechanisms to potentially explain the allelic imbalance at rs12190287 . Recent studies using allelic imbalance sequencing demonstrate that SNPs frequently alter microRNA-mediated repression , by creating or disrupting complementary miRNA binding sites [12] . In silico analyses , based on conservation of miRNA seed regions , predict >60% of human 3′-UTRs are under selective control by miRNAs [13] , [14] . We scanned the TCF21 3′UTR for seed matches using both TargetScan and MiRanda prediction algorithms . Both tools identified rs12190287 ( position 1058 from 5′-UTR ) residing within a 7-mer mammalian conserved binding site for mature miR-224 ( Fig . 2A ) . Alignment of rs12190287 major and minor alleles demonstrated a perfect seed match between the TCF21 3′-UTR containing the major risk allele ( C ) and miR-224 ( nucleotides 2–8; positions 1042–1061 ) , with ΔΔG = −2 . 43 and a seed mismatch between the minor protective allele ( G ) and miR-224 ( ΔΔG = 4 . 67 ) ( Fig . 2B ) . We also investigated the RNA secondary structure of the TCF21 3′-UTR variants . Systematic in silico RNA structural predictions were performed and analyzed as previously described [15] , [16] . Representative predicted local secondary structures of TCF21 rs12190287 C and G variants are shown from positions 941–1141 ( Fig . 2B ) . While both 3′-UTR variants adopt similar global RNA structures , they are predicted to adopt distinct local secondary structures in proximity of the SNP . For instance , the seed matching sequence of the C variant seems to be mostly located within a loop structure and overall , the segment complementary to miR-224 ( shaded grey ) are located in a structurally accessible local structure ( Fig . 2C ) . In contrast , the miR-224 binding sequence segment of the G variant is located in a local structure that does not seem to be accessible , i . e . , the seed-matching element is located near a stem-loop junction within an intra-molecular duplex element ( Fig . 2C ) . Theses observations were consistent among the 180 different structures analyzed , with the C variant SNP typically located in a loop structure and the G variant SNP often located along the stem ( Fig . 2D ) . Similar differences in local RNA structure were predicted using the RNAfold minimal free energy ( MFE ) prediction algorithm ( Supplementary Fig . S1 ) . The overall difference in MFE for these structures is predicted to be only 2 kcal/mol , suggesting that the structure containing the C variant is slightly less stable . Different local RNA structures could also involve differential recruitment of RNA binding proteins ( RBP ) , such as Pumilio , previously shown to alter p27 3′-UTR local structure and miR-221/222 accessibility [17] . In summary , these significant allelic structural differences implicate differences in miR-224 accessibility , binding kinetics , and binding affinity , which may impact miR-224 mediated regulation of TCF21 . We first evaluated the possibility that the TCF21 3′-UTR variants at rs12190287 differentially regulate protein expression through miR-224 targeting using a pmiR-GLO luciferase reporter system . The TCF21 3′-UTR variants ( containing the major or minor alleles ) were inserted downstream of the firefly luciferase gene , luc2 to quantitatively measure post-transcriptional effects of miRNA activity , as previously described [18] . We synthesized miR-224 guide and passenger strands using miRBase sequences to generate double-stranded miR-224 , which has a matched seed sequence of mature miR-224 to the C allele of the TCF21 3′-UTR target site but a mismatch to the G allele of TCF21 3′-UTR ( Supplementary Fig . S2 , top ) . In order to test the specificity of the miRNA-mediated regulation of the C variant we restored base-pairing in the seed region by synthesizing a miR-224 guide strand with a G>C substitution ( referred to as miR-224_SNP , Supplementary Fig . S1 , bottom ) . Using HCASMC co-transfected with the TCF21 3′-UTR reporters and double-stranded miR-224 , we observed selective repression of the C variant compared to the G variant ( Fig . 3A ) . Allele-specific differences in reporter activity were abolished when we co-expressed the adapting miR-224_SNP . Alternatively , using a loss-of-function approach with a selective miR-224 inhibitor , we observed increased reporter activity only by the C variant . These results further suggest that the C variant of the TCF21 3′-UTR can be directly regulated by miR-224 , while the G variant cannot . We also observed similar functional effects in the aortic smooth muscle cell line A7r5 ( Fig . 3B ) and HeLa cells ( Fig . 3C ) . However , the observation that miR-224_SNP did not completely block the allele-specific reporter activity in HeLa , may suggest cell type differences in endogenous miR-224 levels . For instance , both TCF21 and miR-224 are weakly expressed in A7r5 and HeLa cells relative to HCASMC ( unpublished observations ) . Taken together , these data support a functional role of miR-224 in various cell types including HCASMC , by preferentially targeting the TCF21 3′UTR C variant , leading to post-transcriptional repression . A striking positive relationship exists between the extent of regulation and the annealing kinetics of the RNA regulator and its target RNA [19] , [20] . Thus , differential regulation of TCF21 3′UTR variants by miR-224 could result from altered kinetics of mRNA:miRNA complex formation . We monitored the annealing kinetics of miR-224 binding to the TCF21 3′-UTR C and G variants in vitro under experimental conditions that are assumed to mimic known cellular facilitators of RNA:RNA annealing [21] . It is important to note that RNA:RNA annealing can be substantially promoted even at the cost of binding energy , i . e . the association of complementary ribonucleic acids can be greatly increased without lowering the Arrhenius activation energy or even significantly altering RNA structure [21] . The full-length TCF21 3′-UTR variants were generated by in vitro transcription ( IVT ) and incubated in 10-fold excess with 32P-labeled miR-224 for various time points , followed by autoradiography detection . Interestingly , greater amounts of the C variant 3′-UTR:miRNA complexes were generated over time compared with the G variant ( Fig . 4A , left panel ) . Further , the C variant:miRNA complex formation was completely blocked in the presence of 32P-labeled miR-224_SNP , which generates a seed mismatch ( Fig . 4A , right panel ) . However , the miR-224_SNP , which would generate a seed match with the G variant had no effects on G variant:miRNA complex formation . The C variant TCF21 3′-UTR:miR-224 complexes also formed at a faster rate ( kobs = 2 . 2×106 M−1 s−1 ) than the G variant ( kobs = 1 . 4×106 M−1 s−1 ) as determined from second-order reactions ( Fig . 4B and 4C ) . Together these data suggest that miR-224 preferentially binds the major risk C variant of TCF21 , and at a faster rate in vitro , compared to the minor protective G variant . We then used RNA in vitro probing to test and validate our in silico secondary structure predictions for the TCF21 3′-UTR variants , which demonstrated allele-specific local structural alterations . Briefly , we chemically probed the TCF21 3′-UTR variants with Pb2+ ( to monitor all unpaired nucleotide residues ) and probed enzymatically using RNase T1 ( only cleaves unpaired G nucleotide residues ) . After probing , the cleavage patterns were evaluated by primer extension and subsequent denaturing gel electrophoresis , as described [18] . Probing the TCF21 3′-UTR variants ( positions 1040–1075 ) with Pb2+ revealed unique cleavage sites proximal to the miR-224 target site ( positions 1042–1061 ) and rs12190287 ( position 1058 ) ( Fig . 5A ) . For instance , the C variant has stronger and additional sites located proximal to rs12190287 ( positions 1058–1063 ) in comparison to the G variant , which has some unique weak cleavage sites at positions 1045–1049 . The specificity of RNase T1 to cleave G residues explains the occurrence of an additional weak cleavage product at the SNP position ( 1058 ) for the G but not C IVT ( Fig . 5B ) . Additional stronger cleavage by RNase T1 was observed at position 1054 of the G variant , and a weaker cleavage at position 1070 of the G variant , summarized below ( Fig . 5C ) . Together , these results are in line with the in silico predicted RNA structures , which imply there are a number of local structural differences between the two variants , resulting in altered accessibility at sites near rs12190287 . It should be noted , however , that the structure-function relationship of RNA-RNA annealing is complex . Since the pairing mechanism of this TCF21 case is not known , we cannot relate local structures , annealing kinetics , and biological effects . Nonetheless we observe differences at all levels of interaction , strongly suggesting a mechanistically distinct regulation . Next , we investigated the regulatory pattern of endogenous TCF21 and miR-224 gene expression levels in HCASMC . We first explored a potential link between relevant pathways upstream of miR-224 and TCF21 that may account for miR-224-TCF21 3′-UTR allele-specific regulation in HCASMC . Importantly , our previous work identified platelet-derived growth factor ( PDGF ) and transforming growth factor-beta ( TGF-β ) dependent signaling pathways as respective positive and negative mediators of cis-regulatory elements at rs12190287 in HCASMC [10] . PDGF-BB ligand mediates increased SMC proliferation , survival , and migration [22] through PDGFRβ , which is critical for epithelial-mesenchymal transition ( EMT ) and formation of coronary artery SMC [23] . As a pleiotropic vasoactive cytokine , transforming growth factor beta ( TGF-β1 ) also regulates EMT and diverse SMC growth and remodeling processes [24] . Interestingly , we observed a modest negative correlation ( r = −0 . 3287 ) of endogenous TCF21 and miR-224 expression levels in HCASMC treated with PDGF-BB , although this result did not reach statistical significance ( Fig . 6A ) . However , TGF-β1 treatment resulted in significant and highly inverse-correlated endogenous TCF21 and miR-224 expression levels , r = −0 . 7061 , P = 0 . 0015 ( Fig . 6B ) . We then measured the effects of these stimuli on miR-224-mediated regulation of total and allele-specific TCF21 transcript levels . As expected , PDGF-BB treatment led to increased total TCF21 expression levels , whereas TGF-β1 led to reduced TCF21 , which was blunted in all cases by pre-miR-224 ( Fig . 6C ) . We also observed pre-miR-224 to attenuate both PDGF-BB and TGF-β1 stimulated allele-specific TCF21 expression ( shown as the normalized ratio of C/G at rs12190287 ) ( Fig . 6D ) . These results identify PDGF-BB and particularly TGF-β1 as potential upstream mediators of miR-224 directed allele-specific TCF21 expression at rs12190287 . To establish a potential role of miR-224-TCF21 regulation during atherosclerosis progression , we measured endogenous levels of miR-224 and TCF21 in human coronary artery lesions . Immunohistochemical staining of adjacent sections demonstrated TCF21 protein localized within the neointimal and medial layers of the left anterior descending ( LAD ) coronary artery ( n = 4 ) ( Fig . 7A , upper panel ) . TCF21 marked a population of cells resembling smooth muscle cells , indicated by alpha-smooth muscle actin ( a-SMA ) immunoreactivity in similar regions . TCF21 protein was also detected in the adventitia in a few samples , consistent with the expression pattern observed in small intramyocardial coronary arteries [25] . We also localized endogenous miR-224 in these sections using in situ hybridization , which identified miR-224 in both the neointimal and adventitial layers , but not the medial layer ( Fig . 7A , lower panel ) . We validated these findings using microarray based analysis of normal ( no lesions ) , stable ( asymptomatic ) and unstable ( symptomatic ) carotid atherosclerotic lesions . TCF21 mRNA levels were significantly upregulated in both asymptomatic ( P = 0 . 0106 ) and symptomatic ( P = 0 . 0074 ) atherosclerotic plaques ( Fig . 7B ) . Interestingly , miR-224 was significantly downregulated in stable and unstable atherosclerotic plaques ( P = 1 . 5×10−5 and P = 8 . 2×10−6 , respectively ) , as determined by TaqMan qPCR ( Fig . 7C ) . These data confirm that both TCF21 protein and miR-224 are expressed in the diseased vessel wall in vivo , and their expression is inversely regulated during atherosclerosis , consistent with our observations in HCASMC . Together these findings provide additional mechanistic insights into the TCF21 association with respect to coronary heart disease progression . It is also noteworthy that the minor protective allele at rs12190287 disrupts both a TF binding motif TGACTTCA as well as a miRNA seed sequence , GUGACUU in the 3′UTR of TCF21 . Given this unanticipated integration of both positive cis-acting transcription factor binding and negative post-transcriptional miRNA regulation at TCF21 , we sought to estimate the overall frequency of these overlapping regulatory features in humans using publicly available genome-wide datasets . Using validated TF binding ENCODE ChIP-seq regions ( ∼4 , 400 , 000 ) intersected with medium conserved miRcode predicted miRNA binding sites ( ∼1 , 100 , 000 ) we identified approximately 290 , 000 overlapping regions ( approximately 28% of all predicted miRNA binding sites ) ( Supplementary Fig . S3 ) . We then intersected total disease-associated polymorphisms from the National Human Genome Research Institute ( NHGRI ) catalog , including those in LD at r2>0 . 8 ( ∼292 , 000 ) , resulting in 52 , 263 sites ( 17 . 9% ) in TF ChIP regions , 942 sites ( 0 . 32% ) in miRNA binding sites , and 146 sites overlapping with both features ( 0 . 05% ) ( Fig . S3A and Table S1 ) . Interestingly , this overlap was less frequent when applied to all common variants ( 12 . 8% , 0 . 16% , and 0 . 04% , respectively ) ( Supplementary Fig . S3B ) . We also observed 20 , 064 ( 37% ) regions of TargetScan predicted conserved miRNA binding sites residing within TF ChIP-seq peaks . Functional annotation of these regions resulted in significant enrichment of mitogen activated protein kinase ( MAPK ) ( P = 1×10−39 ) , cytokines ( P = 1×10−54 ) , and TGF-β ( P = 1×10−23 ) pathways ( Supplementary Fig . S3D ) , as well as bZip ( P = 1×10−81 ) and p53 ( P = 1×10−29 ) TF binding protein domains versus those expected by chance ( Supplementary Fig . S3C ) . Given the critical role of bZip domain TF families ( e . g . AP-1 , ATF and CREB ) in various cancers , inflammation and developmental processes [26] , concurrent miRNA binding to mRNA regions overlapping these sites ( e . g . miR-224-TCF21 ) may represent an exquisite fine-tuning control of target gene expression .
A large fraction of CHD susceptibility loci recently identified through GWAS do not appear to be mediating risk through effects on traditional risk factors , such as lipid levels and blood pressure . Investigating the mechanism ( s ) of the disease risk association at these loci promises to provide critical new information regarding fundamental disease pathways in the vessel wall that function upstream of the causal variation and the related causal gene [1] , [2] , [27] . One gene that we have chosen to study in this regard is TCF21 , a gene that was originally identified and replicated in the CARDIoGRAM meta-analysis of GWA data , and has now been verified through additional meta-analysis in both subjects of European and Han Chinese descent [1]–[3] . TCF21 encodes a basic-helix-loop-helix transcription factor that is involved in controlling cell fate decisions in developing coronary artery SMC , and may provide insight into the possible causal role of this cell type in atherosclerosis [8] , [9] . Initial eQTL analysis showed that TCF21 expression is related to the genotype at rs12190287 , providing the first suggestion that TCF21 is indeed the causal gene at this locus ( Table S3 ) [2] . These data , in conjunction with evidence that rs12190287 , ( 1 ) is associated with a P-value that is three orders of magnitude lower than that for other associated SNPs within the susceptibility locus [2] , ( 2 ) is only modestly correlated with other SNPs that reach genome-wide significance within the locus [10] , ( 3 ) is found in a region of open chromatin configuration [10] , and ( 4 ) resides within the TCF21 structural gene , collectively suggest that rs12190287 is the causal variant within this susceptibility locus . To further investigate this possibility , we have pursued allele-specific expression ( ASE ) studies as reported here , seeking to correlate ASE with genotype at rs12190287 . These studies employing RNA from circulating leukocytes show highly significant ASE at the TCF21 gene , and consistent allelic expression divergence suggests that rs12190287 is the causal SNP . Although possible , it would seem very unlikely that the ASE is due to another SNP in high linkage disequilibrium with this SNP , since other associated variants are correlated at best with an r2∼0 . 6 . Also , while leukocytes are an appropriate cell type in atherosclerosis and express a number of the signaling components upstream of TCF21 , they may not be the primary cell type reflecting TCF21 function . In this regard , we observed a consistent direction of ASE in a limited study of primary cultured HCASMC grown in the presence of serum . Unfortunately , eQTL studies with circulating leukocytes did not show a significant association with TCF21 expression so we could not compare the directionality between the leukocytes and the adipose and liver tissues employed in the original eQTL studies . miRNAs predominately affect gene expression by decreasing mRNA stability or inhibiting translation . These regulatory effects can be perturbed by allelic variation through SNPs directly interfering with basepair interactions in the seed sequence in the mRNA . Allelic variants can also alter the tertiary structure of the mRNA and hinder miRNA binding even when the SNP is located outside the seed sequence [28] , [29] . Here , we employed reporter gene studies in HeLa cells , rat and human SMCs with both gain and loss of function approaches to demonstrate that miR-224 regulates TCF21 expression at the protein level . Sequence analysis predicts that rs12190287 alters the core miR-224 binding sequence , and folding algorithms that identify lowest energy conformations of the native and variant sequences suggest that the minor G allele at rs12190287 produces a less favorable configuration of mRNA folding for miR-224 binding . These hypotheses were confirmed by kinetic studies showing decreased rate and extent of miR-224 binding , and RNA structural probing studies that revealed decreased availability of the miR-224 binding region in the mRNA containing the minor G allele . The disruption of miRNA binding is a well-established mechanism for alteration of risk for various cancers [30] . However , these data showing that the CHD causal variant rs12190287 can disrupt miR-224 binding provides the first evidence for this type of mechanism for coronary disease associated genes . While a potential role for miR-224 in regulating vascular disease has not been defined , this miRNA has been studied in association with multiple cancer cell types and other cellular systems , and these data provide some insight into upstream pathways that might affect TCF21 expression and thus CHD risk [24] , [31]–[36] . Most significant among these are NFκB , WNT and TGF-β , all of which have been linked to atherosclerotic signaling pathways [24] , [34] , [36] . NFκB is a well-characterized transcription factor and mediator of cellular activation by inflammatory cytokines and chemokines , and in the context of hepatocellular carcinoma , miR-224 was shown to be upregulated by tumor necrosis-α ( TNF-α ) and miR-224 regulation linked to hepatocellular migration and invasion [31] . TGF-β stimulation of miR-224 expression has been characterized in ovarian granulosa cells where it has been implicated in cellular proliferation and estradiol release in this cell type [32] . Further , miR-224 has been shown to be upregulated by the WNT signaling pathway in meduloblastoma where it was linked to inhibition of proliferation , increased radiation sensitivity and reduced anchorage-independent growth of tumor cells [35] . Each of these pathways has been linked to atherosclerotic processes in the diseased blood vessel wall , and could have a role in the TCF21 mediated risk for CHD [24] , [33] , [34] , [36] . Merging these data with that from previous studies of the transcriptional regulation at rs12190287 provides a more complete picture of the complexity of upstream signaling pathways that may regulate TCF21 expression , and may be perturbed by this disease-associated variant . We have shown that rs12190287 resides in an atypical AP-1-like element and that PDGF can stimulate allele-specific expression through this site , as one potential disease-related pathway activated at this region [10] . PDGF has been extensively implicated in atherosclerosis pathogenesis , and in vitro genomic studies have suggested that TCF21 mediates PDGF signaling ( [37] , and data not shown ) . Additionally , transcriptional regulation studies at rs12190287 have also shown that the Wilms tumor factor ( WT1 ) inhibits expression of TCF21 through the AP-1-like site , and PDGF and TGF-β stimulation shown to be upstream inhibitors of WT1 expression in SMC [10] . WT1 is known to inhibit expression of AP-1 like factors , and has been shown to repress TCF21 expression in developmental models [38]–[40] . Combining these data with that derived here for miR-224 provides compelling evidence for multiple signaling pathways , operating by transcriptional and post-transcriptional mechanisms , by which rs12190287 regulates TCF21 expression ( Fig . 8 ) . Importantly , this is the first example of a disease-associated variant that disrupts both transcription factor-DNA and miRNA-mRNA interactions . Our genome-wide analysis provides further support that additional disease-associated variants reside in overlapping TF and miRNA binding regions , which likely have pathophysiological relevance ( Supplementary Fig . S3 ) . We can speculate that this bimodal regulation may partially explain the “dynamic” eQTLs previously observed [41] , [42] which are responsive to intracellular changes in differentiation state . Previous work from this laboratory has characterized a transcriptional regulatory mechanism that mediates TCF21 gene expression differences through variation at rs12190287 , and studies presented here documents a second mechanism by which variation at this SNP can alter expression of individual alleles [10] . Inherent in such mechanistic studies at disease-associated loci is that altered allele-specific expression can alter disease risk through either , 1 ) changing the overall causal gene expression level to alter the normal biological role of the causal gene or to introduce a novel disease-promoting function; or 2 ) changing the overall variance of causal gene expression , such that the gene becomes disconnected from its normal signaling networks [43] . The former may be explained by increased transcription of the rs12190287 risk “C” allele resulting in overall increased TCF21 expression , or the miRNA mechanism by which the “C” allele interacts with miR-224 to decrease overall TCF21 expression . It is possible that these counteracting pathways trigger overall TCF21 expression variance linked to a disease-related phenotype , with different pathways being dominant in different disease contexts . Evidence for directionality of TCF21 expression is provided here in the context of carotid artery disease , with atherosclerotic vessels showing increased TCF21 expression ( Fig . 7 ) . These data are consistent with eQTL data in adipose and liver tissue samples which indicated that the risk “C” allele at rs12190287 is associated with increased TCF21 expression , suggesting that the transcriptional mechanism at rs12190287 may play a dominant role on gene expression levels ( Table S3 ) . In addition , we also demonstrate that miR-224 is reciprocally decreased in these carotid diseased tissues , suggesting that miR-224 may function as a repressor of aberrantly elevated TCF21 levels , but is blunted in the process . It is important to consider that transcription factor and miR-224 pathways are regulated by multiple upstream pathways , which can regulate expression and/or activation of TFs and miR-224 . Verification of the direction of effect for TCF21 expression , and the mechanisms that function at rs12190287 will require additional studies with human vascular disease samples to better assess in vivo gene expression in the disease environment . Also , studies in Tcf21 genetic mouse models should provide additional evidence of the direction , and mechanism of effect , in the setting of vascular disease . It is well known that TCF21 is protective for multiple human cancers and it will be of great interest to determine whether expression of this gene in disease-related cells inhibits or promotes vascular disease processes [44]–[47] . Finally , to fully understand the complexity of transcriptional and miRNA networks regulating TCF21 expression , it will be essential to investigate the risk contributed by additional alleles associated with disease at this locus . GWAS of an East Asian cohort identified the CHD associated variant rs12524865 in the TCF21 locus , and the fact that this SNP is poorly correlated with rs12190287 in this racial ethnic group suggest that it is an independent allele [3] . Previous studies from this laboratory suggested that this variant may directly regulate TCF21 expression through transcriptional pathways similar to those associated with rs12190287 [10] . In addition , fine-mapping studies by the CARDIoGRAM+C4D consortium has identified a second associated allele in Caucasians centered around a variant ∼100 , 000 basepairs upstream of TCF21 , rs17062853 , with this variant being poorly correlated with rs12190287 in Caucasians , again suggesting this is an independent allele [1] . It will be important to investigate these alleles independently and collectively to begin to understand how they contribute to TCF21 regulation in the context of smooth muscle biology and disease processes . To better assess the pathophysiological role of different alleles it will be essential to conduct ASE analyses in cells isolated from human vascular lesions . These future studies may reveal how allelic variation at TCF21 affects relevant upstream signaling pathways during different disease states .
Peripheral blood DNA and RNA were isolated from randomly selected buffy coat samples from individuals of European descent in two human cohort studies , GENEPAD ( Genetic determinants of Peripheral Artery Disease ) and GENESiPS ( GENEticS of insulin sensitivity iPSc ) . Genomic DNA was isolated using the Qiagen DNeasy Blood and Tissue kit according to the manufacturer's instructions . Genotypes at rs12190287 were determined from 10 ng gDNA template using a predesigned TaqMan SNP genotyping assay for rs12190287 ( Applied Biosystems ) and performed in triplicate . Sanger sequencing also confirmed heterozygous samples at rs12192087 . Total RNA was isolated using the Qiagen miRNeasy Mini kit according to the manufacturer's instructions . Total cDNA was prepared from 1 µg RNA using the High Capacity cDNA Reverse Transcription kit ( Applied Biosystems , #4368814 ) . cDNA templates were used to amplify allele-specific TCF21 using the TaqMan SNP genotyping probe ( Applied Biosystems ) . ASE was determined from 22 heterozygous samples using the TaqMan SNP genotyping probe for rs12190287 and expressed as the normalized allelic ratio of cDNA/gDNA . Calibration of the SNP genotyping assay was determined as previously described [10] . DNA and RNA were prepared from 8 individual HCASMC lots determined to be heterozygous at rs12192087 ( confirmed by Sanger sequencing ) . Pyrosequencing assays for rs12190287 were performed as previously described with assays designed using PyroMark Assay Design software ( Qiagen ) . Forward rs12190287 PCR primer , 5′-biotinylated reverse PCR primer , and forward pyrosequencing primers ( Table S2 ) were synthesized by the Protein And Nucleic acid ( PAN ) facility ( Stanford ) . Approximately 20 ng gDNA or cDNA was amplified using forward and reverse pyrosequencing primers under the following conditions: 94°C 4 min , ( 94°C 30 s , 60°C 30 s , 72°C 45 s ) ×45 , 72°C 6 min . PCR products were verified by gel electrophoresis . Pyrosequencing reaction was performed on PCR reactions using a PyroMark Q24 according to manufacturer's instructions . Allelic quantitation was obtained automatically from the mean allele frequencies derived from the peak heights using PyroMark Q24 software . Primary human coronary artery smooth muscle cells ( HCASMC ) were purchased from three different manufacturers , Lonza , PromoCell and Cell Applications and were cultured in complete smooth muscle basal media ( Lonza , #CC-3182 ) according to the manufacturer's instructions . All experiments were performed with HCASMC between passages 4–7 . Genotypes of HCASMC were determined as described above , and lots heterozygous at rs12190287 were used for all experiments . The A7r5 rat aortic SMC line was purchased from ATCC and cells were maintained in Dulbecco's modified Eagle medium ( DMEM , Life Technologies , #11885-084 ) containing low glucose , sodium pyruvate and L-glutamine and supplemented with 10% fetal bovine serum ( FBS ) . HeLa cells were maintained in DMEM containing high glucose , sodium pyruvate and L-glutamine supplemented with 10% FBS . Double stranded DNA sequences containing the TCF21 3′-UTR for rs12190287-C and G were subcloned into the multiple cloning site ( MCS ) of the pmirGLO vector ( Promega , #E1330 ) , located downstream of the translation stop codon and firefly luciferase reporter gene luc2 , driven by the PGK minimal promoter and also carrying the renilla luciferase reporter gene hRluc , as an internal control . PCR and mutagenic primer sequences to generate the TCF21 C and G 3′-UTR reporters are included in Table S2 . Site-directed mutagenesis protocol was adapted from [48] . For gain-of-function studies , single-stranded , unmodified oligonucleotides for miR-224 ( seed-matching TCF21 C allele ) and miR-224_SNP ( seed-matching TCF21 G allele ) were first annealed at an equimolar concentration at 95°C for 3 min and allowed to gradually cool to room temperature . Resulting double-stranded miR-224 , miR-224_SNP or negative control miRNAs ( Ambion/Life Technologies ) were co-transfected at 50 nmol/L along with TCF21 C-3′-UTR or G-3′UTR reporter constructs in HeLa , HCASMC or A7r5 using Lipofectamine 2000 ( Invitrogen/Life Technologies , #11668-019 ) according to the manufacturer's instructions . Alternatively , loss-of-function studies were carried out by co-transfecting 50 nmol/L anti-miR-224 or negative control anti-miR inhibitors ( Ambion/Life Technologies ) . Culture media was changed after 6 hrs , and dual luciferase activity was measured after 24 hrs using either SpectraMax L luminometer ( Molecular Devices ) or anthos Lucy3 luminometer ( anthos Mikrosysteme GmbH ) . Relative luciferase activity ( firefly/Renilla luciferase ratio ) is represented as the fold change of respective control condition as indicated . HCASMC were maintained as described above under normal growth factor and serum supplemented conditions . Upon reaching ∼70% confluence cells were serum-starved overnight prior to stimulation with human recombinant PDGF-BB or TGF-β1 for various times in triplicates . Samples were randomized ( n = 16 ) and total RNA was isolated using the miRNeasy Mini kit ( Qiagen ) . Total cDNA was prepared from 1 µg RNA using the High Capacity cDNA Reverse Transcription kit ( Applied Biosystems , #4368814 ) . Alternatively , miRNA specific cDNA was prepared using the TaqMan miRNA Reverse Transcription kit ( Applied Biosystems/Life Technologies , #4366596 ) and predesigned RT probes for human miR-224 or human control miRNA RNU44 ( Applied Biosystems/Life Technologies ) . cDNA templates were used to measure endogenous human miR-224 and TCF21 variant 1 ( TCF21 v1 ) expression levels using predesigned TaqMan gene expression assay probes ( Applied Biosystems/Life Technologies ) according to the manufacturer's instructions . TCF21 v1 and miR-224 levels were quantitated on a ViiA 7 Real-Time PCR system ( Applied Biosystems ) and normalized to 18S and RNU44 levels , respectively . Pearson's correlation was determined assuming a linear relationship . For expression analyses with miR-224 overexpression , HCASMC were cultured as described above under normal conditions . The day after plating the cells were transfected with either miR negative control ( miR Con ) or miR-224 mimic using Lipofectamine RNAiMAX ( Life Technologies , #13778150 ) for 5 hrs . Culture media was changed to serum-free and cells were incubated overnight prior to stimulation for 6 hrs with either vehicle , 20 ng/ml human recombinant PDGF-BB ( R&D Systems , #220-BB-010 ) , or 5 ng/ml human recombinant TGF-β1 ( R&D Systems , #240-B-002 ) . Total RNA was isolated using the RNeasy isolation kit ( Qiagen , #217004 ) and cDNA was prepared as described above . Total TCF21 or allele-specific expression at rs12192087 was measured as described above using the TaqMan gene expression or TaqMan SNP genotyping probe with expression levels calculated using a standard curve and normalized to the gDNA for each allele . Observed association rate constants ( kobs ) were measured as previously described in detail [49] , [50] . Briefly , 5′ radioactively labeled miR-224 or miR-224_SNP ( 0 . 5 nM final concentration ) was incubated with the TCF21 3′-UTR-C or TCF21 3′-UTR-G target mRNA at 5 nM final concentration in hybridization buffer ( 100 mM NaCl , 20 mM Tris–HCl , pH 7 . 4 , and 10 mM MgCl2 ) in the presence of 10 mM CTAB at 37°C . Aliquots were withdrawn at different time points , transferred into 1 vol of stop buffer ( 20 mM Tris–HCl , pH 7 . 4 , 10 mM EDTA , 2% ( v/v ) SDS , 8 M urea , 0 . 025% ( v/v ) bromophenol blue ) and analyzed by native polyacrylamide gel electrophoresis ( 0 , 1×11×12 cm , run at 4°C and 150 V for 2 h ) . Gels were sealed in polyethylene; exposed to X-ray film stored at −20°C until band intensities were determined using a phosphorimager ( Typhoon 8600 Variable Mode Imager , GE Healthcare ) . ImageQuant 5 . 2-software was used to quantify signals relative to whole lane signal . Second order association rate constants were calculated as described [20] . PCR products harboring the RNA polymerase T7 recognition site were amplified for TCF21 with 5′-GAA ATT AAT ACG ACT CAC TAT AGG GCC TTG GAG TTT GGT ACC TGG-3′ as forward , 5′- TCA GGT CGA CTT GGT GGA ACA AAT CTT TTA TTT TC-3′ as reverse primer and the pmirGLO TCF21 3′-UTR constructs as template and used for in vitro transcription ( T7 RiboMAX Express Large Scale RNA Production System , Promega , #P1320 ) . In vitro transcripts ( IVTs ) were purified by phenol-chloroform-extraction , G-50 column filtration and ethanol precipitation , and subsequently denatured for 10 min at 70°C and refolded at room temperature for 120 min . RNase T1 based hydrolysis: IVTs were incubated at RT for 4 min in 10 µl reaction containing 1 µg tRNA ( Sigma , #83853-25MG ) and increasing RNase T1 ( 0 , 0 . 25 , 1 and 2 units , Fermentas , #EN0541 ) . Cleavage products were purified as described above . Pb2+ based probing: refolded IVTs were incubated for 15 min at RT in a 10 µl reaction mix . Reactions were initiated with 5 µg tRNA and increasing amounts of lead ( II ) acetate ( Pb2+ ) ( Sigma , #32307 ) , terminated after 10 min at RT with EDTA/ethanol , followed by ethanol precipitation . RT reactions with either RNase T1- or lead hydrolysis products were performed for 45 min at 42°C using 1 mM dNTPs , 2 . 5 mM RT-primer ( 5′-32[P] -AGG GCA TCC TGA CAT CTT GA-3′ ) and 1 . 5 units AMV Reverse Transcriptase ( Promega , #M5108 ) . Sequencing reactions were performed in parallel with denatured IVT ( 2 min at 95°C ) for each nucleotide base , as adapted from [49] . After cDNA synthesis , samples were denatured in formamide-containing loading buffer for 3 min at 95°C and resolved on a 10% polyacrylamide sequencing gel under denaturing conditions for 70 min at 52° , and signals analyzed with a PhosphorImager ( Typhoon 8600 Variable Mode Imager , GE Healthcare ) . Single-stranded miRNA guide and passenger strands ( miR-224 and miR-224_SNP , Fig . 2B; miR-224 guide: 5′-CAA GUC ACU AGU GGU UCC GUU-3′ , miR-224_SNP guide: 5′-CAA CUC ACU AGU GGU UCC GUU-3′ and miR-224 passenger: 5′- AAA AUG GUG CCC UAG UGA CUA CA -3′ ) were synthesized by biomers . net GmbH . Double-stranded miRNA was generated by incubating the two strands at a final concentration of 20 µM in 1× RNA annealing buffer ( 6 mM Tris-HCl pH 7 . 4 , 20 mM KCl , 0 . 4 mM MgCl2 ) . The annealing reaction was performed by denaturing the oligonucleotides ( 3 min at 95°C ) and subsequent slow cooling in a heat block . The hybridization product was analyzed by native PAGE . In silico folding of RNA sequences was performed using an adaptation of the mfold package [51] , [52] that has been modified to work with the Accelrys Genetics Computer Group . The calculations were performed with the polymorphic sequence segments containing the SNP at varying internal positions and by defining stepwise ( 10–25 nt ) moving segments with sizes of 100 , 200 , 400 and 800 nt . The resulting structures were compared globally and locally at the SNP position and/or the respective miRNA-binding site and grouped according to the involvement of the SNP-containing sequence segment in intramolecular folding . We validated our predicted structures with the RNAfold package ( University of Vienna ) using minimum free energy ( MFE ) based structure calculations from varying length segments containing the SNP . Genome-wide binding regions for hg19 ENCODE transcription factor ChIP V3 and miRcode V11 or TargetScan miRNA binding sites were extracted using the Galaxy tool . Resulting bed files were intersected with latest GWAS SNP catalog ( in European populations ) from the National Human Genome Research Institute ( NHGRI ) , augmented with SNPs in LD at r2>0 . 8 , to identify overlapping positions . Overlapping genomic regions of transcription factor binding and TargetScan miRNA binding sites were imported into the Genomic Regions Enrichment of Annotations Tool ( GREAT ) for functional assignment by pathway and motif analyses . Statistical enrichments were performed for associations between the overlapping genomic regions and the annotations using the whole genome as a background region . Major coronary arteries were dissected from explanted hearts of patients undergoing heart transplant at Stanford , as previously described [53] . Briefly , left anterior descending ( LAD ) , circumflex , and right coronary arteries were dissected and macroscopically scored as disease ( containing lesion ) or normal ( lesion-free ) , rinsed in saline and fixed in 4% paraformaldehyde overnight at 4°C , followed by cryopreservation in 10% , 20% , and 30% sucrose at 4°C for 30 min , 1 hr , and 2 hrs , respectively . Coronary segments were embedded in OCT media prior to sectioning at 7 µm thickness . Frozen slides were thawed and immunohistochemistry procedure was performed according to the manufacturer's protocol ( Biocare Medical , #RMR625 ) . Briefly , tissue sections were blocked for 30 min using a universal blocking reagent and endogenous peroxidases were quenched prior to incubation with rabbit anti-TCF21 ( Abcam , #ab49475 ) , mouse anti-ACTA2 ( α-SMA; Sigma , #SAB1403519 ) primary antibodies or rabbit serum as a negative control ( purified rabbit or mouse IgG were also used as negative control antibodies ) . Sections were washed in tris buffered saline ( TBS ) and incubated in respective alkaline phosphatase ( AP ) conjugated polymers for 30 min followed by detection using Vulcan Fast Red chromogen ( Biocare Medical , #FR805 ) . Nuclei were counterstained using Methyl Green ( Vector Labs , #H3402 ) . Images were captured on a Zeiss light microscope and total brightness and contrast were uniformly adjusted for each condition . Unlabeled miR-224 locked nucleic acid ( LNA ) and scrambled LNA control oligo probes were purchased from Exiqon and 100 pmol oligos were labeled using the digoxigenin ( DIG ) Oligonucleotide Tailing Kit , 2nd generation ( Roche , #3-353-583 ) according to the manufacturer's instructions . Labeled probes were purified using Sephadex G25 columns ( GE Biosciences , #27-5325-01 ) according to the manufacturer's instructions and labeling efficiency was measured via dot blot analysis using serial dilutions of labeled LNA oligo and Control DIG-dUTP/dATP tailed oligo with detection using an anti-DIG-AP conjugated antibody ( Roche , #1093274 ) and NBT/BCIP developer ( Roche , #11697471001 ) . Probes were diluted in hybridization buffer to a final concentration of 25 or 50 nM and linearized for 5 min at 65°C . Probes were added to thawed slides and incubated at 55°C in a humidified chamber for 2 hrs . Slides were washed with 5X , 1X , 0 . 2X SSC buffer for 15 , 30 , 15 min respectively , followed by 15 min wash in phosphate buffered saline ( PBS ) . Slides were incubated in blocking solution containing 5% heat-inactivated sheep serum , 1% bovine serum albumin , 0 . 1% Tween-20 in RNase-free PBS . Slides were then incubated with AP-conjugated anti-DIG Fab fragment antibody ( 1∶1500 , Roche , #1093274 ) for 2 . 5 hrs at RT . Slides were washed for 2×30 min in PBS-Tween 0 . 1% and 2×20 min in PBS . Signal was detected by incubating with NBT/BCIP developer with 1 mM Levamisole ( Sigma ) for 36–48 hr at RT in the dark . Nuclei were counterstained with Nuclear Fast Red ( Vector ) for 5 min , washed in running H2O and slides coverslipped with aqua-poly/mount ( Polysciences ) . Images were obtained at 20× magnification using a light microscope . Human atherosclerotic carotid artery lesions were obtained from patients undergoing endarterectomy surgery for stable ( asymptomatic ) ( n = 40 ) or unstable ( symptomatic ) ( n = 87 ) carotid stenosis , as part of the Biobank of Karolinska Endarterectomies ( BiKE ) . Normal control arterial samples ( n = 10 ) were obtained from the iliac and radial arteries from healthy organ donors without any history of cardiovascular disease . Briefly , tissue was snap frozen in liquid nitrogen before pulverizing to a fine powder using a pre-chilled mortar and pestle , then resuspended in Qiazol lysis reagent ( Qiagen ) and homogenized with a rotor stator tissue homogenizer . Total RNA was extracted as described above using the miRNeasy Mini Kit ( Qiagen ) and RNA quality assessed using a Bioanalyzer 2100 ( Agilent ) . Global gene expression profiles were analyzed by Affymetrix HG-U133 plus 2 . 0 Genechip microarrays from 127 patient derived plaque samples and 10 donor control samples . Robust multi-array average ( RMA ) normalization was performed and processed gene expression data presented in Log2 scale . For TaqMan based analysis , miRNA-specific cDNA was prepared as described above , and TaqMan qPCR was performed in triplicates using predesigned TaqMan probes for miR-224 and normalized to the RNU44 internal control . Data are represented as mean Log2 fold change of replicates from two independent experiments . Experiments were performed using at least three independent preparations with individual treatments/conditions performed in triplicate [10] . Data is presented as mean ± standard error mean ( SEM ) of replicates . GraphPad Prism 6 . 0 was used for statistical analysis . For all in vitro comparisons between two groups , paired two-tailed t-test was performed . For carotid artery expression analyses between normal donor and endarterectomy plaque samples , unpaired two-tailed t-test with Welch's correction was performed . P values<0 . 05 were considered statistically significant . For multiple comparison testing , two-way analysis of variance ( ANOVA ) accompanied by Tukey's post-hoc test were used as appropriate . All samples reported in this study were obtained with approval of the Institutional Review Board at Stanford University and under written informed consent from patients undergoing orthotopic heart transplantation ( coronary arteries from explanted hearts ) , or those participating in the Genetic Determinants of Peripheral Artery Disease ( GENEPAD ) and Genetics of Insulin Sensitivity iPSC ( GENESiPS ) studies ( peripheral blood ) . All atherosclerotic carotid plaque and donor control samples collected from the Biobank of Karolinska Endarterectomies ( BiKE ) were obtained with informed consent from patients , organ donors or their guardians . The BiKE study is approved by the Ethical Committee of Northern Stockholm . | Both genetic and environmental factors cumulatively contribute to coronary heart disease risk in human populations . Large-scale meta-analyses of genome-wide association studies have now leveraged common genetic variation to identify multiple sites of disease susceptibility; however , the causal mechanisms for these associations largely remain elusive . One of these disease-associated variants , rs12190287 , resides in the 3′untranslated region of the vascular developmental transcription factor , TCF21 . Intriguingly , this variant is shown to disrupt the seed binding sequence for microRNA-224 , and through altered RNA secondary structure and binding kinetics , leads to dysregulated TCF21 gene expression in response to disease-relevant stimuli . Importantly TCF21 and miR-224 expression levels were perturbed in human atherosclerotic lesions . Along with our previous reports on the transcriptional regulatory mechanisms altered by this variant , these studies shed new light on the complex heritable mechanisms of coronary heart disease risk that are amenable to therapeutic intervention . | [
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"atherosclerosis"... | 2014 | Coronary Heart Disease-Associated Variation in TCF21 Disrupts a miR-224 Binding Site and miRNA-Mediated Regulation |
Many infections can be transmitted between animals and humans . The epidemiological roles of different species can vary from important reservoirs to dead-end hosts . Here , we present a method to identify transmission cycles in different combinations of species from field data . We used this method to synthesise epidemiological and ecological data from Bipindi , Cameroon , a historical focus of gambiense Human African Trypanosomiasis ( HAT , sleeping sickness ) , a disease that has often been considered to be maintained mainly by humans . We estimated the basic reproduction number of gambiense HAT in Bipindi and evaluated the potential for transmission in the absence of human cases . We found that under the assumption of random mixing between vectors and hosts , gambiense HAT could not be maintained in this focus without the contribution of animals . This result remains robust under extensive sensitivity analysis . When using the distributions of species among habitats to estimate the amount of mixing between those species , we found indications for an independent transmission cycle in wild animals . Stochastic simulation of the system confirmed that unless vectors moved between species very rarely , reintroduction would usually occur shortly after elimination of the infection from human populations . This suggests that elimination strategies may have to be reconsidered as targeting human cases alone would be insufficient for control , and reintroduction from animal reservoirs would remain a threat . Our approach is broadly applicable and could reveal animal reservoirs critical to the control of other infectious diseases .
Many infections can be transmitted between animals and humans [1] . Human African Trypanosomiasis ( HAT , sleeping sickness ) is a vector-borne disease caused by parasites of the species Trypanosoma brucei and transmitted by flies of the genus Glossina ( tsetse flies ) [2]–[7] . While the east African form of HAT , caused by T . brucei rhodesiense , is a zoonosis with a well-described animal cycle in cattle and wild species , the more chronic west African form , caused by T . brucei gambiense , is often considered a human disease and causes more than 95% of reported cases in humans [8] . Gambiense HAT is endemic in 24 countries and deadly if untreated . While T . b . gambiense has been found in numerous domestic and wild species [2]–[5] , [9]–[13] and transmission between humans and other species been shown to occur both experimentally [9] and naturally [14] , the exact role of animals in gambiense HAT epidemiology remains an unsolved puzzle [15] , [16] . Are they sporadic dead-end hosts , or could they be an important factor for maintaining transmission ? Generally , the incidence of gambiense HAT can be brought to very low levels just by treating human cases , and indeed the latter strategy alone appeared to be sufficient for eliminating gambiense HAT from the island of Bioko in Equatorial Guinea [17] . Such observations have given rise to the notion that T . b . gambiense does not spread in animal populations without the presence of humans . However , the parasite was recently detected in flies on Bioko [18] , suggesting that there is ongoing circulation of the parasite , with the existence of a wild animal reservoir appearing plausible given the lack of detected cases in humans or domestic animals on Bioko . The existence of self-sustained cycles of infection in animals could jeopardise efforts towards gambiense HAT elimination . One of the very few systematic efforts to link the presence of T . b . gambiense in different animal species to human cases was a survey performed over several years in the historical focus of Bipindi , Cameroon , in response to the detection of 44 cases in humans by a newly-installed surveillance network in 1998/99 [19] . Subsequently , data on T . b . gambiense prevalence in domestic [13] and wild animal species [12] , as well as in tsetse flies [20] , biting preferences [21] and the distribution of species among different types of habitat [22] were collected , providing a rich epidemiological and ecological dataset . Synthesising these data in a common modelling framework presents a mathematical and conceptual challenge . Here , we use the concept of the next-generation matrix ( NGM ) [23] to understand the transmission dynamics of gambiense HAT in Bipindi . The NGM describes the number of secondary cases caused in each species by an infected host or vector of any ( other or the same ) species and allows the generalisation of a classical epidemiological quantity , the basic reproduction number , to a situation in which there are different types of hosts or host species . Defining as the spectral radius or largest eigenvalue of the NGM generalises the endemic threshold properties of in single-host systems , in the sense that if there can be sustained transmission and if there cannot . We use a mathematical model of gambiense HAT transmission to understand the prevalence observed in hosts and vectors and estimate the elements of the NGM . Mathematical models of gambiense HAT transmission involving humans only [24] or humans and one animal species [25]–[27] have been derived previously and have yielded valuable insights into HAT epidemiology . For example , it has been shown that there are scenarios in which HAT may require a non-human reservoir host for persistence [25] . From sensitivity analysis of the parameters entering it has been concluded that the proportion of bloodmeals the vector takes from humans is the most important factor , indicating that variation in the exposure to tsetse flies could explain the spatial distribution of T . b . gambiense [26] . Sensitivity of those parameters to expected climate change ( albeit for T . b . rhodesiense ) suggests a shift in the geographical range of infection risk [27] . All these results and , more generally , estimates of for gambiense HAT have not been based on data collected from animals , vector and human systems within the same focus , and instead have relied on the combination of parameter values estimated or drawn from different literature sources . The method we present here is broadly applicable to vector-borne diseases with a potential animal reservoir , and is designed to be informed by data from field surveys . It is based on the premise that the system is in endemic equilibrium , an assumption we revisit in the Discussion section . We show that , in an equilibrium scenario , both and the contribution of different species or groups of species can be estimated using only data on ( a ) relative prevalence of infection in different host species and ( b ) the distribution of bites of the vector on different species . We use this method to assess the potential of each species or combinations of species to maintain gambiense HAT transmission in Bipindi . Further , we extend our method to incorporate ecological data ( the distribution of species across different habitats ) and use this to perform extensive sensitivity analysis .
The human case data come from two active screening campaigns , performed in November 1998 and February 1999 , following the discovery of infected blood sera from the Bipindi area , previously practically ignored in medical surveys [19] . The first of these campaigns concentrated on two neighbouring villages and found 26 infected cases . The second one expanded to a total of 15 villages ( including the two villages screened in the first survey ) , detecting 18 further cases , of which 16 were found in the two villages visited during the first campaign . The data from domestic animals come from a survey performed in 5 villages of the Bipindi area in 2003/04 [13] , including the two villages containing most of the human cases . The data from wild animals come from surveys performed in Bipindi between 1999 and 2001 [12] . The case data are summarised in Tables 1 and 2 . For our analysis , since we were interested in the potential for animal reservoirs to maintain gambiense HAT , we attempted to make our estimates conservative in that regard . We included all the villages screened in Bipindi for our basic estimate of prevalence in humans , as the area comprising these villages region compares well to where the tested animals came from ( see the Results section for sensitivity analysis on the human prevalence estimate ) . Moreover , we combined the two surveys in human populations into a single prevalence estimate , which is equivalent to assuming that the two surveys took place at the same time and ensures we do not underestimate prevalence due to medical interventions in response to the first survey ( i . e . , to estimate prevalence we took all infected cases found in both screening surveys as enumerator and the combined population of the villages screened as denominator ) . The data from both domestic and wild animals were collected later , and are very likely to be affected by vector control installed after the human cases were detected , which could be expected lower the prevalence in all species . Since we did not have access to animal case data separated by location and species , we used all the survey data . As a consequence , in both the data from domestic and wild animals , the prevalence we are using is lower than the one reported from Bipindi alone ( all species combined ) . In summary , we are likely to underestimate equilibrium prevalence in animals , in line with our attempt to be conservative in that regard . In the analyses presented below we assumed infection among a given species to be binomially distributed with fixed infection probability corresponding to an average equilibrium prevalence . The likelihood for equilibrium prevalence in species ( equivalent to the probability of being infected ) , given cases detected among sampled animals , is then proportional to a beta distribution , ( 1 ) This quantifies the uncertainty resulting from small sampling sizes ( the smallest being White-eyelid mangabeys with only 5 sampled animals ) , with correspondingly wide confidence intervals . All other parameters are drawn from flat distributions using Latin Hypercube Sampling [28] , with ranges given in Supporting Text S2 . In setting up the model , we made the following biological assumptions: Assuming random mixing and uncorrelated bites , a simple transmission model for gambiense HAT transmission between host and one vector species is given by the system of ordinary differential equations , based on the Susceptible-Infected-Susceptible ( SIS ) model ( 2a ) ( 2b ) where is the number of infected of host species , is the number of infected vectors , and are the total population sizes of host species and vectors , respectively , and are the forces of infection acting on host species and the vector , respectively , is the rate at which infected hosts of species lose infectiousness ( through recovery or death ) , and and are the natural death rates ( and birth rates , assuming constant population sizes ) of host species and the vector , respectively . The forces of infection are ( 3a ) ( 3b ) where is the probability for an infectious bite on a susceptible host of species to lead to infection , rescaled by the ratio of vector to host population sizes , is the force of infection exerted by species on vectors , is the probability that an infectious bite by a susceptible vector leads to transmission of the parasite and establishment in the vector midgut . These transmission probabilities are treated as unknown quantities to be estimated . The other parameters are measured quantities: is the relative population density of species compared to all other hosts , is the biting rate of vectors , is the fraction of bites taken on species , and and are the prevalence of infection in species and vectors , respectively . Assuming that the system is in equilibrium , we get a relation between force of infection and prevalence , ( 4a ) ( 4b ) where the asterisk denotes equilibrium quantities . The NGM describes transmission between different vector and host species by mapping the distribution of primary cases to the distribution of secondary cases [23] . Once fully quantified , the matrix allows to identify host species that can maintain transmission of a given infection [32] . That is , we can distinguish between maintenance and non-maintenance hosts by calculating the host-specific reproduction number of ( group of or single ) host species , which is interpreted as the average number of secondary cases per generation caused ( via the vector ) by a single primary case belonging to in the absence of hosts other than . If , host ( s ) can maintain gambiense HAT transmission on its ( their ) own . This formalises the definition of maintenance hosts given in [33] . To capture the impact of correlated bites on model dynamics , we separate our vector class into classes and denote these , the number of infected vectors that have last fed on host species If is the average time spent feeding on a given species , the dynamical equations for are ( 5 ) where is the total number of vectors that have last fed on species . In equilibrium , this can be solved for which is used to parametrise the NGM and can be extended to groups of species ( see Supporting Text S1 ) . Extending the scenario of correlated bites to known differences in habitat , we introduce a mixing matrix , the elements of which describes how likely a vector is to switch ( and potentially transmit infection ) from species ( or group of species ) to species ( or group of species ) . The dynamical equations for then become ( 6 ) which , again , is used to parametrise the NGM . With the densities ( or presence/absence ) of the different species in different habitats are given , we estimated mixing rates to ( 7 ) Simulations were performed using the Gillespie algorithm [34] . All parameter estimations where there was no analytical solution were performed using Powell's hybrid method [35] as implemented in the GNU Scientific Library [36] .
In a multi-host system , the basic reproduction number is defined as the spectral radius of the NGM . In the Supporting Text S1 , we show that when we are dealing with only one vector species the basic reproduction number is ( 8 ) where the sum is over all host species and is the average number of infected vectors caused in a completely susceptible vector population by a single host of species , and as the average number of infected hosts of species caused by a single vector in a completely susceptible host population . A special case of this equation for a system composed of humans and one animal species has previously been derived in [26] . The host-specific reproduction number [32] of a group of host species , or their contribution to the basic reproduction number , is ( 9 ) This is equivalent to the value would take in a system of only the subset of species in . The summands are related to the forces of infection via ( 10 ) In equilibrium , we can use Eqs . ( 3 ) and ( 4 ) to rewrite this as ( 11 ) We can use this to calculate the basic reproduction number given only equilibrium prevalence in the vector ( ) and all host species ( ) and vector biting preference ( the fraction of bites taken on species ) , ( 12 ) This does not require any information on vector biting behaviour , host or vector population sizes , or within-host infection dynamics . For the focus we investigated , in the baseline scenario of random mixing of vectors with the different host species ( proportional to biting preference as measured ) we found that the median value of was 1 . 1 ( 95% CI 1 . 0 , 1 . 3 ) ( Fig . 1 ) . The contribution of humans ( i . e . , the hypothetical value of in a system of only humans and vectors ) was 0 . 5 ( 0 . 2 , 0 . 7 ) . When testing for potential cycles of sustained transmission in groups of species , we found that in domestic animals was 0 . 5 ( 0 . 3 , 0 . 8 ) . When adding humans to the system , increased to 0 . 7 ( 0 . 5 , 0 . 9 ) . In wild animals , was 0 . 8 ( 0 . 6 , 1 . 2 ) , with a likelihood of 0 . 14 of being greater than 1 . In all animals ( wild and domestic ) , was 1 . 0 ( 0 . 8 , 1 . 3 ) , with a likelihood of 0 . 46 of being greater than 1 . These results are in contrast to the notion of gambiense HAT as human disease with only accidental animal hosts [7] . However , we could be underestimating the prevalence in ( and , consequently , the importance of ) humans for two main reasons: ( i ) active case detection campaigns might not have detected all cases in the population screened due to problems with diagnostic sensitivity [37] , [38] or the presence of asymptomatic carriers with low parasitemia [29] ( note that our denominator is the population screened , so screening attendance does not change our estimate as long as individuals screened are chosen randomly ) , and ( ii ) the denominator at risk might in fact not be the entire population screened if the risk of infection is unevenly distributed . The effects of these two are equivalent and multiplicative: If a fraction of cases are detected , and a fraction of the population is involved in the transmission cycle , the measured prevalence is and true prevalence is , such that . If we increase the prevalence in humans to account for these potential sources of bias , of the system with only animals and vectors decreases ( Fig . 2a ) . More specifically , if only the 40% of the population of Bipindi living in the two villages with most of the detected cases [19] are at risk of infection , and if we incorporate a low estimate of 90% for screening sensitivity [37] , the likelihood for in animals decreases to 0 . 13 , but the likelihood for in humans is still less than 0 . 01 . Only if we further reduce the population at risk to less than 20% of these villages does the likelihood for in animals drop to less than 0 . 01 . In that case , the likelihood for in humans is 0 . 59 . A second source of potential bias could arise if subsequent bites of the same fly were correlated , or if a fly taking a blood meal on a given species or group of species had a higher probability of biting a host of the same species or another species in that group again [39] , [40] . Our analysis attributes human infection either to other human infections ( via a vector ) or to spillover from animal reservoirs ( again via a vector ) . If the two kinds of host population are fully epidemiologically linked ( i . e . , if we assume random mixing ) , then the analysis inevitably attributes many of the cases in the population with lower ( weighted ) prevalence to spillover from the population with higher ( weighted ) prevalence . The less linkage there is the less likely this is to happen , and eventually in the low-prevalence population is required to explain persistence . When we considered a system of two transmission cycles , one containing humans and domestic animals and the other one wild animals ( i . e . , a system in which there is a sylvatic cycle separate from the human/domestic animal cycle ) , the human contribution to the system was not enough to guarantee in the system of humans and domestic animals . When humans were considered to be part of a transmission cycle completely separate from animals , we got in both the human and the ( wild and domestic ) animal cycle . Introducing only occasional transfer of infection between species , however , means the observed data are not compatible with sustained transmission in the human-vector cycle , with a threshold appearing at a rate of switching of about 1/year ( Fig . 2b ) . in humans was greater than 1 with likelihood greater than 0 . 01 only when vectors switched between species less than once per year . Comparing these with an average fly life expectancy of about one month , this would mean that most flies never change host species in their lifetime , an unrealistic scenario given that in practice flies cannot afford to restrict themselves to one host type . Independent transmission cycles in animal reservoirs , on the other hand , have a likelihood greater than 0 . 5 for any rate of switching less than 30/year , corresponding to 2–3 host switches per fly in its lifetime . To inform this analysis with ecological measurements of habitat distributions of the species found to host gambiense HAT in Bipindi [22] , we incorporated the overlap of habitat ranges between animals in our derivation of the NGM . This version of the model does not support a human-only transmission cycle , and suggests that a sylvatic cycle is possible . Separating the different species by the habitats they can be found in yielded likelihood 0 . 48 for in wildlife species only ( Fig . 3 ) , and likelihood 0 . 97 for in all animal species if switches between groups of species happened at a third of the biting rate . We performed simulations of the different model variants , with a particular focus on how long it would take for the disease to become re-established in a human population from which it had previously been eliminated . We tested different rates of vector switching between a human/domestic and a wild animal cycle , as well as other configurations of cycles . As the rate of switching decreased , the time it can take for cases to reappear in the human population increased ( Fig . 4 ) . For rates of switching greater than 1/year , reintroduction usually occured within a year or less . When , on the other hand , switches between humans , domestic animals and wild animals were as rare as 0 . 01/year per fly ( i . e . , only one in 1000 flies ever switched between these subsystems ) it could take 10 years or longer for infection to be transferred between them .
We have developed a mathematical model to assess transmission dynamics in a focus of gambiense HAT , and analysed it incorporating a variety of epidemiological and ecological measurements , providing one of the first estimates of in gambiense HAT from field data . If vectors and hosts mix randomly , we only need the prevalence in the different vector and host species , as well as the distribution of bites on host species , to determine the NGM and . In this case , the available data strongly suggest that T . b . gambiense cannot be sustained in a human ( and vector ) population alone , whereas independent transmission cycles in animal reservoirs are possible in a realistic parameter range . When reducing the human population at risk , we could not rule out the possibility of transmission cycles in humans and vectors . However , these occured only with a very small likelihood corresponding to very specific parameter combinations unless it was only a very small fraction of the human population that was exposed to the potential infection . While there are occupational hazards associated with trypanosomiasis infection ( especially hunting [41] ) , these do not seem enough to explain such strong heterogeneity in risk . When we relaxed the assumption of random mixing to reduce the amount of infection transfer between humans and other species , human transmission cycles were only possible in parameter regimes where there was a parallel transmission cycle in wildlife . When we informed this analysis with measured distributions of species among habitats , independent transmission cycles in animals occured with high probability . Simulating the transmission dynamics of the model with different rates of vector switching between three subsystems of humans , domestic animals and wild animals , we observed that unless switching was rare , reintroduction of infection in humans usually occurred within less than a year . When , on the other hand , such a switch happened only in a minority of vector lifetimes , reintroduction could take many years , and there was the possibility a human-only cycle in parallel with a separate sylvatic cycle . The disease-free periods of 10 or more years subsequent to human case control that have been observed [17] would point to such a scenario . However , the effect of vector control combined with delayed recognition of new outbreaks due to infrequent screening and lack of gambiense HAT testing in routine health services may also explain long delays observed between apparent elimination of T . b . gambiense from a focus and its re-activation . Our analysis hinges on the assumption of equilibrium , which allowed us to estimate the force of infection from observed prevalence . While fluctuations in the density of the different species or the incidence of infection that they experience are likely , the slow dynamics of gambiense HAT combined with the long history of endemic transmission in Bipindi [42] would appear to justify the assumption of stationarity . Still , since the data underlying our study were taken at different points throughout the year , strong seasonality could mean that the measurements were not a good reflection of the average state of the system , as well as raising theoretical issues in linking persistence of an endemic disease to the value of [43] . While we cannot resolve this issue on the basis of the available data , we note that vector density was found not to vary significantly in the study area [44] , and that the progression of gambiense HAT is slow relative to the progression of seasons , so that fluctuations in tsetse fly density need not translate into significant changes in prevalence . Further , it is worth noting that more detailed data on incidence would enable relaxation of the model assumptions and direct estimation of the force of infection . Moreover , molecular typing of parasite material could be used to quantify the contribution of non-human hosts to the force of infection in humans . Clarifying the precise role of animal hosts in maintaining transmission has important implications for elimination strategies . If wild animals can maintain T . b . gambiense in a separate transmission cycle , elimination ( the permanent interruption of transmission ) will be difficult to achieve with a strategy based on human case detection alone . At the same time , all our estimated likely values are very close to 1 , suggesting that the disease should be controllable , especially if vector control is introduced and maintained . Beyond maintenance , animals could play a role in transmitting infections between communities within a given focus or indeed ( re- ) introduction into old , extinct foci or new areas . Gambiense HAT has remained a west and central African disease confined to persistent foci in spite of large-scale population movements around the continent . If transmission could be maintained in a human-vector system alone , one would expect the distribution of the disease to be more diffuse . Instead , one could speculate that restrictions of animal host ranges are at least to some degree responsible for the observed distribution . An intriguing hypothesis that arises from our results is that the apparent decline in gambiense HAT burden in many areas of west Africa ( e . g . , Gambia , Sierra Leone , Liberia , Nigeria ) where it was previously highly endemic might be attributable mainly to the reduction in wildlife habitats and populations in these regions over the past decades . We have concentrated on an gambiense HAT focus in a region with a well-documented history of endemic transmission [42] . Extrapolation of our results to other settings warrants caution . Focus-specific levels of parasite strain virulence , vector competence or human susceptibility could combine to ensure sustained transmission in human-vector systems elsewhere . Similarly , species and distributions of domestic and wild animals vary considerably across foci . Nevertheless , this study offers an attractive explanation for the mysterious disappearance and re-activation of gambiense HAT foci throughout Africa . Our method is easily generalised to other foci , and further studies on the ecology and epidemiology of T . b . gambiense across different areas would firmly establish the role of wild and domestic animals in the maintenance of sleeping sickness , and to systematically assess the prospects of elimination efforts . In this study , we analysed one of the largest systems for which the NGM has been quantified from field data . Combined with efforts to measure infection prevalence in both humans and animals , our model framework could be applied to better characterise the role of animal hosts in the long-term control of many other diseases , such as yellow fever , rift valley fever or Chagas disease . | Gambiense sleeping sickness is a disease transmitted by tsetse flies that mostly affects rural populations in sub-Saharan Africa . Although the parasite that causes the disease can be found in many different wild and domestic animal species , the disease has often been claimed to be maintained mostly by humans . Currently , fewer than 10 , 000 human cases are reported per year across Africa , and it has been suggested that elimination of gambiense sleeping sickness is feasible . We analysed human and animal case data from a well-known endemic focus of sleeping sickness in Cameroon , to quantify the contribution of the different species to the circulation of the parasite . In a wide range of scenarios , we found that animals are crucial for maintenance in the disease . When informing our model by the distribution of species among habitats as measured in the field , we found indications for independent transmission cycles in animals . This suggests that a risk of reintroduction from animal into human populations would remain even if the disease were eliminated from those human populations . | [
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"dis... | 2013 | Identifying Transmission Cycles at the Human-Animal Interface: The Role of Animal Reservoirs in Maintaining Gambiense Human African Trypanosomiasis |
Sequencing family DNA samples provides an attractive alternative to population based designs to identify rare variants associated with human disease due to the enrichment of causal variants in pedigrees . Previous studies showed that genotype calling accuracy can be improved by modeling family relatedness compared to standard calling algorithms . Current family-based variant calling methods use sequencing data on single variants and ignore the identity-by-descent ( IBD ) sharing along the genome . In this study we describe a new computational framework to accurately estimate the IBD sharing from the sequencing data , and to utilize the inferred IBD among family members to jointly call genotypes in pedigrees . Through simulations and application to real data , we showed that IBD can be reliably estimated across the genome , even at very low coverage ( e . g . 2X ) , and genotype accuracy can be dramatically improved . Moreover , the improvement is more pronounced for variants with low frequencies , especially at low to intermediate coverage ( e . g . 10X to 20X ) , making our approach effective in studying rare variants in cost-effective whole genome sequencing in pedigrees . We hope that our tool is useful to the research community for identifying rare variants for human disease through family-based sequencing .
DNA sequencing is being routinely carried out to identify genetic factors , rare variants in particular , associated with human disease . It has been successful in identifying causal variants for Mendelian disease [1 , 2] , and continues to be a powerful approach to uncovering the genetic basis of rare disease [3] . For complex traits , however , detecting rare variant associations is challenging due to reduced power of statistical tests when the allele frequency is low [4 , 5] . Although large-scale sequencing of unrelated individuals has identified associated rare variants for some complex traits , such as lipid traits [6] , this approach often revealed greater challenges in finding causal genes for complex traits [7 , 8] . Family sequencing provides a promising alternative for identifying rare variant associations due to the enrichment of causal variants in pedigrees . Recent studies demonstrated the effectiveness of sequencing families and identified associated rare variants for a variety of traits , including schizophrenia [9] , Alzheimer [10] , and hypertriglyceridemia [11] . These lines of evidence show that family studies are emerging as a powerful approach in the sequencing era to localize genetic factors for human disease , and will play a key role as a complementary approach to the population based design to help understand the genetic basis of complex traits . A critical step in genetic analysis of family sequence data is to infer genotypes of individuals in pedigrees . For next-generation sequencing data , this is challenging due to base call error , alignment artifacts , possible allele dropout during library preparation and sequencing , especially at low coverage , among others . Although variant calling algorithms developed for unrelated individuals can be applied to family sequencing data , the accuracy is compromised due to the ignorance of family relatedness . Family-aware calling algorithms , e . g . Polymutt [12] and FamSeq [13] , have improved accuracy over the standard methods but assume the same pedigree correlation structure for all sites and therefore ignore the actual identity-by-descent ( IBD ) sharing . For example , in a nuclear family with two siblings and their parents , the IBD sharing between the two siblings can be 0 , 1 or 2 at a particular genomic region , with the probabilities being 0 . 25 , 0 . 5 and 0 . 25 respectively , a priori . Polymutt and FamSeq assume such a priori probabilities for all genomics regions and thus inefficiently model the data when the actual IBD can be inferred . As a concrete example , assuming we know that at a particular position the two siblings share 2 alleles IBD , then their genotypes are identical at this locus and can be inferred with improved accuracy by merging their sequencing data , essentially doubling the sequencing depth . In general , knowledge of IBD sharing helps confine genotypes to be compatible to the IBD patterns in pedigrees and a variant calling framework that models IBD is expected to deliver improved performance over existing methods . Such a framework makes it feasible to design studies with reduced coverage , since data from shared haplotypes in a pedigree are efficiently combined to make reliable genotype calls . This will be particularly beneficial for whole genome sequencing , which is still prohibitively expensive for large-scale sequencing studies . Linkage-disequilibrium ( LD ) -based methods such as Beagle4 [14] , Thunder [15] and SHAPEIT [16] have been extensively used for inferring genotypes from low-depth sequencing utilizing extensive LD among variants . Due to much reduced LD ( in terms of r2 ) among rare variants as well as between rare and common variants , however , LD-based methods are expected to have reduced accuracy for rare than for common variants . In this study , we develop and implement a variant calling framework that infers the IBD sharing ( through the inheritance vector , see Methods ) among family members directly from sequencing data , and utilizes the IBD sharing to jointly infer individual genotypes . The new software , Polymutt2 , provides a complementary tool to our prior work ( Polymutt ) with improved performance for small to moderate pedigrees . By directly modeling the sequencing data , the IBD can be reliably inferred , even for extremely low coverage ( e . g . 2X or below ) , making it a robust tool for sequencing data . In addition to unphased genotypes , when parental data are available haplotypes can be directly constructed from the sequencing data based on the best inferred IBD sharing with little compromise of accuracy compared to unphased genotypes obtained by incorporating the uncertainties of IBD inference . Mendelian error is extremely rare for unphased genotypes , and is completely eliminated for haplotype calls . Through both simulations and real data , we show that Polymutt2 significantly outperforms other tools , including GATK , Beagle4 and Polymutt , for genotype calling on pedigree data , especially for rare variants in low coverage data .
Suppose we have a pedigree with f founders and n non-founders , with sequencing data on M variants across a chromosome . Without loss of generality , we also arrange the pedigree such that the first f members in the pedigree are founders . Define a binary inheritance vector [22] at variant j as Ij = ( p1 , m1 , … , pn , mn ) for the n non-founders in this pedigree . Each of the entries describes the transmission of the paternal ( pi ) or the maternal ( mi ) allele , with 0 ( or 1 ) indicating the grand-paternal ( or the grand-maternal ) allele being transmitted . Therefore an inheritance vector completely determines which of the 2f founder alleles were inherited by each nonfounder . There are N = 2n possibility inheritance vectors and let vk , k = 1 , . . , N , denote individual vectors . Let R denote data of all members in a family across all M variants , Rj be all reads at variant j , and Rij be the reads in family member i at variant j . Similarly , let Gj denote the vector of genotypes at variant j and its ith entry Gij = ( A1 , A2 ) be the ordered genotype of the ith member in the pedigree , where A1 and A2 represent the paternally and maternally transmitted alleles respectively . Assuming that recombination events are independent between all chromosome intervals , i . e . no crossover interference , the likelihood can be framed as a Hidden Markov Model [23] , similarly as that in the Lander-Green algorithm [22] . Specifically , the likelihood of reads across all M variants in the pedigree can be calculated as P ( R ) =∑I1…∑IMP ( I1 ) ∏j=2MP ( Ij|Ij−1 ) ∏j=1MP ( Rj|Ij ) The initial probability , P ( I1 ) , is assumed uniform across all N = 2n inheritance vectors . The transition between adjacent inheritance vectors , P ( Ij|Ij-1 ) , is calculated according to the recombination rate between the jth and j-1th variant , which can be calculated using the HapMap Phase II [24] genetic map . For variants not in the HapMap genetic map linear extrapolation will be used to approximate the genetic distance . The emission probability , P ( Rj|Ij ) , which is the probability of reads in all family members at locus j given the inheritance vector Ij , can be calculated as P ( Rj|Ij ) =∑GjP ( Rj , Gj|Ij ) =∑GjP ( Rj|Gj , Ij ) P ( Gj|Ij ) =∑Gjfounders∏i=1f+nP ( Rij|Gij , Ij ) ∏i=1fP ( Gij ) ( 1 ) Here we assume as in other methods that the sequencing reads depend only on the underlying genotype so that P ( Rj|Gj ) can be factorized into the product of individual genotype likelihoods . Since an inheritance vector specifies precisely how the alleles were transmitted from founders to non-founders , the genotypes of the entire pedigree are determined by the ordered founder genotypes when the inheritance vector is known . Therefore the emission probability involves the summation of only the ordered founder genotypes , whose prior probabilities P ( Gij ) can be either obtained from the external sources , e . g . the 1000 Genome Project [25] , or estimated based on the pedigree data using for example Polymutt [12] , assuming Hardy-Weinberg equilibrium . Since the inheritance vectors usually cannot be determined unambiguously , the goal here is to infer the posterior distribution of the inheritance vectors at each variant using the sequencing data from all M variants; that is , we aim to calculate P ( Ij|R ) . This can be achieved efficiently using the forward-backward procedure in HMM [23] . Let αj ( k ) denote the forward variable at variant j for vk and βj ( k ) be the corresponding backward variable [23] . Then the posterior probability of vk at variant j is P ( Ij=vk|R ) =αj ( k ) βj ( k ) ∑k=1Nαj ( k ) βj ( k ) ( 2 ) From the marginal distribution , the inheritance vector with the maximum posterior probability at variant j , denoted as Ijmarg , can be used to represent the inferred inheritance vector for each variant; the IBD sharing can be directly derived from Ijmarg for any pair of family members . However , since Ijmarg only maximizes the likelihood marginally at variant j , we infer a global optimal path of inheritance vectors along the genome through the Viterbi algorithm [23]; we use Ijbest to denote the optimal inheritance vector at variant j . The Lander-Green algorithm requires that variants are independent , i . e . not in linkage disequilibrium ( LD ) . For sequencing data variants are usually correlated . Since only a limited number of recombination events are expected in a pedigree , it is neither feasible nor necessary to use all data . We built a companion tool to automatically select a subset of independent variants by LD pruning , a similar approach used in Plink and others [26 , 27] . In addition , we filtered variants in genomic regions that are prone to alignment artifacts , including segmental duplications , simple repeats and low complexity regions , and 50bp up and downstream of known insertions and deletions; these data were downloaded from UCSC genome browser ( http://www . genome . ucsc . edu ) and the 1000 Genomes Project . The final set of selected variants is used to contrast a sparse genetic map with high-quality variants for the inference of inheritance vectors , and the genetic distances of these variants are linearly extrapolated based on the HapMap Phase II genetic map [24] . The overall strategy is to build a sparse scaffold of inheritance vectors along the genome using the selected set of variants and utilize the scaffold to boost the variant calling accuracy for all variants . We use the term “scaffold variants” to refer to the sparse set of variants in the map file used to construct the inheritance vectors . We evaluate for each variant the evidence supporting the alternative allele in the data by calculating the posterior probability of being polymorphic . Specifically for each variant we calculate two probabilities , P ( poly|Rj , R ) and P ( mono|Rj , R ) , representing the likelihood of polymorphism and monomorphism respectively given the data at the jth site and scaffold variants . We assume that for poly the two alleles are Aref and Aalt and for mono only Aref is present in the data . The posterior probability of polymorphism given the data is calculated as P ( poly|Rj , R ) =∑IjP ( poly|Ij , Rj , R ) P ( Ij|Rj , R ) =∑IjP ( poly|Ij , Rj ) P ( Ij|Rj , R ) =∑IjP ( Rj|Ij , poly|P ( poly|Ij ) P ( Rj|Ij ) P ( Ij|Rj , R ) = ∑IjP ( Rj|Ij , poly ) P ( poly ) P ( Rj|Ij , poly ) P ( poly ) +P ( Rj|Ij , mono ) P ( mono ) P ( Ij|Rj , R ) The term P ( Rj|Ij , poly ) is calculated based on Eq ( 1 ) , and P ( Rj|Ij , mono ) is simply the product of genotype likelihoods of homozygous reference allele across all family members at variant j . The prior probability of polymorphism , P ( poly ) , is calculated as in Polymutt [12] and P ( mono ) = 1-P ( poly ) . Briefly , in a sample with N founders in the absence of natural selection , according to coalescent theory the prior probability that a site includes non-reference alleles is θ∑i = 12N1i , where θ is the population scaled mutation rate per site and is set to 1/1000 in this study . When variant j is one of the scaffold variants , P ( Ij | Rj , R ) = P ( Ij | R ) , which was obtained in ( 2 ) . Then the Phred-scaled variant quality is calculated as VQ = -10*log10 ( 1-P ( poly|Rj , R ) ) . By construction , only a sparse subset of variants is included in the map file , and the vast majority of variants are located in the intervals of scaffold variants . In an interval within which a crossover occurred , the inheritance vectors on the two sides of the recombination point are different . Assigning wrong inheritance vectors to variants will not only produce wrong IBD sharing among family members but also greatly reduce variant calling accuracy . However it is unknown a priori in which intervals crossovers occurred and where exactly the breakpoint is if a crossover occurred . To address this issue , Polymutt2 calculates for each variant in scaffold intervals the posterior probabilities using the inheritance vectors on the left and right boundary separately , and takes the maximum value , Pmax ( poly| Rj , R ) , as the posterior probability of polymorphism . The Phred-scaled variant quality is calculated as VQ = -10*log10 ( 1-Pmax ( poly|Rj , R ) ) . The inheritance vectors are accordingly assigned to each of the variants in scaffold intervals . This assumes that there is at most one crossover event in any interval , which is reasonable given the limited number of expected recombination events per generation . As a result , the crossovers can be precisely located in intervals in which crossovers occurred . After quantifying the variant quality as described above , the most likely inheritance vectors are assigned to each of the variants . The posterior probability of the genotypes for individual i for variant j can be calculated as P ( Gij|Rj , R ) =∑IjP ( Gij|Ij , Rj , R ) P ( Ij|Rj , R ) =∑IjP ( Gij|Ij , Rj ) P ( Ij|Rj , R ) =∑IjP ( Gij , Rj|Ij ) P ( Rj|Ij ) P ( Ij|Rj , R ) ( 3 ) For a specific genotype Gij = g , the term P ( Gij , Rj|Ij ) can be calculated using Eq ( 1 ) by considering only the terms where Gij = g . For variants in intervals of scaffold variants , P ( Ij|Rj , R ) was obtained in calculating the variant quality as described in the previous section . The genotype with the maximum posterior probability Pmax ( Gij|Rj , R ) is assigned to the individual , and the corresponding genotype quality is calculated as GQ = -10log10 ( 1-Pmax ( Gij|Rj , R ) ) . Since the calculation is repeated for all individuals in the pedigree , the computation can be intensive for larger pedigrees . One remedy is to use inheritance vectors with the largest posterior probabilities in the calculation . Specifically , the top inheritance vectors with cumulative probabilities greater than a cutoff , e . g . 0 . 99 , can be used in ( 3 ) . At the extreme , a single best inheritance vector , Ijbest or Ijmarg , can used to minimize the computation . Given the high accuracy of the inheritance vector inference ( see Results ) , the increase of speed greatly outweighs the negligible loss of accuracy . When parental data available , we generate haplotypes along a chromosome by reconstructing the optimal ordered genotypes jointly for all family members at each position assuming that the inheritance vector is known . We use Ijbest as the optimal inheritance vector for variant j inferred using the Viterbi algorithm as we described before . The posterior probability of each configuration of ordered genotypes at variant j given the sequencing data and Ijbest is calculated as P ( G1j , G2j , … , G ( f+n ) j|Ijbest , Rj ) = P ( Rj|G1j , G2j , … , G ( f+n ) j , Ijbest ) P ( G1j , G2j , … , G ( f+n ) j|Ijbest ) P ( Rj|Ijbest ) = ∏i=1f+nP ( Rij|Gij ) ∏GjfounderP ( Gjfounder ) P ( Rj|Ijbest ) Here P ( Rij|Gij ) is the genotype likelihood calculated before , and the term P ( G1j , G2j , … , G ( f+n ) j|Ijbest ) is simplified to ∏GjfounderP ( Gjfounder ) since given an inheritance vector the probability depends only on the ordered genotypes of founders . The terms P ( Gjfounder ) and P ( Rj|Ijbest ) were calculated in Eq ( 1 ) . The goal is to obtain the posterior probability of each configuration of founder ordered genotypes , and assign the configuration with the maximum posterior to founders as well as nonfounders according to Ijbest . This is repeated for all positions and the haplotypes are automatically constructed by stitching the paternal and maternal alleles at each position along a chromosome . By construction , Mendelian error in haplotype calling is completely eliminated due to the Mendelian transmission dictated by the inheritance vector . Note that the construction of haplotypes is based on the transmission of alleles from parents to offspring . When parental data are not available , such as in sibships , it is not possible to deduce the parental origin of the alleles and therefore haplotypes cannot be reconstructed . We utilized the 1000 Genome Project [25] data to effectively capture the sequencing and mapping error . We generated the founders’ genomes by randomly selecting the CEU phased genotypes ( March 2012 Phase 1 release ) . For non-founders , we simulated cross-overs in the parental haplotypes based on the genetic map in the Phase II HapMap data , and then generated offspring genotypes by randomly selecting one haplotype from each parent . To simulate realistic reads , we first generated paired-end 100bp fragments according to Poisson distribution on the genome , with the insert size following a Gaussian distribution with a mean of 400bp and a standard deviation of 50bp , and then simulated reads based on these fragments assuming a sequencing error rate of 0 . 01 per base . We used BWA [28] to align simulated reads to the reference of hg19 and carried out standard procedures for variant calling , including Indel-realignment and base quality recalibration using GATK and duplication removal using Picard ( http://picard . sourceforge . net ) . The list of known Indels from the 1000 Genomes Project was provided to GATK for Indel re-alignment . We used GATK UnifiedGenotyper to infer variants and genotypes from sequencing . We then applied Polymutt , Polymutt2 and Beagle4 on the GATK-generated VCF files to refine the genotypes utilizing the GL values calculated by GATK and stored in the VCF file . Pedigrees we investigated in this study include sibships of size 2 ( Sib2 ) , 4 ( Sib4 ) and 6 ( Sib6 ) , nuclear families with 4 ( Nuc4 ) and 6 ( Nuc6 ) members , and an extended pedigree with 10 individuals , which is the same as the pedigree investigated in Polymutt [12] . For each pedigree structure , we simulated 20 families at coverage ranging from 2X to 30X . For the Nuc6 we simulated additional 50 and 100 pedigrees to investigate the trend of the genotype calling accuracy of rare variants for increasing numbers of sequenced families . Genotype calling was performed using GATK , Polymutt , Polymutt2 and Beagle4 for each simulated dataset . Note that for trios Polymutt2 and Polymutt are equivalent , and therefore we omitted the investigation of trios in this study . We used two metrics to measure the accuracy of genotype calling . The first is the false negative rate ( FNR ) , defined as the percentage of true genotypes that are called into incorrect genotypes; this measures the sensitivity of the calling and is equal to 1-sensitivity . The second metric is the false discovery rate ( FDR ) , defined as the percentage of called genotypes that are different from the true genotypes; this measures the specificity of the calling; this measures the specificity of the calling and corresponds to 1-precision . A good algorithm is expected to have low values of both FNR and FDR . We used GQ to filter low quality genotype calls and specifically we used GQ = 3 for Polymutt2 and Polymutt , GQ = 5 for Beagle4 and GQ = 10 for GATK; due to different calculations of GQ in these algorithms we found that these filtering criteria have reasonable FNR and FDR values . These criteria were used for all simulated data . Note that when no filtering is used FNR and FDR are the same for overall genotypes , and the difference is due to differential filtering based on GQ cutoffs . For heterozygotes , which are of particular interest in studying rare variants , however , both FNR and FDR are critical metrics to evaluate , as FNR can be made artificially low by aggressive calling of heterozygotes , which will results in high FDR , and conversely conservative calling of heterozygotes can lead to low FDR and high FNR .
We derived the IBD sharing at each position between a pair of family members in a pedigree based on the inferred inheritance vector obtained via the Viterbi algorithm . Fig 1A ) shows the simulated true IBD sharing of the two siblings in the Nuc4 pedigree along chromosome 1 and Fig 1B and 1C and 1D ) show the inferred IBD sharing of the same two siblings at coverage of 30X , 15X and 2X , respectively . From the comparison , we can see that the inferred IBD is extremely close to the true IBD at various coverage , indicating the high accuracy of the inference of inheritance vectors based on sequencing data . Interestingly , for coverage as low as 2X , the accuracy of inferred IBD is not jeopardized ( Fig 1D ) . The high accuracy of inheritance vector inference warrants the increased accuracy of genotype calling when the IBD sharing is utilized to infer genotypes . Fig 2 shows FNR and FDR values for four calling algorithms ( GATK , Polymutt , Polymutt2 and Beagle4 ) on overall genotypes for various pedigrees and sequencing coverage . Polymutt2 significantly outperforms Polymutt and GATK , in terms of both FNR and FDR , and the advantages are more pronounced when pedigree members are more related or coverage is low ( Fig 2 ) . For example , at 10X , the FNR values for Polymutt2 for sibships of size 2 , 4 and 6 are 0 . 71% , 0 . 32% and 0 . 17% , respectively , while the FNR values for GATK are similar across all pedigree types with a mean value of 1 . 65% ( Fig 2A ) . The FDR follows the same patterns ( Fig 2D ) . On the other hand , the relative performance of Polymutt2 vs . Beagle4 depends on pedigree types , and for pedigrees with limited IBD sharing Beagle4 outperformed Polymutt2 . For example for Sib2 Beagle4 calls have smaller FNR and FDR for all sequencing coverage investigated ( Fig 2 ) . For pedigrees with increased IBD sharing , Polymutt2 has either comparable ( e . g . for Sib4 , Nuc4 and Ext10 ) or better ( e . g . for Nuc6 and Sib6 ) genotype calling accuracy , and the advantage of Polymutt2 over Beagle4 becomes more manifest with increasing IBD sharing in pedigrees such as Sib6 and Nuc6 ( Fig 2 ) . If we compare callers without Polymutt2 , Beagle4 consistently outperformed GATK and Polymutt in terms of both FNR and FDR for all pedigrees and sequencing coverage ( Fig 2 ) . It is worth noting that although Beagle4 does not explicitly model family inheritance the algorithm is able to leverage the IBD sharing implicitly so that the genotype accuracy is improved for pedigrees with more IBD sharing . For example , the error rates of Beagle4 calls in Nuc6 are lower than those in Nuc4 calls ( Fig 2 ) . For all algorithms it is clear that sequencing coverage is the key factor influencing the calling accuracy ( Fig 2 ) , and for coverage of 30X the genotype calls are accurate to an extent that the differences among all callers become noncritical ( Fig 2C and 2F ) . In the following sections we only presented results on 10X and 20X simulated data representing intermediate sequencing coverage to investigate the gain of explicit modeling of IBD sharing for genotype calling in such settings . We next investigated the accuracy of the heterozygous genotypes , which are of particular interest for rare variants . S1 Fig shows the error rates for various pedigrees at different coverage . Consistent with the accuracy of overall genotypes ( Fig 2 ) , Polymutt2 and Beagle4 dramatically reduce error rates across all pedigrees and coverage , compared to both GATK and Polymutt , and the reduction is more dramatic when more related individuals are sequenced ( S1 Fig ) . For example , the FNR at 10X is 1% and 0 . 8% for Polymutt2 and Beagle4 respectively , and is increased to 1 . 8% for Polymutt and 4 . 4% for GATK ( S1A Fig ) . The same magnitudes were observed for FDR at 10X ( S1D Fig ) . Consistent with the overall genotypes , both Polymutt2 and Beagle4 achieved better accuracy for pedigrees with more IBD sharing ( S1 Fig ) . Polymutt2 outperformed Beagle4 on pedigrees of Sib6 and Nuc6 due to explicit modeling of the extensive IBD sharing in such pedigrees ( S1 Fig ) . The major interest of sequencing studies , especially in family designs , is to identify rare variants associated with disease . Accurate heterozygote calling is of particular importance due to the challenges associated with rare variant inference from sequencing . We specifically investigated the heterozygote accuracy across different bins of alternative allele frequencies , in the range of ( 0 , 0 . 01] , ( 0 . 01 , 0 . 02] , ( 0 . 02 , 0 . 05] and ( 0 . 05 , 0 . 1] . Fig 3 shows the FNR of heterozygotes for sequencing coverage of 20X and S2 Fig shows the corresponding FDR measurements . It is clear that Polymutt2 achieved superior accuracy compared to others , and for all pedigrees except Sib2 Polymutt2 has lowest error rates in terms of both FNR and FDR across all bins for variants with frequency below 0 . 1 ( Fig 3 and S2 Fig ) . For Sib2 , which is the simplest pedigree with limited IBD sharing , although Beagle4 achieved better accuracy on overall genotypes and heterozygotes ( Fig 2 and S1 Fig ) , Polymutt2 outperformed Beagle4 for variants with frequencies below 0 . 05 ( Fig 3A and S2 Fig ) . Consistent with overall genotypes , the advantage of Polymutt2 increases for pedigrees with more IBD sharing ( Fig 3 and S2 Fig ) . To investigate the effect of increasing numbers of sequenced families on rare variant calling , we simulated additional 50 and 100 Nuc6 families at 10X coverage and carried out genotype calling for both Polymutt2 and Beagle4 . It is evident that the accuracy of Beagle4 heterozygous calls improves with increasing numbers of families for variants with MAF<0 . 02 ( Fig 4 ) . The improvements , however , do not seem to be dramatic , probably due to the limited LD among rare variants even for data with 100 families . In comparison , Polymutt2 achieved superior accuracy than Beagle4 for heterozygotes with MAF<0 . 02 , for both FNR and FDR ( Fig 4 ) , indicating the advantages of Polymutt2 over Beagle4 for calling rare variants . The genotype accuracy of phased genotypes ( haplotypes ) is similar to the unphased genotypes , although on average the error rates are slightly higher for phased genotypes . For example , for Nuc4 pedigrees at 15X , the FNR of overall genotypes is 0 . 30% for phased genotypes and is 0 . 28% for unphased genotypes; the corresponding FDR is 0 . 09% and 0 . 10% respectively . For heterozygotes , the FNR is 0 . 53% and 0 . 51% for phased and unphased genotypes respectively , with corresponding FDR values being 0 . 10% and 0 . 08% . We calculated the Mendelian inconsistency ( MI ) rate as the percentage of parent-offspring trios in which the genotypes violate the Mendelian transmission law . Pedigrees were divided into individual trios for the calculation . We used the minimum GQ of the genotypes in a trio as the filtering criteria to calculate MI rates on relatively high quality genotype calls . When either GQ 5 or 10 was used , both GATK and Beagle4 calls showed considerable Mendelian inconsistencies across various sequencing coverage ( Fig 5 ) . For example , at minimum GQ of 5 , the MI rate is 0 . 76% for GATK at 10X , and 0 . 15% when coverage was increased to 20X . Although Beagle4 achieved reduced MI rates than GATK , there are still noticeable Mendelian inconsistencies in Beagle4 calls ( Fig 5 ) . When the minimum GQ of 10 was used there are still appreciable Mendelian inconsistencies in both GATK and Beagle4 calls ( Fig 5 ) . On the other hand , the MI rates for Polymutt were extremely low , e . g . <10–6 for all scenarios shown above , consistent with the previous report [12] . Strikingly , no Mendelian inconsistencies were observed in Polymutt2 calls in the same settings . We downloaded the whole genome sequencing data in CEPH pedigree 1463 generated on the Illumina HiSeq platform ( http://www . illumina . com/platinumgenomes/ ) . We selected a Nuc6 sub-pedigree for the analysis , which consists of four siblings ( NA12879 , NA12880 , NA12881 and NA12882 ) and their parents ( NA12877 and NA12878 ) . The sequencing coverage of these samples is ~50X . We followed the best-practice procedure for variant calling as we did for simulated data . Since there is only a single family with a few individuals , we used the 1000 Genomes Project reference panel when running Beagle4 ( downloaded from Beagle4 website ) on this pedigree to leverage the extensive LD in the panel . To have a fair comparison of Polymutt2 with Beagle4 , we ran Polymutt2 using the allele frequencies derived from the same reference panel . Based on simulation results it is clear that at high coverage over 30X the accuracy measures of all callers are satisfactory . Our major goal here is to investigate to what extent genotype calls from various callers with a subset of data can recover the original high depth sequencing data . We first created a gold-standard callset from the original high-depth data by taking the consensus of genotype calls from GATK , Polymutt , Polymutt2 and Beagle4; this call set contains genotypes that are agreed by all 4 callers . GATK and Polymutt infer allele frequencies from the sequence data only , and due to the small sample size of the pedigree the estimates are not reliable . Here we focused only on the comparison of Polymutt2 and Beagle4 , two competing methods based on simulated data . Specifically , we randomly extracted 30% and 15% of the reads from the original alignment , corresponding to ~15X and ~7 . 5X of coverage , and carried out variant calling using both Polymutt2 and Beagle4 . For each of the two callers we calculated FNR and FDR using the gold-standard callset . We also compared their performance stratified by allele frequencies , which were calculated based on the same reference panel used in Beagle4 . Since genotype filtering has a strong impact on FNR and FDR , e . g . aggressive filtering results in low FDR and high FNR and vice versa , we calculated the two measurements using GQ values from 3 to 30 and plotted them in FNR-FDR curves to represent genotype accuracy with a wide range of filtering . This is an objective way of comparing genotype accuracy and a curve completely underneath the other indicates consistent high accuracy of genotype calls for all GQ cutoffs in the range of 3 to 30 . First we evaluated the inference of IBD using the full and partial data . Fig 6A shows the IBD of NA12879 and NA12889 across chromosome 1 using full data , and Fig 6B shows the corresponding IBD when 30% data were used . The IBD sharing is very similar using full and partial data with only a few discrepancies ( Fig 6A and 6B ) , indicating the robustness of the inheritance vector inference . For overall genotypes with 30% of the data ( ~15X ) , Polymutt2 calls achieve greater concordance with the gold standard callset than Beagle4 , as manifested by the reduced error rates in the FNR-FDR curves ( Fig 7A ) . When we focused on variants with low frequencies , the advantage of Polymutt2 over Beagle4 is more pronounced ( Fig 7B and 7C ) . For example , with allele frequency <0 . 1 , the FNR-FDR curve of Polymutt2 is more separated from that of Beagle4 , and with allele frequency <0 . 05 we observe further decreasing error rates in Polymutt2 calls than in Beagle4 calls . Interestingly , when 15% of data ( ~7 . 5X ) were used , Beagle4 calls have better overall accuracy than Polymutt2 ( Fig 7D ) , probably due to the increased contribution of LD relative to sequencing data on the genotype calls . However , when we focused on low frequencies variants with allele frequency <0 . 1 and <0 . 05 , Polymutt2 still greatly outperformed Beagle4 ( Fig 7E and 7F ) . When we focused on heterozygotes , Polymutt2 and Beagle4 calls have similar accuracy when all variants were considered with both 30% and 15% of the data ( S3A and S3D Fig ) . When the analyses were carried out on variants with allele frequency <0 . 1 and <0 . 05 , it is clear that Polymutt2 generated more accurate heterozygous calls than Beagle4 ( S3 Fig ) . We observed considerable MI rates in Beagle4 calls with both ~15X and ~7 . 5X data . For example , at ~15X the MI rates are 0 . 14% and 0 . 09% when the minimum GQ was set to 5 and 10 , respectively . The corresponding MI rates at ~7 . 5X are 0 . 15% and 0 . 1% . When we focused on low frequency variants , the MI rates in Beagle4 calls are noticeably increased . For example , for variants with allele frequency <0 . 1 at ~15X , the MI rates are 0 . 27% and 0 . 21% for GQ cutoffs of 5 and 10 , respectively , indicating that increased genotype error rates associated with low allele frequencies in Beagle4 calls resulted in higher MI rates . On the other hand , as a direct comparison , we did not observe Mendelian error in Polymutt2 calls in all of these scenarios investigated , indicating the extremely low Mendelian error rate in Polymutt2 calling in real data .
Sequencing pedigrees has shown its effectiveness in identifying rare variants associated with human disease , and is expected to continue in gene mapping for complex traits in complement to population-based designs . In addition , family designs are not prone to population stratification , which may be more challenging to control for rare variants [29] . In this study we developed a new tool , Polymutt2 , for accurate inference of inheritance vectors and genotype calling for pedigree sequencing data . Through both simulations and application to real data , the new tool achieves markedly improvement of genotype calling accuracy compared to the standard method ( GATK ) and a family-aware algorithm ( Polymutt ) , as well as an LD-based caller ( Beagle4 ) , especially for low frequency variants . The advantages are mainly due to the explicit modeling of the IBD among family members and then the incorporation of the IBD information in genotype calling . This framework efficiently utilizes the relatedness by combining sequencing data from shared haplotypes among all family members across the genome . For the inference of inheritance vectors , which is critical for genotype and haplotype calling , we directly model the sequencing data in an effort to increase the robustness via the incorporation of sequencing error and depth of coverage in the likelihood calculation . Additional increase in performance comes from the careful selection of the scaffold variants in modeling the inheritance vectors . We plan to refine the selection of scaffold variants to further minimize the inadvertent effect of alignment artifacts on the inference of the inheritance vector , e . g . by exploring the alignment files to filter sites with nearby Indels , homopolymers , allelic imbalances , strand and cycle bias , among others . Compared to Polymutt and GATK , Polymutt2 has increased accuracy of genotype calling from all aspects . This is rather unsurprising given that Polymutt2 uses extra information than the other two callers . On the other hand , Polymutt2 and Beagle4 use orthogonal information , i . e . the explicit modeling of IBD sharing in Polymutt2 vs . the utilization of LD among variants in Beagle4 for variant calling . Since the LD ( r2 in this context ) between rare variants and between rare and common variants is low , the effectiveness of LD-based calling for rare variants is reduced . Although for pedigrees with limited IBD sharing ( e . g . sibpairs ) Beagle4 outperformed Polymutt2 when considering all genotypes , Polymutt2 still achieved increased accuracy in calling rare variants . In addition , Mendelian inconsistency in LD-based calls , especially for rare variants , which are usually analyzed in groups , may have inadvertently impact on association analysis since the effect of Mendelian error in individual variants may be aggregated and amplified . As the major focus in sequencing is to identify rare variants we hope that Polymutt2 is useful for gene mapping of rare variants for complex disease . Although most current studies focus on exome sequencing , multiple lines of evidence indicate the need for whole genome sequencing to identify risk factors for complex disease . Given the current cost , it is still not practical to carry out large-scale high coverage whole genome sequencing studies . Our tool makes it feasible for whole genome sequencing of pedigrees with reduced coverage . On the other hand , Polymutt2 is equally effective in targeted sequencing of small genomic regions , such as peaks revealed in linkage analysis , since the inheritance vectors are expected to be reliably inferred by modeling the shallow off-target sequences across the genome . Since the haplotype calling in Polymutt2 is based on inheritance vectors only , the phase cannot be determined for some variants in which parents and offspring are heterozygotes . In such case , the phases are randomly assigned and should not be used without further information . Although LD can be used to phase such variants in trios [30] , the simultaneous modeling of LD and inheritance vectors in complex pedigrees is computationally challenging . On the other hand , this limitation in Polymutt2 has little impact on the analysis since for rare variants , which are the major focus of sequencing studies , such situations are extremely uncommon . Note that for such variants only phasing is affected but the accuracy for both phased and unphased genotypes benefits equally from IBD modeling as other variants . Since the Lander-Green algorithm is the major component for the inference of the inheritance vectors , the computation is linear with respect to the number of variants but can be explosive when pedigrees get large . For a pedigree with f founders and n nonfounders , the possible number of inheritance vectors is 22n . Due to the lack of phasing information of founder alleles , these inheritance vectors are organized into 2f equivalent classes so that only 22n-f inheritance vectors are required to model , a factor of 2f reduction in terms of computation and storage [31] . Furthermore , we implemented the Fast Fourier Transformation in the Lander-Green algorithm [32] , which reduces the computation from O ( N2 ) to O ( NlogN ) in the HMM , where N is the number of inheritance vectors . Even with these speedup techniques , however , the computation can be still very challenging . To further mitigate the problem , we implemented the software using multi-threads so that the computation can be parallelized when possible . The current implementation can handle simple pedigrees efficiently . For example , for sibpairs and sibships of size 4 , and nuclear families of size 4 and 6 , the average time per family using 8 threads for chromosome 1 whole genome sequencing is on the scale of minutes . For sibships of size 6 the time is significant increased and it took over an hour to finish variant calling per family . For pedigree of Ext10 the time is even further increased to over 10 hours to get marginal calls . If computing is limited an option is to use only inheritance vectors with highest posterior probabilities for such pedigrees; for example using the single best inheritance vector the computing is a few minutes . For pedigrees beyond the exact calculation of the likelihoods , Monte Carlo approaches [33–35] are necessary , which is beyond the scope of the current study and will be explored in the future . In the inference of inheritance vectors , we selected the scaffold variants by LD pruning . The results reported in the article were based on the maximum correlation coefficient of R2 = 0 . 2 . We also investigated other thresholds to evaluate the sensitivity of the results to the LD pruning . Specifically we used cutoffs of 0 . 1 and 0 . 5 and observed similar results as 0 . 2 , with the difference below 0 . 01% for most of pedigrees and coverage investigated in Figs 1 and 2 , indicating the robustness of the framework to LD . This robustness makes it flexible to select scaffold makers without comprising the genotype calling accuracy . With the comprehensive catalog generated by the 1000 Genomes Project , identifying known variants in study sample is generally very accurate . However calling novel variants for pedigrees is usually of particular interest . This remains challenging due to potential alignment artifacts . Unannotated structural variants are a major source of alignment artifacts , and when such artifacts do not follow Mendelian transmission laws the variant quality is expected to be dramatically reduced for such sites when the IBD sharing is imposed in the calculation of the likelihood . We believe that Polymutt2 is effective in filtering false novel variant candidates given its efficient use of allele sharing . In our framework the increased accuracy of variant calling is due to the efficient use of the Mendelian inheritance . De novo mutations , however , violate the rule and make the inference of inheritance vector inaccurate . Although it is unlikely to include de novo mutations in scaffold variants , accidental inclusion of such variants makes the results not reliable . To avoid this situation , Polymutt2 internally checks the likelihood of de novo mutations during the calculation and if a strong violation of Mendelian inheritance is detected the algorithm ignores these variants so that the inheritance vectors can be robustly inferred . The current version of Polymutt2 is not designed to call de novo mutations and other methods ( e . g . Polymutt and DeNovoGear [36] ) should be used for that purpose . Our tools were implemented in C++ . The source code and company resources can be downloaded from the authors’ website ( https://medschool . vanderbilt . edu/cgg ) . We hope that our user-friendly software packages are useful to the research community for pedigree sequencing studies to facilitate the identification of rare variants for human disease . | To identify disease variants that occur less frequently in population , sequencing families in which multiple individuals are affected is more powerful due to the enrichment of causal variants . An important step in such studies is to infer individual genotypes from sequencing data . Existing methods do not utilize full familial transmission information and therefore result in reduced accuracy of inferred genotypes . In this study we describe a new method that infers shared genetic materials among family members and then incorporate the shared genomic information in a novel algorithm that can accurately infer genotypes . Our method is particularly advantageous when inferring low frequency variants with fewer sequence data , making it effective in analyzing genome-wide sequence data . We implemented the algorithm in a computationally efficient tool to facilitate cost-effective sequencing in families for identifying disease genetic variants . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Leveraging Identity-by-Descent for Accurate Genotype Inference in Family Sequencing Data |
Faithful DNA replication with correct termination is essential for genome stability and transmission of genetic information . Here we have investigated the potential roles of Topoisomerase II ( Top2 ) and the RecQ helicase Sgs1 during late stages of replication . We find that cells lacking Top2 and Sgs1 ( or Top3 ) display two different characteristics during late S/G2 phase , checkpoint activation and accumulation of asymmetric X-structures , which are both independent of homologous recombination . Our data demonstrate that checkpoint activation is caused by a DNA structure formed at the strongest rDNA replication fork barrier ( RFB ) during replication termination , and consistently , checkpoint activation is dependent on the RFB binding protein , Fob1 . In contrast , asymmetric X-structures are formed independent of Fob1 at less strong rDNA replication fork barriers . However , both checkpoint activation and formation of asymmetric X-structures are sensitive to conditions , which facilitate fork merging and progression of replication forks through replication fork barriers . Our data are consistent with a redundant role of Top2 and Sgs1 together with Top3 ( Sgs1-Top3 ) in replication fork merging at rDNA barriers . At RFB either Top2 or Sgs1-Top3 is essential to prevent formation of a checkpoint activating DNA structure during termination , but at less strong rDNA barriers absence of the enzymes merely delays replication fork merging , causing an accumulation of asymmetric termination structures , which are solved over time .
During DNA replication termination two replication forks coming from opposite directions merge to form two fully replicated sister chromatids . The process is essential for correct transmission of the genetic information to the next generation , but it has so far attracted little attention in eukaryotes . In E . coli , replication terminates in a region defined by two sets of Ter sites bound by the polar terminator protein , Tus . This protein stops replication forks from one direction , but allows free passage of forks from the opposite direction . The Tus-Ter sites are organized so that they form a trap for replication forks , thereby ensuring termination in a region opposite oriC in the circular E . coli genome [1] . Polar replication fork barriers with a function in replication termination have also been identified in yeast . In S . cerevisiae the rDNA locus holds the Replication Fork Barrier sequence ( RFB ) . This barrier binds the Fob1 protein , which mediates polar fork stalling at RFB , resulting in replication termination in this region [2] . In S . pombe polar replication fork barriers are found both at the rDNA and mating type loci , where replication fork arrest occurs at the termination sites TER1-3 and RFP4 [3] and at the Replication Termination Sequence 1 ( RTS1 ) [4] , respectively . At yeast barriers members belonging to the Pif1 family helicases , Rrm3 and Pif1 in S . cerevisiae and Pfh1 in S . pombe , have been demonstrated to play profound roles for fork stalling and fork merging [5 , 6 , 7] . 71 termination regions ( TERs ) have been identified in the S . cerevisiae genome outside the rDNA locus in one of the first large-scale studies performed on this subject in eukaryotes [8] . A common theme to the sequences at the identified TERs was that they contained fork pausing elements and that the Rrm3 protein assisted fork progression through these zones . Furthermore , DNA topoisomerase II located to the TERs during the S and G2/M phases and prevented DNA breaks and genome rearrangements , suggesting that topoisomerase II plays a role to ensure proper replication termination . Over the years several studies have implicated topoisomerase II in the final steps of replication . Early studies of the circular SV40 genome and the yeast-borne 2μ plasmid reported incomplete replication with nascent strands containing smaller or larger gaps upon inhibition of topoisomerase II activity [9 , 10 , 11 , 12] . Based on these studies a model was presented , suggesting that positive supercoiling accumulates between converging forks , leading to a rotation of the replisomes and formation of precatenanes behind the forks . As a consequence , genuine catenanes form following termination in the absence of topoisomerase II activity , since precatenanes are exclusively substrates for this enzyme [13 , 14] . The STR complex , which in S . cerevisiae consists of the Sgs1 RecQ helicase , topoisomerase III ( Top3 ) and the Rmi1 protein , has mainly been studied in relation to its role downstream of homologous recombination ( HR ) [15 , 16 , 17] . Studies have provided evidence that the complex is involved in dissolution of double Holliday Junctions ( dHJ ) in a non-crossover process [18] . In this process Sgs1 is thought to disrupt local annealing between parental and nascent strands , thereby forming hemicatenanes , which can be decatenated by Top3 [16 , 18] . However , based on early results demonstrating an interaction between Sgs1 and topoisomerase II ( Top2 ) , as well as a chromosomal missegregation phenotype of sgs1Δ cells , components of the STR complex have also been proposed to play a role during late stages of replication [19] . In support of this , Marians and co-workers demonstrated that RecQ and topoisomerase III from E . coli in collaboration with the single-stranded DNA-binding protein SSB performed the resolution of a synthetic termination substrate in vitro [20] . The parental duplex DNA region between two stalled replication forks was here unwound by the RecQ helicase , while topoisomerase III simultaneously decatenated the resulting catenated strands , leading to gapped , but untangled termination regions . In line with this , Hickson and co-workers reported that the BLM-TOP3-RMI1-RMI2 complex localized specifically to ultrafine DNA structures , the so-called anaphase bridges , during M phase in human cells . It has been speculated that these bridges represent late replication intermediates that are resolved by the BLM-TOP3-RMI1-RMI2 complex [21 , 22 , 23] . Similarly , Sgs1 and Top3 were found to localize to anaphase bridges in budding yeast [24] . Together , these data indicate that RecQ helicases in concert with topoisomerase III play a role in late stages of replication besides their well-established role in the resolution of recombination structures . Our aim with the present study has been to investigate if S . cerevisiae Top2 and Sgs1-Top3 act redundantly in vivo to ensure faithful replication termination and resolution of replicating chromatids . Our findings demonstrate that they do , and their redundant function in this process is restricted to the rDNA locus .
Replicative stress and perturbations activate the S phase checkpoint pathway , which coordinates replication , repair , and cell cycling [25] . The Rad53 kinase is essential to this pathway and is phosphorylated , when the pathway is activated . If Top2 and the STR complex play redundant functions during final stages of replication , we expect that the absence of Top2 and Sgs1 will cause problems in late S/G2 , which may activate Rad53 . To test this hypothesis we monitored Rad53 phosphorylation in sgs1Δtop2ts cells using the In Situ Autophosphorylation ( ISA ) assay , which takes advantage of the autophosphorylation activity of Rad53 , when it has been primed by upstream kinases [26] . As illustrated in the experimental setup presented in Fig 1A , yeast cells were grown at 25°C , synchronized in the G1 phase of the cell cycle with α-factor and released into the S phase , where samples were withdrawn at different time points and processed for checkpoint analyses . Release was at 34°C , the restrictive temperature for top2ts . In the top2ts mutant , checkpoint activation was seen 120 minutes after release from α-factor ( Fig 1B ) in accordance with the previously identified function of Top2 in chromosome segregation [27 , 28] . Consistent with this , no checkpoint activation was seen in top2ts cells treated with nocodazole , which prevents the cells from entering mitosis by inhibiting microtubule polymerization ( Fig 1C ) . Interestingly , the sgs1Δtop2ts double mutant showed robust checkpoint activation already 60 minutes after release into S phase ( Fig 1B ) . To analyze whether this checkpoint was connected to failure in chromosomal segregation due to lack of Top2 , sgs1Δtop2ts cells were treated with nocodazole . However , the robust checkpoint activation persisted in the presence of nocodazole and was thus independent of chromosome segregation ( Fig 1C ) . As revealed by FACS analyses the observed checkpoint occurred in late S/G2 after bulk DNA synthesis had taken place . Most known functions of Sgs1 are mediated through the STR-complex , where Sgs1 acts in concert with Top3 and Rmi1 . To investigate if this was the case here , we tested a top2tstop3ts strain for checkpoint activation . The top2tstop3ts cells showed robust checkpoint activation 60 minutes after release into S phase ( Fig 1D ) , illustrating that Sgs1 and Top3 are equally important in cells lacking Top2 . Taken together , the data demonstrate that Top2 and components of the STR-complex function in late S/G2 to avoid the accumulation of checkpoint activating structures . Pulsed-field gel electrophoresis ( PFGE ) has earlier been used to demonstrate replication abnormalities in yeast , because DNA structures associated with incompletely replicated chromosomes prevent gel entrance [29 , 30] . We therefore applied this technique to investigate if the checkpoint activating structures observed in sgs1Δtop2ts cells would affect the fate of the individual chromosomes during replication . EtBr stainings of the PFGs revealed a decrease in the intensity of the individual chromosomal bands 40 minutes after α-factor release ( Fig 2A , upper panels ) , which correlated with active replication for all strains as revealed from the FACS profiles . However , after 60 minutes the intensity of the bands had increased , demonstrating completion of replication and that none of the strains experienced any overall defect in replication . However , in the sgs1Δtop2ts cells , chromosome XII ( chr . XII ) , which holds the rDNA locus , never re-entered the gel after replication , as confirmed by Southern blotting with an rDNA specific probe ( Fig 2A , middle panels ) . In contrast , chr . II , which was used as a control for the other chromosomes , re-entered the gel ( Fig 2A , lower panels ) . A quantification of the amount of chr . XII relative to chr . II re-entering the gel is shown in Fig 2B . Thus , although bulk DNA synthesis seems to occur without major problems in sgs1Δtop2ts cells , either Sgs1 or Top2 is required to complete replication of chr . XII . A major part of budding yeast chr . XII is made up of the rDNA locus , consisting of 100–200 repeats of a 9 . 1 kb unit holding the 35S and 5S rRNA genes . Each repeat holds a replication origin ( ARS ) and a replication fork barrier ( RFB ) sequence ( Fig 2C ) , where the latter forms a unidirectional replication fork block , when bound by the Fob1 protein . This block specifically stalls replication forks coming from the direction of the 5S rRNA gene and inhibits head-on collision between replication and transcription of the 35S rRNA gene [31] . Stalling of the leftward moving fork at RFB also ensures that replication terminates within this region . The rDNA locus differentiates chr . XII from the remaining chromosomes and could thus be an obvious cause to the problems observed with this chromosome in sgs1Δtop2ts cells . To address if this was the case and if the underlying cause of the checkpoint activation observed in sgs1Δtop2ts cells was connected to Fob1-mediated unidirectional replication , we investigated the checkpoint response in a sgs1Δtop2tsfob1Δ triple mutant . Interestingly , the checkpoint signal was reduced to background levels in the triple mutant ( Fig 2D ) . Thus , checkpoint activation in sgs1Δtop2ts cells is Fob1-dependent and therefore takes place as a result of events occurring at the rDNA locus . It is well established that excessive amounts of ssDNA coated with the ssDNA binding protein RPA is a signal for checkpoint activation through the Mec1 kinase [32 , 33] . To investigate if the Fob1-dependent checkpoint activation observed in sgs1Δtop2ts cells was related to the formation of DNA structures containing ssDNA , foci analysis were performed with cells having the large subunit of RPA ( Rfa1 ) tagged with CFP and Nop1 ( a marker for the nucleolus [34] ) tagged with RFP ( S1 Fig ) . The results demonstrated that sgs1Δtop2ts cells experienced significantly more RPA foci and thus more ssDNA 60–100 minutes after α-factor release relative to wt cells and single mutants , and most of the foci had a perinucleolar localization ( S1A Fig Furthermore , formation of the ssDNA was Fob1-dependent ( S1B Fig ) . Thus , the Fob1-dependent checkpoint activation observed in sgs1Δtop2ts cells is associated with a Fob1-dependent formation of ssDNA at the rDNA locus . The data indicate that the checkpoint activating DNA-structures observed in sgs1Δtop2ts cells contain regions of ssDNA . To further analyze the nature of the checkpoint activating structures formed in the rDNA in sgs1Δtop2ts cells , we performed Neutral-Neutral two-dimensional ( 2D ) gel electrophoresis with genomic DNA from sgs1Δtop2ts and control strains ( Fig 3A ) . The BglII restriction sites used for generation of a DNA fragment with the RFB site centrally located ( BglIIB ) is shown in Fig 2C , and the migration of the replication structures obtained with this fragment in 2D gels is shown in Fig 3B . 40 minutes after release from α-factor , active rDNA replication occurred in all strains as demonstrated by formation of single and double Y-structures as well as structures generated due to replication fork blockage and convergence at RFB ( Fig 3A ) . 80 minutes after α-factor release replication termination had occurred at most rDNA repeats in wt and single mutants as reflected by the disappearance of the majority of replication intermediates . However , a remarkable accumulation of DNA structures giving rise to a significant X-spike had taken place in sgs1Δtop2ts cells already 60 minutes after α-factor release , which coincided with the timing of checkpoint activation . Notably , the X-spike included the dot from symmetric X-structures representing forks converging at RFB as well as asymmetric X-structures extending the X-spike half way towards the 2N dot ( indicated by the stippled area in Fig 3B ) . Quantification of the X-spike signal relative to the signal from Y-structures demonstrated an increase in the relative amounts of X-spike over time ( Fig 3C ) , although the total amount of replication structures , including the X-structures , had decreased significantly after 100 minutes ( Fig 3A ) . In contrast , sgs1Δtop2ts cells did not show an increase in RFB stalling relative to wt cells as revealed from a quantification of the RFB signal relative to the signal from all Y-structures ( Fig 3D ) . To investigate if X-structure formation was restricted to the area around RFB we analyzed the migration of replication structures obtained in the BglIIA fragment ( see Fig 2C ) covering most of the 35S transcription unit . Asymmetric X-structures were also formed in this fragment with the same timing and Xs/Ys ratio as in the BglIIB fragment ( Fig 3E ) . In contrast , we did not see an accumulation of X-structures in sgs1Δtop2ts cells , when replication structures were analyzed in a fragment outside the rDNA , containing the TER102 site on chr . I [8] ( S2 Fig ) . Thus , lack of Sgs1 and Top2 causes an accumulation of X-structures in late S/G2 , which seems to be restricted to the rDNA locus . Our data demonstrate that sgs1Δtop2ts cells show two strong characteristics , checkpoint activation and formation of X-structures , which are both connected to the rDNA locus . To investigate if the X-structures were the cause of checkpoint activation we took advantage of the Fob1-dependency of checkpoint activation and analyzed replication structures formed in the sgs1Δtop2tsfob1Δ triple mutant by 2D gel electrophoresis ( Fig 4 ) . Interestingly , the X-spike was still present in both BglII fragments . Thus , in contrast to the checkpoint activation ( Fig 2D ) , all X-structures ( except the symmetric X-structure formed due to forks converging at RFB ) are formed independent of Fob1 . Based on this we conclude that asymmetric X-structures are not responsible for checkpoint activation . An investigation of chromosome migration in the sgs1Δtop2tsfob1Δ triple mutant by PFGE furthermore demonstrated that lack of Fob1 in the sgs1Δtop2ts strain was unable to suppress the migration defect of chr . XII ( Fig 4 ) , strongly indicating that the presence of X-structures in the rDNA is responsible for the inability of chr . XII to re-enter the gel after replication . Taken together , our data are most consistent with a formation of two different DNA structures in sgs1Δtop2ts cells , a Fob1-dependent structure , which causes checkpoint activation , and a Fob1-independent structure causing the formation of the X-spike and the migration defect of chr . XII . That checkpoint activation and X-spike formation are caused by different DNA structures was further supported by results obtained from experiments , where we investigated the sensitivity of the different structures to topoisomerase activity . In these experiments we reactivated Top2 in sgs1Δtop2ts cells ( S3B and S3C Fig ) and Top2 and Top3 in top2tstop3ts cells ( S3D and S3E Fig ) either before or after checkpoint activation and X-spike formation ( 25 or 60 minutes after release , respectively ) . We found that checkpoint activation and X-spike formation were inhibited upon early topoisomerase reactivation in both strains . In contrast , checkpoint activation was fully resistant to late reactivation in both strains , whereas X-spike formation was sensitive , showing a small reduction in the Xs/Ys ratio upon Top2 reactivation in sgs1Δtop2ts cells and a significant reduction upon reactivation of both topoisomerases in top2tstop3ts cells . The rDNA locus has previously been demonstrated to be highly recombinogenic with recombination hot spots located close to the RFB [35] . Furthermore , increased recombination activity has been observed within this locus both in sgs1Δ cells and top2ts mutants kept at semi-permissive conditions [29 , 36] . Since HR is visualized as X-structures in 2D gels [37] and since one of the important functions of Sgs1-Top3 is to dissolve dHJs downstream of Rad52-mediated HR [38] , we wanted to investigate the relationship between HR and checkpoint activation as well as formation of X-structures in the sgs1Δtop2ts cells . We therefore deleted RAD52 or RAD51 in the sgs1Δtop2ts strain to investigate if lack of HR would suppress the checkpoint phenotype of sgs1Δtop2ts cells ( Fig 5A and 5B ) . Although single and double mutants with rad52Δ ( Fig 5A and S4 Fig ) or rad51Δ ( Fig 5B ) showed a slight increase in basal checkpoint activation as expected , robust checkpoint activation was observed in the two triple mutants . This demonstrates that the checkpoint activation observed in sgs1Δtop2ts cells occurs independently of HR . Thus , checkpoint activation does not arise due to a HR structure left unresolved in the absence of the Sgs1-Top3 pathway . X-spike generating DNA structures have been observed in several studies both at the rDNA locus [39 , 40 , 41 , 42] and in other chromosomal regions [8 , 42] . To investigate if the X-spike observed in sgs1Δtop2ts cells represents HR structures we investigated replication structures generated in the sgs1Δtop2tsrad52Δ ( Fig 5C ) and sgs1Δtop2tsrad51Δ ( Fig 5D ) triple mutants by 2D gel electrophoresis . In both mutants the X-spike was still present , and it persisted 100 minutes after α-factor release with a relative proportion of Xs to Ys similar to the one obtained in sgs1Δtop2ts cells . The same was true for the X-spike formed in the BglIIA fragment covering most of the 35S transcription unit ( S5 Fig ) . Thus , like checkpoint activation , X-spike formation in sgs1Δtop2ts cells is not caused by unresolved recombination structures . This was further supported by the migration of the structures in 2D gels . Due to branch migration of junctions within HR structures these are expected to form X-spikes that extend with equal intensity over the entire spike , when experiments are performed in the absence of crosslinking agents , which is the case here [8] . In contrast , the X-structures in sgs1Δtop2ts cells only gave rise to signals in the upper half of the spike and often with a punctuate nature of the spike ( see e . g . Figs 4 and 5C ) . By the same token it is unlikely that the X-structures in sgs1Δtop2ts cells represent hemicatenanes , which also form X-spikes in 2D gels [42] . Besides HR structures and hemicatenanes , forks converging during replication termination form X-structures . The X-spike in sgs1Δtop2ts cells could therefore represent termination structures and be indicative of a failure during replication termination . If this is the case converging forks at rDNA replication fork barriers other than RFB should be responsible for the asymmetric X-structures constituting the major part of the X-spike ( indicated by the stippled area in Fig 3B ) and faulty termination at RFB should cause checkpoint activation . Barriers other than RFB have been demonstrated in the rDNA repeat both at ARS and the 5S transcription unit in the BglIIB fragment as well as in the 35S transcription unit in the BglIIA fragment [6] . Dots representing forks stalled at some of these positions were visible both in wt and sgs1Δtop2ts cells ( indicated by arrowheads in Fig 3A–3E ) . If asymmetric X-structures represent forks converging at these barriers and checkpoint activation is a result of faulty termination at RFB we speculated that a general weakening of all barriers would inhibit the accumulation of X-structures and diminish or abolish checkpoint activation . Rrm3 facilitates replication past replication fork barriers and has also been suggested to be involved directly in fork merging [8] . However , Pif1 has been demonstrated to counteract Rrm3 [6] . Therefore , if the DNA structures formed in sgs1Δtop2ts cells would represent termination structures formed at different rDNA barriers we would expect that a deletion of PIF1 should reduce the formation of these structures either by directly facilitating fork merging at the barriers or by reducing fork stalling and thereby termination at these positions . To investigate this we deleted PIF1 and analyzed replication structures generated in the sgs1Δtop2tspif1Δ triple mutant by PFGE ( S6A Fig ) and 2D gel electrophoresis ( Fig 6A and S6B Fig ) to see the implications of a PIF1 deletion for chr . XII migration and formation of asymmetric X-structures , respectively . Interestingly , chr . XII re-entered the PFG after replication in the sgs1Δtop2tspif1Δ triple mutant and only wt levels of X-structures were observed in 2D gels for the triple mutant in both BglII fragments , consistent with X-structures representing termination structures . An alternative explanation for the Pif1 dependency of the asymmetric X-structures could be that sgs1Δtop2ts cells in the presence of Pif1 experience increased fork stalling at the different rDNA barriers , where the stalled forks are processed into X-structures in the mutant . However , we believe this is highly unlikely for several reasons . First , we do not observe increased stalling at RFB in sgs1Δtop2ts cells as expected if the cells in general show increased stalling ( Fig 3D ) . Furthermore , processing of stalled forks into X-structures is expected to require HR , which is not involved ( Fig 5 ) . Finally , we observed an increased Xs/Ys ratio in sgs1Δtop2ts cells ( Fig 3C ) , demonstrating an accumulation of X-structures rather than stalled forks , which indicates that processing of X-structures and not processing of Y-structures becomes the time limiting step in sgs1Δtop2ts cells . Based on this , it seems unlikely that forks stall more often in sgs1Δtop2ts cells than in wt cells . Rather the data suggest that when forks stalled at the different rDNA barriers are met by a fork coming from the opposite direction , fork merging becomes the time limiting step in sgs1Δtop2ts cells , thus resulting in an accumulation of termination X-structures . To investigate , if a PIF1 deletion affected checkpoint activation at RFB as expected if checkpoint activation is a result of faulty termination at this position , we investigated whether or not checkpoint activation occurred in sgs1Δtop2tspif1Δ cells . As seen in Fig 6B checkpoint activation was fully abolished in the triple mutant . Thus , a deletion of either PIF1 or FOB1 inhibits checkpoint activation at RFB , whereas only a deletion of PIF1 eliminates X-spike formation , consistent with a role of Pif1 at all rDNA replication fork barriers . Taken together , the data suggest that lack of Top2 and Sgs1 becomes detrimental during replication termination at the rDNA locus in sgs1Δtop2ts cells . Thus , replication termination at RFB causes checkpoint activation in these cells , which can only be abolished if either Fob1 is fully removed or the barrier is “loosened” as expected in the absence of Pif1 . In contrast , termination at less strong rDNA barriers is merely delayed in sgs1Δtop2ts cells and therefore accompanied by an accumulation of asymmetric termination X-structures , which decrease in amount over time and are sensitive to Top2/Top3 reactivation . Neutral-Alkaline ( N-A ) 2D gels have earlier been used to verify the presence of unsolved termination structures [7] . With this method , X-shaped molecules generated due to termination at RFB are separated from replication forks stalled at RFB in the first dimension due to differences in their molecular weight . After migration in the second dimension , where denaturation will separate DNA strands , both termination structures at RFB and forks stalled at RFB will consist of full length template strands as well as nascent strands of approximately half the size ( Fig 7A ) . The template strands will thus form two dots located on the same horizontal line and the nascent strands will form two dots on a line below . Termination structures generated at positions other than RFB will form asymmetric X-structures . The template strands from these molecules will locate on the upper horizontal line , extending from the dot representing structures terminating at RFB towards the 2N dot . Besides full length parental strands , each asymmetric termination structure contains nascent strands of two sizes , which together make up the size of the parental strand . These strands will therefore in the second dimension form a “<” with legs emanating from the dot representing nascent strands from termination at RFB ( Fig 7A , indicated by thick grey lines ) . Hemicatenanes and HR structures also form asymmetric Xs , but in contrast to termination structures both parental and nascent strands are full length and will locate on the upper horizontal line . When DNA from sgs1Δtop2ts cells was analyzed with this method , spots representing nascent strands from replication forks blocked at RFB ( black arrowhead ) as well as nascent strands from X-shaped structures representing forks converging at RFB ( open arrowhead ) were revealed ( Fig 7B , second row ) . The emergence of these spots 40 minutes after α-factor release correlated with the appearance of corresponding spots in wt cells ( Fig 7B , first row ) in agreement with the observations from Neutral-Neutral 2D gels ( Fig 3 ) . However , whereas the spots representing forks converging at RFB had disappeared to background levels in wt cells 80 minutes after release , they remained at a higher level in sgs1Δtop2ts cells ( Fig 7C ) , although with a more diffuse appearance , strongly suggesting that termination at RFB was faulty . Furthermore , a smear extending as a “<” from the termination dot at RFB was present in wt and sgs1Δtop2ts cells 60 minutes after release , which remained in the mutant but disappeared in wt ( Fig 7D ) . The presence of DNA fragments causing the “<”-smear shows that termination structures are formed at positions other than RFB in the rDNA and they remain for a prolonged time in sgs1Δtop2ts cells . In agreement with this , analysis of DNA isolated from the sgs1Δtop2tspif1Δ and sgs1Δtop2tsfob1Δ triple mutants with this method demonstrated that the “<”-smear was absent in sgs1Δtop2tspif1Δ cells as expected , but still present in sgs1Δtop2tsfob1Δ cells ( except for the dots representing fork merging and stalling at RFB ) ( Fig 7B , third and fourth row and Fig 7D ) .
In this paper we demonstrate that cells lacking Top2 and Sgs1-Top3 show two strong characteristics , checkpoint activation and X-spike formation . Both are connected to the rDNA locus , appear during late stages of replication , and are independent of HR . Interestingly checkpoint activation is Fob1-dependent , whereas X-spike formation is not . In contrast , structures responsible for X-spike formation are sensitive to Top2/Top3 reactivation , whereas those responsible for checkpoint activation are not . Thus , two different structures are formed in sgs1Δtop2ts cells . This could either be due to a redundant function of Top2 and Sgs1-Top3 in two different processes or in a single process , having two different structural outcomes , when the enzymes are absent . Our results strongly suggest that it is the latter situation that is occurring , and that the process in which Top2 and Sgs1-Top3 are involved is replication termination . Thus , checkpoint activation occurs due to lack of Top2 and Sgs1-Top3 during replication termination at RFB , whereas X-spike formation is caused by a delay in replication termination at rDNA barriers other than RFB . The results raise several questions . First , why do sgs1Δtop2ts cells have problems during replication termination and why do these cause checkpoint activation at RFB and only a delay in termination at other fork barriers ? Furthermore , why do sgs1Δtop2ts cells show termination outside the normal RFB termination zone ? The finding that checkpoint activation occurs during termination at the strongest rDNA barrier whereas termination is merely delayed at other rDNA barriers when cells lack both Top2 and Sgs1-Top3 , suggests that the problem experienced in the cells during termination is related to the nature of the barrier as well as to DNA topology . When a moving fork approaches a fork stalled at a barrier the topological tension between the forks increases and eventually leads to the formation of precatenanes behind the fork [13 , 14] . Several results have strongly suggested that the individual rDNA repeats are anchored to the nuclear membrane at RFB due to Fob1 interactions [43 , 44 , 45] . This fixation has been suggested to impose mobility constraints to the rDNA , and thus further increases the topological tension generated when forks converge at RFB . The structural consequence of the increased topological tension at RFB is speculative , but a checkpoint activating structure is finally formed , where either Top2 or Sgs1-Top3 can prevent formation of this structure as well as a deletion of either FOB1 or PIF1 . In this topologically tense region we propose that Top2’s role is to continuously decatenate precatenanes formed in the termination zone behind the replication forks . Top2-mediated decatenation will directly influence replication fork progression . However , the Fob1- and Pif1-dependency of checkpoint activation suggests that Top2 activity furthermore facilitates Rrm3-mediated Fob1 removal/fork merging as has been suggested earlier [8] . Our data demonstrate that Sgs1-Top3 work redundantly with Top2 in this process . In support of this , Rrm3 has been demonstrated to be synthetic lethal with Sgs1 and Top3 [46 , 47] . An obvious role of Sgs1 and Top3 would be to unwind and decatenate , respectively , the DNA between the two converging forks . In support of this the E . coli homologs of Sgs1 and Top3 have been demonstrated to perform this reaction in vitro , when acting on a DNA substrate holding two closely located forks , thus mimicking a late replication structure [20] . Cozzarelli’s lab has earlier demonstrated that the excess topological tension generated between replication forks promotes the formation of chickenfoot structures [48] . An equilibrium may thus exist between formation of precatenanes and chickenfoot structures . Another function of Sgs1-Top3 could therefore be to constantly revert chickenfoot structures to ensure replication fork progression and facilitate Rrm3-mediated Fob1 removal/fork merging together with Top2 . In sgs1Δtop2ts cells the equilibrium may be shifted towards the formation of chickenfoot structures . We observed that ssDNA was generated at the rDNA locus in a Fob1-dependent manner with the same timing as checkpoint activation . If chickenfoot structures are the cause of checkpoint activation they could be subject to DNA end resection , generating a ssDNA overhang , which recruits RPA and mediates checkpoint activation through Mec1 . We would not be able to discern these resected strands in N-A 2D gels due to the smear produced by replication termination at rDNA barriers other than RFB . The two suggested roles for Sgs1/Top3 are not mutually exclusive . At the less strong barriers we expect that the topological tension generated when forks converge in the absence of Top2 and Sgs1-Top3 is allowed to slowly dissipate to more remote areas . This may be possible either because no anchorage is present to inhibit dissipation at these barriers or because the barriers are of a more transient nature . If the topological tension is lower than at RFB , chickenfoot structures may not be generated to an extent , where the amount of ssDNA exceeds the threshold required to trigger checkpoint activation . Rather , replication fork merging is merely delayed , causing an accumulation of asymmetric X-structures , which await dispersal of topological tension for final termination to take place . In correlation with this , the X-structures decreased in amount over time and were sensitive to late reactivation of Top2/Top3 . Furthermore , X-structures were not visible in pif1Δ cells , indicating that the topological tension is easier to deal with when the barrier is more “loose” . This observation furthermore supports that the function of Top2 and Sgs1-Top3 also at the less strong rDNA barriers is to facilitate Rrm3-mediated barrier removal/fork merging . If asymmetric X-structures in sgs1Δtop2ts cells represent termination structures , this means that termination to a great extent occurs outside the general RFB termination zone and thus that some replication forks pass RFB and are trapped at other barriers . Barriers other than RFB have been demonstrated in the rDNA including the 35S and 5S transcription units and the ARS element [6] , where the nature of these barriers is unclear . Fork arrest was observed at some of these positions in wt as well as in the single and double mutants in the present study ( Fig 3 , arrowheads ) . Based on the migration of the asymmetric termination X-structures in 2D gels it seems as if mainly the transcription units act as fork barriers in sgs1Δtop2ts cells besides RFB . At these positions the barrier effect may be caused by collision of the fork with the transcription apparatus or with topological tension generated in excess in these regions due to lack of Top2 [49] . Besides the demonstration that barriers other than RFB exist in the rDNA , it has also been demonstrated that not all leftward moving forks are arrested at RFB despite the general unidirectional replication mode at the rDNA locus [50 , 51] . Thus , relative to wt cells rrm3Δ cells show increased fork arrest both at RFB and at the other rDNA barriers , demonstrating that passage through RFB to some extent occurs in wt cells . RFB escape has also been demonstrated in pif1Δ cells , where the fraction of DNA in leftward moving forks was increased 2 . 5-fold relative to wt cells [6] . Very interestingly , when these observations are taken into account , the increased termination we see in sgs1Δtop2ts cells at barriers other than RFB indicate that termination in general occurs outside RFB in wt cells , but only in sgs1Δtop2ts cells is termination at these positions delayed , resulting in the accumulation of termination X-structures . In support of this , the observation that the relative level of X-structures to Y-structures was very high and remained high in sgs1Δtop2ts cells suggests that termination rather than fork stalling becomes the rate limiting step in these cells . Furthermore , sgs1Δtop2ts cells showed no sign of increased fork stalling . Our data strongly suggests that Sgs1-Top3 becomes essential for replication termination when Top2 is absent , but that this redundant action of Top2 and Sgs1-Top3 is restricted to the rDNA locus . In sgs1Δtop2tsfob1Δ cells , where the anchoring to the nuclear membrane of Fob1 bound rDNA repeats as well as unidirectional replication are abolished the situation is expected to mimic the situation outside the rDNA locus . However , under these conditions asymmetric termination X-structures were still observed in the rDNA , whereas we saw no accumulation of X-structures in sgs1Δtop2ts cells , when analyzing replication structures at TER102 ( S4 Fig ) in correlation with earlier observations , where Top2 , but not Top3 , was found at termination sites outside the rDNA locus [8] . One explanation for this difference could be the high transcriptional activity at the rDNA locus . Transcription will increase the topological tension at the rDNA barriers which could create a need for Sgs1-Top3 besides Top2 and Rrm3 for efficient barrier dispersal/fork merging . Furthermore , it may well be that multiple copies are required to induce the robust cellular response observed in the rDNA . Thus , Top2 and Sgs1-Top3 may still play a role during termination outside the rDNA , but lack of Top2 and Sgs1-Top3 could cause less pronounced effects , which would be able to escape the detection limits of the assays employed in the present study .
The employed strains were constructed using standard genetic techniques and are listed in S2 Table . All strains are derivatives of the original W303-1a . Unless otherwise stated , cells were grown to logarithmic phase in YPD media . Synchronization in G1 was achieved by transferring cells to YPD ( pH 5 . 0 ) containing α-factor ( 2 μg/ml , Lipal Biochem ) followed by incubation at 25°C for 150 min . Additional α-factor ( 1 μg/ml ) was added after 1 hour of incubation to avoid escape from G1 . To release the cells from arrest , they were washed once in water and transferred to fresh , pre-warmed ( 34°C ) YPD medium . G2 arrest was achieved by adding nocodazole ( Calbiochem ) to a final concentration of 15 μg/ml . Pulsed-field gel electrophoresis was performed as described in [52] . Cell cultures were grown to 3 x 107 cells/ml , and approximately 1 . 8 x 107 cells were cast into each plug to be run on the pulsed-field gel . The standard yeast genome size marker ( Bio-Rad ) was included on all gels . Gels were stained with ethidium bromide and transferred to Hybond XL membrane ( GE Healthcare ) . Southern blotting was carried out using probes for chr . XII ( Probe 1 ) and chr . II . Probes were amplified from purified yeast genomic DNA using 5’-CGCTTACCGAATTCTGCTTC and 5’-CTAGCATTCAAGGTCCCATT as forward and reverse primers , respectively , for chr . XII ( Probe 1 ) and 5’-TCTCCGTCTTTAGTTGTTGC and 5’-GCCCTAGCAGTATTGCTTTG as forward and reverse primers , respectively , for chr . II . Experiments were performed 3–4 times with similar results . Samples were taken for FACS analysis during the various experiments and processed as described in [53] . Samples were analyzed in a BD FACSCalibur . All steps of the ISA were as described in [26] , except that 5 μCi/ml [γ-32P] ATP was used . In short , protein extracts were generated from TCA-treated cells . For every sample , protein concentration was determined by Coomassie blue to allow loading of equal amounts of proteins on 10% SDS-polyacrylamide gels along with 5μl of a standard containing a known amount of MMS activated Rad53 ( “+ control” ) . After gel electrophoresis proteins were transferred to PVDF filters ( Immobilon-P , Millipore membranes ) . Filters were subjected to a denaturation/renaturation protocol before the autophosphorylation reaction was performed by incubating membranes in kinase buffer in the presence of [γ-32P] ATP . Dried filters were exposed on a Typhoon Trio+ . After exposure , filters were re-probed with goat anti-Mcm2 ( Santa Cruz ) to check loading and allow comparison among different gels and mutants . Experiments were performed 2–3 times with similar results . MMS control ( “+ control” ) : An Ay-120 culture ( wt ) with a density of 0 . 4 x 107 cells/ml was treated with 0 . 1% MMS for ~60 minutes and harvested . Yeast genomic DNA was isolated from 1 x 109 cells using Genomic-tip 20/G ( QIAGEN ) as described in [54] . After digestion with the BglII restriction enzyme ( New England Biolabs ) half of the purified DNA was subjected to Neutral-Neutral two-dimensional gel analysis as described in [55] . Southern blotting was carried out with the probes shown in Fig 2C , which were generated by PCR using genomic DNA as template . Probe 1 was generated as described above . For probe 2 , recognizing the BglIIA fragment 5’-GTTTCTTTTCCTCCGCTT-3’ and 5’-ATCTCTTGGTTCTCGCAT-3’ were used as forward and reverse primers , respectively . For the probe near TER102 on chr . I 5’-GAAGGTTCAACATCAATTGATTGATTCTGCCGCCATGATC-3’ and 5’- GCTTCCCTAGAACCTTCTTATGTTTTACATGCGCTGGGTA-3’ were used as forward and reverse primers , respectively . For Neutral-Alkaline two-dimensional gel electrophoresis BglII digested DNA was run on a Neutral gel in the first dimension . In the second dimension the excised DNA was run on a 1 . 5% agarose gel in 50 mM NaOH plus 1 mM EDTA at 4°C [7] . Probe 3 ( Fig 2C ) used for southern blotting was made by PCR with 5’-CAGCCATAAGACCCCATC-3’ and 5’-GCAGTTGGACGTGGGTTA-3’ as forward and reverse primers , respectively , and genomic DNA as template . The intensity of DNA structures was measured using QuantityOne software . For Neutral-Neutral 2D gels the relationship between either X-structures and Y-structures or the RFB dot and Y-structures was calculated for each time point . Unless otherwise stated , the ratio at the different time points was related to the ratio at the 0 minute time point to allow comparison between strains . For the Neutral-Alkaline 2D gels the signal of the RFB dot or the “<”-smear at the different time points was related to the signal at the 0 minute time point . For the PFG’s the ratio between chr . XII and chr . II re-entering the gel was calculated for each time point . All strains harbored the pWJ1321 plasmid encoding Nop1-RFP and were therefore grown in synthetic complete media without histidine [56] . Cells were synchronized in G1 by treatment with α-factor for 150 minutes and released into SC-his medium supplemented with 100 μg/ml adenine at 37°C . Cell samples were collected , centrifuged at 2 , 000g and prepared for fluorescence microscopy as described in [57] . Fluorophores were visualized using band-pass CFP ( 31044 ) and RFP ( 41002c ) filter sets from Chroma . Fluorescence images were acquired and processed using Volocity software ( PerkinElmer ) . Statistical probabilities were calculated using Fisher’s exact test ( two-tailed ) . See S1 Table for morphology of cells included in the study . | Replication termination is the final step of the replication process , where the two replication forks converge and finally merge to form fully replicated sister chromatids . During this process topological strain in the form of DNA overwinding is generated between forks , and if not removed this strain will inhibit replication of the remaining DNA and thus faithful termination . In this study , we demonstrate that the cell has two redundant pathways to overcome topological problems during rDNA replication termination , one involving Top2 and the other involving the RecQ helicase Sgs1 , in concert with Top3 . In the absence of both pathways a checkpoint is activated in late S/G2 phase due to faulty replication termination at the strongest rDNA replication fork barrier ( RFB ) . At less strong barriers termination is merely delayed under these conditions resulting in an accumulation of termination X-structures , which are solved over time . | [
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] | [] | 2015 | Top2 and Sgs1-Top3 Act Redundantly to Ensure rDNA Replication Termination |
Substantial experimental evidence suggests the cerebellum is involved in calibrating sensorimotor maps . Consistent with this involvement is the well-known , but little understood , massive cerebellar projection to maps in the superior colliculus . Map calibration would be a significant new role for the cerebellum given the ubiquity of map representations in the brain , but how it could perform such a task is unclear . Here we investigated a dynamic method for map calibration , based on electrophysiological recordings from the superior colliculus , that used a standard adaptive-filter cerebellar model . The method proved effective for complex distortions of both unimodal and bimodal maps , and also for predictive map-based tracking of moving targets . These results provide the first computational evidence for a novel role for the cerebellum in dynamic sensorimotor map calibration , of potential importance for coordinate alignment during ongoing motor control , and for map calibration in future biomimetic systems . This computational evidence also provides testable experimental predictions concerning the role of the connections between cerebellum and superior colliculus in previously observed dynamic coordinate transformations .
Evidence for cerebellar involvement in map calibration comes from studies of prism adaption in primates [1 , 2] and cerebellar patients [3–6] , and from measurements of human brain activity during adaptation [7 , 8] . This evidence suggests that "the cerebellum is particularly involved in the realignment process that is necessary to re-establish a correct spatial mapping among visuo-motor and sensorimotor coordinate systems" ( [7] , p . 176 ) . Given the ubiquity of map representations in the brain , such involvement represents a very significant new role for the cerebellum . However , although computational studies have indicated how the cerebellum could form internal models of a wide variety of dynamic processes [9–11] , it is unclear how these ideas could be applied to the problem of calibrating maps . One possible mechanism for map calibration is suggested by electrophysiological studies of collicular maps that are used to guide orienting movements . These maps receive information about target location from multiple modalities [12] , and issue motor commands to eyes , head and body depending on the species [13] . In primates and humans the superior colliculus primarily controls saccades that bring the target onto the fovea , and these saccades can be artificially miscalibrated by allowing the target to move during the saccade itself [14] . Accuracy can be relearnt , a process termed saccadic adaptation , provided the relevant region of the cerebellum is intact [15] . Current evidence suggests that the cerebellum can act both downstream of collicular maps , and on the maps themselves [16] , consistent with the massive reciprocal connections between the cerebellum and the superior colliculus [17] . In the case of maps combining visual and auditory information , a problem arises when the eyes do not look straight ahead , since the head-based auditory coordinate frame becomes misaligned with the visual coordinate frame . Recordings from primate superior colliculus indicate that auditory receptive fields are appropriately altered by information about the position of the eyes in the orbit [18] . Similar results were obtained for a combined visual and somatosensory map , when the task was to saccade to a tactile signal delivered to the hand [19] . These results suggest that the superior colliculus receives map-calibration signals that can vary dynamically on a trial-by trial basis . We therefore investigated whether such signals could be in principle be generated by current computational models of the cerebellum . We used as a basic framework the standard ‘chip’ metaphor of cerebellar function , which has been employed to represent the combination of a homogeneous cerebellar cortical microcircuit with individual microzones having unique external connections [11 , 20] . In this framework we constrained the model by requiring the cerebellar microcircuit to be represented in a familiar form , so that the novel feature was the architecture connecting cerebellum and superior colliculus . The familiar form we chose was the basic adaptive filter model of the cerebellar microcircuit [21] , a development of the original Marr-Albus theoretical framework that uses the covariance rule to implement the least mean square learning rule for time-varying input signals . This model has been used successfully in a wide variety of sensorimotor contexts [22] , and here we investigated whether it could be applied without change to the very different computational problem of calibrating a topographic map driving an orienting response . We determined whether the model was capable of acquiring two competencies , first correcting a unimodal map that has become distorted and secondly resolving mismatches between modalities in a multimodal map . In addition , since the algorithm we chose naturally results in maps which are predictive , we examined how the cerebellum could be used to calibrate prediction of the future position on the map of a moving target . This competence has been demonstrated for the auditory tectal map in the owl [23] and a colliculus-related map in cat [24] , and is consistent with the demonstrated role of the superior colliculus or optic tectum in prey catching in a number of species [25–29] .
Fig 1 shows in schematic form the architecture for calibration of a unimodal sensory map , in which the adaptive filter learns to produce dynamic modulating inputs to the map that increase its accuracy . The cerebellar cortical microcircuit is modelled as an adaptive filter [21 , 22] . This uses a systems level interpretation , in which each cerebellar microzone has two inputs ( climbing fibre , mossy fibre ) and a single Purkinje cell output . Such a model has previously been applied in a range of sensorimotor contexts [22] . We keep the same model hardwiring ( as described below and in Fig 1A and 1B ) to determine if it can still be applied in the very different context of map calibration . A simplified version of the microcircuit is shown in Fig 1A , in which the mossy-fibre inputs u are recoded in the granular layer to produce parallel-fibre signals pj . These signals influence the simple spike firing z of Purkinje cells via the synapses w . Purkinje cells also a receive a climbing-fibre input e . In the adaptive-filter interpretation of this circuit ( Fig 1B ) processing in the granular layer is represented by a set of fixed filters G1 … GN whose outputs p1 … pN are weighted by w1 … wN where the weights correspond to the efficacies of the synapses between parallel fibres and Purkinje cells . Purkinje cells linearly sum the weighted parallel-fibre signals to produce their simple-spike output z = Σwipi . The climbing-fibre input e acts as a teaching or error signal that alters the weights w1 … wN using the covariance learning rule 𝛿wi = -β<epi> , which corresponds to the Least Mean Square learning rule [30] . In this form of supervised learning the weights are altered until correlations between presynaptic inputs and output error are removed [30 , 31] , hence the term decorrelation learning [32] . A compact schematic of the adaptive filter ( Fig 1C ) is used in subsequent diagrams . In the simplest version of the architecture the superior colliculus was represented by a single topographic map ( Fig 1D ) . Target locations xd = ( xd , yd ) are selected from within a two dimensional grid , then transformed into sensor data which is written into the collicular map , modelled as a square grid with each grid point corresponding to a collicular neuron . The sensor data are generated by a linear sensor model sd = Kxd , where K is a 2 x 2 matrix that defines the sensor model and is determined from the sensor scaling , noise level and rotation of target ( e . g . [33] ) . The sensor data are then written into the topographic collicular map to provide a distributed representation of the target location ( Fig 1D ) . Neurons in the map had receptive field centres ( xi , yj ) , so that if only an individual neuron fired , it would produce an orienting response to the real-world location ( xi , yj ) . It assumed here that the map’s connections to the motor system are fixed , and that neuron centres are assumed to be dense enough to code the target location accurately . A 2D elliptical Gaussian function was used to provide the distributed target position which when sent to the motor system generates an orienting response to the estimated position of the target . For an accurate map this corresponds to the actual position of the target xd , thus bringing the target onto the fovea ( in primates ) or the area of the mouth ( in rodents ) . The two components are calculated from the distributed firing rates of the collicular neurons ( details in Materials and Methods ) . The distributed collicular response is also sent to the cerebellum as mossy-fibre input , where it is processed in the granular layer to produce a coarse coded map carried by the parallel fibres ( Fig 1D ) . Coarse coding was used to provide a sparser representation to ensure both an acceptable speed of learning and an acceptable degree of precision . An evenly spaced k by k grid of Gaussian receptive fields , GPn ( where n denotes the nth Gaussian in the k by k grid ) was used to coarse code the topographic map . The activity of each grid point was found by multiplying each Gaussian receptive field by the topographic map activity and summing and normalising . When the collicular map is correctly calibrated , the target positions estimated by the map are accurate , and so are the orienting movements it generates . In the absence of orienting errors the climbing fibres to the cerebellum will not carry any error signals , and the weights between parallel fibres and Purkinje cells will stay fixed . When the collicular map is inaccurate it generates an erroneous estimate xg = ( xg , yg ) of the actual target location xd = ( xd , yd ) so that the resulting orienting movement will be in error ( e = xd−xg ) . This would be foveation error in the case of saccade generation , or a tactile signal provided by micro-vibrissae in the case of rodent prey acquisition . The cerebellum receives a corresponding error signal via climbing fibres , a signal assumed here to be signed and two dimensional , with axes approximately aligned with horizontal and vertical ( x and y directions ) . This error signal is used to adjust the weights of the synapses between parallel fibres and Purkinje cells so that the output to the superior colliculus sent from the cerebellar cortex via the deep cerebellar nuclei biases the collicular map in order to shift the position of peak map activity ( Fig 1D ) . The simplest way for the cerebellum to act on a topographic map is to assume a 2D output z which is fed to all neurons in the map and biases their centre position . That is , for a given sensory map , a cerebellar bias input z to a target neuron with centre x will make it act as though it has centre x+z . In effect cerebellar input ‘slides’ map activity across the map by an amount z = ( δx , δy ) . We therefore assume there are 2 biasing microzones for each sensory map , so that map activity can be shifted independently in 2 dimensions . Using a global map shift is a simplification that can be applied when considering single targets . For multiple targets , different regions of the map are likely to require shifting by different amounts . To achieve this , the map could be split into different regions , calibrated by a separate cerebellar zones . We consider single targets to avoid overcomplicating the problem . We use the notation xa = ( xa , ya ) to denote the adjusted target location xa = xg+z . Subsequent orienting errors are calculated from the shifted estimated location e = xd−xa = ( ex , ey ) ( further details in Fig 1 ) . The bias signal is generated as follows . A weight is associated with each parallel fibre signal . The cerebellar weights to bias the map in the x—and y–directions are learnt from initial values of zero . As indicated above , the learning rule is given by Δwx = −β ex P , Δwy = −β ey P , where ex and ey are the errors , P the coarse coded parallel fibre signals , and β is a learning rate . In the first problem we asked the cerebellar-collicular architecture described above to calibrate a unimodal map ( green grid in Fig 2A , left-hand panel ) that had been distorted as a result of sensor changes ( red grid ) . The nature of the distortion varied with stimulus location , as indicated by the arrows which show the changes to the map that are needed to restore its accuracy . The sensory map after 3000 trials of cerebellar recalibration ( blue dashed grid ) is shown in the centre panel of Fig 2A , and is very substantially restored to its undistorted from . The right hand panel shows the combined learnt weights in the x- and y-directions corresponding to each coarse coded set of parallel fibre signal ( weights initially zero ) . The time course of the recalibration is shown in Fig 2B , which plots the RMS error of the orienting response against number of stimulus presentations . The impact of learning maps with a low quality error signal was also investigated by testing a version of the learning rule that simply used the sign of the error signal . Learning with the full signal ( Fig 2B ) gave RMS errors with mean 0 . 008 over the last 2500–3000 iterations . When the sign of the error was used this was increased to 0 . 015 . However , both signals substantially restored the map to the undistorted form . The model is robust to reductions in the quality of the error signal , even if it is sign only , learning is little affected . The details of dynamic recalibration for a particular target location are illustrated in Fig 2C . The shift needed to restore response accuracy to this location is shown as a red arrow on the collicular map image in the left panel . The coarse-coded , normalised parallel fibre signals generated by the inaccurate target location are shown in the centre panel ( cf . Fig 1D ) . At the start of recalibration , each of the weights of these signals ( corresponding to the efficacy of the corresponding synapses on Purkinje cells ) were zero . After learning the weights had changed to produce a cerebellar output that shifted the map appropriately ( Fig 2C , right-hand panel ) . It is important to emphasise that recalibration by the architecture described above is a dynamic process since the cerebellar bias signal depends on the current target position . This means that , although the whole map receives the same bias signal , the bias signal changes according to the position of the target . The parallel-fibre representation used here contains enough terms to allow affine recalibrations . In general the complexity of possible re-calibrations depends only on the completeness of the parallel-fibre representation , e . g . radial basis function inputs could generate very general calibrations . The superior colliculus has both unimodal and multimodal maps ( e . g . [34] ) . In the example illustrated in Fig 3 , information from a visual and a somatosensory map are combined into a multimodal map that drives the orienting response . If one or both unimodal maps are distorted , the output of the multimodal map produces an inaccurate orienting response . The problem is to use this error information to calibrate all three maps . The architecture used to address this problem ( Fig 4A ) was an extension of that used for calibrating a single map ( Fig 1 ) . For the case of two sensors we assume two sets of PCs , where each set consists of an x- bias and y-bias PC . Writing undistorted sensory data into each map used linear sensor models as before , where the sensory signals were generated from target locations xd by s1d = K_1xd , s2d = K_2xd . Both K_1 and K_2 were set to the same value to simplify the simulation . The sensor data were then written into the topographic collicular map to provide a distributed representation of the target location as previously , using identical 2D elliptical Gaussian functions . The outputs of the unimodal maps were combined to generate the multimodal map using element by element multiplication of the individual multimodal maps , a method that implements Bayes’ rule ( Materials and Methods ) . Copies of the distributed neuronal responses in the unimodal maps were also sent to the cerebellum as parallel-fibre inputs ( Fig 4A ) . Coarse-coded parallel-fibre signals for each map were generated as before , with the same values for the parameters for each set . The total parallel fibre signal P is thus a vector consisting of the values of P1 at each grid point and P2 at each grid point . When one or both maps were distorted , the output of the multimodal map produced an inaccurate orienting response . In the first method tried for calibrating the unimodal maps , this erroneous response was used to bias the unimodal maps , just as in Task 1 where there was only a single map . Application of this simple method revealed a fundamental calibration ambiguity . Since estimated target position is a weighted combination of individual map estimates , multiple sensors can be miscalibrated in such a way that their combined errors cancel on average ( Fig 5A ) . In principle this ambiguity can be resolved if the sensors have varying accuracies , because the relative weightings of different sensors will vary so that cancellation cannot be exact . However , the learning architecture above cannot utilise this information about sensor accuracy , because all sensor calibration modules are trained by the same error signal ( from the combined , single map ) and so any behavioural error is necessarily attributed to all sensors . This generates a credit attribution problem: since any error is attributed to all sensors , a sensor is forced to learn even when it is accurate ( Fig 5B ) . The required teaching signal , calculated theoretically by the method of gradient descent , is target error inversely weighted by sensor accuracy . But even in simple cases this requires detailed information about sensor accuracy to modulate the target error signal , and is therefore biologically implausible . A more plausible solution would use available sensory signals as teaching signals . Map calibration is often regarded as a static , target independent process . The architecture used here , however , implements a dynamic process since the cerebellar bias signal depends on the current target position . This means that , although the whole map receives the same bias signal , the bias signal changes according to the position of the target . This allows position dependent curvilinear recalibration using a single biasing output as illustrated in Figs 1 and 2 . The dynamic formulation turns also leads to a natural implementation of predictive calibration . This is because in the adaptive filter the granular layer is assumed to act as an information processing reservoir , so that the parallel fibres carry information not only about current mossy fibre inputs , but also about the history of those inputs [22] . If we idealise this process by adding further parallel fibre inputs to the biasing microzones which contain the coarse coded map information filtered by leaky integrators at a range of time scales , then , in the presence of delay in either the sensory or motor systems , the adaptive filter learns to predict target position so as to acquire the target accurately . Fig 7A illustrates this predictive architecture and Fig 7B , 7C and 7D show the results when applied to a target which moves along a smooth curve ( Methods ) whose position is both distorted by miscalibration and delayed by sensory processing with respect to the raw sensory input . The algorithm can be seen to successfully reduce mean square acquisition error ( Fig 7C ) , and both remove the distortion and shift the target peak at its predicted position ( Fig 7D ) . There are two time scales involved in the calibration process . Learning the weights ( or corrections ) is relatively slow and takes place over many iterations . Once the weights are learnt then the application of the corrective signals during dynamic behaviours is fast . This is only possible because the target motion is predictable; in effect the cerebellum learns an internal dynamic model of target behaviour and uses it to predict future positions . Fig 7 shows that this internal model is optimally adapted to the statistics of the target behaviour , which in this case were bandpassed white noise trajectories chosen as an example of a stochastic motion with an adjustable level of predictability . If the target trajectory only contains low frequencies then prediction is more accurate and uses a simpler internal model based on fewer filter inputs . When higher frequency components are present the trajectory is less accurately predictable and requires a more complex internal model utilising a larger range of filter time-scales . Similar predictive shifts in target position have been observed experimentally , for example in the map of auditory space found in the optic tectum of the barn owl [23] . The optic tectum is homologous to the mammalian superior colliculus , and is used by the barn owl to generate orienting movements required for prey capture ( Fig 8A ) . If the prey is moving , then the orienting response must be directed to its predicted not current location , requiring a shift in tectal receptive fields . The nature of such shifts in response to horizontal stimulus movement was examined by manipulating the cue used for localising horizontal position , namely interaural time difference ( ITD ) using dichotic presentation of sounds through earphones . Sound presentation corresponding to a stimulus location moving at constant velocity elicited receptive field changes corresponding to predicted location ( Fig 8B , 8C and 8D ) . Consistent with this interpretation , the size of the receptive-field change increases with ( virtual ) stimulus velocity ( Fig 8D ) . The changes are consistent with a predictive time-lead of ~100 msec , which corresponds to the time taken to complete saccadic gaze shifts produced by electrical stimulation of the tectum [37] . The predictive recalibration architecture ( Fig 7A , with simulation parameters provided in the Methods ) was able to reproduce this pattern of changes ( See Fig 1 in [23] for experimental results ) . Here no sensor distortion was applied , so the algorithm just learns to account for the delay between the estimated and actual target location . The time scales of the experimental and simulated results differ , however the simulation is not intended to replicate the experiment , but to demonstrate that the adaptive cerebellar filter is able to explain the behaviour seen . Note that an even better correspondence could be obtained if evidence accumulation was added to the salience map write-mechanism , so that sensor inputs were optimally combined over time in the map . This would result in a tighter bound on target location over time , mimicking the behaviour seen in the experimental data .
Both the cerebellum and superior colliculus have been implicated in saccadic adaptation [14] . The precise nature of that involvement has proved difficult to identify , because saccadic adaptation has turned out to be more complex than it originally appeared , with evidence for different mechanisms being involved depending on whether the adaptation is gain-up or gain-down , short term or long term , or of reactive or voluntary saccades ( e . g . [39 , 40] ) . It appears that sensory remapping is likely to be involved in the gain-up adaptation of reactive saccades , and more generally in the adaptation of voluntary saccades ( e . g . [41] ) . In the former case it seems likely that the altered map is within the superior colliculus [16 , 33] , whereas for the latter spatiotopic cortical maps appear to be implicated [42 , 43] . A possible anatomical basis for dynamical cerebellar remapping of maps in the superior colliculus is the extensive projection from the deep cerebellar nuclei to the superior colliculus [17] . However , little is known about the signals sent by these projections , though it has been suggested they may be “involved in correlating the modality maps within the SC” ( [17] , p . 352 ) . There is evidence for tonic excitatory inputs in anaesthetised rats [44–46] that directly influence collicular sensory cells , and affect movements resembling pursuit , but how that influence works during normal behaviour is not understood . There is good evidence that the cerebellum is involved in the sensory remapping that occurs in prism adaptation [1–5] . The location of the recalibrated maps is however unclear , though event-related FMRI implicates the superior temporal cortex [7] . Adaptation of voluntary saccades has been argued to be similar to prism adaptation [41 , 43] and also appears to involve alterations of maps in higher level frameworks than the retinotopic maps in the superior colliculus . The basic framework for map recalibration proposed here should in principle work for such higher-level maps . A necessary requirement for this is the existence of a recurrent architecture involving cerebral cortex rather than the superior colliculus . Evidence for such an architecture connecting multiple cerebellar and cortical areas has been summarised by Ramnani [47] . Overall , the biological evidence appears to be consistent in broad terms with the map calibration scheme proposed here . The next step is to consider more detailed evidence , that could be provided by testing specific predictions generated from the present results . As mentioned in the Introduction , accurate saccades to auditory targets can be made when the eyes are in an eccentric starting position , causing auditory and visual maps to become misaligned [18] . The scheme investigated here predicts that saccadic accuracy to auditory stimuli in this situation will be severely impaired after selective inactivation of cerebellar inputs to the superior colliculus , or of collicular outputs to the cerebellum . It also predicts that this impairment will be accompanied by a loss of the shift in auditory receptive fields that normally results from change in eye position , again as demonstrated by Jay and Sparks [18] Accurate saccades can also be made to somatosensory targets ( stimulation delivered to the hands which are not visible ) from different starting positions of the eye [19] . We again predict that saccadic accuracy to these somatosensory stimuli under these conditions will be severely impaired after selective inactivation of connections between cerebellum and superior colliculus , and that this impairment will be accompanied by a loss of the shift in somatosensory receptive fields that normally results from change in eye position [19] . Finally , owls are able to capture moving prey , an ability connected with predictive shifts in the receptive field of auditory neurons in the optic tectum [23] . We predict that selective inactivation of connections between the cerebellum and optic tectum will seriously affect the ability to capture moving prey , and abolish the predictive shifts in auditory receptive fields . The recalibration mechanisms investigated here may have application to the generic problems of realigning collicular maps when the body moves that were outlined in the Introduction . In the absence of a recalibrating input auditory and visual maps would become misaligned when the head moves ( e . g . [18] ) , as would tactile and visual maps when the hands move ( e . g . [19] ) . Dynamic recalibration appears to be particularly useful for such problems , and a role for the cerebellum is suggested by consideration of the computational complexities of determining target position in eye-centred coordinates of a tactile target delivered to a hand . “If the stimulus is delivered to the finger , the angles of the finger joints , wrist , elbow , shoulder , neck and eyes must be known … a neural implementation of a multi-dimensional lookup table with indexes for all the intervening joint angles could convert stimulus position from body-centred space to eye-centred space” ( [19] , p . 450 , p . 450 ) . Dynamic coordinate alignment is crucial for motor coordination in multi-jointed animals , and its implementation by the cerebellum could greatly simplify higher-level motor control . One suggestion for future work would be to investigate to what extent the tactile/visual map exemplar could be considered as ( or rephrased as ) an eye-position/retinotopic . Finally , dynamic recalibration might also prove useful for biomimetic control schemes in robotics . The adaptive-filter model of the cerebellum has been applied to a number of robot control problems , including plant compensation [48 , 49] and the reafference problem [50] . Preliminary results suggest that adaptive-filter based dynamic remapping can be utilised with a robotic platform to improve the accuracy of orienting responses [51] . More generally , the dynamic coordinate transformations referred to above are also required for control of multijoint robots , and it is possible the scheme investigated here could be useful in that context .
When the collicular map is not correctly calibrated , the estimated target position xg will differ from the actual location xd , and the orienting movement will be in error ( e = xd−xg = ( ex , ey ) ) . The cerebellum receives a corresponding error signal via climbing fibres , a signal assumed here to be signed and two dimensional with axes approximately aligned with horizontal and vertical ( x and y directions ) , which is used to adjust the weights associated with each parallel fibre signal Δwx=−βexP Eq ( 12 ) Δwy=−βeyP where ex and ey are the error components , P the coarse coded parallel fibre signals , and β is a learning rate here set to 1 . The initial value of the weights was zero . The learnt weights were used to bias the map in the x—and y–directions by generating a cerebellar signal ( δx , δy ) corresponding to the sum of the weighted parallel fibre signals δx=∑wxP Eq ( 13 ) δy=∑wyP The cerebellar bias signal in effect slides map activity across the map by an amount ( δx , δy ) . Sensory maps can be used for the pursuit of moving targets . We therefore examined whether the proposed role of the cerebellum in calibrating a unimodal sensory map using stationary targets ( Fig 1 ) could be extended to pursuit . For moving targets delays in sensory processing ( for example in the retina ) become important , because the map no longer has access to the current target location x ( T+Δ ) ( where Δ is the delay and T the trial number , Fig 7A ) but only to its delayed location x ( T ) . In addition the error signal is no longer the difference between current estimated and actual target locations , but between current estimated location and actual location Δ times steps earlier ( Fig 7A ) . To solve this calibration problem the system must learn to predict future target location , hence the term predictive recalibration . The parallel-fibre signals from map to cerebellum now conveyed temporal information , required for the prediction of target trajectories . The new temporal signals were generated by a bank of fixed temporal filters ( Fig 7A ) . Incorporating fixed filters increases the number of parallel fibre signals and corresponding weights to adjust , but does not change the rest of the algorithm . | The human brain contains a structure known as the cerebellum , which contains a vast number of neurons–around 80% of the total ~90 billion . We believe the cerebellum is involved in learning motor skills , and so is vitally important for accurately controlling the movements of our body , amongst other things . However , like most regions of the brain , we still do not fully understand the role of the cerebellum and evidence for new roles is appearing all the time . One such new role is in the calibration of sensorimotor maps in the brain that link our sensory perception to motor function , such as when a visual stimulus causes a redirect of our gaze . We investigated this problem by connecting a mathematical model of the cerebellar cortical microcircuit to simulated sensory maps in the superior colliculus that are used to control orienting movements . We found the error signal generated by inaccurate orienting movements could be used to accurately calibrate sensorimotor maps , and to allow predictive tracking of moving targets . This finding points to a potentially widespread role for the cerebellum in calibrating the sensorimotor maps that are ubiquitous in the brain and could prove useful in controlling the movements of multi-joint robots . | [
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"filte... | 2019 | Sensorimotor maps can be dynamically calibrated using an adaptive-filter model of the cerebellum |
The phenotypic effect of some single nucleotide polymorphisms ( SNPs ) depends on their parental origin . We present a novel approach to detect parent-of-origin effects ( POEs ) in genome-wide genotype data of unrelated individuals . The method exploits increased phenotypic variance in the heterozygous genotype group relative to the homozygous groups . We applied the method to >56 , 000 unrelated individuals to search for POEs influencing body mass index ( BMI ) . Six lead SNPs were carried forward for replication in five family-based studies ( of ∼4 , 000 trios ) . Two SNPs replicated: the paternal rs2471083-C allele ( located near the imprinted KCNK9 gene ) and the paternal rs3091869-T allele ( located near the SLC2A10 gene ) increased BMI equally ( beta = 0 . 11 ( SD ) , P<0 . 0027 ) compared to the respective maternal alleles . Real-time PCR experiments of lymphoblastoid cell lines from the CEPH families showed that expression of both genes was dependent on parental origin of the SNPs alleles ( P<0 . 01 ) . Our scheme opens new opportunities to exploit GWAS data of unrelated individuals to identify POEs and demonstrates that they play an important role in adult obesity .
The effect of genetic variants on phenotypes may depend upon the parent from whom the variant was inherited [1] , [2] . Parent-of-origin effects ( POEs ) may arise through imprinting; mechanisms of which include cytosine methylation and histone deacetylation [2] . To date around 50 human genes are known to be imprinted and for most mammalian species less than 1% of the genome is confirmed to be imprinted [3] . One plausible explanation for this phenomenon is the parental conflict hypothesis , whereby both parents would like to maximize the influence of their genome on their offspring [4] . Current methods for detecting parent-of-origin effects rely on assigning parental ancestry to the inherited alleles . This is straightforward in linkage studies , which have identified potential POEs on type 2 diabetes , body mass index ( BMI ) [5] , [6] , and alcohol intake [7]–[9] . However , only a very few of these findings have been replicated and the identified linkage peaks often span large chromosomal regions harbouring hundreds of genes , hence the causal gene or regulatory sequence is unknown . A notable exception is the work of Kong et al [1] who inferred parental origin through genealogy information and long-range phasing to subsequently test for POEs . This study identified six SNPs , four associated with risk of type 2 diabetes and the other two associated with each of breast cancer and basal-cell carcinoma . Genome-wide association studies ( GWASs ) of unrelated individuals have very precisely identified a large number of genetic loci harbouring SNPs whose ( alternative ) allele counts associate with common traits . Since GWASs predominantly include unrelated individuals , the parental origin of the alleles cannot be determined , hence genetic effects influenced by the parental origin of the alleles are typically not considered . Here we present a novel approach that is able to detect POEs using genome-wide genotype data of unrelated individuals . We chose BMI as our target trait , due to previous findings [5] , [6] and the large available sample size . We report the discovery of two novel loci affecting BMI in a manner dependent on the parent-of-origin of the transmitted alleles .
The replication stage utilised five family-based studies ( see Tables S1–S3 ) to test for parent-of-origin effects at the six selected SNPs . Only heterozygous individuals are informative when testing for parent-of-origin effects; the number of heterozygous individuals for each of the tested SNPs ranged from 1 , 122 to 4 , 128 ( see Table 2 and Table S4 ) . A simplified parental asymmetry test ( PAT , see Materials and Methods ) was applied and SNPs successfully replicated if their PAT P-values were below 0 . 0083 ( Bonferroni corrected significance threshold for family-wise error of 0 . 05 with six tests ) . Two of these SNPs , rs2471083 [T/C] ( GWAS discovery BMI variance ( het vs . hom ) : 1 . 058 vs 0 . 963 , PPOE = 9 . 34×10−7; replication PAT P = 0 . 00264 ) and rs3091869 [T/C] ( GWAS discovery BMI variance ( het vs . hom ) : 1 . 046 vs 0 . 957 , PPOE = 4 . 7×10−6; replication PAT P = 0 . 00245 ) successfully replicated . In particular , we found that heterozygous individuals who carry the rs2471083-C allele paternally have 0 . 11 ( SD unit ) higher BMI on average than those carrying the C-allele maternally ( P = 0 . 00264 ) . Heterozygous carriers of the paternal rs3091869-C allele have 0 . 11 ( SD unit ) lower BMI on average than those carrying the maternal C-allele ( P = 0 . 00245 ) . Figure 2 shows the locuszoom plots of the POE association P-values for the two replicated loci ( KCNK9 and SLC2A10 ) . By combining the effect difference estimates from the family-based studies and the marginal association effect sizes from the largest-to-date meta-analytic study on BMI [10] , we estimated the effects of the maternal and paternal alleles . For both rs2471083-C and rs3091869-T we obtained and . Using these effect sizes and the population frequency of these SNPs , we calculated the explained variance of these SNPs ( if their parent of origin is known ) to be 0 . 24% and 0 . 30% for rs2471083 and rs3091869 , respectively . These effects are comparable to that of the strongest BMI-associated variant in the FTO gene ( 0 . 34% ) [10] . Notably , rs2471083 is located 105 kb upstream of the imprinted gene KCNK9 . Mutations in this potassium channel gene cause Birk-Barel syndrome , a maternally transmitted syndrome of mental retardation , hypotonia , and unique dysmorphism , resulting from genomic-imprinting [11] . SNPs within 2 kb have been shown to be associated with HDL cholesterol , adiponectin levels [12] and blood pressure [13] . Its impact on hypertension is potentially via a mechanism involving aldosterone , the concentration of which correlates strongly with fat mass . Interestingly , KCNK9 knock-out mice exhibited more fragmented sleep episodes [14] and 7 . 1%–9 . 6% increased weight gain ( P = 0 . 02 ) at 19–20 weeks of age [15] . SNP rs3091869 is 61 kb upstream of SLC2A10 , a glucose transporter involved in arterial morphogenesis . SNPs in low LD ( in CEU r2 = 0 . 05 ) with rs3091869 have been shown to alter body fat distribution [16] . We tested these two confirmed SNPs in 705 trios with paediatric ( extreme ) obese offspring in which parental origin of the alleles was known in up to 255 individuals [17] . No significant effect was observed ( see Table S5 ) . This could be due to insufficient power , different genetic mechanisms between young individuals and adults or that our association is specific to variations within the range of normal BMI . We evaluated whether the parent of origin effect of the rs2471083-T and rs3092611-T ( proxy for rs3091869-T , r2 = 0 . 998 ) alleles can be observed in the expression levels of their respective genes ( KCNK9 or SLC2A10 ) . To test this we carried out quantitative PCR ( qPCR ) experiments using lymphoblastoid cell lines ( LCL ) of the CEPH families . These cell lines have been used extensively to identify imprinted genes [18] , [19] . Using the available trio data we could infer the parental origin of the alleles of rs2471083 and rs3092611 in 33 ( 9 maternal T alleles , 24 paternal T alleles ) and 24 ( 16 maternal T alleles , 8 paternal T alleles ) individuals respectively ( Table S7 ) . We performed between 2 and 10 technical replicates per individual ( mean of 7 . 75 ) and samples with high coefficient of variation ( >5% ) were discarded in order to ensure robustness . After quality control , 124 expression values from 23 ( mat:pat = 4∶19 ) samples for KCNK9 and 240 expression values from 24 ( mat:pat = 16∶8 ) for SLC2A10 were available for analysis . We fitted a linear mixed model to test for association between expression levels ( Ct values ) and allelic origin . The paternal T allele of rs2471083 was associated with lower KCNK9 expression levels ( +1 . 08 [SD unit] Ct values , P = 0 . 0096 ) , and the paternal T allele of rs3092611 was associated with higher SLC2A10 expression values ( −1 . 09 [SD unit] Ct values , P = 0 . 0023 ) . To ensure there was no systematic bias in our experiments giving rise to spurious POE associations we repeated the qPCR experiments for two housekeeping genes GAPDH and HRPT1 . Both analyses gave non-significant POE P-values ( P>0 . 3 ) . POEs can be driven by differences between inherited paternal and maternal methylation . To explore whether the observed parent-of-origin effects at our discovered SNPs were driven by differential methylation we tested whether methylation in the regions ( Chr8: 140 . 45–140 . 65 Mb and Chr20: 45 . 3–45 . 55 Mb ) was ( i ) associated with the two respective SNPs ( rs2471083 , rs3091869 ) in 262 unrelated individuals from the TwinsUK cohort and ( ii ) associated with BMI in two independent cohorts: 79 BMI discordant ( difference >0 . 5 SD ) monozygotic twin pairs from the TwinsUK cohort and a sample of 412 unrelated individuals from the EPIC-Italy cohort . None of these analyses showed significant association ( see Supplemental Data S1 , Figures S2 , S3 and Table S8 for further details ) .
Our novel approach revealed two SNPs , located near the genes KCNK9 and SLC2A10 , influencing BMI in a parent-of-origin specific fashion . These loci were the first and fourth most significant genome-wide in our new POE test for unrelated individuals and both showed significant parent-of-origin effects in family studies . Both SNPs exhibit polar overdominance , where homozygous individuals have equal ( baseline ) phenotypes and heterozygous genotypes confer relative risk/protection , depending on the parental origin . Polar overdominance , has been observed in humans for type2 diabetes [1] and BMI [20] , however it is very rare and its molecular mechanism is unknown . RT-PCR experiments revealed that gene expression levels of KCNK9 and SLC2A10 in LCLs were also influenced in a parent-of-origin manner . The expression of these genes is highest in the brain ( although it is also expressed in testis , liver , colon , adrenal gland and kidney; see http://www . genecards . org/ ) indicating a potential neuronal involvement . Expression levels of KCNK9 and SLC2A10 in living brain cells might have been more informative and robust , however , such information is not available . The applied qPCR method was optimised to ensure that the expression levels measured in LCLs were representative only of the target transcript and amplification efficiency was assessed to be sensitive enough to allow the detection of even small changes in gene expression . Interestingly , rs2471083 alleles , regardless of their parental origin , show marginally significant association ( P = 0 . 03 ) with KCNK9 expression levels in the hippocampus ( http://www . broadinstitute . org/gtex/ ) . Our methylation analyses did not reveal any evidence that the POEs were driven by differences in inherited paternal and maternal methylation . Neither of our two SNPs tag common copy number variants ( CNVs ) ( based on the CNV reference data used in Heid et al . [21] ) and we found only one sample ( out of 14 , 315 available in-house , whose BMI Z-score was +1 . 18 ) with a 76 kb deletion overlapping rs2471083 . Hence , the effect of the two discovered SNPs are unlikely to be driven by CNVs . To check whether the two confirmed SNPs ( rs3091869 , rs2471083 ) , or SNPs in LD ( 10 with r2>0 . 8 in 1000 Genomes EUR population ) , show regulatory activity , we queried RegulomeDB ( http://regulome . stanford . edu ) . None of these SNPs were annotated to have more than minimal binding evidence ( RegulomeDB score below 4 ) . A previous study proposed to detect POE in inbred F2 mice by a two-component mixture distribution fitting of the heterozygous genotype group and further two components for the homozygous groups [22] . This method requires a parametric distribution of the phenotype to be assumed , small violations of this assumption can result in heavily biased parameter estimates . The method we chose is more robust to a wide range of phenotype distributions ( due to the underlying Brown-Forsythe test employed ) , computationally faster ( making it attractive for testing millions of SNPs ) and applicable to probabilistic genotype calls . Our POE test for unrelated GWAS samples is similar to a test proposed to detect gene-environment interactions [23] in that it exploits differences in phenotypic variance to detect a phenomenon not directly measured . Inflated phenotypic variance in the heterozygous group might also be the result of other phenomenon: ( i ) a phenotype altering effect ( be it genetic or environmental ) acting only on the heterozygous group; ( ii ) an overdominant effect combined with a genetic or environmental interaction or non-linear , monotonic phenotype transformation that has different derivatives for low and high trait values; ( iii ) a large marginal additive effect combined with a ( monotonic ) transformation for which the second derivative is maximised at the mean phenotype value of the heterozygous group ( see Materials and Methods for details ) . More generally , the combination of the scale on which the phenotype is measured and a strong marginal association with an allelic dosage may give rise to spurious associations using variance tests [24] . Recently some evidence has emerged about loci which effect the variance of phenotypes ( through impacting environmental plasticity , canalization , developmental stability , etc . ) that can be detected via association with phenotypic variability [25] . Therefore , the top hits obtained by our POE test may need further prioritisation before proceeding to trio-based confirmation . We recommend the following checks: ( a ) Exclude SNPs with overdominant effects; ( b ) For SNPs with low POE P-value , test gene-environment ( GxE ) interaction ( as done in [23] ) via modelling phenotypic variance as a function of the genotype dosage ( coded in additive , recessive or dominant fashion ) . If this test is more significant than the POE test , it is probably a GxE that is driving the POE association and also as a side effect we will observe significant difference in the variance between the two homozygous groups . ( c ) If a SNP with low POE P-value has marginal effect on the trait , repeat the POE test for various transformed versions of the phenotype such as log and inverse-normal quantile . If the resulting POE P-values are not robust , give lower priority to the examined SNP . For our confirmed SNPs multiple lines of evidence show that the parent-of-origin effects are real , most convincingly clear replication in independent family data of parent-of-origin associations of the hit SNPs with both BMI and gene expression levels . Further , the GWAS discovery associations are very unlikely to be artefacts of the factors discussed above: ( i ) there is no evidence of overdominant , additive , recessive or dominant effects ( the mean BMI values are near identical in the three genotype groups ) , hence the signals cannot be driven by gene-environment interactions or be an artefact of the scale on which the phenotype is measured ( ii ) no SNP within 500 kb has any detectable marginal effect on BMI thus the association cannot be driven by haplotype-specific marginal effects [26]; ( iii ) the phenotypic variances in the two homozygous groups , are almost identical ( rs2471083: , , and rs3091869: , , ) ; ( iv ) POE test with log- and inverse-normal quantile transformed BMI values resulted in similar results ( Table S6 ) , further reducing the likelihood of an artefact resulting from the scale on which the phenotype is measured [24] . Some of the negative results of the other SNPs carried forward to the replication phase in the family data could be explained by lack of power . The power to replicate POE associations in family-based studies is dependent on the available number of heterozygous individuals ( for details see Supplemental Data S1 ) and thus increases with minor allele frequency ( MAF ) . Therefore , it is unsurprising that the two SNPs which replicated had relatively high MAF ( >27% ) . Linkage studies have identified four regions exerting POE on BMI ( 10p12 , 12q24 , 13q32 ) [5] and 2q31 [27] ) . We looked up SNPs in these regions in our genome-wide discovery POE association results . The reported linkage regions showed enrichment for lower than expected POE P-values ( see Figure S4 for regional QQ-plots ) , however , no SNPs survived Bonferroni correction . We also tried to replicate a SNP in exon 5 of DLK1 ( rs1802710 ) because this SNP showed polar overdominance for obesity in children [20] , but only a very slight trend ( P = 0 . 32 ) was visible in our study . Previously reported BMI-associated loci [10] show some enrichment for lower POE P-values ( Supplemental Data S1 , Tables S9 , S10 and Figure S5 ) , however these need to be replicated in family studies . Previous work comparing strength of associations of mother-offspring BMI with father-offspring BMI did not reveal intrauterine influence on obesity in children [28] . A similar conclusion was reached in a systematic review of seven studies [29] , while stronger maternal influence was observed in a recent longitudinal study [30] . The difference in conclusions may be due to that fact that the former studies included predominantly older children than the longitudinal study ( 0–3 . 5 years ) . At early age the diet of the offspring may be more similar to that of the mother than the father ( e . g . due to breastfeeding ) , which might have contributed to the higher mother-offspring BMI similarity found by Linabery et al . [30] . In summary , our findings indicate that POEs may play a role in adult obesity . The two identified SNPs have strong parent-of-origin effect on BMI , close to that of the FTO , contributing substantially to the heritability of BMI . Our follow-up experiments demonstrated parent-of-origin specific gene expression modulation , but failed to link methylation activity of these loci to BMI values . Inevitably for newly discovered loci , further studies are warranted to determine how these variations functionally influence obesity in humans . The reliance of our approach on difference in phenotypic variance means that it cannot be extended to binary outcomes . Since there are other phenomena which can give rise to significant POE association , we recommend that top hits from our method are followed up in family studies , where parental origin of alleles can be inferred . In addition , our variance based POE test for GWAS data is naturally much less powerful than actually testing the mean values of the two heterozygous subgroups in trios . However , GWASs of unrelated individuals are several-fold more numerous and typically much larger than studies with a trio design , hence our methodology provides a great advance in parent-of-origin research by providing means to exploit all available GWAS data of unrelated individuals in order to identify parent-of-origin effects on continuous phenotypes .
All participating studies were approved by the respective institutional Ethics Committees . All study participants gave written consent including for genetic studies . If we denote the alleles of a bi-allelic SNP by “A” ( reference ) and “B” ( alternative ) the possible genotypes are A/A , A/B and B/B . Standard GWASs estimate the effect of the alternative allele dosage on the phenotype in question . In this work we are interested in associations in which a phenotype ( y ) is influenced by the alleles of a particular SNP and the effect depends on the parental origin of these alleles . In the presence of a parent-of-origin effect the heterozygous genotype group is split into two subgroups , depending on the parental origin of the A and B alleles . We assume that the phenotype of any individual in the A/A genotype group is modelled by , where is the mean and is an individual level error with mean zero and variance . If the maternal and paternal effects of the B allele are and , it follows that the phenotype of an individual in the B/B group is and its variance is . ( Note that as a consequence the maternal and paternal effects of the A allele are and . ) Here we assume is constant across genotype groups ( A/A , A/B and B/B ) and and are fixed effects . The effects of violations of these assumptions are covered in the discussion . The phenotype in the heterozygous group is a 50%–50% mixture of two distributions ( Fig 1a ) : where is a Bernoulli random variable ( with parameter ½ ) , taking values if the B allele is inherited from the mother and if inherited from the father . The heterozygous phenotype distribution can be simplified to Since and are independent random variables , the phenotypic variance of the heterozygous genotype group is If a parent-of-origin effect is present andare different , thus is larger than the variance observed in the homozygous groups ( ) ( Figure 1 ) . Therefore , although in regular GWAS data we cannot identify the two subgroups within A/B genotypes , we can detect POE via increased phenotypic variance in the heterozygous group relative to the homozygous groups . In the presence of a marginal association a phenotype transformation could alter the genotype group variances and introduce bias into the test [24] . For this reason we analysed untransformed age- , age2-corrected BMI values ( normalised to have zero mean and unit variance ) separately for men and women . Standard variance tests ( such as the F-test ) are , however , sensitive to deviations from the Gaussian distribution . Therefore , we used a robust version of the Brown-Forsythe test . Briefly , we first centred the phenotype values ( at zero ) in each genotype group to avoid inflated variance in the presence of marginal effects in the group of all homozygote individuals . We denote these centred phenotypes by , where Here stands for the genotype of individual , and represents the median phenotype value in genotype group , where can take the values of AA , AB or BB . We then regress the absolute deviations from the median onto a 0–1 coded genotype group identifier ( 1 for heterozygous and 0 for homozygous individuals ) in order to estimate the POE effect size [31] . This regression result in a slope estimatewhere and . The corresponding standard error iswhere Finally , the POE P-value is assigned based on the test statistic . The test was extended to imputed genotype probabilities and implemented in the latest version ( v0 . 98 ) of the Quicktest software ( http://www3 . unil . ch/wpmu/sgg/quicktest/ ) . The robustness of this test to deviations from normality has been studied in [32] and its power in [31] . We applied our POE test genome-wide to all HapMap imputed markers in a set of cohorts and results were combined across cohorts using fixed-effect inverse-variance weighting meta-analysis . SNPs were selected for replication if they met at least one of the following two criteria: ( 1 ) POE P-value <5×10−6 or ( 2 ) POE P-value <5×10−4 and within 500 kb of previously reported imprinted regions according to the Catalogue of Parent of Origin Effects database ( http://igc . otago . ac . nz/home . html ) . At loci which met either criteria , a lead SNP ( with the strongest POE association ) was identified; other markers within 1 Mb or in LD ( r2>0 . 1 ) were excluded from further investigations . In total 2 , 673 , 768 HapMap imputed and genotyped SNPs were analysed , of which 29 , 457 were considered as lying in imprinted regions , criterion ( 2 ) . Using the procedure of Gao et al . [33] we estimated the effective number of tests considered by each criterion to be ∼1 , 000 , 000 and 6 , 100 respectively , justifying the ∼100 fold drop in the P-value threshold applied to the second criterion . We tested our findings in family-based studies using a simplified parental asymmetry test [34] ( PAT ) . For each target SNP , in each family we searched for trios ( or parent-offspring pairs ) with heterozygous offspring and determined the parent of origin of the alleles ( whenever possible , i . e . at least one homozygous parent ) . From each family at most one heterozygous offspring with known parental origin was then collected and grouped according to the parental origin of the alleles . Note that although POE is acting in every genotype group , it can only be detected in the heterozygous group . As at the discovery phase , we used sex- , age- and age2-corrected BMI residuals as phenotype . The equality of phenotypic means in the two groups was tested using a Student t-test . When significant differences were detected we also estimated the difference between paternal and maternal effect sizes , which is simply the difference between the phenotype averages in the paternal- and maternal- groups . In order to estimate paternal ) and maternal ( ) effect sizes it is sufficient to know their mean and their difference . The difference between paternal and maternal effect alleles can also be derived from GWAS of unrelated individuals . It is easy to see that the test statistic defined asgives an unbiased estimate of . Since , the variance of T is . Therefore , the absolute difference in paternal and maternal effects ( ) can be estimated if the phenotypic variances in the three genotype groups are known . However , these estimates will be strongly subject to the winner's curse [35] , thus we used the family studies to derive more reliable estimates of . To reduce the effect of differences in the distribution of BMI between the family-based studies , we meta-analysed the difference estimates from each study in order to obtain a combined estimate of . The average of the maternal- and paternal effects , , is the association effect size using a simple additive genetic model , which can be most accurately estimated from the largest-to-date meta-analytic study on BMI [10] ( including ∼250 , 000 individuals ) . If there is an additive marginal genetic effect influencing the trait certain transformations may inflate the phenotypic variance of the heterozygous group . Let be the phenotypic mean in the heterozygous group and the marginal effect of the SNP ( on the original scale ) . Let denote an S-shaped transformation function of the form that is applied to the trait . In the following we show that for any value arbitrarily large phenotypic variance inflation can be achieved in the heterozygous group , compared to the two homozygous groups by an appropriate parameter choice for . Using a second order Taylor expansion the variance of the transformed phenotype in the heterozygous group can be estimated by If we assume the phenotype follows a Gaussian distribution then , simplifies to and thus Without loss of generality one can assume that . The variance in AA genotype group can be estimated similarly and thus Using the special form of , the variance difference can be expressed asand since and It is easy to see that for a fixed As , faster than , thus for any effect size we can find a transformation function such that the variance inflation of the heterozygous group exceeds any arbitrary threshold . Lymphoblastoid cell lines were derived from peripheral blood leukocytes of 95 members of 11 CEPH families [36] ( #102 , #884 , #1333 , #1340 , #1341 , #1345 , #1346 , #1347 , #1362 , #1408 , #13292 ) . They were purchased from the Coriell Cell Repository ( http://ccr . coriell . org/ ) , and cultured as previously described [37] . DNA was extracted by using the QIAamp DNA Mini kit ( QIAGEN ) , and RNA by using the RNeasy Mini kit ( QIAGEN ) , according to the manufacturer's instructions . Primer sequences were designed to amplify a 328-bp region on chromosome 8 that spans the rs2471083 polymorphism ( forward primer: 5′-ACCACAGAAGTCAGTAGACGAG-3′; reverse primer: 5′- GTGACATTGGGAGCATGGGA-3′ ) and a 146-bp region on chromosome 20 that spans the rs3092611 polymorphism ( forward primer: 5′-GCCACCAGTGGTCTGATAGT-3′; reverse primer: 5′- TAACTCGTCATTCTGCCCTGG -3′ ) . PCR amplification was performed in a 25 µl reaction using GoTaq polymerase ( Promega ) . After purification of PCR products ( ExoSAP-IT , USB ) , sequencing reactions were carried out by using 1 µl of each of the 3 . 2 µM sequencing primers and 0 . 5 µl of BigDye Terminator v1 . 1 ( Applied Biosystems ) . Following on-column purification ( EdgeBio ) , sequencing products were run on an ABI-3130 XLS sequencer ( Applied Biosystem ) . To synthesize cDNA , 2 µg of total RNA was retrotranscribed using the Superscript III reverse transcriptase ( Invitrogen/Life Technologies ) according to the manufacturer's instructions and a mix of random hexamers and oligo-dT that facilitate the detection of poorly expressed genes . To validate primers for qPCR , we first performed a series of test amplifications by using a defined range of primer concentrations ( 50–200 nM ) . We then loaded 10 µl of each qPCR product on 1% agarose gels to check the specificity of the amplification product , which should correspond to a 113-bp ( KCNK9 ) and 148-bp ( SLC2A10 ) fragment . To test KCNK9 and SLC2A10 PCR efficiency a standard curve made of five serial dilutions of brain and lung cDNA were used , respectively , since the two genes are known to be highly expressed in these organs . We obtained a standard curve slope of −3 . 49 for KCNK9 and of −3 . 37 for SLC2A10 , corresponding to 94% and 98% PCR efficiency . For more details see Supplemental Data S1 . The output of the analysis was threshold cycles ( Ct ) , i . e . the number of cycles at which the fluorescent signal of the reaction crosses a pre-determined threshold value . Since standard quantification methods ( including normalization by housekeeping genes ) introduce a considerable amount of experimental noise for very lowly expressed genes , raw Ct values were used to perform an absolute quantification of KCNK9 and SLC2A10 transcripts . As negative controls , housekeeping genes ( HPRT1 , GAPDH ) were also tested for parent-origin-effect to exclude the possibility that the observed difference in KCNK9 and SLC2A10 expression levels was due to the sample preparation process . Raw Ct values were inverse-normal quantile transformed and a linear mixed model was fitted ( using the R function lmer ) modelling the technical replicates as random effects and parental origin as a fixed effect . | Large genetic association studies have revealed many genetic factors influencing common traits , such as body mass index ( BMI ) . These studies assume that the effect of genetic variants is the same regardless of whether they are inherited from the mother or the father . In our study , we have developed a new approach that allows us to investigate variants whose impact depends on their parental origin ( parent-of-origin effects ) , in unrelated samples when the parental origin cannot be inferred . This is feasible because at genetic markers at which such effects occur there is increased variability of the trait among individuals who inherited different genetic codes from their mother and their father compared to individuals who inherited the same genetic code from both parents . We applied this methodology to discover genetic markers with parent-of-origin effects ( POEs ) on BMI . This resulted in six candidate markers showing strong POE association . We then attempted to replicate the POE effects of these markers in family studies ( where one can infer the parental origin of the inherited variants ) . Two of our candidates showed significant association in the family studies , the paternal and maternal effects of these markers were in the opposite direction . | [
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"phys... | 2014 | Novel Approach Identifies SNPs in SLC2A10 and KCNK9 with Evidence for Parent-of-Origin Effect on Body Mass Index |
Chromoblastomycosis ( CBM ) is a difficult-to-treat chronic subcutaneous mycosis . In Brazil , the main agent of this disease is Fonsecaea pedrosoi , which is phenotypically very similar to other Fonsecaea species , differing only genetically . The correct species identification is relevant since different species may differ in their epidemiologic aspects , clinical presentation , and treatment response . Partial sequencing of the internal transcribed spacer ( ITS ) was used to identify twenty clinical isolates of Fonsecaea spp . Their in vitro antifungal susceptibility was determined using the broth microdilution method , according to the M38-A2 protocol . Amphotericin B ( AMB ) , flucytosine ( 5FC ) , terbinafine ( TRB ) , fluconazole ( FLC ) , itraconazole ( ITC ) , ketoconazole ( KTC ) , posaconazole ( POS ) , voriconazole ( VRC ) , ravuconazole ( RVC ) , caspofungin ( CAS ) , and micafungin ( MFG ) were tested . The association between ITC/TRB , AMB/5FC , and ITC/CAS was studied by the checkerboard method to check synergism . The available patients’ data were correlated with the obtained laboratory results . Fonsecaea monophora ( n = 10 ) , F . pedrosoi ( n = 5 ) , and F . nubica ( n = 5 ) were identified as CBM’ agents in the study . TRB and VRC were the drugs with the best in vitro activity with minimal inhibitory concentrations ( MIC ) lower than 0 . 25 mg/L . On the other hand , FLC , 5FC , AMB , and MFG showed high MICs . The AMB/5FC combination was synergistic for three F . monophora strains while the others were indifferent . Patients had moderate or severe CBM , and ITC therapy was not sufficient for complete cure in most of the cases , requiring adjuvant surgical approaches . F . monophora , the second most frequent Fonsecaea species in South America , predominated in patients raised and born in Rio de Janeiro , Brazil , without cerebral involvement in these cases . TRB , VRC , and the AMB/5FC combination should be further investigated as a treatment option for CBM .
Chromoblastomycosis ( CBM ) is a chronic fungal infection of cutaneous and subcutaneous tissues caused by traumatic implantation of several species of dematiaceous fungi [1] . In 2017 , this mycosis was recognized as a neglected tropical disease by the World Health Organization [2] . The etiological agents of CBM belong mainly to the genera Cladophialophora , Phialophora , and Fonsecaea [3] . In the last decade , new species of the genus Fonsecaea have been described , based on molecular criteria: Fonsecaea monophora [4] , Fonsecaea nubica [5] , Fonsecaea multimorphosa [6] , and Fonsecaea pugnacius [7] . These species can be found in nature , trace amounts in plant debris , thorns , and wood cortex , which provide microhabitats for these fungi [8] . In the Brazilian State of Maranhão , on the border of the Brazilian Amazon rainforest , several agricultural communities work on harvesting babassu ( Orbignya phalerata ) , a wild palmacea specimen that was described as probable infection source in this area [9] . In the environment , all agents of CBM present in their mycelial form , which is composed by dematiaceous hyphae and conidia , which are specific for each genus . Infection usually follows a human trauma with a contaminated organic material such as plant thorns , wood , plant debris , grass , tree cortex among others , leading to the implantation of the fungus in the subcutaneous tissues , where the fungus changes to its parasitic form composed by muriform cells . These cells are heavily melanised and are extremely resistant to the harsh conditions imposed by the host immune system [10] . CBM can be caused by four Fonsecaea species: F . pedrosoi , F . nubica [4–6] , F . monophora and F . pugnacius . The latter two show significant neurotropism , eventually leading to dissemination to the brain and other organs [4 , 7] or causing primary brain infection without skin lesions , which are classified as phaeohyphomycosis since no muriform cells are seen in tissues [10 , 11] . CBM may assume several clinical forms with different degrees of severity [10] . There is no treatment protocol to be followed , and antifungal therapy is often combined with physical methods such as cryosurgery or surgical excision for small lesions [12] . Itraconazole ( ITC ) and terbinafine ( TRB ) are the most used drugs in the treatment of CBM [10 , 13–15] . Other drugs used include posaconazole ( POS ) , voriconazole ( VRC ) , amphotericin B ( AMB ) and flucytosine ( 5FC ) [10 , 16 , 17] . In addition , combined therapies of ITC with TRB [10 , 18] , 5FC with AMB [17] , or ITC with 5FC have been used [19] . It is important to determine the in vitro susceptibility of these isolates because of the difficulty found in the treatment of this mycosis and the frequency of refractory cases and relapses . The present study aimed to molecularly identify the species , to evaluate the in vitro susceptibility to antifungals , and to identify possible combinations of drugs with synergism against strains isolated from patients with CBM diagnosed in Rio de Janeiro state , Southeast Brazil , an area of low occurrence of this mycosis . Moreover , a clinical and laboratorial data association is provided for some patients .
This study was approved by the Ethics Committee Board of the Evandro Chagas National Institute of Infectious Diseases ( INI ) , Oswaldo Cruz Foundation ( Fiocruz ) , under the number CAAE: 52247016 . 0 . 0000 . 5262 . Twenty isolates of dematiaceous fungi from skin lesions of 17 patients with CBM were included in this study . These isolates were stored from 1999 to 2015 at the INI Mycology Laboratory and identified phenotypically as Fonsecaea pedrosoi . From the total of 17 patients , 12 were treated at the INI's Infectious Dermatology Outpatient Clinic , 7 of which were previously studied by Mouchalouat et al . , [20] and the remaining 5 were followed up at other institutions after mycological diagnosis at the INI ( Table 1 ) . All stored fungi were recovered on Sabouraud Dextrose Agar ( Difco Laboratories , Sparks , MD , USA ) incubated at 25°C for 10 days . Microscopically , hyphae were septate , branched , and brown staining with the predominance of conidiophores with short chains of smooth , thin-walled dematiaceous conidia . Putative Fonsecaea spp . colonies were assessed on potato dextrose agar ( PDA ) ( HiMedia Laboratories Pvt . Ltd . , India ) at 25°C after 7–14 days of inoculation . The DNA extraction and polymerase chain reaction ( PCR ) of the ITS1-5 . 8S-ITS2 region , the official Fungal DNA barcode , were performed according to Brito-Santos et al . [21] . PCR products were purified using the Wizard SV Gel and PCR Clean-Up System kit ( Promega Corporation , Madison , USA ) and sequenced at the Platform for DNA Sequencing PDTIS/Fiocruz . Sequences were edited using Sequencher 4 . 9 ( Gene Codes Corporation , Ann Arbor , MI , USA ) , aligned and analyzed with MEGA 6 . 06 [22] , and compared by BLAST with sequences available at the ISHAM ITS database ( http://its . mycologylab . org ) . The molecular identification was considered valid when it presented more than 98 . 5% of identity , compared to the sequences available in the ISHAM-ITS database [23] . The evolutionary history was inferred by using the Maximum Likelihood method based on the Tamura-Nei model [24] . In vitro antifungal susceptibility testing was performed according to the recommendations proposed in the Clinical and Laboratory Standards Institute ( CLSI ) M38-A2 protocol [25] with modifications . AMB , FLC , ketoconazole ( KTC ) , POS , ITC , VRC , ravuconazole ( RVC ) , 5FC , TRB , caspofungin ( CAS ) ( all from Sigma-Aldrich Chemical Corporation , St . Louis , MO , USA ) and micafungin ( MFG ) ( Astellas Pharma Tech Corporation , Takaoka city , Toyama , Japan ) were tested . The inoculum was prepared from a seven-day old PDA culture; the cells were harvested in RPMI medium and diluted to approximately 0 . 4–5 × 104 cells/mL . The plates were incubated at 35°C for five days [14] . The minimal inhibitory concentration ( MIC ) for AMB , FLC , KTC , POS , ITC , VRC , RVC , 5FC , and TRB; and the minimal effective concentration ( MEC ) for CAS and MFG were determined according to the CLSI M38-A2 protocol [25] . The reference strains Aspergillus flavus ( ATCC 204304 ) , Aspergillus fumigatus ( ATCC 204305 ) , Candida krusei ( ATCC 6258 ) and Candida parapsilosis ( ATCC 22019 ) were used for quality control . The susceptibility test with antifungal combinations was performed by the checkerboard method , where two different drugs were applied at different concentrations in a single 96-well plate , so that in each well there were different concentrations of the antifungals in combination . The concentrations assayed in the combinations were ITC 0 . 0075–4 mg/L with TRB 0 . 015–1 mg/L; AMB 0 . 0075–4 mg/L with 5FC 0 . 06–4 mg/L; CAS 0 . 0075–4 mg/L with ITC 0 . 06–4 mg/L . Drug interaction , classified according to the fractional inhibitory concentration index ( FICI ) , which defines the type of interaction between the antifungal agents in combination , was as follows: synergism if FICI ≤ 0 . 5; indifference if 0 . 5 < FICI ≤ 4 and antagonism if FICI > 4 [26 , 27] . The FICI was obtained by the sum of the fractional inhibitory concentrations ( FIC ) or by the formula: FICI ═ ( A/MIC ( a ) ) + ( B/MIC ( b ) ) , where: A = MIC of the drug ( a ) in combination; MIC ( a ) = MIC of drug ( a ) alone; B = MIC of the drug ( b ) in combination; MIC ( b ) = MIC of drug ( b ) alone [28] . The geometric mean of MIC/MECs , MIC/MEC50 , MIC/MEC90 and the MIC/MEC ranges were calculated using the Statistical Package for the Social Sciences v . 17 . 0 ( SPSS Inc , USA ) . Data analysis was performed in the GraphPad Prism 5 software . Kruskal-Wallis test was used to compare MIC of each antifungal drug between the different species . The Wilcoxon matched pairs test was used to compare MICs of two different drugs and the Friedman test to compare MICs of three or more antifungal drugs . P values lower than 0 . 05 were considered significant .
Ten isolates were identified as F . monophora ( 50% ) , five as F . pedrosoi ( 25% ) and five as F . nubica ( 25% ) . The ITS sequencing alignment scores of the fungal isolates herein studied exhibited 99–100% identity compared with corresponding ITS sequences deposited in the ISHAM-ITS database ( Fig 1 ) . The ITS sequences obtained during this study were deposited in NCBI/GenBank under the accession numbers MF616485 –MF616504 . Table 2 depicts the susceptibility profile of the strains included in this study . TRB ( MIC range 0 . 015–0 . 25 mg/L ) and VRC ( MIC range 0 . 12–0 . 25 mg/L ) were the antifungal drugs that showed the best in vitro activity against the Fonsecaea spp . isolates . FLC ( MIC range 8–32 mg/L ) , 5FC ( MIC range 2–32 mg/L ) , AMB ( MIC range 4->16 mg/L ) and echinocandins ( MEC range 1–8 mg/L ) showed higher MIC/MEC values . Overall , FLC was the azole with the poorest activity ( P<0 . 0001 ) and among the echinocandins , CAS was more effective than MFG ( P = 0 . 003 ) . The susceptibility profile between the different species was very similar for the drugs tested . The few differences observed were as follows: F . pedrosoi presented MEC90 of 1 mg/L for MFG , while F . monophora and F . nubica presented both MEC90 of 8 mg/L ( P = 0 . 0009 ) and F . monophora presented MIC50 and MIC90 of 8 mg/L for AMB , while F . pedrosoi and F . nubica presented MIC50 and MIC90 of 4mg/L for the same polyene drug ( P = 0 . 0447 ) . Although not significant ( P = 0 . 0871 ) , F . nubica presented MIC90 of 4 mg/L for 5FC , while for the other species the MIC90 was two-dilutions higher , that is , 16 mg/L . According to the FICI , when 5FC and AMB were tested in combination , synergistic interaction ( FICI ≤ 0 . 5 ) was observed in 3 F . monophora isolates ( 30% ) . For the combinations ITC/TRB and ITC/CAS , an indifferent interaction ( 0 . 5 < FICI ≤ 4 . 0 ) was observed for all isolates tested . S1 Table depicts the results of the three combinations of antifungal drugs herein studied . It was possible to determine the probable site of infection for 9 out of the 12 patients with documented data , 8 of them in Rio de Janeiro and 1 in the Espírito Santo state . Among the patients infected in Rio de Janeiro , six were by F . monophora ( 75% ) and two by F . nubica ( 25% ) . Regarding the geographic location , it is important to note that the three patients born and raised in Rio de Janeiro state were infected with F . monophora and all patients infected with F . pedrosoi were born outside the Rio de Janeiro state . In addition , two cases of F . pedrosoi without clinical data available ( cases 13 and 14 , Table 1 ) were diagnosed in patients from the Brazilian Amazon region . Of the 12 patients followed up at INI , nine were cured; three of them used only antifungal drugs , two underwent surgical procedures , three used antifungal drugs associated with physical methods ( cryosurgery and/or surgery ) and one underwent surgery plus two sessions of cryosurgery ( Table 3 ) . The extension of treatment considering only the six patients who used antifungal drugs ( ITC alone or in combination ) ranged from 1 to 87 months ( median = 9 months ) . Regarding the severity of the disease , of the nine patients infected by F . monophora , clinical information was available for eight , five of which were characterized by the moderate form and three with the severe form . Moderate and severe CBM was also observed in patients infected with F . nubica , and moderate CBM was observed in the patient infected by F . pedrosoi with available clinical data . The only case of cutaneous disseminated CBM was observed in a patient with F . monophora . The only case with tumor lesion was observed in another patient with F . monophora . Of the patients infected by F . monophora , seven were cured and one had no outcome information . Regarding the five patients with no clinical data , three were infected with F . pedrosoi , one with F . monophora and one with F . nubica . Extracutaneous manifestations of the disease were not observed in any case , regardless of the isolated species .
This work represents a case series study of CBM for a period of 16 years in one of the main reference centers for infectious diseases in Rio de Janeiro . Studies on CBM in other Brazilian regions have been conducted in this decade by several groups [29–31] , but in all these studies , data from Rio de Janeiro was missing . We believe that this study can be an important clinic-laboratorial contribution to the knowledge of the disease and an update to the actual Brazilian situation of CBM . All four species of Fonsecaea up to now related to CBM are found in Brazil [5 , 7 , 32 , 33] . F . pedrosoi is the predominant species in South America , followed by F . monophora [34] . Rio de Janeiro , the geographic region where this study was conducted , is an area of low occurrence of CBM in Brazil [20] , which explains the limited number of cases during the studied period . The high frequency of F . monophora in our study may indicate a reservoir for this species in this region . The fact that all patients born and raised in Rio de Janeiro were infected by F . monophora supports this hypothesis . This is first study that evaluate in vitro antifungal susceptibility of CBM isolates in Rio de Janeiro . We found high MIC values for AMB corroborating other authors [35 , 36] . Treatment with AMB alone or combined with 5FC has not been used since the introduction of ITC during the 1980s . The frequent occurrence of nephrotoxicity , due to the drug characteristics and prolonged treatment [37 , 38] , together with the reactivation of the infection with the drug discontinuation [10 , 34] are factors that hinder the use of AMB for CBM therapy . The Fonsecaea strains included in this study presented low MIC values for KTC , similar to other studies [14 , 35 , 39] . This drug was the first systemic imidazole available , but it is rarely used due to serious hepatic reactions , as well as severe drug interactions [40 , 41] . Nowadays , ITC is considered the most commonly used drug for CBM treatment [10] . In this study , the MIC values for this drug indicate susceptibility of the isolates to the antifungal agent [42] , and the schemes using ITC alone or associated with other antifungal or surgical modalities was able to lead 7 patients to cure . POS , VRC and RVC represent the new generation of triazoles with a broad spectrum of activity and a favourable pharmacokinetic profile [43] . POS is known to have a better in vitro activity than ITC against clinical Fonsecaea isolates [44] in accordance with the results of this study . VRC has good in vitro activity against CBM agents , including Fonsecaea spp . [45] . However , in addition to its high cost [46] , the prescription of VRC should be done with caution , since it presents risk of photo toxicity and cutaneous carcinoma in prolonged periods of treatment [47] . To the best of our knowledge , this is the first study on Fonsecaea spp . susceptibility to RVC using the CLSI M38-A2 protocol . González et al . [48] reported the in vitro activity of RVC against isolates of F . pedrosoi with MICs ranging between 0 . 125–0 . 5 mg/L using M38-A CLSI protocol . Despite the use of another protocol , this work reports MIC values ≤ 1 mg/L for the same drug against clinical Fonsecaea isolates . However , based on our results that showed POS and VRC with a better in vitro profile than RVC , the first two azoles should be considered in the treatment of CBM , instead of RVC . The MIC values found for 5FC and FLC were compatible with other studies , showing that these drugs are ineffective in vitro against Fonsecaea spp . , discouraging their use in CBM treatment [35 , 39 , 44] . As for 5FC , its use in the mid-1960s marked the beginning of chemotherapy approaches for CBM [45] . However , it was later observed that F . pedrosoi is able to develop in vitro resistance to 5FC [45 , 49–53] . Although not prohibited , the drug is not registered in the Brazilian regulatory agency ( ANVISA ) and is not commercialized on the Brazilian market [54] , because there is no pharmaceutical industry that manufactures this antifungal drug in our country , which leads to a need to its import by tertiary hospitals . Several studies have reported the susceptibility of Fonsecaea spp . to TRB , revealing its potent action against various filamentous fungi and in the treatment of CBM , demonstrating up to 80% of cure rate [14 , 39 , 55 , 56] . Our results were consistent with those studies showing low MIC values for this drug . In addition , TRB shows a potent antifibrotic effect in recent lesions [57] . This drug has little affinity for the cytochrome P450 enzyme system , resulting in less interaction with other substances [58] and in a general way , is well-tolerated , indicating an effective option for the treatment of CBM . Echinocandins have a limited role in the treatment of CBM due to high MEC values for F . monophora and F . nubica , as observed in other studies [44 , 59] . Isolates of F . pedrosoi presented a better in vitro response to MFG . Nevertheless , due to the low number of analysed isolates , it is suggested that further studies will assess whether this echinocandin is specifically effective against F . pedrosoi . According to some studies , in particular CBM cases , the best therapeutic strategy would be the association of two antifungals based on the results of previous susceptibility tests [17] . Some of the suggested combinations are AMB and 5FC [45 , 60] or ITC and TRB [61] . Our work showed a synergism between AMB and 5FC in three F . monophora isolates . This combination has two distinct mechanisms enhancing the antifungal action: AMB binds to ergosterol of the fungus membrane forming pores and 5FC acts inhibiting the synthesis of nucleic acids . This combination is widely used in cases of cryptococcal meningitis because it has a more effective penetration into the SNC [62] . In the past , the combination AMB/5FC had been used for CBM treatment [17 , 63 , 64] , but now a days it is no longer considered due to the adverse side effects [10] . However , we believe that this association could be beneficial in severe cases of CBM , especially those with brain involvement . The synergism found for 100% of the isolates of Phialophora verrucosa by Li et al . [18] encouraged the use of ITC and CAS combination in our study . A single study [65] found synergism for isolates of F . monophora . An indifferent interaction was observed in all Fonsecaea isolates of this study , which is compatible with most studies [15 , 17 , 61] . There are few studies comparing in vitro susceptibility among clinical Fonsecaea isolates . Najafzadeh et al . [44] found no significant differences among species in the activity of eight antifungals ( AMB , FLC , ITC , VRC , POS , CAS , anidulafungin and isavuconazole ) against F . pedrosoi , F . monophora and F . nubica . However , in this study , F . monophora showed higher MIC values than F . nubica for AMB ( P = 0 . 0447 ) , and the species F . monophora and F . nubica ( P = 0 . 0009 ) had higher MEC values for MFG when compared to F . pedrosoi . CBM is known as a difficult-to-treat disease , there is no standard drug of choice and relapses are frequent [10 , 34 , 45 , 57 , 66] . There are many factors that can influence the patient outcome that can be related to the host or the fungal species . The host immune response , local lymphedema , and fibrosis become a barrier for a proper drug bioavailability at the site of infection . In addition , muriform cells are heavily pigmented and represent a resistant fungal form against antimicrobial compounds [67] . In the same vein , there is no clear correlation between in vitro susceptibility and clinical practice [45] . In general , MIC values ≤ 1 mg/L usually indicate a potential susceptibility of most drugs used in the treatment of infections by dematiaceous fungi [14 , 42] , as occurred in this study . It is also possible that the broth microdilution test is not the best method to guide therapeutic management in CBM . In fact , in a correlation study between different antifungal susceptibility methodologies and clinical outcome of cryptococcosis patients treated with AMB showed that time-kill assays are more suitable to predict treatment failure than broth microdilution and gradient diffusion methods [68] . Further studies are necessary to check if a similar scenario occurs in CBM . Due to the hardships to obtain the parasitic form of the CBM agents in vitro [67 , 69] , most authors perform antifungal susceptibility testing using conidia . This is not an invalid strategy , since a similar scenario occurs in sporotrichosis , another deep mycosis , for what the official antifungal susceptibility testing guidelines suggest the use of conidia instead of the parasitic yeast-like form [25] . However , we are aware that this can be a bias in the correlation of in vitro and in vivo results . We were not able to observe clear relationships between the treatment responses and the antifungal susceptibility of the isolates . ITC was used in almost all clinical cases , because it is distributed free of charge in our institution . However , most of patients only presented a slight improvement with this drug , despite the MIC observed . It is common the development of fibrosis in lesions of CBM [70] , what hinders the action of the drugs , since it prevents their penetration [71] . In addition , ITC needs an acidic gastric environment to be properly absorbed [72] . A decrease in the production of gastric juice may result in a higher pH of the stomach , thus reducing the bioavailability of this drug and therefore its activity [73 , 74] . No synergism was observed between ITC and TRB in this study and the two cases treated with this drug combination required surgical approaches for complete cure . In a study with CBM cases , to which drugs were administered together for a long period of time , failure was observed . So , the authors chose to weekly alternate these drugs , with a positive outcome in some cases [61] . In summary , this study demonstrated that TRB and VRC exhibited better in vitro activity against Fonsecaea spp . , while AMB , FLC , 5FC and echinocandins played a limited role in the CBM treatment because of their relatively high MICs . However , AMB and 5FC presented in vitro synergism for a few strains , which may be useful as a salvage therapy . ITC , although with higher MICs , were used alone or in association and lead to cure in moderate to severe clinical cases . Despite the fact that we did not use TRB as the sole therapeutic drug in the patients herein described , we believe that more attention should be given to this antifungal in the context of CBM treatment , due to the low MIC values observed in this study as well as safety and effectiveness in other studies [56] . Our work provides perspectives for future studies of clinical follow-up , treatment and outcome of patients with CBM , as well as the determination of in vitro susceptibility to antifungal and new compounds with fungicidal action , especially in melanized fungi . | Chromoblastomycosis is a disfiguring disease usually occurring in rural workers from poor and remote communities . In Brazil , the most frequent agents of this neglected disease are the species belonging to the genus Fonsecaea . The disease occurs after traumatic inoculation during work . As the lesions progress , itching becomes severe , and scratching may result in further inoculation to other body sites . When patients seek medical help , the lesions are usually extensive and disfiguring . For this reason , a more effective and less time-consuming treatment is important . Oral antifungal therapy is not very effective , must be taken for months or years , it is costly for most patients and often unavailable . Hence , it is important to determine the in vitro antifungal susceptibility and correlate it with the isolated species . In this study , Fonsecaea monophora was the predominant species and , differently from some studies , dissemination to the central nervous system was not observed . In vitro analysis showed that the most effective antifungal drugs were terbinafine and voriconazole , followed by itraconazole , the most used drug in the treatment of this disease . The combination of amphotericin B and flucytosine may be synergistic , depending on the infective strain . | [
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"s... | 2018 | Molecular identification and antifungal susceptibility profiles of clinical strains of Fonsecaea spp. isolated from patients with chromoblastomycosis in Rio de Janeiro, Brazil |
Striped skunks are one of the most important terrestrial reservoirs of rabies virus in North America , and yet the prevalence of rabies among this host is only passively monitored and the disease among this host remains largely unmanaged . Oral vaccination campaigns have not efficiently targeted striped skunks , while periodic spillovers of striped skunk variant viruses to other animals , including some domestic animals , are routinely recorded . In this study we evaluated the spatial and spatio-temporal patterns of infection status among striped skunk cases submitted for rabies testing in the North Central Plains of US in a Bayesian hierarchical framework , and also evaluated potential eco-climatological drivers of such patterns . Two Bayesian hierarchical models were fitted to point-referenced striped skunk rabies cases [n = 656 ( negative ) , and n = 310 ( positive ) ] received at a leading rabies diagnostic facility between the years 2007–2013 . The first model included only spatial and temporal terms and a second covariate model included additional covariates representing eco-climatic conditions within a 4km2 home-range area for striped skunks . The better performing covariate model indicated the presence of significant spatial and temporal trends in the dataset and identified higher amounts of land covered by low-intensity developed areas [Odds ratio ( OR ) = 3 . 41; 95% Bayesian Credible Intervals ( CrI ) = 2 . 08 , 3 . 85] , higher level of patch fragmentation ( OR = 1 . 70; 95% CrI = 1 . 25 , 2 . 89 ) , and diurnal temperature range ( OR = 0 . 54; 95% CrI = 0 . 27 , 0 . 91 ) to be important drivers of striped skunk rabies incidence in the study area . Model validation statistics indicated satisfactory performance for both models; however , the covariate model fared better . The findings of this study are important in the context of rabies management among striped skunks in North America , and the relevance of physical and climatological factors as risk factors for skunk to human rabies transmission and the space-time patterns of striped skunk rabies are discussed .
Rabies is one of the oldest known zoonosis , a central nervous system disease of mammals caused by viruses in the Rhabdoviridae family . Rabies continues to kill people throughout the world although human deaths due to this disease in the US have become increasingly rare [1] . The main global source of rabies occurs among domestic dogs but dog-to-dog transmitted rabies has been pushed to near extinction in North America since the mid-20th century due to intensive dog vaccination campaigns and stray dog control [1] . Numerous variants of the virus however circulate in wildlife today in North America , primarily among those species in the orders Carnivora and Chiroptera . In California , Texas and the north-central United States , the disease is well established among skunks in the Mephitis genera , and although all skunk species are susceptible , the striped skunk , Mephitis mephitis is the most reported rabid species to diagnostic facilities in these regions [2] [3] . Two distinct variants of the virus are recognized for striped skunk rabies in the central Midwestern US , one occurring in the South Central and the other in North Central Plains [2] . Striped skunks are widely distributed habitat-generalists in North America , with an ability to colonize periurban and rural environments [4] . Efforts to control or manage rabies among skunks has been a challenge even though they have been known as a major terrestrial reservoir for rabies in central and western US and as well as a public health concern for many decades [5–7] . Unlike the widely acknowledged success of oral rabies vaccine ( ORV ) programs in immunizing raccoons , foxes and coyotes [8–10] , ORV baits are less effective on striped skunks and has not produced detectable levels of immunity [10] [11] . The authors are aware of at least one current effort in the Midwestern US that aims to understand the efficacy of ORV baits in immunizing striped skunks in some parts of Texas but results have not been brought to public attention at the time of this study . It can be safely said therefore that striped skunk rabies remains largely if not fully unmanaged , and there are no efforts undertaken in the region to actively monitor disease prevalence . An important cause for concern as a result of this is the potential for skunk variant virus to spillover to non-reservoir species , including domestic animals , which occur time to time in the North Central Plains and other enzootic regions . Geographically scattered skunk rabies cases are usually recorded throughout the year in the North Central Plains [2] . However , the temporal and spatial dynamics of striped skunk rabies , or in other words whether or not this disease among skunks has been increasing or decreasing over the years , and whether it is expanding or contracting in geographic extent within the areas where it is known to occur is not clearly known since active surveillance is lacking . Striped skunk habitats overlap with those of humans , particularly in the periurban areas , and evidence suggests that striped skunk abundance is relatively higher in urban and forest edge environments . The magnitude of risk posed by striped skunks to humans and domestic animals in such environments are however not clear . Further , research on environmental factors , including climate and land cover that could be influencing the epizootiology of striped skunks in the north central US are not available in the literature . Wildlife mammalian distribution and their movement , and therefore the pathogens they vector are influenced by environmental and climatological pressures , some of which are undergoing rapid changes , and an understanding of such factors is crucial for effective disease management . Our objectives in this study were to explore the spatial and temporal patterns of rabies infection status among striped skunk cases submitted for testing in the North Central Plains , and to evaluate any influential environmental and climatological factors that drive such patterns . We hypothesized that the spatio-temporal patterns of passively surveilled striped skunk rabies cases can be described by modeling the underlying spatially structured and unstructured heterogeneities , nonparametric time trend and space-time interaction in a Bayesian hierarchical construct; and , that such a model can be extended to identify important eco-climatic drivers of such patterns , whose posterior estimates when further exponentiated can be used to describe the risk of skunk to human rabies transmission and as well as the risk of enzootic skunk rabies .
This study utilized a retrospective case-control study design , with samples for cases and controls obtained from medical records maintained at KSVDL . Records of tests performed for the presence of rabies virus in striped skunk tissue samples that were submitted during January 2007 –December 2013 were obtained from a laboratory information management system . Observations were coded in a binary fashion , those with positive test result ( case ) as ‘1’ , and ‘0’ for negative test result ( control ) . KSVDL receives test requests for rabies predominantly from the US states of Kansas and Nebraska , and records from these states alone were kept for statistical evaluations . Address information provided along with submission forms were geocoded in ArcGIS environment for mapping and were projected into North American Datum of 1983 ( NAD83 ) State Plane coordinate system . Presence of address locations within rural vs . urban areas was assessed in ArcGIS using US Census Bureau’s 2010 census urban and rural classification criteria . Urban areas according to US Census Bureau’s definition includes densely developed territory and encompass residential , commercial and other non-residential urban land uses . The US Census Bureau identifies two types of urban areas , viz . , urbanized areas of 50 , 000 or more people and urban clusters of at least 2 , 500 but less than 50 , 000 people . Any other territory , population or housing not included in ‘urban’ are considered rural [12] . Striped skunk home range is sex and age dependent and could vary from 3 . 75–5 . 0 km2 [13–15] . We assumed an area covering 4 km2 as home-range for the purposes of this study . A polygon layer with 4 km2 area was created in ArcGIS , and environmental and climatic data were extracted from publicly available sources surrounding case locations . The 2006 National Land Cover Dataset [16] was obtained from the United States Geological Survey ( USGS ) in a raster grid format . Grids representing different land cover type within each areal unit ( 4 km2 area ) were extracted from the raster dataset and the percentage area they occupy were estimated . A list of land cover variables evaluated in the study is present in Table 1 . In addition to deriving percent land-cover areas , a landscape metric , Total Edge Contrast Index ( TECI ) was derived . TECI was calculated in FRAGSTATS [17] program by TECI=[∑i=1m∑k=i+1meikdik]−E* ( 100 ) . where eik is the total length of edge between patch types i and k , and E*is the total length of edge in landscape , and dik is the dissimilarity ( edge contrast weight ) between patches i and k . Climatic variables including the maximum normalized vegetation index ( NDVI ) , minimum land surface temperature LSTmin , mean LST ( min ) , diurnal temperature range ( DTR ) ( the difference between daily maximum and minimum temperatures averaged over a thirty day period ) , precipitation and humidity were extracted for each 4 km2 area in the study area . The LST and NDVI estimates were derived from MODIS ( Moderate Resolution Imaging Spectroradiometer ) imagery [18] . DTR , precipitation and relative humidity were derived from the Prediction of Worldwide Renewable Energy ( POWER ) web portal of the NASA Langley Research Center [19] [20] . These datasets were resampled in ArcGIS environment whenever required to derive 4km2 resolution data .
A total of 1027 tests were performed for rabies virus on striped skunk cases in the region during the 2007–2013 study period . Among these , 705 specimens had negative diagnosis and 318 were tested to be positive . During the same period 33 specimens were determined unsuitable for diagnostic testing , and were not included in the analyses . Geographic coordinates were obtainable for 656 ( 93% ) negative and 310 ( 97 . 4% ) positive specimens , whose spatial distribution is present in Fig 1 . A predominant number of striped skunk specimens tested had originated from locations that were completely present within areas classified as rural ( n = 905 , [93 . 7%] ) and the remaining ( n = 61 , [6 . 3%] ) within urban boundaries . Of twenty three covariates evaluated in the univariate parameter estimation , six were retained for Bayesian hierarchical analysis ( Table 1 ) . No multicollinearity was noted among the retained covariates , and univariate non-linearity in logit with relation to positive infection among striped skunks over time and geographic extent was not noted . The covariate model with independent random effects for spatial and temporal terms , and fixed land cover/land use and climatological covariates performed best among the three Bayesian hierarchical models considered in this study , indicating that the inclusion of covariate terms explained additional variability among striped skunk rabies submissions that were unaccounted by purely random-effect terms . The summary of obtained posterior estimates for the hyperparameters ( τu , τv and τψ ) in the three models ( Table 2 ) showed that the spatially unstructured regional effect , indicating spatial heterogeneity , and the spatially structured random effects , indicating spatial clustering were significant in all models , while the spatio-temporal effect was negligible . The estimates for the former terms decreased in the covariate model , likely due to the fixed effect covariate terms competing to explain the same processes in the model . The non-parametric term , γj for structured temporal trend in all three models ( Fig 2 ) indicated that there was an increasing overall time trend , albeit by very small amounts in the data during the initial years followed by a stable trend throughout the remaining years in the study . The covariate model indicated that higher percentages of low intensity—developed areas , total edge contrast index ( henceforth referred to as patch fragmentation ) within skunk home ranges ( 4 km2 units ) , and a climate variable , diurnal temperature range were significantly associated with positive rabies infection among submitted cases of striped skunks ( Table 2 ) . All further interpretations are made on this model alone . The ORs and 95% Bayes CrIs indicate the risk of rabies infection among striped skunks submitted for testing in the study region for each of the 4 km2 habitat cells . For every percentage increase in developed—low density area within a 4 km2 habitat cell , the odds of a striped skunk specimen collected within that cell to test positive for rabies is 3 . 41 times larger than being negative . Likewise , for every percentage increase in patch fragmentation ( as measured by total edge contrast index ) , the odds of testing positive was 1 . 7 times larger than testing negative; and , for every unit increase in DTR , the odds of testing positive reduced by 0 . 54 times; or in other words , the ongoing decrease in DTR due to climate change increased the odds of rabies infection . Developed—low intensity areas and patch fragmentation indirectly point to the relatively higher human density in such areas , which increases striped skunk encounter rates and the probability of positive diagnosis . The addition of an interactive space-time term , Ψij to the partial ST model yielded a Deviance Information Criterion ( DIC ) value of 1 , 459 , marginally higher than the DIC value ( 1 , 431 ) without the term included . Additionally , the inclusion of Ψij term and covariate terms to the final covariate model failed to improve model performance , indicating a lack of space-time process in the dataset ( Table 3 ) . The posterior estimates used to derive ORs and 95% CrI correspond to the median of the posterior predictive distribution of the covariate model ( Table 3 ) . Model validation statistics based on the mean error and mean absolute error derived by randomly partitioning incidence data within 4 km2 areas into test and model groups did not indicate model inadequacies and the area under curve ( AUC ) values for the covariate model indicated good discriminative capacity ( Table 4 ) .
This study presents current spatiotemporal pattern of striped skunk rabies in the North Central US based on passive surveillance , and identifies influential eco-climatological drivers of this disease using Bayesian hierarchical modeling approach . Developed , low intensity areas and highly fragmented landscapes are mostly periurban environments and areas where human habitats overlap striped skunk habitats . In these environments higher rates of human—striped skunk contact is possible . There is a relatively higher risk of rabies transmission from striped skunks to humans who reside in developed low intensity areas and highly fragmented landscapes such as edges of woodlands and agricultural lands than other places . Diurnal temperature range , a climate change indicator is decreasing at a slow but steady rate , and increases the enzootic risk of rabies to striped skunks . Human mediated landscape changes and climate-change appears likely to exacerbate the prevalence of this disease in this species , and further studies are necessary to more fully understand the dynamics of skunk rabies in the study region and its impact upon the prevention of rabies among humans and other animals . | Despite the long recognition that skunks are an important reservoir host for rabies , the control of this disease among this host has not been achieved , and the disease is currently only passively monitored in North America . The need for rabies control among striped skunks is , however , well acknowledged , and reports of occasional spill-over of skunk variant rabies viruses to non-reservoir species , including some domestic animals , remains a cause for public health concern and a major roadblock for eradicating rabies from North America . An understanding of the spatial and temporal dynamics of diseases is important in management and for setting future research agendas , and such knowledge could assist in effective striped skunk rabies control . In this study , we evaluated whether rabies among striped skunk cases submitted for testing in the North Central Plains exhibit discernable spatial and temporal patterns , and if there are any eco-climatic factors that influenced such patterns . Our findings indicate that the year-to-year and spatial origins of rabies incidences in the states of Kansas and Nebraska in the North Central Plains are currently stable , and certain physical environment ( developed low-intensity areas and patch fragmentation ) and climatic ( diurnal temperature range ) factors play an important role in determining such temporal and spatial patterns . | [
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"... | 2016 | Bayesian Spatiotemporal Pattern and Eco-climatological Drivers of Striped Skunk Rabies in the North Central Plains |
Metabolic changes within the cell and its niche affect cell fate and are involved in many diseases and disorders including cancer and viral infections . Kaposi’s sarcoma-associated herpesvirus ( KSHV ) is the etiological agent of Kaposi’s sarcoma ( KS ) . KSHV latently infected cells express only a subset of viral genes , mainly located within the latency-associated region , among them 12 microRNAs . Notably , these miRNAs are responsible for inducing the Warburg effect in infected cells . Here we identify a novel mechanism enabling KSHV to manipulate the metabolic nature of the tumour microenvironment . We demonstrate that KSHV infected cells specifically transfer the virus-encoded microRNAs to surrounding cells via exosomes . This flow of genetic information results in a metabolic shift toward aerobic glycolysis in the surrounding non-infected cells . Importantly , this exosome-mediated metabolic reprogramming of neighbouring cells supports the growth of infected cells , thereby contributing to viral fitness . Finally , our data show that this miRNA transfer-based regulation of cell metabolism is a general mechanism used by other herpesviruses , such as EBV , as well as for the transfer of non-viral onco-miRs . This exosome-based crosstalk provides viruses with a mechanism for non-infectious transfer of genetic material without production of new viral particles , which might expose them to the immune system . We suggest that viruses and cancer cells use this mechanism to shape a specific metabolic niche that will contribute to their fitness .
Altered metabolism is regarded as a hallmark of cancer . It is thought that cancer cells rewire metabolic pathways in such a way that biosynthetic processes are balanced against ATP production to support high rates of proliferation [1] . One of the most characteristic metabolic hallmarks of tumour metabolism is aerobic glycolysis . Despite the inefficiency of glycolysis in energy production , the glycolytic phenotype provides cancer cells with several advantages such as increased biosynthesis of intermediate macromolecules and anti-apoptosis and signalling through metabolites [2] . Recently , it has been suggested that cancer cells , in addition to their intrinsic metabolic alteration can also induce aerobic glycolysis in adjacent stromal cells , a phenomenon termed the ‘reverse Warburg Effect’ [3 , 4] . The reverse Warburg effect emphasises the importance of tumour stromal cells in supplying energy metabolites and chemical building blocks to the rapidly proliferating cancer cells . Oncogenic viruses cause more than 15% of human cancers and it is predicted that effective treatment against them will lead to 25% fewer cancers in developing countries and 7% in developed countries [5] . Pathogenicity of these viruses involves the hijacking of host cellular pathways , including those controlling cell metabolism , suggesting they can use as a model system to study cancer development . Kaposi’s sarcoma herpesvirus ( KSHV ) is the etiological agent of Kaposi’s sarcoma ( KS ) and certain lymphoid neoplasms . KS is the most common neoplasm in HIV-1-infected individuals and also induces significant morbidity in other immunosuppressed individuals ( e . g . , post organ transplantation ) and in populations where KSHV infection is endemic ( para-Mediterranean regions ) [6] . To date KSHV infection does not have an effective treatment and new therapeutic approaches are needed . KS cells , as other cancers , have a distinct metabolism and KSHV was shown to alter several metabolic pathways in its host cell [7–10] . We have recently shown that the KSHV-encoded microRNAs ( miRNAs ) induce aerobic glycolysis in infected cells through regulation of key cellular genes involved in mitochondrial activity and regulation of glucose metabolism [11] . Interestingly , interaction between KSHV infected cells and their microenvironment was shown to be important for primary effusion lymphoma growth in vivo[12] . It was recently shown that the KSHV miRNAs are present in exosomes isolated from KS patient and KS mouse model [13] but the role of this in KS biology is still unknown . We suggest that exosomes present a new possible platform allowing KSHV in infected cells to interact with their microenvironment and improve its host cell fitness . Exosomes are spherical structures sealed with membranes released from the endosomal compartment of most cell types . They vary in size and molecular composition , depending on the cell of origin [14] . The functional impact of exosomes is imparted by the molecular components ( protein and RNA cargo ) they carry [15] . Exosome uptake is thought potentially to modulate many physiological and pathological processes including cell growth , immune regulation , angiogenesis and metastasis [16 , 17] . Exosomes uptake was also shown to affect metabolism; exosomes secreted from cancer associated fibroblast were suggested to modulate cancer cells metabolism by transferring miRNAs and metabolites [18] , and breast cancer secreted exosomes were shown to reduce glucose metabolism in cells in the pre-metastatic niche by transferring miR-122 [19] . Interestingly , it has been suggested that viruses , including KSHV , modulate the secretion of exosomes from infected cells [13 , 20–22] . Here we have used KSHV infection as a model to study whether cells utilise the exosomal pathway to regulate the metabolism of their microenvironment . We show that KSHV-infected primary lymphatic endothelial cells secrete exosomes containing viral-encoded miRNAs . These exosomes transfer the miRNAs to surrounding uninfected cells to induce a reverse Warburg effect . We show that these miRNAs function in the recipient cells to regulate known target genes and this results in reduced mitochondria biogenesis and induction of aerobic glycolysis . Most importantly , we found that this metabolic cross-talk between infected and neighbouring non-infected cells has physiological implications in KSHV life cycle supporting the growth of latent infected cells . Finally , our results show that regulation of microenvironment metabolism by miRNA transfer might be a global mechanism used by other viruses and cancer cells . Taken together our results reveal a novel mechanism whereby virally infected cells and cancer cells regulate the metabolism of surrounding cells via miRNA transfer . This allows these cells to control their microenvironment without producing new viral particles , which might trigger host immune recognition .
KSHV miRNAs regulate mitochondria and glucose metabolism in infected cells [11] . In addition , it has been demonstrated that patient- and mouse model-derived exosomes carry in them the KSHV miRNAs [13] . We hypothesised , therefore , that KSHV modulates exosomes secretion to regulate the metabolism of neighbouring cells . We test our hypothesis in lymphatic endothelial cells ( LEC ) , the precursor cells for KS . We infected primary LEC with BAC16-derived WT KSHV ( KLEC ) or miRNA cluster-deleted KSHV ( ΔmiR-KLEC ) [23] . Cells were selected to produce a 100% infected population and cultured for 6 days to allow establishment of latency ( Fig 1A and S1A Fig ) . Post selection , both KLEC and ΔmiR-KLEC were found to have similar KSHV genome copy number ( S1B Fig ) and expression levels of other latent genes ( S1C Fig ) . Growth media was then collected every 48 hours for a period of 14 days after each infection and extracellular vesicles were purified by standard differential centrifugation from 150ml of media ( S1D Fig ) . To determine if these nano-vesicles are exosomes we performed imaging , particle sizing , and measured biochemical properties of purified vesicles released from infected and non-infected LEC . Size and population characterisation using NanoSight Nanoparticle Tracking Analysis technology measured particles with diameter distribution between 50-150nm , corresponding to the expected size of exosomes ( S1E Fig ) . This analysis also showed that 150ml of growth media contains between 2-3x1011 particles/ml ( S1F Fig ) . Purity of the exosome preparations was determined by both electron microscopy ( Fig 1B ) and immunoblot analysis for the known exosome markers CD63 , CD9 and ALIX ( Fig 1C and S1G Fig ) . Because our hypothesis is a non-cell autonomous regulation on cell metabolism , we confirmed that our purified exosomes are not contaminated with KSHV particles . Immunoblot analysis showed no detectable KSHV envelope-associated protein ORF8 in the purified exosome fraction ( Fig 1D , bottom panel , 3 left lanes ) and no viral particles were identified by electron microscopy . As expected , CD63 can be detected also in the KSHV sample ( Fig 1D , top panel , last lane ) since it is collected from the media of activated cells using high speed centrifugation . Finally , after incubation with purified exosomes , we could not detect the KSHV DNA in uninfected cells , and these cells did not become GFP positive nor kanamycin resistant , as would be expected had they become infected with the recombinant virus . Taken together , we concluded that the purified exosome fraction is KSHV free and therefore any effect this fraction has , is not due to de novo KSHV infection . To test whether the KSHV miRNAs are present in secreted exosomes , RNA was extracted from LEC- , KLEC- and ΔmiR-KLEC-derived exosomes . To ascertain that the RNA is protected within the vesicles , exosomes were treated with 0 . 4μg/μl RNase for 10 min at 37°C prior to RNA extraction [24] . qRT-PCR analysis using the KSHV-miR LNA PCR primer sets ( Exiqon ) , showed all 12 KSHV encoded miRNAs are present in KLEC derived exosomes ( S1H Fig ) . Importantly , using these primer sets we could not detect any signal for RNA extracted from exosomes derived from LEC . Similarly , we could only detect miR-K12-10 and miR-K12-12 , which are encoded out of the miR-cluster , in exosomes derived from ΔmiR-KLEC ( S1I Fig ) . To better quantify the viral miRNAs in these exosomes as well as the effect of KSHV infection on transfer of cellular miRNAs in exosomes , we sequenced the small RNAs from LEC and KLEC as well as from exosomes secreted from these cells . We detected around 1800 different miRNAs in LEC and KLEC and around 1200 miRNAs in exosomes secreted from these cells . Importantly , we found that while in KLEC , the viral miRNAs present around 5% of the total miRNAs reads , in exosomes secreted from them , these miRNAs are responsible for around 10% of the total miRNAs reads ( Fig 1E ) . In addition , we found differences in the expression profile of the viral miRNAs between the cells and exosomes . For example , KSHV miR-K12-10a-3p , K12-4-3p and K12-8-3p are over represented in the exosomes while KSHV miR-K12-11-3p and K12-4-5p are underrepresented ( Fig 1F and S1 Table ) . Moreover , specific cellular miRNAs that are over represented in KLEC compared to LEC such as miR-145-5p and 143-3p ( S2 Table ) , were not found to be enriched in KLEC-derived exosomes ( S3 Table ) . On the other hand , we identified cellular miRNAs which are enriched in KLEC-derived exosomes compared to LEC-derived exosomes although these are not enriched in the respective cells ( S2 and S3 Tables ) . For example , hsa-miR-216a is highly enriched in KLEC derived exosomes . miR-216a was suggested to function as an oncomiR and to induce epithelial-mesenchymal transition ( EMT ) by targeting PTEN and SMAD7 [25] . This suggests that transfer of human miRNAs in exosomes secreted from KSHV infected cells may play an additional role in KSHV pathogenicity . Taken together these results suggest that KSHV manipulates its host cells secretion system to selectively enrich the packaging of the viral miRNAs together with specific cellular miRNAs in exosomes secreted from infected cells . Having established the presence of KSHV miRNAs in exosomes we sought to explore their trafficking and more importantly , biological function . We initially tested whether exosomes secreted from KLEC are taken up by non-infected LEC , by labelling internal protein in purified exosomes using the Exo-Green ( System Bioscience ) . Untreated LEC were first labelled using CellMask Deep Red Plasma Membrane Stain ( molecular probes ) , then incubated with labelled exosomes and analysed using confocal microscopy . As shown in Fig 2B , upon incubation with labelled exosomes , GFP signal can be detected within the cells , indicating uptake of these exosomes . Flow cytometry analysis of these cells showed that positive staining is still detectable 24 hours’ post uptake by target cells ( S2A Fig ) . Importantly , uptake of KLEC-derived exosomes results in transfer of the viral miRNAs to these cells ( Fig 2C ) . miRNAs function by binding to their target genes to inhibit their translation and induce their mRNA degradation . Therefore , we next tested if the KSHV miRNAs are active in the cytosol of recipient cells to inhibit the expression of known target genes . We have previously shown that the KSHV miRNAs function as cluster to regulate EGLN2 and HSPA9 and induce aerobic glycolysis [11] . We inserted the 3’ UTRs of these genes downstream of a luciferase coding sequence to generate a reporter of KSHV miRNA activity . Cells expressing this reporter were incubated with exosomes , secreted from LEC or KLEC for 48 hours ( 1x109 particles ) . Incubation with KLEC-derived exosomes resulted in a ~25–30% reduction in luciferase activity relative to cells incubated with control LEC-secreted exosomes ( Fig 2D ) . To further confirm the activity of the KSHV miRNAs in receptive cells we specifically tested the activity of miR-K12-10 , which is expressed separately from the other miRNAs , using blue fluorescent protein ( BFP ) fused to 8 repeats of the miRNA target site . When these cells were incubated with KLEC-secreted exosomes , we found a ~25% reduction in BFP intensity compared to incubation with exosomes secreted from LEC ( Fig 2E ) . To further test if these KLEC-derived exosomes regulate the expression of these genes under more physiological exosomes-transfer conditions we used Transwell plates [26] to co-culture non-infected LEC ( bottom compartment ) with either ΔmiR-KLEC or KLEC ( upper compartment ) . We then analysed the RNA from the non-infected LEC and found 30% and 40% down regulation of EGLN2 and HSPA9 mRNAs levels respectively ( Fig 2F ) . Taken together these results show that the KSHV encoded miRNAs are transferred to non-infected cells and maintain their ability to down-regulate specific target genes . We have previously shown that the KSHV miRNAs induce aerobic glycolysis in infected cells . We therefore predicted that they would have a similar effect in exosome recipient cells . To test this hypothesis , we educated non-infected LEC by either growing them in the presence of isolated exosomes for 48 hours ( Fig 3A ) or by co-culturing them with infected cells using Transwell plates ( Fig 3B ) . We first measured the oxygen consumption rate of educated LEC using the Seahorse XF24 Analyzer . The Seahorse Extracellular Flux Analyzer determines oxygen consumption rate ( OCR ) and extracellular acidification rate ( ECAR ) , in order to assess cellular functions such as oxidative phosphorylation and glycolysis . We educated non-infected cells with increasing numbers of exosomes and found a dose dependent reduction in oxygen consumption of educated cells with maximal effect using 2 . 5x109 exosomes ( S3A Fig ) . We therefore decided to use this number of exosomes for cell education for all future experiments . Education using KLEC-derived exosomes reduced the baseline oxygen consumption in recipient cells by ~30% compared to education using LEC-derived exosomes ( Fig 3C and S3A Fig ) . Importantly , exosomes derived from ΔmiR-KLEC did not affect oxygen consumption ( Fig 3C ) , suggesting the viral miRNAs are the driving force behind this phenotype . Similarly , after co-culturing KLEC and LEC for 5 days , LEC were found to have reduced oxygen consumption compared to uneducated LEC ( S3B Fig ) . This suggests that transfer of the KSHV miRNAs into non-infected cells reduces mitochondrial respiration . Similarly , we also observed a 30% increase in glucose uptake , consistent with increased aerobic glycolysis ( Fig 3D ) . Mitochondria are key players in normal glucose metabolism during aerobic conditions and as part of the Warburg effect , many cancer types show altered mitochondrial activity [27] . Since we have previously shown that expression of the KSHV-encoded miRNAs reduces mitochondria biogenesis [11] , we tested whether KLEC-derived exosomes have a similar effect on mitochondrial volume by loading cells with MitoTracker together with Calcein AM . MitoTracker is a fluorescent dye that labels mitochondria within live cells utilising the mitochondrial membrane potential . It therefore allowed us to calculate mitochondrial volume ( MitoTracker staining ) relative to total cell volume ( Calcein staining ) ( Fig 3E ) . Upon incubation with KLEC-derived exosomes we found a ~40% decrease in mitochondria volume in educated cells compared to cells incubated with LEC derived exosomes ( Fig 3F ) . Importantly , we did not detect any significant difference between cells educated using LEC- and ΔmiR-KLEC-derived exosomes , strengthening our notion that the KSHV encoded miRNAs are responsible for this phenotype . The hypoxia-induced factor alpha ( HIF1α ) is a known regulator of glucose metabolism [28 , 29] and can mediate the Warburg effect in cancer cells [30] . KSHV has been shown to activate HIF1α and HIF2α during latency [31] and we have observed that expression of KSHV miRNAs induces HIF1α stabilisation [11] . As shown in Fig 3G , HIF1α expression was increased in cells educated using KLEC-derived exosomes compared to those educated using LEC- or ΔmiR-KLEC-derived exosomes . To further characterise the metabolic effect induced by exosomes secreted from KSHV infected cells , we performed targeted quantitative analysis using capillary electrophoresis mass spectrometry ( CE-MS ) . This analysis showed significant increase in lactate and pyruvate as well as decrease levels of TCA cycle metabolites and ATP , in cells educated by KLEC derived exosomes ( Fig 3H and 3I and S3C Fig ) , supporting our notion that these exosomes reduce mitochondrial activity in educated cells . Importantly , when educated cells were grown for additional 5 days without exosomes , the metabolic phenotype was reversed and their oxygen consumption was comparable to that of untreated LEC ( S3D Fig ) . This supports our notion that these cells are not infected by KSHV and that this phenotype depends on constant transfer of miRNAs from infected cells . Finally , we tested whether exosomes secreted from KLEC have the same metabolic effect on other cell types relevant to KS . Education of Human Umbilical Vein Endothelial Cells ( HUVEC ) , with exosomes extracted from KLEC induced aerobic glycolysis , as shown by reduced oxygen consumption and mitochondria volume ( S3E and S3F Fig ) . Taken together these results suggest that transfer of the KSHV miRNAs via exosomes induces aerobic glycolysis , reduces mitochondria biogenesis and leads to HIF1α stabilisation in surrounding non-infected cells . Many viruses other than KSHV express miRNAs , and we speculated that miRNA transfer via exosomes might be a general mechanism used by viruses to regulate their microenvironment . Epstein Barr Virus ( EBV ) is a human gamma herpes virus which , like KSHV , is the etiological agent for several lymphoid malignancies [32] . EBV encodes at least 40 miRNAs , which were shown to be present in exosomes secreted from EBV transform cells [21 , 33] . The EBV encoded miRNAs are thought to have many target genes in common with KSHV [34–36] and EBV-miR-BART1 has been suggested to regulate metabolism-associated genes [37] . To test whether exosomes secreted from EBV infected cells have similar effect to those secreted from KSHV infected cells , we collected exosomes from the growth media of EBV positive and negative AKATA cell lines ( S4A and S4B Fig ) . We have found that EBV-encoded miRNAs were present in only exosomes secreted by the EBV positive AKATA cells ( Fig 4A ) . Educating human fibroblasts using these exosomes for 48 hours resulted in a 25% decrease in oxygen consumption ( Fig 4B ) , stabilisation of HIF1α ( Fig 4C and 4D ) and expression of its target gene VEGFA ( Fig 4E ) . This suggests EBV can also use exosomes to alter its microenvironment metabolism in a similar way to KSHV . Regulating energy metabolism using miRNAs is not exclusive to viruses , and many cellular miRNAs are also known to control energy metabolism [38] . miR-210 , for example , is known to regulate cell metabolism , is associated with mitochondrial defects and glycolytic phenotype [39–41] , and was suggested to be secreted in exosomes under hypoxic conditions [42] . To test if miR-210 can be transferred in exosomes to alter the metabolism of cells within the microenvironment we forced the expression of miR-210 in HEK293T and HCT116 cell lines . Exosomes secreted from these cells had much higher levels of miR-210 compared to exosomes derived from cells infected with a control vector ( S5A Fig ) . miR-210 directly targets the iron-sulphur assembly proteins ISCU1/2 [43] . To test if miR-210 can be transferred in exosomes and be active in recipient cells , we tested ISCU1 mRNA levels in human fibroblasts , educated using exosomes secreted from control or miR-210 over expressing cells . Education using exosomes contain high levels of miR-210 leads to a ~50% decrease of ISCU1 mRNA levels ( S5B Fig ) . Importantly , these educated cells also reduced their oxygen consumption by 30–40% ( S5C Fig ) . Taken together these results suggest that transfer of miRNAs via exosomes is a general mechanism that can be used by cells in a variety of pathological contexts to regulate the metabolism of cells in their microenvironment . How might metabolic transformation of the microenvironment enhance the fitness of KSHV ? One possibility is that it sensitises surrounding cells to viral infection . To test this , we infected different educated cells with KSHV ( Fig 5A ) . Contrary to our expectations we observed a 50% decrease in infection of cells educated using KLEC-derived exosomes compared to those educated using LEC exosomes ( Fig 5B ) . Similarly , DNA analysis of cell educated using KLEC and ΔmiR-KLEC exosomes showed a similar decrease in the viral copy number in cells educated using KLEC derived exosomes ( Fig 5C ) . To test whether this unexpected effect is due to the metabolic changes induced by these exosomes , we mimicked the exosome metabolic effect by expressing the HIF1α P402A/P564A stable mutant [44] in LEC ( Fig 5D ) . Over-expression of HIF1α had a similar effect to exosome treatment , leading to a 40% reduction in the viral copy number 48 hours’ post KSHV infection ( Fig 5E ) . Thus , it appears that aerobic glycolysis inhibits KSHV infection , and that exosomes secreted from infected cells in fact prevent KSHV spreading into new cells . The fact that uptake of exosomes secreted from LEC reduces viral spreading suggests other benefit for the virus . It has been suggested that the reverse Warburg effect in stromal cells supports growth of cancer cells [3 , 4] . We therefore hypothesised that inducing aerobic glycolysis in nearby non-infected cells supports the growth of KSHV-infected cells . To test this hypothesis , we co-cultured KLEC in Transwell plates with HUVEC educated with different exosomes ( Fig 5F ) . We found that growing in the presence of cell per-educated with KLEC derived exosomes , promoted KLEC growth by ~40% compared to growing in the presence of cells educated with exosomes from ΔmiR-KLEC ( Fig 5G ) . HUVEC over-expressing stable mutant HIF1α had a similar effect on KLEC growth ( Fig 5H ) , supporting the notion that this increased growth is indeed due to the reverse Warburg effect . In cancer models , the reverse Warburg effect is thought to support cancer cell growth by promoting the secretion of energy-rich metabolites such as lactate and pyruvate from non-cancerous neighbours . These metabolites are taken up by the cancer cells and used in the mitochondrial TCA cycle , thereby promoting efficient energy production and higher proliferative capacity [3] . Since we have found that uptake of exosomes secreted from KLEC leads to increased levels of lactate and pyruvate in educated cells ( Fig 3H and 3I ) , we tested whether lactate supports KLEC growth . We found that supplementing the growth medium with Lactate promotes KLEC growth , though to a lesser extent than with HUVEC co-culture ( Fig 5I ) , suggesting that lactate responsible for part of this phenotype . Consistent with this , we found that KSHV infection leads to over expression of the monocarboxylate transporters MCT1 and 2 ( S6 Fig ) , supporting our notion that these cells uptake high energy molecules such as lactate and pyruvate to support their growth . Taken together , these results suggest a metabolic feedback where KSHV infected cells induce aerobic glycolysis in cells in their microenvironment , and those as a result secrete high energy metabolites that support the KSHV infected cells . Kaposi's sarcoma ( KS ) is a highly-vascularised tumour supporting large amounts of neo-angiogenesis . It has been proposed that KSHV directly induces angiogenesis in a paracrine fashion [45] . Consistent with this KSHV infection of endothelial cells in culture induces a number of host pathways involved in activation of angiogenesis and a number of KSHV genes themselves can induce pathways involved in angiogenesis . We have previously shown that expression of the KSHV miRNAs leads to stabilisation of HIF1α in infected cells [11] . Here we found that exosomal transfer of KSHV miRNAs leads to similar affect also in non-infected cells ( Fig 3G ) . Since HIF1α is as a master regulator of angiogenesis[46] , we hypothesis that KSHV uses exosomes to induce angiogenesis also in non-infected cells . To test this , we determined the angiogenic ability of non-infected LEC using an endothelial cell tube-formation assay . As shown in Fig 6A and 6B , LEC educated by KLEC-derived exosomes have greater angiogenic potential compared to LEC educated by LEC or ΔmiR-KLEC derived exosomes . This suggests that viruses can use exosomes secretion as a mechanism to enrich their growth environment by increasing the angiogenic potential of non-infected endothelial cells . It was previously shown that exosomes collected from patient blood or KS models can induce migration [13] . In order to directly test if this phenotype is due to transfer of the viral miRNAs we educated HUVEC using exosomes from LEC , KLEC and ΔmiR-KLEC , and tested their migration capability using wound assay . We found that while HUVEC educated using LEC or ΔmiR-KLEC exosomes migrate similarly , HUVEC educated using KLEC derived exosomes migrate faster ( Fig 6C and 6D ) . Taken together these results suggest that KSHV uses exosomes to induce angiogenesis and migration of non-infected cells around its host cell , and present a potential mechanism allowing the virus to enrich its microenvironment .
Viruses have long served as tools in molecular and cellular biology to study a variety of complex processes . In this study , we reveal a novel mechanism by which oncogenic herpesviruses can regulate the nature of their microenvironment , which has implications for cancer cell biology . We have found that KSHV not only regulates its host cell metabolism , but also alters the metabolism of neighbouring non-infected cells . We report that exosomes secreted from latently infected primary LEC selectively transfer the viral miRNAs into neighbouring cells . While these cells remain uninfected , the viral miRNAs are active in them and down regulate expression of their target genes . This results in a metabolic shift toward aerobic glycolysis and reduced mitochondria biogenesis . Moreover , our data show that this exosomal transfer of miRNAs transforms the exosome-recipient cells into ‘feeder cells’ producing a microenvironment that is more supportive for host cell growth . We suggest this is due to secretion of high energy molecules , such as lactate and pyruvate , that can be used by the infected cells ( Fig 7 ) . Moreover , we found that this flow of genetic information increases the angiogenic potential of non-infected cells and therefore , could further enrich the infected cells microenvironment to support their fitness . Our data suggest that KSHV uses a miRNAs-based mechanism to manipulate the metabolism of cells in its microenvironment . This is based on: ( i ) our previous observation that expression of the KSHV miRNAs is sufficient to induce aerobic glycolysis , ( ii ) our observation that the KSHV miRNAs are active in recipient cells to regulate the same metabolic target genes as in KSHV infected cells and ( iii ) the fact that exosomes secreted from ΔmiR-KLEC , which do not contain the viral miRNAs , do not have the same effect . Nevertheless , while our data show that the KSHV miRNAs are the driving force behind this metabolic phenotype , the possibility that other components in these exosomes might support it , still needs to be further investigated . To some extent it is not unexpected that KSHV miRNAs will be present in exosomes secreted from latent cells , since these miRNAs are highly expressed in them . Indeed , it was shown that these miRNAs are present in exosomes collected from plasma of KS patient or KS mouse models [13] . While identification of viral miRNAs in the blood might be useful for diagnostic purposes , it is hard to appreciate the biological advantage for the virus by transferring these miRNAs in the blood stream . Here we show for the first time that the KSHV miRNAs are selectively enriched in exosomes secreted from infected cells . Critically we show the physiological functionality of this exosomal transfer in shaping the metabolic feature of the infected cells microenvironment and the advantage of this local transfer for virally infected cells . Although exosomes secreted from LEC and KLEC contain both viral and cellular miRNAs , our results support the notion that the KSHV miRNAs are the driving force behind this metabolic transformation since it is not induced by exosomes secreted from cell infected with a mutant virus , lacking the miRNAs cluster ( ΔmiR-KLEC ) . Our results also show that although the KSHV miRNAs are highly expressed in infected cells , much lower levels are sufficient to regulate their target genes and to induce aerobic glycolysis in surrounding cells . These results are consistent with other studies showing that exosomes can transfer miRNAs in sufficient levels to regulate their target genes in the recipient cells [19 , 47–49] . We therefore suggest that infected cells express the viral miRNAs in much higher levels than those required to regulate their target genes , to ensure their inclusion in the secreted exosomes . Our results suggest that regulation of cell metabolism by miRNAs transfer is not unique for KSHV but may present a more general mechanism used by other viruses and cancer cells . EBV , a close relative of KSHV , encodes at least 40 miRNAs , many of which regulate the same genes and pathways as the KSHV miRNAs [35] . We show here that EBV can transfer these miRNAs in exosomes and these can also affect mitochondrial respiration in exosome recipient cells . It was previously shown that exosomes secreted from EBV infected cells also transfer proteins that might be involved in altering the metabolism of recipient cells [22] . While our results do not rule out this possibility , the fact that EBV and KSHV are suggested to share many of their miRNAs target genes supports our model that viral miRNAs transfer is the driving force behind this metabolic regulation . Herpesviruses account for most of the viral encoded miRNAs , though other DNA and even RNA viruses also encode miRNAs [50] . miRNAs are likely to be invisible to the adaptive immune response . Therefore , transferring miRNAs via exosomes would be advantageous during persistent infection since it allows viruses to recruit cells in their vicinity without producing and releasing new viral particles , a process that requires energy and that exposes them to the immune system . Moreover , many cellular miRNAs are also known to be involved in regulation of energy metabolism [38] . miR-210 is highly expressed under hypoxic conditions to alter cellular processes including cell cycle regulation , mitochondria function , apoptosis and angiogenesis [51] . Hypoxia can arise because of oxygen diffusion limitation in avascular primary tumours or due to abnormal tumour microvascularization . For these reasons , these cells might also have poor nutrient supply . Our results suggest the miR-210 , which is over-expressed under these conditions , can be transferred by exosomes into cells in the tumour microenvironment . Inducing the Warburg effect in their microenvironment by transferring miR-210 into normoxic non-cancer cells can support the growth of the hypoxic cancer cells by supplying high-energy molecules such as lactate and pyruvate . Our results with miR-210 , raise the possibility that other onco-miRs can be transferred into the microenvironment to support the tumour growth . Our results suggest that altering their host metabolism by miRNA transfer is a novel mechanism used by oncogenic viruses to influence their hosts . One outcome of this , is growth support for the virus host cell . However , cell metabolism has also been shown to be involved in regulation of other cellular processes that are relevant for KS development and prolong infection . Cell metabolism and specifically oxidative metabolism and glycolysis were also shown to regulate both innate and adaptive immune systems [52] . Moreover , it was recently shown that glucose consumption by tumours metabolically restricts T cells , thereby allowing tumour progression [53] . Our results suggest that viruses might use exosomes to create a hypoglycaemic microenvironment that similarly suppress the immune response against infected cells . Thus , our results raise the possibility that viruses use exosomes to shape the metabolism of their microenvironment during persistent infection , as a mechanism to evade the immune system . It was previously shown that exosomes secreted from breast cancer cells can inhibit glycolysis in the pre-metastatic niche [19] and that exosomes secreted from cancer associated fibroblast can induce aerobic glycolysis in nearby cancer cells [18] . Here we show the opposite effect , whereby uptake of exosomes from herpes viruses-infected cells induce aerobic glycolysis in surrounding normal cells . This is the first-time exosomes are shown to induce the reverse Warburg effect and presents a new mechanism by which cancer cells can recruit cells in their microenvironment to support their growth . KSHV miRNAs were shown to regulate many other cellular processes such as cytokine responses , immune recognition , cell survival , transcriptional reprogramming and angiogenesis [54–56] . Therefore , transfer of the viral miRNAs via exosomes may alter a wide spectrum of processes , adding to the complexity of the relationship between viruses and their hosts . The cross talk between KS cells and their microenvironment warrants further study , with a view to identifying novel therapeutic targets . We present a novel mechanism allowing viruses to regulate the metabolism and migration of cells in their vicinity in a way that supports their fitness by transferring their genetic material via exosomes . We suggest that viruses and cancer cells use this mechanism to shape a specific metabolic niche that will favour their proliferation . It also implies that , similar to cancer cells , latently infected cells depend on their environments for sustained growth . Targeting this miRNA transfer-based metabolic cross-talk between diseased cells and their microenvironment could therefore open a new therapeutic window .
HUVEC and LEC were purchased from Promocell and grown in endothelial growth medium 2 and MV2 ( Promocell ) respectively . Both cell types were used for experiments before passage 8 . To exclude exosomes derived from the FBS , it was subjected to centrifugation of 120 , 000g for 3 hours . iSLK producing cells were kindly provided by Rolf Renne ( University of Florida ) . KSHV producing iSLK cells were cultured in DMEM ( Invitrogen ) , supplemented with 10% FBS , 1μg/ml Puromycin , 250μg/ml Geneticin and 1200μg/ml Hygromycin . HEK293T ( ATCC ) and HCT116 ( ATCC ) cells were cultured in DMEM ( Invitrogen ) , supplemented with 10% FBS . EBV producer line Akata ( kindly provided by Paul Farrell , Imperial College ) and EBV negative Akata ( kindly provided by Andrew Bell , University of Birmingham ) were cultured in RPMI 1640 ( Invitrogen ) supplemented with 10% FCS . Wild type and ΔmiR-cluster KSHV were prepared from iSLK cells as previously described [23] . Early passage LEC were infected and selected using 50ug/ml Hygromycin B ( Invitrogen ) . Cells were tested for 100% infection ( GFP positive ) before carrying on any experiment . Repeats for each experiment were performed using different KSHV infections . Uninfected LEC and LEC infected with WT or ΔmiR-cluster were grown up to approximately 80% confluency . Infected cells were cultured for at least 6 days before collecting media to verify latency establishment , and were grown up to passage 8 . Medium was replaced every 48 , and kept at 4°C for up to 7 days . The medium was subjected to centrifugation of 300g for 5 minutes , 2000g for 10 minutes and concentrated with a Centricon Plus-70 filter ( Millipore ) according to the manufacturer’s instructions . The media was then subjected to centrifugation of 10 , 000g for 1 hour , and was filtered using 0 . 22μM filters . To purify exosomes , the sample was subjected to ultracentrifugation at 120 , 000xg for 1 hour and washed once with PBS . Exosomes pellet was resuspended in PBS , quantified for protein concentration and particle number , and stored at -80°C . The exosome suspensions in PBS were incubated on formvar- and 2 nm carbon-coated copper grids overnight at 4°C in a humidified chamber . They were then washed twice in ddH20 by dipping onto the surface of a water droplet and then stained with 2% aqueous uranyl acetate for 2 . 5 minutes . The stain was drawn off with cartridge paper to leave a thin negative stain . The sections were examined in a Jeol 1010 microscope . Images were taken with a Gatan Orius SC100B charge-coupled device camera and analysed with Gatan Digital Micrograph . Characterisation of vesicle size distribution and concentration was performed using Nanoparticle Tracking Analysis ( NTA ) ( Malvern Instruments , Nanosight NS300 ) . Sample size distributions were calibrated in a liquid suspension by the analysis of Brownian motion via light scattering . Nanosight provides single particle size and concentration measurements . Total RNA was extracted from exosomes or from their respective parental cells ( 3 biological replicates for each condition ) using the miRNeasy mini-kit ( Qiagen ) and was quantified using Qubit RNA HS assay kit . 200ng or 500ng of RNA from exosomes or cells respectively , were used for small RNA libraries using the NEBNext Multiplex Small RNA Library Prep Set for Illumina ( NEB ) according to the manufacturer’s instructions . Constructed libraries were assessed using the BioAnalyzer 2100 ( Agilent ) and KAPA Library Quantification Kit ( KAPA Biosystems ) . Quantified libraries were then sequenced using the single-end sequencing protocol at 36bp . Expression of human and KSHV miRNA reads were quantified using PaTMaN [57] rapid aligner to the miRbase release 21 database of miRNA sequences [http://mirbase . org] allowing for 1 mismatch ( or counting only perfect matches if more than one miRNA was matched ) . Normalisation and differential expression were performed using the DESeq2 method [58] , whilst multiple testing correction employed independent hypothesis weighting ( IHW ) for greater statistical power [59] . For education experiments 105 non-infected cells were incubated for total of 48 hours when 2 . 5x109 exosomes were added every 24 hours . For biological replicates of all experiments , we used exosomes from separate KSHV infections . For Transwell co-culture experiments , indicated cells were grown on both compartments for total of 5 days and media was replaced every other day . For exosome-tracking experiments , purified exosomes were fluorescently labelled using Carboxyfluorescein succinimidyl diacetate ester ( System Biosciences ) . Labelled exosomes were washed in 20 ml of PBS , collected by ultracentrifugation , and resuspended in PBS . Cells were seeded in XF 24-well cell culture microplates ( Seahorse Bioscience ) at 4 × 104 cells/well ( 0 . 32 cm2 ) in 200μl growth medium and then incubated at 37°C/5% CO2 for 20–24 hours . Assays were initiated by removing the growth medium from each well and replacing it with 600μl of assay medium pre-warmed to 37°C . The cells were incubated at 37°C for 30 minutes to allow media temperature and pH to reach equilibrium before the first-rate measurement . Prior to each rate measurement , the XF24 Analyzer gently mixed the assay media in each well for 3 min to allow the oxygen partial pressure to reach equilibrium . Following mixing , OCR and ECAR were measured simultaneously for 4 min to establish a baseline rate . The assay medium was then gently mixed again for 3 min between each rate measurement to restore normal oxygen tension and pH in the microenvironment surrounding the cells . Uncoupled , maximal and non-mitochondrial respiration was determined after the addition of 5 μM oligomycin , 1 μM carbonyl cyanide 4- ( trifluoromethoxy ) phenylhydrazone ( FCCP ) and 2 μM antimycin-A . All chemicals were from Sigma-Aldrich . Glucose uptake was measured by incubating cells with 30μM glucose analogue 6-NBDG ( Invitrogen ) for 15 minutes . Cells where then washed and trypsinized and their fluorescence ( λex: 465 nm , λem: 540nm ) was measured by flow cytometry . Cells were loaded with 5μM Calcein-AM and 50nM MitoTracker Red ( Invitrogen; 37 °C , 30 minutes ) in growth media for 30 minutes and Z-series of images were acquired using a Zeiss LSM 510 system ( Carl Zeiss , Inc . , Cambridge , UK ) , as previously described [11] . Maximal projection of images was used to quantify the area of green ( Calcein ) and red ( mitoTracker Red ) signal . Mitochondrial area was defined relative to cytoplasmic area as ‘area red/area green’ . Images were analyzed using the MetaMorph Microscopy Automation & Image Analysis Software ( Molecular Devices ) . The two channels ( Calcein-AM and MitoTracker Red ) were separated and threshold in order to acquire two separate binary images . To ensure reproducibility , for each biological repeat , we analysed images of at least 10 fields , when each field contain between 5–10 cells . Metabolites were extracted according to the Human Metabolome Technologies , Inc . protocol . Shortly , 2x106 cells were washed twice using 5% mannitol solution and metabolites were extracted by adding methanol for 30 second . The extracted solution was centrifuged 2300g at 4°C for 5 minutes , filtered and evaporated using centrifugal evaporator . Analysing the ionic metabolites including in these cells by CE-TOFMS and CE-QqQMS was performed by Human Metabolome Technologies , Inc . The results in the manuscript present 3 biological repeats , where cells were educated using exosomes from different KSHV infections . Cells were lysed in RIPA buffer ( 300mM Sodium Chloride , 1% NP-40 , 0 . 5% Sodium deoxycholate , 0 . 1% Sodium dodecyl sulphate and 50mM Tris pH 8 . 0 ) . Exosomes were lysed directly into 1x Laemmli sample buffer ( Bio-Rad Laboratories ) . Equal amounts of protein were resolved on Mini-PROTEAN TGX Precast gels ( Bio-Rad Laboratories ) . Antibodies against CD63 ( Invitrogen ) , α-TUBULIN ( Sigma-Aldrich ) , HIF1α ( BD Transduction Laboratories ) , LAMIN A/C ( Santa Cruz Biotechnology ) , EBV-GP125 ( GeneTex ) and KSHV-ORF8 ( ThermoFisher Scientific ) were detected with IRDye secondary antibodies ( LI-COR ) or HRP-conjugated secondary antibodies . Images were acquired using the Li-COR Odyssey Imaging System or the GE Healthcare Imagequant LAS 4000 . Genomic DNA for qPCR was extracted using the QIAamp DNA mini-kit ( Qiagen ) . Cell total RNA was extracted using either the RNeasy mini-kit or the miRNeasy mini-kit ( Qiagen ) . Exosomal RNA was extracted using the miRNeasy mini-kit ( Qiagen ) . KSHV genome copy numbers were quantified by qPCR as previously described [11] . cDNA synthesis for qRT–PCR quantification of mature miRNAs was performed using the Exiqon Universal cDNA Synthesis Kit II according to the manufacturer's instructions . Detection of the mature KSHV miRNAs was performed using the KSHV-miR LNA PCR primer sets ( Exiqon ) . Where indicated cellular small nucleolar RNA RNU66 or S5 rRNA were used as a reference RNA . Importantly , the KSHV LNA PCR primer sets do not give any background detection for negative control ( such as non-infected cells or ΔmiR-KLEC ) . The reporter plasmids for the 3’UTRs of the indicated genes were previously described [11] . Cells expressing each of the reporter plasmids were incubated for 48 hours with the indicated exosomes for 48 hours . Cells were harvested according to the Dual-Luciferase Reporter assay system ( Promega ) . Luciferase activity was measured using a Fluoroskan Ascent FL luminometer ( ThermoScientific ) . Firefly activity was normalised to internal Renilla luciferase levels . Growth factor–reduced Matrigel ( Becton Dickinson ) was placed in 96-well tissue culture plates ( 75μl/well ) and allowed to gel at 37°C for 30 minutes . Then 1x104 LEC , pre-incubated with exosomes derived from LEC , KLEC or ΔmiR-KLEC , were added to each well and incubated at 37°C for 24 hours . Morphological changes were visualised using phase contrast microscope . Educated HUVEC were seeded in 12 well plates and grown overnight to confluence . A 200μl tip was used to make a straight scratch and plates were immediately placed in Nikon Biostation CT or Zeiss Cell Observer . Images were acquired for 16 hours and analysed for scratch area using ImageJ . miR-210 was cloned using the Gateway Cloning protocol ( Invitrogen ) . Shortly , the mature microRNA was amplified using the primers: The PCR product subcloned into the gateway entry vector pENTR/pTER+ [60] . The miRNA was further cloned into the 3rd gen lentiviral promoter-less Gateway destination vector pLenti X1 Puro DEST using the Gateway LR Clonase II enzyme mix ( Invitrogen ) . | The metabolic state within a cell and its local environment is altered in many diseases and disorders including those caused by viral infections . The gamma-herpesviruses Kaposi’s Sarcoma Associated Herpesvirus ( KSHV ) is a viral agent associated with a large number of human malignancies . KSHV was shown to manipulate the metabolism of host cells and to induce similar metabolic changes to those found in non-viral cancers . Our work demonstrates that KSHV not only regulates host cell metabolism , but also alters the metabolism of neighbouring non-infected cells . We report that exosomes secreted from KSHV-infected cells selectively transfer the viral miRNAs into neighbouring cells . While these cells remain uninfected , the viral miRNAs are active in them to induce the Warburg effect . Moreover , our data show that this exosomal transfer of miRNAs transforms the exosome-recipient cells into ‘feeder cells’ producing a microenvironment that is more supportive for host cell growth . Finally , we found that this flow of genetic information increases the angiogenic potential of non-infected cells and therefore , could further enrich the infected cell microenvironment to support their fitness . This previously undescribed mechanism provides important insight into cancer and viral pathology and suggests new avenues for therapeutic intervention . | [
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"processes... | 2017 | Herpesviruses shape tumour microenvironment through exosomal transfer of viral microRNAs |
Mammalian circadian rhythm is established by the negative feedback loops consisting of a set of clock genes , which lead to the circadian expression of thousands of downstream genes in vivo . As genome-wide transcription is organized under the high-order chromosome structure , it is largely uncharted how circadian gene expression is influenced by chromosome architecture . We focus on the function of chromatin structure proteins cohesin as well as CTCF ( CCCTC-binding factor ) in circadian rhythm . Using circular chromosome conformation capture sequencing , we systematically examined the interacting loci of a Bmal1-bound super-enhancer upstream of a clock gene Nr1d1 in mouse liver . These interactions are largely stable in the circadian cycle and cohesin binding sites are enriched in the interactome . Global analysis showed that cohesin-CTCF co-binding sites tend to insulate the phases of circadian oscillating genes while cohesin-non-CTCF sites are associated with high circadian rhythmicity of transcription . A model integrating the effects of cohesin and CTCF markedly improved the mechanistic understanding of circadian gene expression . Further experiments in cohesin knockout cells demonstrated that cohesin is required at least in part for driving the circadian gene expression by facilitating the enhancer-promoter looping . This study provided a novel insight into the relationship between circadian transcriptome and the high-order chromosome structure .
Circadian rhythm is a daily oscillation of physiological processes and behaviors in varieties of living systems [1 , 2] . In mammals , the endogenous clock is established by interconnected transcriptional-translational feedback loops including a series of clock genes , for instance , Bmal1 , Clock , Nr1d1 , Nr1d2 , Per and Cry family genes [3 , 4] . Transcription factor complex Bmal1-Clock drives Nr1d1 , Nr1d2 , Per and Cry family gene expression via cis-regulatory element E-box . Conversely , Per and Cry proteins repress the transcriptional activity of Bmal1-Clock by protein-protein interaction . In addition , transcription repressors Nr1d1 ( Rev-erbα ) and Nr1d2 ( Rev-erbβ ) inhibit the transcription of Bmal1 through retinoic acid-related orphan receptor response element ( RRE ) . Other clock genes like Dbp , Tef , Dec1 , and Dec2 are also involved in the feedback loops . These genes constitute the molecular makeup of central clock system that robustly oscillates across different tissues and generate the circadian expression of thousands of downstream genes . In mammals , master clock residing in suprachiasmatic nucleus ( SCN ) directs tissue-specific circadian clocks in peripheral tissues . Circadian oscillating genes ( COGs ) showing 24-hour rhythm in mRNA expression level in mouse liver have been intensively studied by transcriptomic profiling technologies [5 , 6] . High-throughput studies on circadian transcription factor binding [6 , 7] and histone modifications [6 , 8] by ChIP-Seq , and enhancer RNAs by GRO-Seq [9] have hinted the circadian regulation in intergenic regions distal to gene promoters . Furthermore , the cycling profiles of many COGs were found to be inconsistent with the proximal binding of circadian transcription factors [10] . Thus , long-range chromasome interactions between promoters and enhancers may be required for a deeper understanding of the temporal organization of widespread COGs . Over the past few years , the development of comprehensive chromosomal interaction mapping technologies facilitated our current understanding of three-dimensional architecture in chromosome conformation [11] . It was found that the boundaries of chromatin interaction domains are enriched for binding sites of CTCF ( CCCTC-binding factor ) [12 , 13] , which is commonly accepted as a barrier protein binding to the insulators [14] . Cohesin is another chromosome structure protein with crucial function in sister chromatin cohesion and chromosome remodeling [15] . Cohesin complex contains four subunits , Smc1 , Smc3 , Scc1 ( also called Rad21 ) , and Scc3 ( known as Stag1 and Stag2 in mammalian cells ) , which form an open-close ring structure to hold DNA [16 , 17] . Cohesin cooperates with Mediator or CTCF [18 , 19] in controlling gene expression independent of its function in sister chromatid cohesion [20] . The co-binding sites of CTCF and cohesin repress gene expression by insulating enhancer action [18 , 21] . In comparison , CTCF-independent cohesin binding sites are reported to be cell type specific and predominately associated with transcriptional factor binding sites [22 , 23] . The high-order chromosome structure conveys important message on the transcription [24] , which should also apply to the regulation of COGs . An earlier study in mouse embryonic fibroblast ( MEF ) cells analyzed the chromosomal interactions anchored to a COG Dbp [25] . However , the roles of chromosome structure proteins were not yet explored . In this study , we systematically identified long-range interactions involving a Bmal1 bound super-enhancer upstream of a clock gene Nr1d1 in mouse liver . Notably , we found that cohesin binding sites are enriched in these interactions . With bioinformatics analysis and further experiments in cohesin-deficient MEF cells , our study provides the first line of evidences that cohesin can exert the influence upon genome-wide circadian expression by mediating long-range chromosome interactions .
To study the effect of high-order chromatin structure on circadian rhythm , we focus on a pioneer-like transcription factor in circadian regulation: Bmal1 [26] . We identified 3 , 244 Bmal1 enhancers [27] in mouse liver from published Bmal1 binding sites and histone marks of enhancers . Among them , the top 3% with highest Bmal1 binding signals were defined as super-enhancers [28] ( Methods , S1 Table ) . To reveal the long-range interactions involved in circadian enhancers , we selected a Bmal1 super-enhancer located ~8 kb upstream of a clock gene Nr1d1 ( S1A Fig ) . This enhancer harbors the strongest Bmal1 binding site in mouse liver with rhythmic binding ( S1B Fig ) . Using this enhancer as the bait , we detected its interacting regions in mouse liver by circular chromosome conformation capture sequencing ( 4C-Seq ) at CT6 ( CT: circadian time , n = 3 ) and CT18 ( n = 3 ) when Bmal1 binding is at its peak and trough respectively . Genomic regions consistently enriched in 4C signals in at least two out of three biological replicates at a given time point were identified as enhancer interacting regions , resulting 49 regions at CT6 and 51 regions at CT18 respectively within 2 Mb to the enhancer ( FDR = 0 . 01 , Methods and S2 Table ) . A highly interacting region spanning approximately ~150 kb around the bait region shows markedly elevated signals at both CT6 and CT18 ( Fig 1A and S1C Fig ) . We next obtained 3 , 018 COGs and their circadian phases from a published microarray data of high temporal resolution in mouse liver [5] . Out of them , Fbxl20 , Cdk12 , Med24 , Thra , and Nr1d1 show interactions with the enhancer at CT6 . Quantitative chromosome conformation capture ( 3C-qPCR ) analysis was performed to validate the interactions between selected COGs and the enhancer at CT6 . In all cases tested , the interactions identified by 4C are highly consistent with 3C-qPCR results ( Fig 1A and 1B ) . Cdk12 shows a weak interaction with the enhancer . Ormdl3 , a non-interacting COG at CT6 , shows a lower interaction with the enhancer than the control . In comparison , Nr1d1 , Thra , and Med24 demonstrate strong interactions with the enhancer of 6–30 folds over the nearby control regions . The highly interacting region identified in our study falls into one of topologically associating domains ( TAD ) identified by Hi-C in mouse embryonic stem ( ES ) cells [12] ( S2 Fig ) . The circadian phases of Thra and Med24 are both around CT0 ( Fig 2A ) . The closeness of their circadian phases suggests that they are likely co-regulated by the same enhancer [8] . Interestingly , the interactions with the bait within highly interacting region are significantly enriched of chromatin loops from cohesin ChIA-PET ( chromatin interaction analysis by paired-end tag ) data [29] ( Fig 1A , Chi-squared test p < 10−16 ) but are devoid of chromatin loops from CTCF ChIA-PET data in mouse ES cells [30] . The interactome data in mouse ES cells implies the potential involvement of cohesin in the long-range interactions with Nr1d1 enhancer . When we examined broader regions of interactions , nearly 50% of enhancer interacting regions at CT6 or CT18 overlapped with cohesin-non-CTCF sites as compared to merely 20% for cohesin-CTCF sites or random sites obtained by the permutation of cohesin-CTCF or cohesin-non-CTCF sites ( Fig 2B ) . Furthermore , we profiled all Bmal1 super-enhancers in mouse liver and found that they have high occupancy of cohesin ( Fig 2C ) . Therefore , our 4C-Seq data suggested that cohesin is implicated in facilitating circadian enhancer and promoter interactions . To globally investigate the relationship between circadian gene expression and chromosome structure proteins , we collected circadian cistrome data consisting of different DNA-binding proteins including architectural proteins cohesin and CTCF [22] , core circadian transcription factors Bmal1 and Nr1d1 [6] , as well as a non-circadian transcription factor Gabpa [22] from published ChIP-Seq datasets in mouse liver ( Methods ) . All datasets were analyzed from the raw data and with the same pipeline . None of the components of cohesin and CTCF are circadian oscillating in their expression levels in mouse liver [3] . Because of the distinct function of cohesin from CTCF [22] , we further classified the cohesin binding sites into cohesin-CTCF co-binding sites and cohesin-non-CTCF binding sites . Compared to cohesin-non-CTCF , the number of CTCF-non-cohesin sites is much fewer and therefore has been omitted from the analysis . In total , we obtained 10 , 948 , 28 , 883 , 23 , 662 , 41 , 690 , and 32 , 899 binding sites for Bmal1 , Nr1d1 , Gabpa , cohesin-CTCF , and cohesin-non-CTCF in mouse liver respectively . We defined a nucleotide-level circadian index using circadian time-series GRO-Seq data [9] to quantify circadian transcriptional activities across whole mouse genome in mouse liver ( Methods ) . In our definition , higher circadian index indicates stronger rhythmicity . As expected , the binding centers of Bmal1 and Nr1d1 have overall higher circadian indices than the other factors or genomic background . Interestingly , the profile of cohesin-non-CTCF sites is between Bmal1/Nr1d1 and the random sites , which implicates a positive role of cohesin-non-CTCF on circadian rhythmicity ( Fig 3A , S3A Fig ) . This phenomenon is again observed when defining circadian index using circadian time-series RNA-Seq data [6] ( S3B Fig ) . Moreover , both Bmal1 and Nr1d1 binding sites prefer to overlap with cohesin-non-CTCF binding sites rather than cohesin-CTCF binding sites in mouse liver ( Fisher’s exact test p < 10−22 , Fig 3B ) . Therefore , cohesin-non-CTCF sites are associated with high circadian rhythmicity of transcription . Cohesin-CTCF co-binding sites are known to play the role of genomic insulator [32] . To study whether cohesin-CTCF sites affect the circadian gene expression in mouse liver , we compared the phase differences of two neighboring COGs separated by a given binding site to the genomic background ( Methods , S3C Fig ) . The phase differences of two adjacent windows were significantly smaller than the phase differences of two windows that were randomly picked from the genome ( Mann-Whitney U test p = 10−10 , S3C Fig ) . This demonstrated that the neighboring COGs across the genome tend to have similar circadian phases . Interestingly , the phase differences across cohesin-CTCF sites show a bimodal distribution and are significantly larger than those in genomic background ( Mann-Whitney U test p = 0 . 03 , Fig 3C ) . On the contrary , Bmal1 and Nr1d1 binding reduced the phase differences of COGs across their binding sites ( Mann-Whitney U test p = 0 . 03 and 0 . 006 respectively ) . It indicated that Bmal1 and Nr1d1 might lead to the oscillation of genes in the similar phases in both directions flanking the binding sites . The effect of cohesin-non-CTCF was again similar to those of Bmal1 or Nr1d1 , although it is only moderately statistically significant from the background ( Mann-Whitney U test p = 0 . 1 ) . In comparison , the distribution of phase differences across Gabpa sites was similar to that of genomic background . These results revealed that cohesin-CTCF co-binding sites tend to disrupt the phase continuity of neighboring COGs . We then asked whether the COGs within the same domain defined by interacting cohesin-CTCF sites show similar circadian phases . Due to the lack of cohesin-CTCF domains in mouse liver , we inferred tissue/cell type invariant cohesin loops from the ChIA-PET data in mouse ES cells [29] in which only loops with both anchors overlapped with cohesin-CTCF binding sites in mouse liver were selected ( S3D Fig ) . The variance of circadian phases was used to measure the phase difference of two or more COGs . We observed that the phase variance of genomic background increases as an exponential function of the domain size ( S3D Fig , p < 10−16 , Pearson’s correlation coefficient = 0 . 37 ) . To take into account of this size effect , we divided cohesin-CTCF domains into three categories according to their sizes and compared to genomic background in the corresponding sizes ( Methods ) . The phase variances were smaller in cohesin-CTCF domains of medium and large sizes compared to genomic background at significance levels of p = 0 . 01 and 0 . 003 respectively ( Fig 3D , Mann-Whitney U test ) . In summary , the chromosomal domains defined by cohesin-CTCF co-binding sites tend to lock the phases of COGs . In light of the above observations , we proposed a model that incorporated the effects of cohesin mediated enhancer-promoter interactions on the gene regulation in chromosomal domains defined by the co-binding of cohesin and CTCF ( Fig 4A ) . We adopted the concept of regulatory potential to quantify the regulation of a gene by a given circadian transcription factor [33] . The regulatory potentials of Bmal1 on all annotated genes in mouse genome were calculated with or without considering the effect of cohesin and CTCF ( Methods , Fig 4B and S3 Table ) . In the background model , the regulatory potential Bi of Bmal1 on a given gene i was computed as the sum of contributions from all available Bmal1 binding sites j within 2 Mb of the gene , that is , Bi=∑Dij<2Mb ( e−Dij/λ1⋅Sj ) , where Dij is the distance between gene i and Bmal1 binding site j and Sj is the strength of Bmal1 binding at site j . Here we assumed that the regulatory effect of transcriptional factor on its target gene decays exponentially with distance from the binding site to its target gene and λ1 is the characteristic distance . In the cohesin/CTCF dependent model , the contribution of gene i and Bmal1 binding site j was further multiplied by three factors corresponding to the enhancing effects of a cohesin-non-CTCF site either near gene i ( CNCi ) or a Bmal1 binding site j ( CNCj ) as well as the insulating effect of cohesin-CTCF site ( CCij ) , i . e . Pi=∑Dij<2Mb ( e−Dij/λ1⋅Sj ) ⋅CNCi⋅CNCj⋅CCij , ( Methods ) . At last , the regulatory potentials were normalized to the ranks across all genes to ensure the robustness of model parameters . Comparing with the background model , the regulatory potentials of Bmal1 on COGs were significantly higher in the cohesin/CTCF dependent model ( Fig 4C , Kolmogorov-Smirnov test p = 10−16 ) . It was known that the circadian phase of Bmal1 binding occurs around CT6 [7] and the phases of COGs directly controlled by Bmal1 are typically between CT6 and CT12 . We found that the phases of COGs with top ranked Bmal1 regulatory potentials in cohesin/CTCF dependent model are more enriched in CT6-CT12 following the binding peak of Bmal1 at CT6 compared to the background model ( Fig 4D ) . Using Nr1d1 ChIP-Seq data , we observed that Nr1d1 regulatory potentials in cohesin/CTCF dependent model could also distinguish COGs from non-COGs ( S4A and S4B Fig ) . The fact that most core circadian clock genes have higher regulatory potentials in cohesin/CTCF dependent model suggested that chromosome structure proteins might facilitate the transcription of core components of circadian clock ( S4C Fig ) . Taken together , our cohesin/CTCF dependent model is a more sophisticated model that integrated circadian transcription factors and chromatin organizers to explain the circadian gene expression . To validate the regulatory potentials of circadian transcription factors , we examined the differentially expressed genes in the livers of Bmal1 knockout ( KO ) ( Fan et al . , manuscript in preparation ) and Nr1d1 KO mice [9] . We observed that under-expressed genes in Bmal1 KO have higher Bmal1 regulatory potentials in cohesin/CTCF dependent model than those in the background model , while over-expressed genes in Bmal1 KO have similar regulatory potentials between two models ( Fig 4E ) . In contrast , over-expressed genes rather than under-expressed genes in Nr1d1 KO showed much higher Nr1d1 regulatory potentials in cohesin/CTCF dependent model than those in the background model ( Fig 4E ) . This is consistent with the current notion that Bmal1 functions as an activator and Nr1d1 as a repressor in circadian regulation . The knock-out of cohesin subunits , Smc3 , Scc1 , and Scc3 , lead to the embryonic lethality in mice [34] . To establish a knock-out system of cohesin in vitro , we transfected the post-mitotic Smc3-flox/flox MEF cells by Cre/GFP adenovirus such that the expression of Smc3 decreased by 80–90% in Smc3-/- cells compared to control cells ( Methods ) . We measured the mRNA levels of four clock genes in Smc3-/- cells by RT-PCR assays after synchronizing the cells with dexamethasone treatment ( Fig 5A ) . All genes showed significant oscillations both in KO and control cells ( cosine fitting , p < 0 . 05 ) except for Nr1d1 in KO cells . Nr1d1 showed under-expression in KO cells ( ANOVA , p = 10−7 ) . The peak-trough ratio of Bmal1 dropped from 3 . 7 in control to 2 . 4 in KO cells . The circadian oscillations of Dbp and Per3 were not affected upon cohesin KO . Although the core clock genes have consistent cycling expression in vivo across tissues [3] , the number of circadian oscillating genes in vitro in cell lines is much fewer than in vivo . To examine the gene regulation of circadian transcriptional factors in MEFs , we conducted Bmal1 ChIP-seq data in control MEF cells ( Methods ) . However , only 244 Bmal1 binding sites were identified ( S4 Table ) including those on the promoters of core clock genes , Nr1d1 ( S1A Fig ) , as well as Nr1d2 , Cry1 , Cry2 , Per1 , Bhlhe41 , and Dbp ( S4 Table ) . The lack of Bmal1 binding sites on most hepatic COGs is consistent with the fact that they are not oscillating in synchronized MEF cells . To reveal the broader impact of cohesin on gene expression , we then applied RNA-Seq to measure gene expression in Smc3-/- MEFs vs . control MEF cells . In total , 248 and 1 , 064 genes were identified as over-expressed and under-expressed genes respectively in cohesin KO ( log2 fold change > 0 . 8 , Fig 5B and S4 Table ) . The promoter regions of differentially expressed genes upon cohesin KO were enriched with cohesin binding sites in MEFs ( Fisher’s exact test p = 0 . 002 ) . Interestingly , the genes involved in circadian clock were significantly enriched among the under-expressed genes by Gene Set Enrichment Analysis [35] among the canonical pathways ( FDR = 10−8 ) . To extrapolate our result in cohesin KO MEFs to mouse liver , we next focus on tissue/cell type invariant enhancer-promoter interactions mediated by cohesin . We found that 22% of differentially expressed genes in cohesin KO have their promoter regions situated near an anchor of cohesin loops in mouse ES cells [29] , suggesting they are regulated by invariant enhancer-promoter loops . To identify the invariant cohesin loops , we required that both anchors of the cohesin loop in ES cells are also bound by cohesin in mouse liver . Furthermore , one anchor of the loop is situated within 15 kb near either a Bmal1 or Nr1d1 binding site in liver and the other anchor resides within 5 kb near the transcription start site of a hepatic COG that was also differentially expressed in cohesin KO in MEFs . We also required that the circadian phases of candidate genes fall into either Bmal1 controlled phase regime ( CT6-CT12 ) or Nr1d1 controlled phase regime ( CT20-CT2 ) . The candidate pairs of COGs and enhancers identified were listed in Table 1 . We then used 3C-qPCR experiments to confirm the presence of enhancer-promoter interactions in two such cases , Phldb2 and Ahnak in mouse liver ( Fig 6B and S5B Fig ) . We also found that both interactions were significantly weakened in cohesin KO MEF cells compared to control cells ( Fig 6C and S5C Fig ) . Phldb2 encodes a microtubule-anchoring factor that binds to phosphoinositides and filamin [36] . Phldb2 shows circadian phase at CT7 in mouse liver and is interacting with a Bmal1-bound enhancer situated 126 kb upstream in the intron of another gene Plxd2 ( Fig 6A ) . This Bmal1 binding site is confirmed by ChIP-PCR in mouse liver ( S6A Fig ) . Ahnak protein is a mediator in calcium signaling and transforming growth factor β signaling pathways [37] . Ahnak shows circadian phase at CT21 and is interacting with an Nr1d1-controlled enhancer ( S5A Fig ) . The promoters of Phldb2 and Ahnak are devoid of any Bmal1 or Nr1d1 binding sites in liver . Furthermore , we found conserved histone modification marks of active transcription and cohesin binding sites at these two genes and their enhancer loci in both MEFs and liver . This supports that the cohesin-mediated loops in Phldb2 and Ahnak are invariant between tissues or cell types . Finally , to show that these interactions are functional for gene regulation , we used CRISPR-CAS9 system to delete the cohesin binding site near the enhancer of Phldb2 in Hepa1-6 cells and found a significant reduction of 41% in the expression of Phldb2 ( Fig 6D and S6B Fig ) . Phldb2 is not circadian oscillating in synchronized MEFs or Hepa1-6 cells due to the lack of Bmal1 binding in their enhancers ( Fig 6A ) . The binding of Bmal1 on the enhancer of Phldb2 renders its circadian expression in liver . Taken together , our results suggest that the stable and invariant enhancer-promoter loop mediated by cohesin is a prerequisite for temporal gene regulation in circadian rhythm .
In complex organisms , it is known that genome-wide transcription is highly organized under high-order chromosome structure . In particular , distal enhancer has been considered to play a key role in gene regulation through long-range interactions . Given its far-reaching effect on gene expression , circadian clock is an ideal system to investigate the interplay between chromosome architecture and temporal regulation of gene expression under homeostasis . It was proposed that orchestrated transcription takes place at the so-called “transcription factories” where genes from distant loci across the genome are physically in contact . COGs within the same transcription factory may be regulated under the common circadian regulators such as Bmal1 . Our 4C-Seq data for a super-enhancer upstream of Nr1d1 provided evidence of physical interactions between the enhancer and multiple COGs . This super-enhancer contains the binding sites of both Bmal1 and Nr1d1 ( S1A Fig ) , a common feature in circadian cistrome [6] . Among the interacting genes within 2 Mb of the super-enhancer , the circadian phases of Fbxl20 , Nr1d1 , and Eif1 follows the phase of Bmal1 binding , while the phases of Ormdl3 , Med22 , and Thra suggests that they are more likely co-regulated by Nr1d1 ( Fig 2A ) . We also found that strong interactions within 150 kb of the super-enhancer were independent of circadian time and restricted in a cell type invariant TAD . These results hinted that the long-range interaction acts as a stable backbone rather than a dynamic driving force for circadian regulation . Similar findings of stable interactions have been reported in other temporal processes such as animal development [38] although chromosome domains are highly dynamic during the stages of cell cycle [39] . Comparing with cohesin ChIA-PET data in mouse ES cells [29] , we found the presence of multiple cohesin-mediated loops coinciding with the highly interacting regions of the enhancer . The enrichment of cohesin binding signals in both 4C interacting regions and on Bmal1 super-enhancers conforms to the general role of cohesin in organizing genome structure for gene regulation , although this may not be unique for circadian regulation . The availability of cohesin and CTCF ChIP-Seq data in mouse liver provided us a unique opportunity to investigate the genome-wide association between cohesin and CTCF binding sites with circadian genomic features . To examine the effect of cohesin in circadian system , we designed a pipeline to capture the continuous change of circadian rhythmicity of transcription across the binding sites of several proteins including Bmal1 , Nr1d1 , Gabpa , CTCF , and cohesin at 2 kb resolution . This unbiased approach allowed us to include un-annotated transcripts as well as unconventional transcription that does not take place from transcription start sites [40] . We observed that the profile of cohesin-non-CTCF binding sites resembles that of Bmal1 as compared to other non-circadian transcription factors . It suggests that cohesin-non-CTCF sites have a positive effect on circadian rhythmicity of transcription although cohesin itself is not known to be a circadian regulator . From circadian phase analysis , we noted that neighboring circadian genes tend to have similar circadian phases while the co-binding of CTCF and cohesin leads to the insulation of circadian phases . Our finding is consistent with a recent study reporting that CTCF attenuates the transcription of circadian oscillating genes by mediating their contacts to the nuclear lamina [41] . Previous study showed that the nearest genes around Bmal1 sites were not always rhythmically expressed or peaking in the phase regime predicted for a Bmal1 controlled gene [7] . Based on 4C-Seq and bioinformatics analysis , our model incorporating the disparate effects of cohesin-non-CTCF and cohesin-CTCF provides a better predictor of the circadian expression of genes and their phases . In this study , we have utilized a range of datasets from mouse liver and several cell lines . We chose mouse liver for our main analysis because it shows genome-wide circadian oscillation of gene expression [5] and it has the most comprehensive circadian transcriptomic and cistromic data to date . Mouse cell lines were used when genetic manipulations are not possible in liver as cohesin-deficiency is embryonic lethal in vivo [34] . Although synchronized MEF cells have been widely used for circadian studies [25 , 42–44] , there are much fewer genes oscillating in MEFs and only core circadian genes are oscillating in both liver and MEFs . This is largely due to the lack of Bmal1 binding sites in MEFs as shown by our Bmal1 ChIP-Seq data in MEFs . For this reason , we used cohesin-deficient MEFs only to select candidate genes regulated by the invariant enhancer-promoter interactions mediated by cohesin even if these genes including Phldb2 and Ahnak are not oscillating in MEFs . However , the histone marks and cohesin binding sites were very conserved on the enhancer loci of the two cases between liver and MEFs indicating these are tissue/cell type invariant TADs . This is further supported by the cohesin ChIA-PET data in ES cells even though ES cells lack a functional circadian clock [45] . We used Hepa1-6 cells here because of the convenience for CRISPR-CAS9 experiment in these cells . These data in cell lines collectively suggest that these invariant enhancer-promoter interactions are both cohesin-dependent and functional in gene regulation . These DNA loops were confirmed to be also present in liver and the binding of Bmal1 in these enhancers renders the circadian expression to these two genes in liver . This picture is in line with our model that cohesin-mediated enhancer-promoter loop provides a stable and tissue/cell type invariant backbone and circadian gene regulation is a result of dynamic Bmal1 binding on the stable chromosome structure . We are also aware that the DNA loops mediated by architectural proteins seem to be developmentally regulated at specific loci within the TADs [46] . Whether our finding has general applicability for long-range circadian regulation still awaits future studies with other experimental strategies . Overall , our study sheds new light on the transcriptional landscape of circadian genes under high-order chromosome structure .
All animal experiments performed in this study were approved by the Institutional Animal Care and Use Committee of Shanghai Institutes for Biological Sciences and conformed to institutional guidelines of vertebrates study . The general strategy for screening Bmal1 bound super-enhancers followed the pipeline described in [28] . We first defined 3 , 244 Bmal1 enhancers in mouse liver with the following rules: ( 1 ) the co-occurrence of H3K4me1 and H3K27ac marks [47 , 48] , ( 2 ) positioning at least 1 kb away from any transcription start sites of annotated genes [49] , ( 3 ) overlapping with Bmal1 binding sites at ZT8 from Koike et al . ’s data [6] ( see Methods section ChIP-Seq data analysis ) , ( 4 ) at least 100 bp in length . H3K4me1 and H3K27ac ChIP-Seq data in the livers of eight-week-old mice were used [31] . Because the signals on Bmal1 binding site do not show broad distribution , we skipped the step of merging enhancers in close distance . To obtain confident super-enhancers , the read numbers per million reads per kilobase from Koike et al . ’s and Rey et al . ’s Bmal1 ChIP-Seq experiments were added to rank Bmal1 enhancers [6 , 7] . Finally , 97 Bmal1 enhancers ranked at top 3% were defined as Bmal1 super-enhancers in mouse liver . 4C-Seq assays were performed as previously described [50 , 51] with modifications . Briefly , six-week-old male C57BL/6 mice were entrained to 24 hr cycles of 12 hr light and 12 hr dark for one week and then switched into constant darkness . Three mice each were sacrificed in the dark at CT6 and CT18 , respectively . Mouse liver cells were quickly dispersed and filtered through the 40 mm cell strainer to make a single-cell suspension . Approximately 50-million cells were fixed in 1% formaldehyde for 10 min at room temperature before being quenched with 0 . 125 M glycine . Cells were then lysed in cold lysis buffer ( 10 mM Tris HCl , 10 mM NaCl , 0 . 2% NP-40 , 1×protease inhibitor ) for 15 min on ice . After being washed twice , cell nuclei were re-suspended in Buffer 2 . 1 ( New England Biolabs ) including 0 . 1% SDS and were incubated for 10 min at 65°C . 1% ( final concentration ) of Triton X-100 was added to quench SDS and centrifuged to remove SDS and Triton . Nuclei were then digested overnight by 800U HindIII ( New England Biolabs ) at 37°C with shaking . After inactivation by 1 . 6% ( final concentration ) of SDS at 65°C for 20 min , samples were washed and re-suspended in ligation buffer and ligated by 100U T4 DNA ligase ( Thermo Fisher Scientific ) at 16°C for 4 hr and then room temperature for 30 min . Ligated chromatin was digested by proteinase K before DNA purification . The purified DNA was further digested by DpnII ( New England Biolabs ) and then circularized using T4 DNA ligase ( Thermo Fisher Scientific ) . After purification , 200 ng of DNA from the 4C library was used as the template for the PCR amplification using DyNAzyme EXT ( Finnzymes ) . Primers specific to bait region ( S5 Table ) were applied to amplify the interactome of interest in a 25 μl reaction volume under the following PCR conditions: 1 cycle at 94°C for 2 min; ( 94°C 30 sec; 60°C 30 min; 72°C 2 min ) ×18 cycles; 1 cycle of 72°C 7 min . PCR products ( 1 μl ) were used as the templates for a second PCR reaction utilizing the primers with the addition of Illumina adaptors in a 50 μl volume under the same PCR conditions . The PCR-amplified library was purified and sequenced with a 100 bp read length using Illumina HiSeq 2000 ( S6 Table ) . Sequencing reads of 4C-Seq were de-multiplexed using the bait primers , i . e . removing the upstream of HindIII restriction site ( AAGCTT ) and the downstream of DpnII restriction site ( GATC ) . Then the reads were aligned to mouse genome ( mm9 ) by Bowtie [52] . The self-ligated reads and non-cut reads were removed [53] . Only the reads uniquely mapped to the HindIII restriction sites on the cis-chromosome of the bait were kept and assigned the HindIII restricted fragments defined by two neighboring restriction sites . Peak calling was performed with a custom-designed pipeline generally following FourSig [54] . Previous interactome studies reported that 99% interactions were less than 1 Mb and inter-chromosomal interactions were hard to be validated [55] . Hence , we only considered intra-interactions within 2 Mb of the bait . The highly interacting region ( 150 kb to the bait , S1C Fig ) was masked out during the peak calling on other regions . A sliding window with size of 3 fragments was used to calculate the cutoff based on the comparison between 100 permutations of raw reads and true data . The distribution of cutoffs under FDR = 0 . 01 was profiled and the final cutoff was determined as the 95% quantile . For highly interacting region , this cutoff was multiplied by the reads ratio between highly interacting region and other regions . Then the merged peaks in highly interacting region and other regions were considered as the peaks in each sample . We required that the peaks at each time point were consistently called in at least two out of three biological duplicates . In total , 49 and 51 peaks were obtained at CT6 and CT18 respectively . To compare highly interacting regions with ChIA-PET , we selected 1000 random regions of the same size and applied Chi-squared test to evaluate the significance between overlapped loops in highly interacting regions and in the random regions . The Gene Expression Omnibus ( GEO ) accession number for the 4C dataset is GSE68830 . 3C-qPCR was performed as previously described with modifications [56] . Briefly , 10 μg of cross-linked nuclei were collected and shaken in 1 ml lysis buffer ( 1% SDS , 0 . 5% TritionX-100 , proteinase inhibitor cocktail in TE buffer ) for 1 hr at 37°C , followed by centrifugation for 3 min at 1000 rpm at room temperature . After removing the supernatant , the pellet was re-suspended in 500 μl digestion buffer ( 1% TritonX-100 , 1xRE buffer , PI , 20 μl Quickcut HindIII in H2O ) and digested overnight at 37°C with shaking . The reaction was terminated by the addition of SDS at a final concentration of 1 . 5% and the incubation at 65°C for 30 min . SDS and RE buffer were removed by centrifugation and the pellet was re-suspended for the next ligation . Reverse crosslinking was performed in the presence of proteinase K at 60°C overnight followed by RNaseA treatment at 37°C for 1 hr . The genomic DNA was extracted by phenol-chloroform . All 3C primers were designed by Primer Premier 6 ( S5 Table ) . The ChIP-Seq data of CTCF , Rad21 , Stag1 , Stag2 , and Gabpa in mouse liver were downloaded from ArrayExpress database ( accession: E-MTAB-941 ) [22] . The ChIP-Seq data of Bmal1 at CT8 in mouse liver was downloaded from Gene Expression Ominbus ( GEO ) database ( accession: GSE39860 ) [6] . The ChIP-Seq data of Nr1d1 at CT10 in mouse liver was downloaded from GEO ( accession: GSE26345 ) [57] . The ChIP-Seq data of CTCF and Smc1 in MEFs were downloaded from GEO ( accession: GSE22557 ) [19] . Rad21 , Stag1 , Stag2 , and Smc1 are the subunits of cohesin . Gabpa is a non-oscillating transcription factor in mouse liver chosen as a negative control . It is known that Bmal1 and Nr1d1 rhythmically bind to the genome and their binding peaks are around CT6 and CT10 , respectively . To ensure that different datasets are directly comparable , all these ChIP-Seq data were analyzed in the same pipeline described as below . Raw reads in FASTQ files were mapped on mouse genome ( mm9 assembly ) by Bowtie [52] . Only reads uniquely mapped with no more than two mismatches were considered as valid reads . Peak calling was implemented by MACS with default parameters and cutoff p < 10−5 [58] . The signal files generated from MACS were normalized to per million total reads . Broad peaks with multiple peaks were split to accurately determine the peak region by PeakSplitter [59] , requiring per million reads larger than 1 . Peaks generated from PeakSplitter were considered as the binding sites and the centers of peaks were considered as the binding centers . The binding sites of Smc1 were considered to represent the binding sites of cohesin in MEFs . The binding sites of cohesin in liver were defined as the union of binding regions of Rad21 , Stag1 , and Stag2 . Consequently , we obtained 10 , 948 , 28 , 883 , 23 , 662 , 50 , 683 , and 74 , 589 binding sites for Bmal1 , Nr1d1 , Gabpa , CTCF , and cohesin in mouse liver respectively . In MEFs , we obtained 5 , 738 and 8 , 756 binding sites for CTCF and cohesin respectively . These cohesin binding sites that overlap with CTCF binding sites in liver were defined as cohesin-CTCF sites ( 41 , 690 ) and the cohesin binding sites not overlapping with CTCF binding sites were defined as cohesin-non-CTCF sites ( 32 , 899 ) . GRO-Seq ( accession: GSE59486 ) [9] and RNA-Seq ( accession: GSE39860 ) [6] data in mouse liver sampled every 3 or 4 hours over 1 day or 2 days were downloaded from GEO to obtain the genome-wide circadian gene expression . For each DNA binding factor including Bmal1 , Gabpa , CTCF , and cohesin , the upstream 20 kb and downstream 20 kb relative to the binding centers were extracted . These regions were further divided into 2 kb bins as the basic unit for analyzing circadian rhythmicity of transcription across the genome . The 2-kb bin was considered as a valid bin if it contains at least one read at more than 7 ( GRO-Seq ) or 10 ( RNA-Seq ) time points . To exclude the binding sites in the region without any transcript , the binding site was considered for downstream analysis only if there is at least one valid bin in its proximity , i . e . the upstream and downstream 20 kb . BEDTools [60] were used to calculate the normalized read coverage in these bins at each time point . JTK_CYCLE [61] was applied to detect the circadian oscillation . We defined the minus logarithm of Bonferroni-adjusted p value of JTK_CYCLE , i . e . -log2 ( p ) , as the circadian index to measure circadian rhythmicity . To generate a meta-site for each binding factor , we computed the mean circadian index in each bin in the proximity of binding sites . The mock meta-site was obtained from randomly selected 100 , 000 sites of 40 kb in length over whole genome . The circadian time-series microarray data in mouse liver sampled every 1 hour for 48 hours were downloaded from GEO ( accession: GSE11923 ) [5] to analyze the phases of COGs . We chose this time-series data for phase analysis because of its high temporal resolution . The raw data in CEL files were normalized by robust multi-array average ( RMA ) . JTK_CYCLE was performed to obtain circadian phases at the probeset level on the microarray . R package mouse4302 . db was used to annotate the gene symbols of 45 , 101 probesets . If one gene corresponds to multiple probesets , we only kept the one with the minimum Benjamini and Hochberg ( BH ) q value from JTK_CYCLE . 3 , 018 COGs were selected with the threshold of BH q value < 0 . 01 . The genomic locations of these genes were obtained from UCSC genome ( mm9 assembly ) . To examine whether the neighboring COGs tend to have similar phases , we scanned the whole genome for COGs with neighboring double windows consisting of the upstream 20 kb and downstream 20 kb windows ( S3C Fig ) . The phase differences were computed between two COGs situated in each of the double windows . If multiple COGs were found in one window , only the COG closest to the other window was retained . Next we increased the distance of two windows apart to 10 , 20 , 30 , 40 , and 50 kb and re-calculated the phase differences of COGs in the double windows . For a random genomic background , a pair of two 20 kb windows were randomly selected on the genome and searched for COGs . The phase differences were calculated for 1 , 000 such random pairs of windows . Compared to the strategy of just considering contiguous genes [62] , our fixed-size window approach eliminates the distance factor between neighboring genes . Mann-Whitney U test was applied to detect the significance of difference in the distributions of phase difference between double windows and randomly chosen windows . To obtain neighboring COGs separated by the binding sites of Bmal1 , Nr1d1 , Gabpa , cohesin-non-CTCF , and cohesin-CTCF , the transcription start sites of COGs were searched upstream 20 kb and downstream 20 kb relative to protein binding centers providing that the whole transcripts do not overlap with the binding centers . We selected the binding sites flanked by COGs and calculated the phase difference between the opposite sides of these binding sites . Mann-Whitney U test was applied to detect the significance of difference in the distributions of phase difference between across the binding factors and genomic background . The phase variance is calculated based on a method used to measure the dispersion of directional data [63] . In brief , the phase pi of COG i is given by polar co-ordinates of unit length ( cos θi , sin θi ) , i = 1 , … , n . The mean of phases p0 is defined as the direction of the vector resulted from the vector summation ( ∑i=1ncosθi|p0| , ∑i=1nsinθi|p0| ) , where |p0|= ( ∑i=1ncosθi ) 2+ ( ∑i=1nsinθi ) 2 . The dispersion of phases is measured by D ( p0 , p1 ) =∑i=1n[1−cos ( θ0−θi ) ]=n− ( ∑i=1ncosθi ) 2+ ( ∑i=1nsinθi ) 2|p0|=n−|p0| . Hence , the phase variance is defined as 1 − |p0|/n after normalization by the sample size n . R package circular was used to calculate the phase variance . We collected 23 , 724 intra-chromatin interactions from cohesin ChIA-PET data in mouse embryonic stem cells [29] . The invariant domains in mouse liver were inferred if two anchors of cohesin loops both overlapped with cohesin-CTCF binding sites in mouse liver . As a result , we obtained 16 , 837 invariant cohesin-CTCF domains . To explore the relationship between phase variance and window size , we scanned the whole genome with different sizes of windows 5×4i kb , i = 1 , 2 , … , 5 to extract COGs and calculate the phase variance ( S3D Fig ) . The Pearson’s correlation coefficient ( PCC ) was calculated between phase variance and log2 transformed window size . The p value for testing null hypothesis ( PCC = 0 ) was computed based on Pearson’s product moment correlation coefficient . To reduce size effect in the comparison of phase variances between cohesin-CTCF domains and background , we classified cohesin-CTCF domains into small , medium , and large categories with sizes of [10×4i , 10×4i+1] kb ( i = 1 , 2 , 3 ) respectively . The genomic background for each category is generated by the scan across genome with window of size 5×4i+1 ( i = 1 , 2 , 3 ) . We first defined a background model only considering the circadian regulation from nearby Bmal1 binding sites . In the background model , the regulatory potential Bi of Bmal1 on gene i is given by Bi=∑Dij<2Mb ( e−Dij/λ1⋅Sj ) , where j is Bmal1 binding site located within 2 Mb to gene i , Sj is the weight representing the signal of Bmal1 binding site j in ChIP-Seq data , and Dij is the distance between gene i and Bmal1 binding site j . For cohesin/CTCF dependent model , the effects of cohesin-non-CTCF and cohesin-CTCF sites were multiplied upon the background model . For a given gene or Bmal1 site , we searched for the nearby cohesin-non-CTCF site within 5 kb that may facilitate gene regulation . We assigned a weight larger than 1 to the gene or Bmal1 binding site to increase the circadian regulatory potential . Between each pair of gene and Bmal1 binding site , we counted the number of cohesin-CTCF sites in between and assigned a weight less than 1 to reduce the circadian regulatory potential of Bmal1 on that gene . Taken together , the regulatory potential Pi of Bmal1 on gene i is given by Pi=∑Dij<2Mb[ ( e−Dijλ1⋅Sj ) ⋅CNCi⋅CNCj⋅CCij]=∑Dij<2Mb[ ( e−Dijλ1⋅Sj ) ⋅ ( 1+e−NDiλ2⋅SCi ) ⋅ ( 1+e−NDjλ2⋅SCj ) ⋅ ( 12 ) mij] , where NDi and NDj are the distances between the nearest cohesin-non-CTCF sites to gene i or Bmal1 site j respectively , SCi and SCj are the weights representing their signals on cohesin ChIP-Seq data , and mij is the number of cohesin-CTCF sites between gene i and Bmal1 j . If there is no cohesin-non-CTCF within 5 kb of gene i or Bmal1 site j , e−NDiλ2 or e−NDjλ2 was assigned to 0 . The weights S and SC are defined by 1+e−rλ3 , where r is the rank of ChIP signal among all Bmal1 or cohesin binding sites respectively . The parameters λ1 , λ2 , λ3 are set to be 2000000/4 , 5000/4 , and ( total number of peaks in ChIP-Seq ) /4 respectively as suggested by an empirical model of gene regulation [64] . To render the circadian regulatory potentials directly comparable between two models , we finally converted them to their respective ranks in the models as Rank ( Pi ) /n where n is the total number of genes considered . Smc3-flox/flox MEF ( mouse embryonic fibroblast ) cells was originally derived from European conditional mouse mutagenesis program [65] ( http://www . informatics . jax . org/allele/MGI:4434007 ) . The Cre/GFP adenovirus and GFP adenovirus ( 1010 pfu/ml ) were purchased from Hanbio biotechnology , Shanghai . MEF cells were cultured with 10% FBS in DMEM ( Life technology ) . To avoid the loss of viability in Smc3-/- cells when they enter mitosis , we infected the cells at G0/1 stage of the cell cycle . The medium was changed two days after the cells reaching the complete confluence . 109 pfu GFP and Cre/GFP adenovirus were used in 8-hr treatment for wild type and Smc3-/- MEF cells respectively . To allow the cells to recover from viral infection , we changed the medium into serum-free DMEM and kept the cells for 6 days at high confluence . MEF cells were then synchronized by dexamethasone ( Sigma ) with the final concentration of 100 nM for 1 hr . The cells were rinsed with PBS and cultured with serum-free DMEM . Wild type and Smc3-/- MEF cells were collected at 20 , 24 , 28 , 32 , 36 , and 40 hr after synchronization . Total RNA was extracted using Trizol reagent and reverse-transcribed into cDNA by SuperScript II RT ( Life Technologies ) . RNA-Seq libraries for 20 hr and 32 hr samples were prepared by using Illumina TruSeq RNA Sample Prep Kit V2 and were subjected to deep sequencing with 1×100 bp read on HiSeq 2000 at CAS-MPG Partner Institute for Computational Biology Omics Core , Shanghai , China ( S6 Table ) . RNA-Seq reads were mapped to mouse reference genome ( mm9 assembly ) by Tophat [66] . HTSeq was used to count the number of uniquely mapped reads that are located on the exons of genes [67] . Only genes with at least one read in all samples were kept for downstream analysis . Treating 20 hr and 32 hr samples as biological replicates , we applied DESeq to select differentially expressed genes between cohesin knockout and control cells with log2 fold change > 0 . 8 [68] . The Gene Expression Omnibus ( GEO ) accession number for RNA-Seq dataset is GSE68831 . Bmal1 ChIP in MEF cells were performed following the protocol by Shimomura et al . [69] with modification . Briefly , 107 cells were washed with PBS and cross-linked by 1% formaldehyde for 10 min on a rocker at room temperature . The cross-linking was quenched by 2 . 5 M Glycine with final concentration of 125 mM . The nuclei was extracted at 4°C from the homogenate by lysis buffer containing protease inhibitors [50mM Hepes-KOH , pH 7 . 5 , 140mM NaCl , 1mM EDTA , 10% glycerol , 0 . 5% NP-40 , 0 . 25% Triton X-100] , [10mM Tris-HCl , pH 8 . 0 , 200mM NaCl , 1mM EDTA , 0 . 5 mM EGTA] , and [10mM Tris-HCl , pH 8 . 0 , 200mM NaCl , 1mM EDTA , 0 . 5 mM EGTA , 0 . 1% Na-Deoxycholate , 0 . 5% N-lauroylsarcosine] . DNA was fragmented with sonication into 150–300 bp at 4°C . 50 μl DNA fragments were stored in 4°C as the input DNA . The rest of DNA fragments were incubated on rocker at 4°C for 6 hr with 1:1 ChIP buffer [20% Triton , NaDOC , NaCl , TE , inhibitor] and 4 μl Bmal1 antibody ( Santa Cruz: sc-8550 ) . Then 15 μl protein A/G agarose beads were added into DNA and incubated on rocker at 4°C overnight . Co-immunoprecipitated DNA was washed with 1 ml buffers [5% Triton , 1% SDS , 1% NaDOC , 93% TE] twice , [5% Triton , 1% SDS , 1% NaDOC , 6% NaCl , 87% TE] twice , [10% LiCl , 5% NP40 , 5% Na-DOC , 80% TE] twice , [10% Triton , 90% TE] , and TE . Then DNA was reverse cross-linked at 50°C for 2 hr with TE 100 μl , 10% SDS 3 μl , and protease K 5 μl . QIAquick PCR Purification Kit ( QIAGEN ) was used to purify ChIP DNA . Input and ChIP DNA library were prepared by using Illumina TruSeq ChIP Sample Prep Kit and were subject to deep sequencing with 1×100 bp read on HiSeq 2000 at CAS-MPG Partner Institute for Computational Biology Omics Core , Shanghai , China ( S6 Table ) . ChIP-Seq data analysis was performed in the same pipeline described above . The Gene Expression Omnibus ( GEO ) accession number for Bmal1 ChIP-Seq data set is GSE77162 . CRISPR-Cas9 method [70] was used to delete the cohesin binding site near the enhancer of Phldb2 in Hepa1-6 cells . The gRNA target sequences ( GTCTTTCACGTGGGACGGAT and GAGACCTCAAGGACATGTGC ) were designed by E-CRISP [71] . The homologous arms for donor plasmids are ( chr16: 45967525–45967702 ) and ( chr16: 45967935–45968129 ) . The regulatory module ( hPGK promoter/PuroR ) was amplified from commercially available expression vector pLKO . 1 . Two homologous arms and PGK/puroR were assembled into pGEM-T Easy vector ( Promega ) . Hepa1-6 cells were cultured with 10% FBS in DMEM ( Life technology ) and co-transfected with two gRNA/Cas9 vectors and linearized donor DNA . Then the cells were screened with 3 μg/ml puromycin ( Merck/ millipore ) for 2 weeks . Gel electrophoresis analysis of the homologous arms , control region , and the regulatory module ( PGK-puroR ) in WT and CRISPR-CAS9 treated cells validated the successful deletion of target DNA region ( S6B Fig ) . Primers used in PCR and RT-PCR are listed in S5 Table . The whole-genome scans in this study were implemented in Java language ( JDK 6 ) . All statistical analyses were performed in R 2 . 11 . | Circadian rhythm regulates daily oscillations of many physiological processes in a wide range of organisms . In mammals , circadian rhythm drives the cycling expression of thousands of downstream genes . The temporal control of transcription takes place under high-order chromosome structure , which is established by looping distant loci on the linear DNA double strands . The most important chromatin structure proteins studied so far are cohesin and CTCF . Using circular chromosome conformation capture technologies , we found that cohesin binding sites are enriched in interacting regions of an enhancer bound by a key circadian transcription factor , Bmal1 . Globally , cohesin and CTCF have disparate functions on transcriptional regulation . We developed a quantitative model integrating the effects of cohesin and CTCF in circadian gene regulation . With further computational and experimental approaches , we validated several cases of circadian oscillating genes where cohesin facilitates the enhancer-promoter looping . Taken together , this study showed that circadian gene expression is orchestrated under the long-range interactions mediated by cohesin . | [
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"prot... | 2016 | Long-Range Chromosome Interactions Mediated by Cohesin Shape Circadian Gene Expression |
This paper discusses the presence of Aedes aegypti pupae in different types of containers considering: volume , pH of the container , among other variables . A nonlinear method for selection was applied , based on Mutual Information , by placing in order of importance the most appropriate variables for identifying containers with and without Aedes aegypti pupae . Such variables were used for input into a Neural Network in layers for classification . Among the experiments carried out , the best result obtained used the first eight variables selected by order of importance . The percentage of hits for containers which had no Aedes aegypti pupae was 73 . 3% , and 80 . 9% for those which did have Aedes aegypti pupae in the containers . This Neural Network method , a model with the capacity to emulate non-linear data , got better results in comparison with the discriminant power of the Logistic Regression model . Thus , the outcomes of using the Neural Networks method achieved better separability in classifying the containers with pupae and those with no pupae . This type of analysis will aid in the efforts to design an efficient program to control Aedes aegypti that can concentrate principally on containers which present the greatest productivity .
The incidence of dengue has grown rapidly around the world over the last decades . Batth et al [1] estimated that around 390 million ( 95% CI: 284–528 ) dengue infections occurred per year , of which 96 million ( 95% CI: 67–136 ) are symptomatic ( any level of disease severity ) . Dengue is currently an important public health problem worldwide . Brady et al [2] estimated that 3 . 9 billion people , in 128 countries , live in areas at risk for dengue viruses’ infection . In Asia , dengue hemorrhagic fever is predominant in children , while in the Americas adults are more often affected [3 , 4] . The worldwide emergence of the chikungunya and the Zika viruses and the serious consequences for public health has increased the need for more effective Aedes aegypti control programs [5 , 6 , 7] . Recently , Brazil has had the largest outbreak of sylvatic yellow fever in recent decades [8] and Aedes aegytpi can transmit urban yellow fever . The emergence of epidemics of dengue , chikungunya , yellow fever , and Zika virus disease is an alert for governments , academia , funders and World Health Organization to improve programs and enhance research in Aedes-transmitted diseases [9] . Aedes aegypti proliferates in various domestic containers which are used to store water and for ornamental plants . There are also various habitats that receive rain water and may be potential breeding sites for the mosquito , such as used tires , drinking containers , clogged gutters and buildings under construction . Some artificial containers produce large numbers of adult mosquitoes , while others are less productive . Consequently , efforts to control highly productive containers should be priority , especially when resources are limited . This strategy requires extensive knowledge on the ecology of vectors . The control of Aedes aegypti is achieved mainly through the elimination of breeding sites which are sites favorable for oviposition that also enable the development of the aquatic phases of the vector . Besides that , control programs for the vector have also sought a greater involvement from the community and have encouraged intersectoral activities [10] . Without effective control of the vector in the American continent , the successive epidemics which are taking place will bring on a depletion of susceptible individuals , and the disease will tend to affect more children . As there is no specific vaccine or specific treatment for dengue [11] , the only fragile link in the transmission chain of the disease is its vector , and the best efforts should be taken for its effective control . A great variety of factors influence the spatial and temporal dynamics of the Aedes aegypti and , therefore , the patterns of the dengue transmission in humans . Temperature , rain and humidity interfere at all phases of the development of the vector as well as its dispersion in the environment [12 , 13] . In underdeveloped countries , the unorganized urbanization , the increased population density , the precariousness of the waste collection and provision of water , along with the inefficiency of the programs for fighting the vector favor the wide expansion of Aedes aegypti [14 , 15 , 16] . In these countries , the absence or intermittent supply of water drives a large part of the population to store water in various tanks to supply their daily needs . These tanks provide a place for the procreation of the vector in urban areas [14 , 15] . Other breeding sites propitious for the multiplication of mosquitoes are disposable containers ( bottles , cans , plastics etc . ) , most often found around the outside of homes , and very often inappropriately disposed of , due to the lack of regular waste collection in various areas [17 , 18 , 19] . In Brazil , fighting the vector during the transmission periods is sought or achieved through the elimination of potential breeding sites and the application of larvicide in water containers , as well as the use of insecticide for the adult forms [10] . The Ministry of Health in Brazil recommends the indiscriminate removal of containers which have the potential for the reproduction of Aedes aegypti , no matter their type or size . However , the feasibility of some small sites to produce adult forms of the vector has not actually been well established . This information is essential for designing a more efficient and cost-effective removal program . Results obtained from research on the pupae are often used as a proxy to produce adult mosquitoes , as the pupae mortality is considered low [18] . The objective of this study is to investigate the production of Aedes aegypti pupae according to the type of use , volume and manufacturing material of the different containers which may potentially be breeding sites for the mosquito , applying non-linear method , and to demonstrate the improvement in results obtained in comparing with linear methods . The identification of the most productive breeding place of the mosquito allows guiding more efficient dengue control program , since concentrates the efforts in the places of greater infestation of the mosquito .
The set of data used in this study was collected in 2004 from the municipality of Nova Iguaçu , state of Rio de Janeiro , Brazil , situated at latitude 22°45’33” South and longitude 43°27’04” West , with a total area of 523 , 888 m2 . This county had a population of 750 , 485 inhabitants and a demographic density of 1 , 413 . 8 inhabitants/km2 . The temperature and the average annual precipitation of rain are 21 . 8° C and 2 , 105 mm , respectively . The Secretary of Health Surveillance of the Ministry of Health carried out a Rapid Survey Index ( RSI ) of Aedes aegypti [20] between November 22 and 26 of 2004 . Breteau indexes were calculated from the results of this RSI for all quarters of the sample . The Breteau index is calculated as the number of positive containers per 100 houses inspected . The six quarters which presented the greatest Breteau indexes were selected for further monitoring . These quarters were in the following neighborhoods: Centro , Califórnia , Vila Operária , Cerâmica , Nova América and Moquetá . All potential breeding sites in the survey delimited region were monitored in a summer week between the 22sd and the 29th December 2004 , aiming to identify and collect all immature mosquito specimens . Every container or non-hermetically closed site which contained water ( in any volume ) found around or inside sample homes during the visits was a potential breeding ground . Samples of specimens from containers with a capacity of less than 10 liters were collected by aspiration using rubber “pears” or with the help of “shrimp nets” . In the containers with a capacity of over 10 liters , specimens were collected from the drainage system , via the flow of water through the shrimp net . In fixed and large containers , the collection of pupae was carried out by the “sweep net” method , proposed by Tun-Lin et al [21] , modified by Kubota et al [22] . The collected specimens were identified with the aid of binocular bacteriologic microscopes . The database was composed of 5 , 954 inspected containers for the presence or absence of Aedes aegypti pupae , and the following eleven independent variables: All existing containers on a 100-meter radius around the center of the six quarters with the highest Breteau indexes were monitored . It was considered as a potential breeding place all non-hermetically closed deposits containing any volume of water . All water holding containers were examined , after oral consent for getting into people’s house . Immature specimens were collected biweekly during monitoring . The breeding sites were also daily monitored between the collections , to verify the presence of pupae , indicating the adults hatching , which would anticipate the interval between the collections ( less than fifteen days ) , to avoid the proliferation of the vector . The problem of an eventual closed house was minimized by the repeated visits . In this study , the decision to analyze the Aedes aegypti pupae is based in the fact that the Aedes aegypti larvae mortality is high; on the other hand , when it reaches the pupal stage , it evolves into the adult form giving a better idea about the breeding site productivity . As previously cited , the objective of this study was to estimate the presence of Aedes aegypti pupae considering all the eleven variables cited above in their various containers . The discrimination potential of the selected parameters is assessed in some studies with linear statistical methods , such as the Linear Discriminant Analysis or Principal Component Analysis [23] . In this study , a non-linear method was applied , based on Mutual Information ( MI ) [24] , to select by order of importance the most appropriate variables for distinguishing containers with and without Aedes aegypti pupae . These variables were then used as inputs for a Neural Network in layers for classification . For comparison with the proposed model , a logistic regression model was used under the same conditions .
Table 1 presents the Breteau indexes of the quarters studied . It is noted that the Cerâmica quarter , located in slum , has the highest Breateau index ( 283 . 3 ) , while the Vila Operária quarter , located in an urbanized area with a supply of water and garbage collection , presented the lowest Breteau index ( 90 . 8 ) . Table 2 presents the results of the selection and ordering of variables in relation to the outcome ( presence or absence of Aedes aegypti pupae ) , as obtained by the MIFS-U algorithm . The first three most relevant variables in order of importance for the presence or absence of pupae took size into consideration ( categorization of container volume ) , presence or absence of another type of pupae apart from Aedes aegypti , and the location of the container ( outside or inside ) . On the other hand , the less important variables in relation to the presence or absence of Aedes aegypti pupae were the container pH and the quarter temperature . Table 3 presents the performance of the proposed method ( Neural Networks ) regarding the number of variables used . The data base is made up of information on 5 , 954 containers , where 5 , 560 had no pupae and 394 had Aedes aegypti pupae . Of these 5 , 954 containers , 70% were used for training the network and the other 30% were used for testing; that is , the network was trained with 3 , 857 containers with no pupae and 279 containers with pupae , in a total of 4 , 136 containers . The containers used for testing ( apart from the training sample ) were made up of 1 , 703 with no pupae and 115 with pupae . The following results presented refer to the containers not included in the training sample . Eight experiments were carried out comparing the first variable on Table 2 with a varying number of the other variables taking into consideration the order of importance . Comparing the results of these 8 experiments it can be observed , for example , that an increase in the ‘container volume’ ( ninth variable in order of importance ) effectively introduces a worse result regarding predictability of presence of Aedes aegypti pupae . The best prediction of the presence of Aedes aegypti pupae is obtained in experiment 6 , comparing variable 1 with the following 8 variables selected by the MIFS-U algorithm . In this experiment percentage of hits in containers that had no Aedes aegypti pupae was 73 . 3% ( specificity ) and 80 . 9% in containers with Aedes aegypti pupae ( sensitivity ) , with an accuracy of 73 . 8% . For this experiment , considering the prevalence of containers with Aedes aegypti pupae of 6 . 3% , the negative predictive value of the Neural Network was 98 . 3% and the positive predictive value was 17 , 0% . A logistic regression was also carried out in the same way , for the purposes of comparison , the results of which are shown in Table 4 . A percentage of 63 . 6% of hits was found in the containers that did not have any Aedes aegypti pupae ( specificity ) and 71 . 7% in the containers with Aedes aegypti pupae ( sensitivity ) , with accuracy of 64 . 1% . Thus , the capacity for predicting the presence of Aedes aegypti pupae of this method was found to be inferior to the method proposed in this study . Considering the prevalence of containers with Aedes aegypti pupae of 6 . 3% , the negative predictive value of the logistic regression was 97 . 1% and the positive predictive value was 11 . 7% . For coherence , the logistic regression analysis concentrated on the same set of data considered in the same 8 variables selected where the result from the Artificial Neural Network was more significant , that is , size of the container , presence of other pupae in the container , location of the container , the container’s exposure to sunlight , manufacturing material of the container , mobility and permanence , type of usage and quarter pluviometric index .
Using the Neural Network model , the variables that better discriminate the containers with Aedes aegypti pupae from the containers without pupae were size—the larger the container volume the greater the proportion of containers positive for pupae– , the presence of another type of pupa other than Aedes aegypti and the location of the container outside the home . Arunachalam et al [33] found that the most productive breeding sites for Aedes aegypti were the containers of water located outdoors , principally those that were uncovered , under trees and not having been used for at least a week . Areas around and inside the homes were much more important to produce pupae than commercial and public areas . While Martins et al [34] found no significant association between volume of the breeding place and infestation by Aedes aegypti , they did find however that the absence of immature forms of Aedes albopictus and Culex spp in the breeding sites favors its infestation by Aedes aegypti . The Neural Network method , a model with the capacity to emulate non-linear data , got better results in comparison with the discriminant power of the Logistic Regression model . Thus , the outcomes of the Neural Networks method achieved better separability in classifying the containers with pupae and those with no pupae . According to Medronho et al [35] , the containers with a greater percentage of pupae are tires , barrels , cisterns , drums and water tanks . In that study , most containers cited ( with the exception of tires ) are all concerned with the problem of the region’s supply of water . Romero-Vivas et al [36] found similar results for the containers used for storage of the supply of water , whereas Barrera et al [37] found a larger proportion of containers related to waste positive for pupae . The containers with the largest proportion of immature forms of the vector were denominated by Tun-Lin et al [38] as key containers and according to these authors they should be prioritized in the activities of vector control to make the program more efficient . In this sense , the efforts of an efficient program for controlling Aedes aegypti should be primarily concentrated on being able to identify the containers which present greater productivity . We have shown here that the use of a Neural Networks model to aid in monitoring various types of containers can significantly help achieve this goal . Literature shows that Neural Network have been successfully considered for solving many medical problems , including the prediction of the occurrence of dengue cases [39] , the risk classification of dengue patients [40 , 41] and the identification and classification of species of the Anopheles , Aedes , and Culex , based on wing shape characters with identification of 100% of Aedes aegypti [42] . However , the identification of the most productive breeding sites of Aedes aegypti using Neural Network is relatively new and we did not find published articles in this field . Identification of the most productive breeding sites and their elimination or appropriate treatment may contribute to a more effective mosquito control program . | The authors discuss a nonlinear method , based on Mutual Information , for selection of the presence of Aedes aegypti pupae in different breeding sites . The authors compare this method with the logistic regression model . In this study , using the Neural Network model , the variables that better discriminate the containers with Aedes aegypti pupae from the containers without pupae were size—the larger the container volume the greater the proportion of containers positive for pupae– , the presence of another type of pupa other than Aedes aegypti and the location of the container outside the home . The Neural Network method , a model with the capacity to emulate non-linear data , got better results in comparison with the discriminant power of the Logistic Regression model . Thus , the outcomes of the Neural Networks method achieved better separability in classifying the containers with pupae and those with no pupae . This type of analysis will aid in the efforts to design an efficient program to control Aedes aegypti that can concentrate principally on containers which present the greatest productivity . | [
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"alpha... | 2018 | Classification of containers with Aedes aegypti pupae using a Neural Networks model |
Accurate chromosome segregation during meiosis relies on the prior establishment of at least one crossover recombination event between homologous chromosomes . Most meiotic recombination intermediates that give rise to interhomolog crossovers are embedded within a hallmark chromosomal structure called the synaptonemal complex ( SC ) , but the mechanisms that coordinate the processes of SC assembly ( synapsis ) and crossover recombination remain poorly understood . Among known structural components of the budding yeast SC , the Zip1 protein is unique for its independent role in promoting crossover recombination; Zip1 is specifically required for the large subset of crossovers that also rely on the meiosis-specific MutSγ complex . Here we report that adjacent regions within Zip1’s N terminus encompass its crossover and synapsis functions . We previously showed that deletion of Zip1 residues 21–163 abolishes tripartite SC assembly and prevents robust SUMOylation of the SC central element component , Ecm11 , but allows excess MutSγ crossover recombination . We find the reciprocal phenotype when Zip1 residues 2–9 or 10–14 are deleted; in these mutants SC assembles and Ecm11 is hyperSUMOylated , but MutSγ crossovers are strongly diminished . Interestingly , Zip1 residues 2–9 or 2–14 are required for the normal localization of Zip3 , a putative E3 SUMO ligase and pro-MutSγ crossover factor , to Zip1 polycomplex structures and to recombination initiation sites . By contrast , deletion of Zip1 residues 15–20 does not detectably prevent Zip3’s localization at Zip1 polycomplex and supports some MutSγ crossing over but prevents normal SC assembly and Ecm11 SUMOylation . Our results highlight distinct N terminal regions that are differentially critical for Zip1’s roles in crossing over and SC assembly; we speculate that the adjacency of these regions enables Zip1 to serve as a liaison , facilitating crosstalk between the two processes by bringing crossover recombination and synapsis factors within close proximity of one another .
A unique feature of the meiotic cell cycle is how chromosomes are segregated at the first division: Homologous chromosomes ( homologs ) orient and precisely separate from one another on the meiosis I spindle due to the prior establishment of recombination-based associations between homologs . Interhomolog crossover recombination creates a reciprocal splice between non-sister DNA molecules; in conjunction with sister cohesion , this DNA exchange provides a physical association between replicated homologs that is stable but nevertheless can be released to allow disjunction after the bivalent has acquired a proper orientation on the spindle [1] . Interhomolog crossovers form during meiotic prophase through the homologous recombination-based repair of a large number of programmed DSBs catalyzed by the meiosis-specific , topoisomerase-like protein , Spo11 [2] . For many organisms , the repair pathway that allows a subset of Spo11-mediated DSBs to become interhomolog crossovers involves the formation and processing of Holliday junction intermediates by meiosis-specific proteins [3 , 4] . This set of crossover-promoting proteins includes the MutSγ ( Msh4-Msh5 ) and MutLγ ( Mlh1-Mlh3 ) heterodimeric complexes which have homology to the bacterial MutS and MutL protein families , respectively [5–10] . DSB repair processes in meiotic cells also rely on meiosis-specific proteins and pathways to ensure desired outcomes unique to meiosis: for example that crossovers preferentially involve non-sister chromatids of homologous chromosomes ( as opposed to involving the sister chromatids that comprise a single chromosome ) , and that every chromosome pair , no matter how small , receives at least one crossover . Many of the meiosis-specific factors that function in crossover recombination also function to promote the assembly of a widely-conserved , prominent feature of meiotic prophase chromosomes: the synaptonemal complex ( SC ) [11] . The SC is a proteinaceous macromolecular structure comprised largely of proteins ( called transverse filaments ) that feature extensive regions of predicted coiled-coil secondary structure . These rod-like transverse filament proteins assemble in an ordered fashion to create “rungs” connecting aligned chromosome axes along their entire lengths ( chromosome axis structures are referred to as lateral elements in the context of the mature SC structure ) . As demonstrated in multiple organisms by electron and super-resolution microscopy using epitope-specific antibodies , SC transverse filament protein units span the conserved 100 nm width of the SC and orient with their opposing C termini toward lateral element structures [12–17] . In many organisms including budding yeast and mammals , a distinct set of SC structural proteins assemble near the N terminal regions of transverse filaments at the midline of the SC , comprising the “central element” substructure [13 , 18 , 19] . While SC assembly ( synapsis ) and recombination are mechanistically independent and separable , crosstalk exists between the two processes . One widely-conserved example of such crosstalk is the reliance of meiotic crossover events on proteins that are also essential for SC assembly . Especially noteworthy is the fact that mutants missing structural building block components of the SC , particularly transverse filament proteins such as the budding yeast Zip1 protein , are typically deficient in MutSγ crossover formation [11] . The reliance of crossover recombination on SC proteins is perhaps unsurprising given that meiotic recombination intermediate-associated complexes fated to become chiasmata ( cytological manifestations of the crossover links between homologs ) embed directly within the central region of the SC [20–23] . However we note that , at least in budding yeast , not all SC structural components play a role in crossing over and the mature SC structure itself is not a prerequisite for crossover recombination: Budding yeast mutants deficient in the SC central element components Ecm11 or Gmc2 , or expressing an ecm11 allele that prevents Ecm11 SUMOylation , fail to assemble tripartite SC but nevertheless exhibit crossovers , which remain MutSγ-dependent and actually occur in excess compared to wild-type [24] . This finding indicates not only that tripartite SC is dispensable for crossing over , but that SC is associated with an activity that antagonizes interhomolog crossover formation; at least one aspect of the observed anti-crossover activity of the budding yeast SC is likely to be a capacity to inhibit Spo11 DSB formation [25 , 26] . Genetic evidence from multiple systems also suggests that meiotic recombination directly influences SC assembly . In organisms including budding yeast and mammals , early steps in homologous recombination are a prerequisite for proper synapsis . In spo11 mutants , which fail to initiate meiotic recombination , SC assembly does not occur extensively in mammals , or at all in budding yeast [2 , 27] . Furthermore , SC assembly in budding yeast is initiated both from centromeres and also from interstitial chromosomal sites that are presumed to be recombination-associated [28 , 29] . A subset of proteins that co-localize with MutSγ on budding yeast meiotic chromosomes are required downstream of DSB formation not only for the formation of stable , crossover-designated recombination intermediates but also for robust SC assembly [30 , 31] . This group of proteins includes the so-called “Synapsis Initiation Complex” ( SIC ) factors ( Zip2 , Zip3 , Zip4 and Spo16 [28 , 32–34] ) . In the absence of any one of these proteins , the MutSγ heterodimer , or the SC transverse filament protein Zip1 , recombination intermediates fail to form stable joint molecule ( Holliday junction ) structures [30 , 35] . Recent studies indicate that Zip2 , Zip4 and Spo16 form a complex and that Zip2 and Spo16 together can bind branched DNA structures , which suggests a structural basis for the role of these proteins in stabilizing recombination intermediates [36 , 37] . In the absence of Zip2 , Zip4 or Spo16 ( and , by default , Zip1 ) , SC assembly is also abolished . The absence of MutSγ or the putative E3 SUMO ligase , Zip3 , does not cause a complete absence but rather a diminishment of SC assembly , presumably due to a failure or severe delay in synapsis initiation from non-centromeric chromosomal sites [28 , 29] . Finally , SIC protein activity has been found to be required for normal SUMOylation of the SC central element component , Ecm11 , which is critical for SC elaboration [38] . Taken together , these data suggest that intermediate events in the budding yeast meiotic recombination process mediate the gradual , stepwise assembly of a recombination intermediate-associated complex that has the capacity to trigger SC elaboration . Interestingly , even in C . elegans where SCs assemble in the absence of recombination initiation , MutSγ-associated crossover recombination intermediates locally influence the physical and dynamic properties of the C . elegans SC , through a Polo-like kinase ( PLK-2 ) signaling mechanism [39] . The observed interdependencies between synapsis and meiotic recombination indicate that the two processes are not only spatially correlated but also functionally intertwined . However , we currently lack a substantial molecular understanding of the how these hallmark meiotic processes intersect . In budding yeast it is clear that the SC transverse filament protein , Zip1 , serves an early role in promoting crossover recombination independent of ( and prior to ) its structural role in assembling the SC [24 , 30] . In this case , a single protein evolved dual functions to promote these distinct but coordinated meiotic prophase processes . The budding yeast Zip1 protein thus provides an opportunity to understand how SC transverse filaments can both promote and coordinate interhomolog recombination and SC assembly . Here we present a phenotypic analysis of mutants carrying a series of non-null zip1 alleles that encode small in-frame deletions within Zip1’s ( putatively unstructured ) N terminus; taken together with our previously-published analysis of the zip1[Δ21–163] mutant , these zip1 alleles encompass distinct and nearly reciprocal phenotypes with respect to synapsis and crossover recombination . These separation-of-function mutants reveal critical N-terminal residues that correspond to Zip1’s dual function in regulating crossing over and synapsis , and suggest that these residues may encompass adjacent interaction sites for the pro-crossover factor and putative E3-SUMO ligase , Zip3 , and the SUMOylated SC central element component , Ecm11 .
The primary amino acid sequence of Zip1 suggests that the region encompassing residues ~175–748 of the 875 residue protein has the capacity to assemble an extended coiled-coil structure , while the flanking N- and C-terminal regions are likely unstructured . Mirror-image Zip1 units assemble in a head-to-head fashion to span the ~100 nm width of the budding yeast SC central region [12 , 13]; Zip1’s C termini orient toward aligned chromosome axes ( lateral elements ) while its N termini orient toward the central element substructure ( comprised of–at least–the SUMO , Ecm11 , and Gmc2 proteins ) at the midline of the budding yeast SC . We previously reported that the non-null zip1[Δ21–163] mutant phenocopies SC central element-deficient ecm11 and gmc2 null mutants . In the zip1[Δ21–163] , ecm11 ( null ) or gmc2 ( null ) mutant , tripartite SC assembly fails but MutSγ-mediated crossover recombination events occur in excess [24] . In order to identify residues within the Zip1 and Zip1[Δ21–163] proteins that are critical for Zip1’s crossover activity , we created and analyzed additional non-null zip1 alleles ( Fig 1A ) . We found that alleles encoding disruptions in Zip1’s first twenty residues severely diminish Zip1’s capacity to promote MutSγ crossovers ( Fig 1B , 1C and 1D ) . Unlike zip1[Δ21–163] mutants but similar to a zip1 null ( corresponding to a complete deletion of the ZIP1 ORF ) in our BR1919 strain background , zip1[Δ2–163] meiotic cells exhibit a severely diminished capacity to form spores ( 1% in zip1[Δ2–163] vs 5% in zip1 vs 57% in the wild-type strain , n > 1000; S1 Table ) . The meiotic checkpoint that prevents zip1 null meiotic cells from producing spores relies on the AAA+ ATPase protein , Pch2 [40 , 41] , thus we removed PCH2 activity from zip1[Δ2–163] strains in order to evaluate the spore viability and meiotic crossover recombination phenotypes associated with this allele . As expected , the diminished spore formation phenotype of zip1[Δ2–163] is partially bypassed by removal of PCH2 ( 28%; S1 Table ) . We found that meiotic products from pch2 zip1[Δ2–163] strains were only slightly more viable than those from pch2 zip1 strains ( 58% in zip1[Δ2–163] vs . 39% in zip1; S1 Table ) , raising the possibility that the pro-crossover activity of Zip1 ( and of Zip1[Δ21–163] ) relies on Zip1 residues 2–20 . Indeed , consistent with previously published data implicating Zip1 in the formation of MutSγ crossovers , tetrad or random spore analysis to deduce meiotic crossover recombination frequencies within seven distinct intervals ( spanning most of chromosome III and a substantial length of chromosome VIII; Fig 1B ) revealed that pch2 msh4 , pch2 zip1 , and pch2 zip1[Δ2–163] meiotic cells exhibit similarly diminished interhomolog crossing over relative to the pch2 single mutant ( Fig 1C , Table 1 ) . These data in conjunction with our prior phenotypic analysis of zip1[Δ21–163] and zip1[Δ21–163] msh4 [24] indicates a critical role for the first twenty residues of Zip1 in promoting MutSγ crossovers . We next assessed the sporulation and interhomolog crossover recombination phenotypes of zip1 alleles that encode proteins with smaller internal deletions of Zip1’s N terminus ( Fig 1A ) . We found that , in contrast to zip1[Δ2–163] , strains expressing zip1[Δ2–9] , zip1[Δ10–14] , and zip1[Δ15–20] sporulate as efficiently as zip1[Δ21–163] and wild type BR strains ( 57% , 54% , and 51% , respectively; S1 Table ) . zip1[Δ2–20] gave an intermediate sporulation efficiency at 20% . These data indicate that Zip1’s residues 2–20 and 21–163 contribute redundantly to the mechanism ( s ) that normally prevent a PCH2-mediated checkpoint block to meiotic progression . Despite their near normal sporulation efficiency and high spore viability ( S1 Table ) , zip1[Δ2–20] , zip1[Δ2–9] , zip1[Δ10–14] , and zip1[Δ15–20] mutants exhibit deficiencies in MutSγ-mediated crossover recombination . Removal of the MutSγ complex protein , Msh4 , reduces detectable crossovers in chromosome III and VIII genetic intervals to about 50% of wild-type levels ( Fig 1D , Table 2 ) . By contrast to zip1[Δ21–163] single mutants , which exhibit elevated crossovers [24] , zip1[Δ2–20] , zip1[Δ2–9] , zip1[Δ10–14] , and zip1[Δ15–20] mutants exhibit 69% , 57% , 57% and 76% of the corresponding wild-type crossover level , respectively; Fig 1D , Table 2 ) . Importantly , removal of MSH4 from zip1[Δ2–20] , zip1[Δ2–9] , and zip1[Δ10–14] strains resulted in little change in the crossover phenotype relative to their zip1 single mutant allele counterparts , ( zip1[Δ2–20] msh4 , zip1[Δ2–9] msh4 , and zip1[Δ10–14] msh4 double mutants exhibit 63% , 52% , and 54% of wild-type crossovers , respectively ) . Because removal of Msh4 does not cause a substantial further reduction in crossovers , we conclude that few , if any , MutSγ-mediated crossovers form in zip1[Δ2–20] , zip1[Δ2–9] , and zip1[Δ10–14] mutant strains . By contrast , removal of MSH4 did reduce the crossover level of the zip1[Δ15–20] mutant ( 76% in zip1[Δ15–20] versus 55% in zip1[Δ15–20] msh4; Fig 1C and Table 2 ) , suggesting that the Zip1[Δ15–20] protein may support an intermediate level of MutSγ-mediated crossing over . Taken together , the interhomolog crossover recombination phenotypes of zip1[Δ2–9] , zip1[Δ10–14] , and zip1[Δ15–20] mutants indicate that Zip1’s first twenty residues are critical for its meiotic crossover promoting activity . We note that each msh4 strain bearing one of these four non-null zip1 alleles exhibits a slightly higher crossover frequency than the msh4 single mutant . This phenomenon is particularly dramatic for the zip1[2–20] msh4 strain , which exhibits 63% of wild type crossover frequency whereas the msh4 single mutant exhibits 46% of wild-type crossovers ( Fig 1D , Table 2 ) . The curious result that crossovers are elevated in msh4 mutants when Zip1’s N terminus is altered ( but not when Zip1 is absent altogether; Fig 1C ) , raises the possibility that Zip1’s N terminal residues might normally constrain the processing of some interhomolog recombination intermediates in a manner that ensures they are MutSγ-dependent . Residues 21–163 within Zip1’s N terminal unstructured region are dispensable for Zip1’s function in crossover recombination but essential for tripartite SC assembly [24] . We investigated whether residues 2–20 are critical for the formation of mature SC by asking whether coincident linear structures of the SC transverse filament protein ( Zip1 ) and the SC central element protein Ecm11 assemble on surface-spread meiotic prophase nuclei from strains carrying wild-type or a mutant zip1 allele and missing the Ndt80 transcription factor . Ndt80 is required for progression beyond a mid-meiotic prophase stage when full-length SCs are normally assembled [42] , thus the ndt80 null background allows us to maintain cells in sporulation medium for prolonged periods in order to assess the overall capacity of a strain to assemble SC . We utilized a polyclonal antibody targeted against Zip1’s C terminal 264 residues [43] together with an antibody against the MYC epitope tag that is fused to the C terminus of one copy of the ECM11 gene in these strains . As expected based on the SC-deficient phenotype of the zip1[Δ21–163] mutant , meiotic prophase nuclei from the zip1[Δ2–163] mutant strain fail to exhibit extensive Zip1 or Ecm11-MYC coincident linear structures on meiotic chromosomes . At 24 hours after placement into sporulation medium , when ~85% of ZIP1 ndt80 strains in our BR genetic background exhibit nearly full synapsis [44] , zip1[Δ2–163] mutants instead display Zip1 or Ecm11-MYC foci of varying sizes , sometimes accompanied by a large “polycomplex” aggregate of these SC central region proteins ( S1 Fig ) . Interestingly , ndt80 meiotic cells expressing zip1[Δ2–20] also fail to exhibit any detectable SC formation , even after 24 hours in sporulation medium ( S1 Fig ) . These data in conjunction with the deficient SC assembly phenotype of zip1[Δ21–163] [24] indicate that residues within both the 2–20 and 21–163 regions of Zip1 are required for Zip1’s capacity to assemble SC . However , we found that not all residues within Zip1’s 2–20 region are critical for SC assembly . In our initial examination of surface-spread meiotic prophase nuclei in zip1[Δ2–9] and zip1[Δ10–14] strains at the 24 hour time point , we observed many nuclei with extensive SC , as reflected by long linear assemblies of coincident anti-Zip1 and anti-Ecm11-MYC label ( Fig 2 ) . In addition , we observed extensive SCs in meiotic nuclei from a strain expressing zip1[10–14→A] , where each of Zip1’s residues 10–14 is replaced by alanine ( Fig 2 ) . The tripartite SC in budding yeast is assembled by Zip1 transverse filament proteins whose C termini orient toward homologous axes and whose N termini orient nearby to the central element substructure positioned at the midline of the SC . We used structured illumination microscopy ( SIM ) to ask whether the SC structures in zip1[Δ2–9] meiotic nuclei have a canonical , tripartite organization . This tripartite organization can be detected using SIM on surface-spread meiotic chromosomes dually labeled with antibodies that target the C terminal region of Zip1 and SC central element components [13] . With the increased resolution that SIM affords , our anti-Zip1 antibody localizes as a wide ribbon on linear SC structures; one can often observe parallel tracts of Zip1 C termini flanking the central element protein ( s ) within subsections of such a Zip1 linear element . We observed no detectable difference in the organization of Zip1 and the central element proteins within SCs assembled by wild type Zip1 versus Zip1[Δ2–9] protein: Antibodies targeting the C terminus of Zip1 were observed to flank the SC central element substructure within SCs assembled by Zip1[Δ2–9] ( Fig 3 ) . These observations indicate that , in reciprocal fashion to residues 21–163 , residues 2–15 are critically required for Zip1’s MutSγ crossover-promoting activity but dispensable for its capacity to assemble tripartite SC . In contrast to the robust synapsis observed in zip1[Δ2–9] and zip1[Δ10–14] strains at the 24 hour time point , extensive coincident linear assemblies of Zip1 and Ecm11 were not detectable in meiotic prophase nuclei from zip1[Δ15–20] or zip1[15–20→A] mutant strains ( Fig 2 ) . Instead , the vast majority of meiotic prophase nuclei from these strains exhibit foci of Zip1 and Ecm11 of varying sizes , which are sometimes , but not always , coincident . Our phenotypic assessment of three novel non-null zip1 alleles thus indicates that Zip1’s N terminal twenty residues correspond to adjacent regions that function somewhat independently of one another: residues 2–14 are essential for normal MutSγ-dependent crossovers but dispensable for SC assembly , whereas residues 21–163; [24] , and possibly residues 15–20 are less critical for MutSγ crossovers but crucial for SC assembly . Our initial 24 hour time point data hinted at a possible deficiency in the number of meiotic nuclei with extensive SC in zip1[Δ2–9] , and zip1[Δ10–14] , relative to wild-type strains . To explore the possibility that SC assembly occurs with altered timing in these zip1 mutants , we examined SC abundance in at least 50 meiotic surface-spread nuclei from zip1[Δ2–9] , zip1[Δ10–14] and zip1[[Δ15–20] strains at 15 , 18 , 21 and 24 hours after placement into sporulation medium . Nuclei were selected solely based on DAPI-stained morphology of the DNA; this method can be used to select nuclei in mid or late meiotic prophase . In addition to a wild-type control strain , our analysis included the zip3 mutant , which resembles zip1[Δ 2–9] and zip1[Δ10–14] mutants in its failure to form MutSγ crossovers but proficiency for SC assembly ( albeit diminished ) . We quantified the extent of SC assembly in the selected meiotic nuclei at each time point by measuring the number of linear assemblies of Zip1 , Ecm11-MYC , or coincident Zip1-Ecm11-MYC ( Fig 4 , left column ) and the cumulative length of Zip1 , Ecm11-MYC , and coincident Zip1-Ecm11-MYC linear structures ( Fig 4 , center column ) . Our analysis revealed that SC cumulative length per meiotic nucleus ( as measured by coincident Zip1 and Ecm11-MYC; purple circles in Fig 4 , center scatterplots ) was only slightly reduced in zip1[Δ2–9] and zip1[Δ10–14] meiocytes relative to wild type: zip1[Δ2–9] and zip1[Δ10–14] populations of meiotic nuclei exhibited a maximum of 32 and 38 microns , and on average 17 and 20 microns of cumulative SC length per nucleus , respectively , at the time point displaying the most abundant SC . ZIP1 control meiotic nuclei exhibited a maximum of 41 and average of 23 microns at the time point with the most abundant SC . zip3 mutants exhibited a more dramatic reduction in SC cumulative length relative to wild type , with a maximum of 25 and a mean of 8 microns at the time point exhibiting most abundant SC ( Fig 4 , center column ) . Interestingly , SC cumulative length was highest for wild-type populations at the 24 hour time point , while SC cumulative length in zip1[Δ2–9] , zip1[Δ10–14] , and zip3 strains peaked at the 21 hour time point in our time course . Moreover , at the earliest time point ( 15 hour ) both the number of SC linear assemblies and SC cumulative length per meiotic nucleus was substantially higher in zip1[Δ2–9] , zip1[Δ10–14] and zip3 populations ( exhibiting a maximum of 25 , 31 , 17 microns and mean of 4 , 4 , 2 microns , respectively , at this time point ) relative to the ZIP1 ZIP3 control population , which exhibited a mean of 0 . 04 micron of cumulative SC length per nucleus . The difference in SC accumulation at the earliest time point in zip1[Δ2–9] , zip1[Δ10–14] and zip3 relative to wild type populations of meiotic nuclei is significant ( using an unpaired t-test or the non-parametric Mann Whitney test , the two-tailed P value is <0 . 0001 for each mutant strain ) and suggests that SC initiates earlier and/or assembles faster in zip1[Δ2–9] , zip1[Δ10–14] , and zip3 meiocytes , relative to wild type . We repeated a similar time course analysis of SC assembly on these strains and observed consistent results ( S2 Fig ) . Early synapsis may have been missed by two earlier analyses of SC assembly in zip3 because of technicalities: In one study , wild-type nuclei already exhibited substantial SC at the earliest time point examined [45] . An earlier time course spanned appropriate pre-synapsis time points , but only those nuclei with extensive SC were tallied , thus the less extensive SC structures potentially present in the zip3 mutant at early time points were likely excluded [28] . Using a lacO array inserted near the centromere of chromosome IV and GFP-LacI expressed in trans , we observed that SC assembles between aligned homologous chromosomes in most meiotic nuclei that display moderate to extensive synapsis in zip1[Δ2–9] , zip1[Δ10–14] and zip3 strains ( n > 50; Fig 5 ) . Thus , while SC assembly initiates early and potentially in a manner that is uncoupled from normal regulatory cues in zip1[Δ2–9] , zip1[Δ10–14] and zip3 strains , SC assembly in these mutants remains an event that is triggered downstream of the homologous pairing process . SC assembly appeared less extensive in zip1[Δ2–9] , and zip1[Δ10–14] mutants at the 24 hour relative to the 21 hour time point ( Fig 4 ) . Although the decrease in cumulative SC at 24 hours is not dramatic , both the range and average cumulative SC length per nucleus shifted lower at the 24 hour time point compared to the 21 hour time point in zip1[Δ2–9] , and zip1[Δ10–14] strains ( Mann Whitney two-tailed P = 0 . 015 for zip1[Δ2–9] and 0 . 071 for zip1[Δ10–14] ) , while cumulative SC increased in wild-type meiotic nuclei at 24 versus 21 hour timepoints ( P = 0 . 0014 ) . Furthermore , Zip1 polycomplex aggregates , while undetectable in 100 zip1[Δ2–9] and zip1[Δ10–14] meiotic nuclei from 15 and 18 hour time points , were observed at the 21 hour time point and were observed even more frequently ( 30–40% of nuclei , n = 50 ) at the 24 hour time point ( Fig 4 , right column ) . As polycomplex structures tend to assemble when SC assembly is disrupted , the sub-peak level of assembled SC at the 24 and 26 hour time points and the increased occurrence of polycomplex structures together suggest the possibility that SCs assembled in zip1[Δ2–9] , and perhaps zip1[Δ10–14] strains are less stable than SCs assembled in wild-type strains . The early appearance of polycomplex structures and overall lower extent of assembled SC observed in the zip3 mutant ( ranging from 1 . 7 microns at 15 hours to 8 . 4 microns at the time point with peak SC assembly , 21 hours ) furthermore suggests that SCs assembled in the absence of Zip3 may be unstable . A replicate time course analysis comparing 21 to 26 hour timepoints gave consistent results ( S2 Fig ) : While the cumulative length of SC in populations of wild-type meiotic nuclei increased between 21 and 26 hour time points ( Mann Whitney two-tailed P = 0 . 010 ) , SC decreased between 21 and 26 hours in zip1[Δ2–9] , zip1[Δ10–14] and zip3 populations of meiotic nuclei ( Mann Whitney two-tailed P = 0 . 0015 , <0 . 0001 , and 0 . 0289 respectively ) . Interestingly , a small number of meiotic nuclei with relatively extensive synapsis from zip1[Δ2–9] , zip1[Δ10–14] and zip3 mutant strains displayed unpaired GFP-LacI-lacO signals , one or more of which associated with a linear assembly of SC protein ( Fig 5 ) . This observation is consistent with the possibility that SC structures prematurely fall apart in these mutants , and either re-assemble on or remain attached to chromosome axes after an original SC breaks down . Our time course experiment furthermore confirmed that zip1[Δ15–20] meiotic nuclei fail to assemble extensive SC at any point during meiotic prophase ( prior to the ndt80 late meiotic prophase arrest ) . Out of the 200 zip1[Δ15–20] meiotic nuclei analyzed over the time course , zero exhibited robust long linear structures containing coincident Zip1 and Ecm11-MYC . However , large or adjacent foci of coincident Zip1 and Ecm11 were sometimes included in our SC measurements ( which recorded any Zip1 or Ecm11 continuous structures with a dimension of 0 . 7 micron or more ) , and occasionally a meiotic nucleus displayed linear elements of Ecm11 and Zip1 with a frayed and diffuse appearance ( Fig 6 ) . The average cumulative length of SC per nucleus detected in zip1[Δ15–20] populations was 1 . 7 microns at the peak time point ( 24 hours; Fig 4 ) . Zip1 polycomplex structures were observed only at the 24 hour time point in zip1[Δ15–20] strains ( Fig 4 , right column ) . While the earliest SC assembly events that occur during meiosis in budding yeast have been found to preferentially initiate at centromeres [29] , SC assembly events are also associated with non-centromeric sites ( presumably recombination sites ) in wild-type meiotic nuclei at intermediate stages of synapsis . In the zip3 mutant , by contrast , new SC assembly events associate predominantly with centromeres at both early and later meiotic prophase stages [29] . As the SC assembly and meiotic crossover phenotypes in zip1[Δ2–9] and zip1[Δ10–14] strains resemble the phenotypes found in zip3 mutant meiotic cells , we asked whether nascent SC assembly events in zip1[Δ2–9] and zip1[Δ10–14] meiotic nuclei associate with centromeres more often than wild-type nuclei at intermediate stages of synapsis . Surface-spread meiotic chromosomes from 15 , and 18 hour time points were co-labeled with antibodies that target Zip1 , and that target the MYC epitope that is fused to the Ctf19 centromere protein in these strains . In order to enrich for new , singular SC assembly events in our analysis , we identified the total number of Zip1 linear stretches measuring between 0 . 7–1 . 0 micron in length , and measured the number that are directly adjacent to ( overlapping ) a Ctf19-MYC focus . We found that 83% ( 29 out of 35 ) of such short SC structures were associated with a Ctf19-MYC focus in zip3 meiotic nuclei , consistent with previously-published data [29] . By contrast , 48% , 52% and 64% ( 35/73 , 27/52 , and 23/36 ) of short Zip1 linear structures were associated with Ctf19-MYC in zip1[Δ2–9] , zip1[Δ10–14] , or ZIP1 ZIP3 meiocytes , respectively ( Fig 7 ) . Thus , in contrast to zip3 mutants , zip1[Δ2–9] , and zip1[Δ10–14] mutants display a wild-type capacity to initiate SC assembly from non-centromeric sites on meiotic chromosomes . Humphryes et . al ( 2013 ) demonstrated that SUMOylated Ecm11 is required for SC assembly , and that the central element component Gmc2 , transverse filament Zip1 , along with SIC proteins Zip2 , Spo16 and Zip4 ( but not Zip3 ) are required for robust Ecm11 SUMOylation during meiosis . This report also revealed that Ecm11 is hyper-SUMOylated in mutants missing the putative SUMO E3 ligase and SIC protein , Zip3 . To ask whether the N terminal twenty residues of Zip1 are required for its capacity to regulate Ecm11 SUMOylation , we evaluated the abundance of Ecm11 forms in meiotic extracts from strains homozygous for ZIP1 , zip1[Δ2–9] , zip1[Δ10–14] , zip1[Δ15–20] , a zip1 null , or a zip3 null allele . These strains also are homozygous for the ndt80 mutation , in order to allow our asynchronous meiotic cultures to accumulate ( by 24 hours after placement into sporulation medium ) at a mid-late meiotic prophase stage , when the Ecm11 SUMOylation that accompanies synapsis is at a maximum [42] . A Western blot can readily detect three forms of Ecm11-MYC in protein extracts from meiotic cells homozygous for MYC-tagged Ecm11 [13 , 35 , 38] . UnSUMOylated Ecm11-MYC migrates near the 75 kD marker on a protein gel , whereas monoSUMOylated and polySUMOylated Ecm11-MYC is positioned near the 100 kD and 150 kD positions , respectively . HyperSUMOylated Ecm11-MYC , which is abundant in zip3 meiotic extracts , migrates at various positions between the 150 kD and 250 kD markers ( Fig 8A ) . We found the proportion of SUMOylated Ecm11-MYC in ZIP1 ZIP3 ndt80 meiotic extracts at the 24 hour time point to be , on average , 14% , wherein 11% of total Ecm11-MYC was of the monoSUMOylated form and 3% was of the polySUMOylated form ( three replicates; Fig 8B ) . Consistent with prior results [13 , 38] , zip1 null strains exhibited a relatively low level of SUMOylated Ecm11: An average of 4% of total Ecm11-MYC was of the monoSUMOylated form , while polySUMOylated Ecm11-MYC was below levels of detection ( less than 1%; Fig 8B ) . Again consistent with prior findings [38] , zip3 meiotic extracts exhibited not only an elevated level of polySUMOylated Ecm11-MYC ( 15% of total Ecm11-MYC , on average , over three replicates ) , but also an abundance of hyperSUMOylated Ecm11 ( 19% of total Ecm11-MYC , on average , over three replicates; Fig 8B ) . We found that residues 15–20 are important for Zip1’s capacity to promote Ecm11 SUMOylation . In zip1[Δ15–20] meiotic extracts at the 24 hour time point , on average only 8% of total Ecm11 was of a SUMOylated form ( Fig 8B ) . Given the SC assembly defect of zip1[Δ15–20] strains , this result bolsters a direct correlation between Ecm11 SUMOylation and SC assembly . In striking contrast , cells expressing the zip1[Δ2–9] or zip1[Δ10–14] alleles exhibit robust levels of Ecm11 SUMOylation as well as the hyperSUMOylated forms of Ecm11 that are characteristic of the zip3 mutant . An average of 40% of Ecm11-MYC was SUMOylated in zip1[Δ2–9] meiotic extracts at the 24 hour time point , with 9% in the hyperSUMOlated form ( Fig 8B ) . Likewise , an average of 54% of Ecm11-MYC was SUMOylated in zip1[Δ10–14] meiotic extracts , with 13% in the hyperSUMOylated form ( Fig 8B ) . It has been proposed that the hyperSUMOylated forms of Ecm11 that occur in zip3 mutant meiotic cells correspond to Ecm11 linked to poly-SUMO branched chain structures of various sizes and shapes [38 , 46] . The accumulation of such extensively SUMOylated Ecm11 protein in zip1[Δ2–9] and zip1[Δ10–14] mutants indicates that residues within the 2–14 region of Zip1 are dispensable for Ecm11 SUMOylation per se , but regulate the extent and/or manner of Ecm11 SUMOylation . Zip1’s residues 2–14 appear to control similar aspects of Ecm11 SUMOylation as the putative SUMO E3 ligase , Zip3 , raising the possibility that Zip1’s N terminal residues regulate Ecm11 SUMOylation in part through an interaction with Zip3 . We found that zip1 alleles encoding proteins with alanine substitutions in place of dual or triple residues within Zip1’s N terminus exhibit the distinguishing Ecm11 SUMOylation and synapsis phenotypes of corresponding internal deletion zip1 alleles . zip1[N3A , R6A , D7A] , zip1[F4A , F5A] , and zip1[P14A , P16A] exhibited elevated levels of SUMOylated Ecm11 during meiosis , reminiscent of the zip1[Δ2–9] and zip1[Δ10–14] mutants , although we note that zip1[N3A , R6A , D7A] and zip1[P14A , P16A] strains exhibit a particular abundance of monoSUMOylated relative to polySUMOylated and hyperSUMOylated Ecm11 , which differs slightly from the distribution of SUMOylated Ecm11 forms in zip1[F4A , F5A] , zip1[Δ2–9] , zip1[Δ 10–14] or zip3 mutants ( Fig 8B ) . By contrast , meiotic extracts from zip1[I18A , F19A] strains exhibit a dramatic reduction in SUMOylated Ecm11 , reminiscent of meiotic extracts from zip1[Δ15–20] and zip1 null strains ( Fig 8B ) . We also found that zip1[N3A , R6A , D7A] and zip1[I18A , F19A] exhibit SC assembly phenotypes that are generally reminiscent of corresponding internal deletion zip1 alleles . Linear stretches of coincident Zip1 and Ecm11-MYC were often detectable on surface-spread meiotic chromosomes from zip1[N3A , R6A , D7A] ndt80 and zip1[F4A , F5A] ndt80 strains at multiple time points in a meiotic time course , while such extensive SC structures were absent from meiotic chromosomes in zip1[I18A , F19A] ndt80 strains at all time points ( Fig 9 ) . We furthermore found that meiotic crossovers are reduced in zip1[N3A , R6A , D7A] , zip1[F4A , F5A] , and zip1[I18A , F19A] point mutants in a manner that resembles the corresponding deletion strain ( S2 Table ) . Specifically , zip1[F4A , F5A] exhibited the most dramatic deficit in meiotic crossovers , and crossovers in this mutant are not further reduced by removal of MSH4 , indicating that zip1[F4A , F5A] meiotic cells lack MutSγ-mediated crossovers ( S2 Table ) . Crossovers did further diminish from their intermediate level when MSH4 was removed from zip1[I18A , F19A] mutants ( S2 Table ) . The phenotypes of these novel zip1 dual- and triple-residue substitution alleles strengthen the idea that Zip1’s first twenty residues encompass both crossover recombination and SC assembly functionalities and that adjacent sites within this region maintain different and independent roles in regulating synapsis . The shared phenotypes of zip1[Δ2–9] , zip1[Δ10–14] and zip3 mutants prompted us to wonder whether the N terminus of Zip1 directly or indirectly interacts with the Zip3 protein . Prior evidence for an interaction between Zip1 and Zip3 includes the observation that Zip3 is detected throughout Zip1 polycomplex structures that assemble in contexts where SC assembly fails [28 , 34] . To explore the possibility that Zip1’s N terminus mediates an interaction with the Zip3 protein , we examined the distribution of Zip3 at Zip1 polycomplex structures assembled in spo11 meiotic cells , which fail to initiate recombination and thus also SC assembly [45 , 47] . Of the polycomplex structures assembled by wild-type Zip1 and Zip1[Δ15–20] protein , 100% ( 20/20 ) exhibited Zip3-MYC distributed uniformly across the entire structure ( Fig 10A ) . Frequently , additional “capping” structures of coincident Zip3-MYC and Zip4-HA protein flank the Zip1 polycomplex , as has been reported previously [34] . Intriguingly , however , among more than 20 meiotic nuclei examined from spo11 zip1[Δ2–9] and spo11 zip1[Δ10–14] strains , Zip3-MYC was completely absent from the bulk of the Zip1 polycomplex structure ( Fig 10A ) . Instead , Zip3-MYC co-localized with Zip4-HA in the capping configuration at opposite ends of the polycomplex . Often these “capping” structures of coincident Zip3-MYC and Zip4-HA were observed at a substantial distance away from the polycomplex aggregate of Zip1 protein . We furthermore found that Zip3 is diminished at polycomplexes assembled by Zip1[N3A , R6A , D7A] or Zip1[F4A , F5A] protein ( Fig 10B ) , although the absence of Zip3 from the bulk of these Zip1 polycomplex structures is less dramatic than what is observed at Zip1[Δ2–9] or Zip1[Δ10–14] polycomplex . Finally , as expected based on the robust localization of Zip3 to Zip1[Δ15–20] polycomplex , we found that Zip3 also localizes uniformly throughout polycomplexes built of Zip1[Δ21–163] protein ( Fig 10B ) . These data indicate that , at least in the context of polycomplex structure , Zip1’s residues 2–14 mediate a direct or indirect interaction with the pro-crossover and putative E3 SUMO ligase protein , Zip3 . Zip3 and other SIC proteins ( such as Zip2 , Zip4 and Spo16 ) form foci that co-localize with MutSγ along aligned homologous chromosomes at mid meiotic prophase [28 , 34] . Consistent with the notion that such Zip3 foci mark recombination intermediates , Zip3 has been detected at DSB hotspots using chromatin immunoprecipitation ( ChIP ) [37 , 48] . Zip1 was found to be required for the recruitment of Zip3 to the DSB sites examined , thus we asked whether the capacity of Zip1 to recruit Zip3 to DSB sites relies on the N terminal residues of Zip1 that facilitate Zip3’s localization to Zip1 polycomplex . We performed ChIP in conjunction with quantitative PCR ( ChIP-qPCR ) on meiotic cell extracts from ZIP1 , zip1[Δ2–9] , zip1[Δ10–14] , and zip1 null strains expressing a Zip3 protein with three copies of the FLAG epitope fused to its C terminus . Strains for this experiment were built in the SK1 genetic background , to ensure maximal synchrony over a meiotic time course ( SK1 strains enter and/or progress through meiosis more synchronously than the BR strain background used for all other experiments in this study ) . ChIP-qPCR was performed at multiple time points during sporulation , and the time course experiment was performed in duplicate for each strain except for the zip1 null negative control , where the single experiment performed gave results that are consistent with prior published data [48] . We examined Zip3-6xHIS-3xFLAG association with chromosomal sites corresponding to three known DSB hotspots , a centromere , or the chromosome axis [37 , 48] . Sequences enriched for Rec8 that are embedded in the proteinaceous chromosome axis are generally anti-correlated with DSB sites , but are thought to associate with DSB repair intermediates , according to a “loop-tether” model for DSB formation in budding yeast [49] . A sequence internal to the large NFT1 open reading frame was previously found to be devoid of Zip3 binding [50 , 51] and thus served as a negative control for Zip3 enrichment . In strains carrying wild-type ZIP1 within two hours after placement of into sporulation medium , centromeric DNA was more abundant in Zip3 immunoprecipitates relative to axis or DSB site DNA , consistent with previously published results [37 , 48] . Between two and four hours after placement into sporulation medium , DNA sequences corresponding to chromosome axis sites and three DSB hotspots ( GAT1 , BUD23 , and ERG1; Fig 11 ) significantly increased their abundance within Zip3 immunoprecipitates , reflecting Zip3 recruitment to these chromosomal sites . Zip3 localization to all sites peaked at the 4 hour time point in ZIP1 meiotic cells , which corresponds to maximal DSB activity at the BUD23 locus in this SK1 strain background [48] . At this four hour time point , Zip3 enrichment was found to be two to three fold greater at GAT1 and BUD23 compared to DNA sequences at the chromosome axis ( Fig 11 ) . Consistent with DSB repair timing in this genetic background , Zip3 enrichment at all sites dramatically diminished between four and six hours , and was at pre-meiotic levels by eight hours after placement in sporulation medium . Consistent with prior findings , Zip3 was virtually undetectable at DSB , axis and centromere sites in the zip1 null strain ( in which the ZIP1 ORF is deleted; Fig 11; [48] ) . Similar to a zip1 null strain , little Zip3 was detectable at any of the three DSB sites examined , nor at the axis or centromere site , in zip1[Δ2–9] or zip1[Δ10–14] strains ( Fig 11 ) . The phenotype of zip1[Δ2–9] in this experiment appeared indistinguishable from the zip1 null , whereas slight Zip3 enrichment was detected at the BUD23 DSB site in the zip1[Δ10–14] meiotic time course . These data indicate that the capacity of Zip1 to recruit Zip3 to DSB sites during meiosis relies on Zip1’s N terminal residues 2–14 . As expected based on ultrastructural images of recombination nodules along the length of synapsed chromosomes [20 , 22 , 52] , we found that meiotic recombination proteins embed within the SC central region in budding yeast . Structured illumination microscopy ( SIM ) in conjunction with antibodies targeting the meiotic axis protein Red1 and the central element protein ( s ) Ecm11 or Gmc2 reveal MutSγ and Zip3 foci at the midline of the SC , embedded within the SC central element . Singular foci of epitope-tagged MutSγ protein Msh4-MYC and Zip3-MYC localize directly between aligned Red1-labeled axes , where the SC central element substructure is positioned ( Figs 12 and 13 ) . When antibodies targeting SC central element proteins are used to label the SC central element directly , Msh4 foci are observed embedded directly in the linear Ecm11-Gmc2 structures at the midline of the SC ( Fig 13 ) . In SCs assembled by Zip1[Δ2–9] protein , we observe Zip3-MYC foci embedded within the SC central element , but such Zip3-MYC foci appear strongly diminished in both number and intensity relative to Zip3-MYC foci on meiotic chromosomes from ZIP1 strains ( Fig 12 ) . Similarly , using conventional wide-field fluorescence microscopy we observe a diminished number of bright Msh4-MYC foci on aligned mid-meiotic prophase chromosomes in strains expressing zip1[Δ2–9] , zip1[Δ10–14] and zip1[Δ15–20] relative to ZIP1 strains , mimicking the Msh4-MYC pattern seen in a zip1 null strain ( Fig 14A and 14B ) . Furthermore , the Msh4-MYC foci observed on synapsed meiotic prophase chromosomes in zip1[Δ2–9] strains do not robustly co-localize with other SIC proteins , such as Zip4-HA , relative to the Msh4-MYC foci assembled on synapsed chromosomes in wild-type meiotic nuclei ( S3 Fig ) . Our ChIP and cytological studies together indicate that the N terminal twenty residues of the budding yeast transverse filament protein , Zip1 , are essential for the proper enrichment of pro-crossover protein Zip3 to meiotic centromeres , to DSBs , and to chromosomal axis sites , and for the accumulation of robust Zip3 and MutSγ foci within the central region of the SC .
Our analysis of the novel zip1 alleles reported here leads us to propose that Zip1’s first twenty residues correspond to adjacent interaction domains that directly engage with pro-crossover and pro-synapsis machinery and/or mechanisms , as illustrated in Fig 15B . The absence of Zip1’s residues 2–9 or 10–14 confers a phenotype that strongly resembles the unique phenotype of cells missing the pro-crossover SIC protein , Zip3: zip1[Δ2–9] , zip1[Δ10–14] and zip3 strains display some SC assembly despite severely diminished MutSγ crossovers , and hyperSUMOylated forms of the SC central element protein , Ecm11 . This constellation of phenotypes is a striking contrast to the synapsis-deficiency and diminished Ecm11 SUMOylation phenotype displayed by mutants that are missing Zip1 altogether or missing other SIC proteins that colocalize with Zip3 at presumed recombination intermediates embedded in the SC , such as Zip2 , Zip4 , Spo16 or the MutSγ complex [32–34 , 37 , 38 , 55] . The unique , zip3-like phenotype of zip1[Δ2–9] and zip1[Δ10–14] mutants suggests that these mutant meiocytes are missing a specialized capacity to promote Zip3 function . Zip3 localizes , along with other SIC proteins , to MutSγ foci on mid-meiotic prophase chromosomes in budding yeast and is ( along with the other SIC proteins ) required for MutSγ -mediated crossover formation [28 , 35] . Unlike Zip2 , Zip4 and Spo16 , however , Zip3 has been implicated in preventing unwarranted ( recombination-independent ) SC formation [45] and is not required per se for SC assembly [28] . Zip4 directly interacts with a Zip2-Spo16 complex that is capable of binding branched DNA structures , and can interact with the meiotic axis component Red1 , the MutSγ factor Msh5 , as well as Zip3 [37] . One possibility is that while Zip2 , Zip4 and Spo16 are absolutely critical at recombination sites to establish a suitable foundation on which to initiate SC assembly , Zip3 may be specifically involved in the licensing of SC assembly at certain recombination sites . Interestingly , we found evidence in support of a specific regulatory role for Zip3 in meiotic recombination as well . Our data indicates that crossover levels in zip3 , zip3 msh4 , or zip1[2–20] msh4 mutants are elevated over the msh4 single mutant ( Table 2 , S2 Table ) while crossovers in zip1 meiotic cells resemble the level observed in msh4 and zip1 msh4 strains ( Table 1 ) . This observation suggests that Zip3 acts not only to promote MutSγ crossing over , but also to ensure that Zip1-associated recombination intermediates are processed in an MutSγ-dependent manner . Under this model , recombination intermediates associated with both Zip3 and Zip1 fail to resolve into interhomolog crossover events when Msh4 is absent , but when Zip3 or both Msh4 and Zip3 are absent , at least some Zip1-associated recombination intermediates can resolve into interhomolog crossovers ( through a MutSγ-independent pathway ) . This model is supported by sequence signatures observed at interhomolog recombination events in msh4 versus zip3 mutants [31] , which suggest that unbiased resolution of joint molecule recombination intermediates—a type of resolution associated with MutSγ-independent pathways [5 , 56]—occurs to a greater extent in zip3 relative to msh4 mutants . Zip3 is a RING domain protein that has been shown to have SUMO ligase activity in vitro [46] , and to be responsible for the SUMOylation of a fraction of Red1 , a meiosis-specific axis associated protein that is required for normal recombination and SC assembly [57–59] . On the other hand , Zip3 normally prevents hyperSUMOylation of the SC central element protein , Ecm11 . Thus Zip3 likely impacts meiotic recombination and SC assembly mechanisms through both negatively and positively regulating the SUMOylation of several distinct target proteins . Consistent with a particular role for Zip3 in preventing unwarranted SC assembly via an interaction with Zip1’s N terminal residues , and also with the idea that this Zip1-Zip3 interaction might be required to “trigger” synapsis initiation at MutSγ crossover sites , zip1[Δ2–9] and zip1[Δ10–14] mutants display an absence of MutSγ crossovers , a failure of stable Zip3 recruitment to several DSB sites , and premature SC assembly ( albeit still between homologs ) . These phenotypes are consistent with the idea that , through an interaction with Zip1’s N terminus , Zip3 not only constrains Zip1-associated recombination intermediates to be MutSγ-dependent ( possibly through the SUMOylation of targets such as the Red1 axis-associated protein ) , but also negatively regulates SC assembly until the successful completion of a specific intermediate event associated with the recombination process . Perhaps negative regulation of Ecm11 SUMOylation is linked to Zip3’s switch-like function in ensuring that SC assembly occurs “in the right place at the right time” ( i . e . at a specific type of recombination intermediate , such as one designated to become a MutSγ crossover event ) . We note , however , that SC assembly is more robust and non-centromeric synapsis initiation events are more abundant in zip1[Δ2–9] and zip1[Δ10–14] relative to zip3 mutant strains; this difference could reflect additional pro-SC assembly activities of Zip3 , carried out in a manner that is independent of Zip3’s engagement with Zip1’s N terminal tip . While multiple attempts at two-hybrid and pull-down experiments have failed to reveal evidence of a strong physical interaction between Zip3 and Zip1’s N terminus , cytological support for this interaction comes from the localization of Zip3 at polycomplex structures , aggregates of Zip1 and other SC-associated proteins that form when SC assembly is compromised . While many if not all SIC proteins have been found to localize to Zip1 polycomplex structures , Zip3’s localization shows a greater degree of coincidence with Zip1 throughout the bulk of the polycomplex , for example relative to Zip4 ( [34]; Fig 10A ) or the MutSγ component , Msh4 [60] . Importantly , Zip3’s localization throughout the bulk of Zip1 polycomplex is not abolished when Zip4 ( which has been found to interact with Zip3 [37] ) is absent [34] , however Zip3’s localization to the bulk of Zip1 polycomplex is completely abolished by the loss of Zip1’s residues 2–9 or 10–14 ( Fig 10A ) . Finally , Zip1[Δ2–9] and Zip1[Δ10–14] have lost Zip1’s capacity to recruit Zip3 to sites of recombination initiation ( Fig 11 ) . Given our inability to observe a stable interaction between Zip1 and Zip3 via pull downs or two hybrid methods , it seems likely that Zip3 interfaces with Zip1 in a manner that is dependent on other proteins , on molecular structures nearby , such as the DNA joint molecule itself , or on a relatively unstable Zip1 structural configuration . Based on the absence of SC assembly in meiotic cells expressing zip1[Δ15–20] , zip1[15–20→A] and zip1[I18A , I19A] , we furthermore conclude that Zip1’s first twenty residues correspond to ( perhaps in an overlapping manner ) at least one interaction domain for an SC assembly factor or complex of factors . Unlike the limited nature of the region within Zip1’s N terminal 163 amino acids that is required for MutSγ crossovers ( twenty residues , based on the fact that zip1[Δ21–163] is fully capable of MutSγ crossing over [24] ) , groups of residues that are critical for allowing Zip1 to assemble SC may be distributed throughout the entire N terminal region encompassed by residues 15–163 , as both zip1[Δ15–20] and zip1[Δ21–163] fail to assemble SC . Nevertheless , we propose that the region overlapping residues 15–20 interfaces with components serving an SC assembly function , based on the fact that alteration of two adjacent residues ( I18 and I19 ) at Zip1’s extreme N terminus ( a change that is unlikely to alter the overall length or structure of the rod-like protein ) completely abolishes Zip1’s capacity to build the SC . We previously demonstrated that zip1[Δ21–163] phenocopies the ecm11 and gmc2 null mutant phenotype ( a failure in SC assembly but proficiency in crossing over ) , suggesting that this N terminal region of Zip1 functionally interacts with the central element in order to assemble SC [24] . Moreover , Leung et . al ( 2015 ) demonstrated that Zip1’s N terminal 346 residues is sufficient to promote Ecm11 SUMOylation in vegetative ( non-meiotic ) cells , provided that Gmc2 is also expressed . These data suggest that the N terminal region of Zip1 is able to engage with the Ecm11-Gmc2 proteins , perhaps in a direct manner or perhaps indirectly through a protein expressed in both meiotic as well as mitotic cells [61] . Similar to the uncertainty about whether Zip3 interacts with Zip1 in a direct manner , apart from the genetic interactions found for Zip1 and Ecm11 and the coincidence of Ecm11 and Gmc2 at the midline of SC ( where Zip1 N termini also reside [13] ) , strong evidence of a direct physical interaction between Zip1 and Ecm11 or Gmc2 does not yet exist . Finally , we note the tantalizing possibility that the adjacency between the putative pro-crossover and pro-synapsis regions of Zip1’s N terminus is functionally important for ensuring that SC assembly occurs in coordination with intermediate steps in the MutSγ crossover recombination pathway . Specifically , we speculate that Zip1 may physically connect crossover recombination events to SC assembly through a mechanism that is based , at least in part , on its capacity to stabilize Zip3 at its N terminus . Here , Zip3 would be expected to be in close proximity to putative pro-synapsis factors stabilized ( perhaps in conjunction with other SIC proteins ) by the adjacent region in Zip1 , and thus could potentially be oriented appropriately to regulate the extent of SUMOylation of SC central element protein Ecm11 . During the course of analyzing SC assembly over a time course of meiotic progression in our mutants we found that SCs assembled in zip3 , zip1[D2-9] and zip1[10–14] strains ( corresponding to SCs assembled in the absence of MutSγ crossovers ) assemble earlier than wild-type SC structures , and appear to be less capable of persisting during an ndt80-mediated , meiotic prophase arrest ( Fig 4 , S2 Fig ) . Pattabiraman ( 2017 ) found that a MutSγ-associated process affects the dynamic properties of C . elegans SC [39]; our set of preliminary observations raises the intriguing possibility that the MutSγ crossover pathway influences the structure and/or dynamics of budding yeast SCs in a similar fashion .
Yeast strains used in this study are isogenic to BR1919-8B [62] and were created using standard genetic crosses and manipulation procedures . CRISPR-Cas9 methodology was utilized to create unmarked alleles ( described below ) . Strains for crossover analysis carry an hphMX4 cassette inserted near the chromosome III centromere , ADE2 inserted upstream of the RAD18 locus , a natMX4 cassette inserted near the HMR locus , TRP1MX4 inserted 62 bp downstream of the SPO11 locus ( Kee and Keeney , 2002 ) , URA3 replacing SPO13 , and LYS2 inserted on chromosome VIII at coordinate 210 , 400 bp . Strains SYC107 , SYC149 , SYC151 and K914 do not carry THR1 nor LYS2 on chromosome VIII . Zip3 and Zip4 epitope tags ( MYC and HA , respectively ) are positioned internal to the gene ORFs , as described in [34] . ChIP and qPCR experiments were performed in strains of the SK1 genetic background ( S4 Table ) . Custom zip1 alleles were created in two sequential transformation steps: The first step is the creation of a “base strain” , which entails replacing the DNA sequences to be altered ( in this case , within the ZIP1 locus ) with the kanMX4 dominant drug resistance cassette . Next , ~500 ng of a CRISPR-Cas plasmid pRS425-Cas9-kanMX , created by Gang Zhao in Bruce Futcher’s laboratory ( Stony Brook University ) , is transformed into the zip1-kanMX4 base strain along with custom DNA sequences ( “healing fragments” ) that carry the desired alteration in DNA sequence as well as homology to sequences flanking the kanMX4 insert . pRS425-Cas9-kanMX is a yeast two micron plasmid carrying sequences that encode the LEU2 gene , the CAS9 gene ( driven by the TEF1 promoter ) and a unique CRISPR guide RNA ( driven by the RNA polymerase III promoter , SNR52 ) that targets the kanMX gene . For our strains , “healing fragments” typically correspond to two overlapping ZIP1 DNA sequences sharing at least twenty bases of overlap that encode the DNA changes desired in the new allele . These DNA fragments were amplified by PCR using a template with sequences containing the wild type ZIP1 gene . The 5’ healing fragment is created using a reverse primer containing the desired DNA changes and a forward primer that has homology to ZIP1 sequences 5’ of the kanMX4 insertion , whereas the 3’ healing fragment is created using a forward primer containing the desired DNA changes ( usually a reverse complement DNA fragment to the reverse primer used to create the 5’ fragment ) and a reverse primer corresponding to ZIP1 sequences 3’ to the kanMX4 insertion . Primers are positioned such that the PCR products contain have at least 50bp of DNA homologous to the regions flanking kanMX4 in the base strain . After transformation of the zip1-kanMX4 strain with pRS425-Cas9-kanMX plasmid DNA along with both “healing” DNA fragments , cells are plated onto synthetic medium lacking leucine and incubated for up to 10 days at thirty degrees Celsius . Leu+ colonies survive due to the presence of the pRS425-Cas9-kanMX plasmid and because of a DNA repair event that replaced kanMX4 with “healing fragment” sequences . Leu+ colonies are screened for G418 sensitivity; G418-sensitive transformants are struck out on YPD media to isolate single colonies and then genotyped by PCR . For potential mutant allele transformants ( based on the PCR genotyping ) , the entire gene ORF is sequence verified . Meiotic nuclei were surface spread on glass slides and imaged as described in [24] . The following primary antibodies were used: affinity purified rabbit anti-Zip1 ( 1:100 , raised at YenZym Antibodies , LLC , against a C terminal fragment of Zip1 , as described in [43] , mouse anti-cMYC ( 1:200 , clone 9E10 , Abcam ) . Mouse anti-Gmc2 antibodies were raised against purified Gmc2 protein , and guinea pig anti-Gmc2_Ecm11 antibodies were raised against a co-purified protein complex ( ProSci Inc . ) . These antibodies were used at 1:800 . Chicken anti-HA ( 1:100 , Abcam ) , and rabbit anti-Red1 ( 1:100 , a kind gift from G . S . Roeder , [59] ) were also used . Secondary antibodies conjugated with Alexa Fluor dyes were purchased from Jackson ImmunoResearch and used at 1:200 dilution . Microscopy and image processing were performed using a Deltavision RT imaging system ( General Electric ) adapted to an Olympus ( IX71 ) microscope . Measurements of Zip1 , Ecm11 , and SC linear structures in Fig 4 and S2 Fig ( raw data in S5 Table ) were measured manually by K . V . M . ( “ImageK” ) , using the measurement tool in the SoftWorx program associated with the Deltavision RT system . Structured illumination microscopy was carried out using Applied Precision’s OMX Blaze Structured Illumination Microscope system at The Rockefeller University’s Bio-Imaging Resource Center . Genetic crossover data was compiled and processed using an Excel Linkage Macro program , created by Jonathan Greene ( Rhona Borts , pers . comm . ) and donated by Eva Hoffmann ( University of Copenhagen , Denmark ) . Crossover values ( and their standard errors ) were obtained using the Stahl lab online tools ( https://elizabethhousworth . com/StahlLabOnlineTools/ ) , with the method of Perkins [63] . Non-mendelian segregation is reported in S3 Table . Recombinant spore values were calculated according to the following: 100 ( r/t ) , where r = the number of colonies carrying a chromosome which is recombinant in the interval and t; the total number of colonies assessed . Standard error ( S . E . ) values for random spore analysis were calculated according to the formula: 100 ( √ ( r/t ) ( 1-r/t ) /t ) [64] . All other statistical analyses were carried out using Graphpad Prism or Graphpad InStat ( www . graphpad . com ) . Western blotting was performed as described previously [13] with the following modifications: Amersham Protran 0 . 2μm NC was used as the transfer membrane following the manufacturer’s recommendation; after secondary antibody incubation the membrane was processed with a final wash in 100mM Tris-Cl pH 9 . 5 , 100mM NaCl , 5mM MgCl2 to boost the HRP- mediated chemiluminescence using Amersham ECL Prime Western Blotting Detection Reagent . Signals were detected on a Syngene G:Box and measured using Syngene GeneTools software; the areas being assessed for measurements were manually refined in order to ensure all and only appropriate data from each lane is collected . Meiotic cells were processed as described [65] , with the following modifications: Lysis was performed in Lysis buffer plus 1 mM PMSF , 50 μg/mL Aprotinin and 1X Complete Mini EDTA-Free ( Roche ) , using 0 . 5 mm zirconium/silica beads ( Biospec Products , Bartlesville , OK ) . 2 μg of the mouse monoclonal anti-FLAG antibody M2 ( Sigma ) and 30 μL Protein G magnetic beads ( New England Biolabs ) were used . Quantitative PCR was performed from the immunoprecipitated DNA or the whole-cell extract using a 7900HT Fast Real-Time PCR System ( Applied Biosystems , Thermo Scientific ) and SYBR Green PCR master mix ( Applied Biosystems ) as described [65] . Results were expressed as % of DNA in the total input present in the immunoprecipitated sample . Primers for GAT1 , BUD23 , ERG1 , Axis and NFT1 loci have been described [50 , 51 , 66] . | Reproductive cell formation relies on a nuclear division cycle called meiosis , wherein two homologous sets of chromosomes are reduced to one . At the crux of ( and critically required for ) meiotic chromosome segregation is a transient association between homologous chromosomes established by a crossover recombination event . Recombination intermediates embed within a ~100 nm wide proteinaceous structure that connects aligned homologous axes , the synaptonemal complex ( SC ) . While genetic data implicate certain SC structural proteins in crossover formation , it is unclear how such coiled-coil , rod-like proteins carry out their recombination function . Our structure-function analysis of the yeast SC transverse filament protein , Zip1 , reveals pro-crossover and pro-synapsis functions that are encompassed by adjacent N terminal regions . We also discovered that the pro-crossover region of Zip1 promotes proper localization of pro-crossover factor and putative SUMO ligase , Zip3 , to meiotic recombination sites . Zip3 is known to not only promote crossovers but also to influence the post-translational modification of another SC structural component , Ecm11 , which is dispensable for crossovers . Our findings raise the possibility that Zip1’s N terminus acts as a liaison to connect pro-crossover factors ( like Zip3 ) to SC assembly proteins ( such as Ecm11 ) in order to coordinate the two landmark meiotic chromosomal processes . | [
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"an... | 2019 | Crossover recombination and synapsis are linked by adjacent regions within the N terminus of the Zip1 synaptonemal complex protein |
As part of on-going efforts to control hookworm infection , the “human hookworm vaccine initiative” has recognised blood feeding as a feasible therapeutic target for inducing immunity against hookworm infection . To this end , molecular approaches have been used to identify candidate targets , such as Necator americanus ( Na ) haemoglobinase aspartic protease-1 ( APR-1 ) , with immunogenicity profiled in canine and hamster models . We sought to accelerate the immune analysis of these identified therapeutic targets by developing an appropriate mouse model . Here we demonstrate that Nippostrongylus brasiliensis ( Nb ) , a phylogenetically distant strongylid nematode of rodents , begins blood feeding early in its development and that immunisation with Na-APR-1 can block its growth and completion of its life cycle . Furthermore , we identify a new haem detoxification pathway in Nb required for blood feeding that can be blocked by drugs of the quinolone family , reducing both infection burden and the associated anaemia in rodents . Collectively , our findings show that haem metabolism has potential as a checkpoint for interrupting hookworm development in early stages of the hookworm life cycle and that the Nippostrongylus brasiliensis rodent model is relevant for identifying novel therapeutic targets against human hookworm .
Hookworms ( Ancylostomatoidea ) are agents of one of the major Neglected Tropical Diseases , affecting 450 million people worldwide [1] . Human hookworm disease is caused principally by Na and A . duodenale and manifests as anaemia through blood-loss , stunted development in childhood and complications during pregnancy [2 , 3] . Blood-loss is thought to be associated with the feeding activity of the parasite in the gut throughout the L4 and adult stages , during which the parasite attaches to the gut mucosa and ruptures capillaries . The blood-feeding mechanisms have been partially characterised in these nematodes , and some proteins involved in this pathway such as the Na hemoglobinase aspartic protease 1 ( Na-APR-1 ) and the haem transporter Na gluthatione-S-transferase-1 ( Na-GST-1 ) , that are essential to the digestion process , are now the targets of vaccine development [4–7] . Haem , an essential prosthetic group , is one of the byproducts of the degradation of haemoglobin . Most nematode parasites lack the de novo production of haem and are as such dependent on haem scavenging from the host [8] . However , haem in its free form is highly toxic , and its detoxification is essential to the survival of haematophagous parasites [8] . This process has been partially studied in hookworms with the discovery of a haem catabolism pathway involving the GST and GSH proteins , similar to that described for the malaria parasites Plasmodium spp . and other haematophagous parasites [9–12] . In malaria , several pathways of haem detoxification have been described . One of these pathways involves the crystallisation of haem into a β-haematin complex called hemozoin [13 , 14] . Hemozoin is a dark-brown non-toxic pigment and has been characterised in both Plasmodium spp . and in the blood flukes Schistosoma spp . [15] . Given the presence of hemozoin in such distantly related parasites , we hypothesized its possible formation in hookworms . As human hookworms do not develop in mice , we used a phylogenetically distant strongylid nematode that is widely used to study the type 2 immune response , namely Nb ( Trichostrongyloidea ) . This parasite has a similar life cycle to Na , migrating from the skin to the lungs during the infective L3 stage ( iL3 ) , and maturing to adulthood in the gut from where it releases eggs into the faeces . Larvae can be found in the lungs approximately 11 hours post-subcutaneous infection . There , they enter the 3rd molt that differentiates them from the L3 to the L4 stage in around 48 hours . This ecdysis is rarely observed in the lungs , but all the larvae that reach the gut by 72 hours are L4 . The morphological changes associated with the 3rd molt are considerable , and can be summarized as follows: significant growth of the larvae ( increasing more in width than in length ) , shortening and widening of the buccal cavity , increase in length of the oesophagus , increase in number and widening of intestinal cells , and accumulation of a dark-brown intestinal pigment [16] . We designed in vitro and in vivo assays to demonstrate that Nb is a haematophagous parasite from the iL3 stage to the adult stage , causing anaemia in its host just as described in human hookworm infection . We have shown that the uptake of RBC , or of haemoglobin , induces growth of the parasite and the formation of a dark brown pigment that we characterized as hemozoin-like . Drugs of the quinolone family targeting hemozoin formation are able to arrest the development of the iL3 and the reproductive capacity of the adults both in vitro and in vivo .
Anaemia is the main pathology associated with hookworm infection and an important cause of adverse pregnancy outcomes and developmental stunting in children in endemic areas [2 , 3] . Here , we report that mice infected with Nb develop a mild anaemia , during both the lung phase and the gut phase of the parasite life cycle ( Fig 1A ) . As anaemia could be due to a combination of lung damage caused by the worm migration [17] and damage through feeding in the gut , we sought direct evidence of parasite ingestion of blood by using fluorescently-labelled whole blood or Ter-119-labelled red blood cells ( RBC ) . In all stages tested ( iL3 in lungs , L4 or adults in the gut ) we observed fluorescence in the intestine of the parasite , confirming that Nb is ingesting blood in vivo ( Fig 1B & 1C ) . We further confirmed that infection by gavage of L4 , which is not a migratory stage , also causes anaemia ( Fig 1D ) . In order to confirm the blood-feeding behaviour of Nb , we searched for homologues of Na-APR-1 , the first enzyme of the haemoglobin digestion cascade in Na [18] within the Nb secretome and transcriptome [19] . Amongst the excretory-secretory products of Nb iL3 and adult stages , we identified a potential homologue of Na-APR-1 , hereafter Nb-APR-1 , presenting 83% amino-acid identity over 91% of the protein length , notably including a conserved active site ( Fig 2A , S1 Text ) . A protein-based neighbour-joining phylogram of several homologues from related organisms confirms the proximity of the Nb-APR-1 homologue to those of the hookworm family ( Fig 2A ) . We next explored the pattern of expression of Nb-APR-1 throughout the Nb life cycle by western blot using a monoclonal antibody raised against the Na-APR-1 protein , as its binding site was fully conserved between both species [5] ( Fig 2B ) . We found that Nb-APR-1 is expressed in both gut stages ( L4 and adults ) but also , surprisingly , in the iL3 . In Necator , such expression of APR-1 in the iL3 stage has not been reported , although APR-1 mRNA has been detected [20] . Next , we assessed whether anti-APR-1 antibody could bind to APR-1 in live parasites , the expression of which is restricted to the nematode intestine [7] . By culturing serum-activated Nb iL3 in vitro with an anti-APR-1 antibody in the presence or absence of murine blood , we show that the antibody is naturally ingested by the parasite and binds to the intestinal brush border ( Fig 2C ) . Remarkably , we did not observe any binding of the antibody to the intestinal border in unfed iL3 , suggesting that Nb-APR-1 could be transported to the luminal surface upon initiation of blood feeding ( Fig 2C ) . As APR-1 is shared between the rodent hookworm Nb and the human hookworm Na , we assessed whether there was an immune cross-reactivity between these pathogens . Even though Na is unable to develop into L4 in an incompatible host such as the mouse [21] , intravenous injection of Na iL3 was sufficient to induce protection against Nb both during the lung and the gut stages ( Fig 2D & 2E ) . More strikingly , vaccination with a wild-type Na-APR-1 recombinant protein formulation with alum was sufficient to elicit protection against Nb , even as early as the lung phase of the infection ( Fig 2D & 2E ) . To further validate the relevance of Nb as a vaccine target discovery model for hookworms , we assessed the cross-protection potential of Na-GST-1 , the other lead vaccine candidate against hookworms , thought to be involved in haem detoxification [22–25] . First , we identified 11 homologous sequences with a high identity ( >55% ) to Na-GST-1 in the Nb transcriptome ( S1 Fig ) [19] . Two of those proteins are secreted by the adult stage but not the iL3 ( m . 154242 and m . 83139 ) , similar to the pattern of expression described in A . caninum [9 , 26] . Importantly , intraperitoneal vaccination with recombinant Na-GST-1 with alum also conferred protection against the lung stage of Nb infection ( Fig 2F ) . Altogether , the blood-feeding-induced anaemia and conservation of the molecular blood-feeding pathways in Nb show that this parasite can be used as a relevant tool for vaccine and drug identification against hookworms . In hookworms , blood-feeding was thought to be restricted to the adult stage , with its main role being to support the reproduction of the parasites [3] . However , APR-1 is expressed in the iL3 stage , raising the possibility that hookworms are blood-feeders throughout their parasitic life cycle , and as such could be targeted by vaccination earlier than previously suggested [27] . This prospect is supported by mRNA analysis of Na [20] . We thus investigated the potential importance of blood-feeding in the development of iL3 . First , we show that Nb iL3 can ingest blood and that this feeding is specific to RBC , as only Ter119-labelled RBC and not CD45-labelled leukocytes are ingested by the parasite ( Fig 3A ) . Using intravital imaging of the parasite we additionally observed the movement of a RBC bolus in the intestine of the worms ( S1 Video ) . Within 24 hours of RBC-feeding in vitro , Nb iL3 develop a dark brown pigmentation that clearly accumulates inside the gut epithelial cells ( Fig 3B ) . This is reminiscent of the characteristic intestinal pigmentation that appears in the lung molt 3 larvae both in Nb and Na [16 , 28] ( S2A Fig ) . The percentage of iL3 presenting with this pigmentation through time in vitro increased in a dose-dependent manner with RBC number , and with haemoglobin concentration ( Fig 3C & 3D ) . Interestingly , myoglobin but not other iron-carrier proteins ( hemin , hematin nor transferrin ) caused development of pigmentation in the parasite , suggesting that the blood digestion cascade is very specific and similar to that of hookworms ( Fig 3E , S2B Fig ) . As the intestinal pigment appears specifically after RBC or haemoglobin feeding of Nb iL3 , we hypothesised that the pigment could be part of a haem detoxification pathway . Microscopic examination of RBC-fed iL3 identified the black pigment within the digestive tract as birefringent , a feature consistent with crystals of haem [29] . The haem nature of the pigment was further confirmed by its specific absorbance at 400 nm , and its presence only in RBC-fed larvae ( Fig 3F ) . Liquid chromatography mass spectrometry ( LC-MS ) analysis of the purified pigment fraction ( RBC+L3 ) identified a peak at 616 . 17 consistent with a haem B signature . LC-MS analysis of the isolated pigment fraction from unfed larvae ( iL3 ) and medium-fed larvae ( med . + iL3 ) did not present with this characteristic peak ( Fig 3G ) . Taken together , these results suggest that the intestinal pigment forming in the Nb intestine after blood-feeding is a detoxified form of the haem released from haemoglobin digestion , similar to hemozoin described in other blood-feeding parasites [30 , 31] . Hookworms cause extensive haemorrhage by damaging the lungs during their migration , and are thus exposed to potentially harmful haemoglobin that requires detoxification . We sought to establish if accumulation of the haem-derived pigment in iL3 Nb is an artefact of the haemorrhage caused by tissue damage or whether active blood-feeding is required for the development of iL3 into more mature stages . First , we show that in vitro feeding of iL3 with haemoglobin is sufficient for the larvae to grow in size ( Fig 4A ) . Using fluorescent staining of nuclei in whole mount Nb iL3 , we further show an increase in differentiation of the intestine with a proliferation of the intestinal epithelial cells , from 8 to 12 cells ( Fig 4B ) . We further addressed whether other signs of molt 3 in the parasite were initiated after blood consumption . In accordance with the molt 3 specific morphological criteria described for Nb [16] , we observed ( i ) an increase of the overall size of the larvae ( Fig 4A ) , ( ii ) the elongation of the oesophagus , ( iii ) the increase in the length of the intestinal cells , and finally ( iv ) the widening and shortening of the buccal capsule after RBC or haemoglobin feeding ( Fig 4C & 4D ) . In summary , blood-feeding is essential for the early development of Nb larvae . We thus considered whether haem metabolism could be a checkpoint in worm development that could be leveraged to control infection . The pigment we identified in Nb presents very similar physiochemical characteristics to hemozoin , a haem-detoxification crystal that has been described in other blood-feeding organisms , such as the protozoan Plasmodium and the trematode S . mansoni [30 , 31] . Hemozoin formation in S . mansoni is prevented by RNAi blockade of cathepsin D , the homologue of APR-1 in this trematode [32] . Consistent with the possibility of a hemozoin-like pigment in Nb , APR-1 blockade with a monoclonal antibody prevents pigment formation in Nb iL3 ( Fig 5A ) . Furthermore , iL3 fed with RBC in vitro and fasted for 48 hours lose their pigmentation , suggesting that hemozoin could also be a form of iron storage as described in S . japonicum [33] ( S3A Fig ) . Additionally , anti-APR-1 ( mAb 11F3 ) blocks larval growth after feeding with haemoglobin , in a dose-dependent manner ( Fig 5B ) . Together , these results suggest that Nb possesses an alternate haem detoxification pathway to GST that could lead to new drug/vaccine targeting in hookworm infection . To further evaluate whether the hemozoin-like pigment in Nb iL3 is the product of a haem detoxification pathway , we carried out pigment formation inhibition experiments using quinolines , compounds that have been described as specifically able to target the formation of hemozoin in both malaria and Schistosoma [34 , 35] [36] . Consistent with our hypothesis , chloroquine ( CLQ ) , quinine ( QN ) and quinidine ( QND ) were all able to block pigment formation in Nb iL3 in a dose-dependent manner ( Fig 5C ) . Associated with the reduction in the proportion of worms that develop the intestinal pigmentation , we observed a diminution of pigment intensity per worm ( Fig 5D ) and a decrease in worm viability as measured by ATP levels ( Fig 5E ) . Notably , none of the drugs caused toxicity to the parasite in the absence of blood-feeding ( S3B Fig ) . As the intestinal pigment was also identified in adult stages ( S3C Fig ) , we next assessed the effect of blocking the haem detoxification in sexually mature adults . In line with our observations with iL3 , we noticed a diminution in worm pigmentation and fecundity after quinine treatment in a dose-dependent manner ( Fig 5F , S3D Fig ) . To confirm that the hemozoin-like pigment could be a useful target against hookworms , we assessed the effect of blocking the haem detoxification pathway in hookworms in vivo in mice . We administered QND intraperitoneally at 25 mg/kg daily , and assessed Nb worm burden 2 days post-infection in the lungs and 6 days post-infection in the gut , thus investigating both the developing larvae and the sexually mature parasite . As expected , QND treatment caused a significant reduction in worm burden at both 2 and 6 days post-infection , as well as a significant reduction in numbers of eggs released by the adults ( Fig 5G–5I ) . More strikingly , the mild anaemia caused by Nb compared to uninfected control mice , was also countered by QND treatment ( Fig 5J ) . Altogether , these results show haem metabolism to be a promising target for both vaccine-based and chemotherapeutic hookworm interventions .
Hookworms are considered to cause one of the major neglected tropical diseases , and infect around 450 million people worldwide [1] . The human disease is caused principally by either N . americanus or A . duodenale infection , and is characterized clinically by anaemia , malnutrition in pregnant women , and an impairment of cognitive development in children [2 , 37 , 38] . While the host immune response to hookworm infection is robust and comprehensive in scope , activating both strong humoral and cellular responses , it fails to elicit protection against subsequent infection , highlighting the need for design of an efficient vaccine [27 , 39] . In this work we re-describe a well-known murine model of hookworm infection in light of new insights into its life cycle . Nb , which belongs to the Strongylida order and yet is phylogenetically distant from human hookworms , has so far been used only as a surrogate for understanding the immune response against hookworms , given that the parasite was believed to be non-blood-feeding [40] . Here we demonstrate that , contrary to those beliefs , Nb is indeed a blood-feeding nematode and that its haemoglobin digestion cascade is conserved with Na and A . duodenale . Notably , we report that Nb is blood-feeding early in its lifecycle with the APR-1 protein being expressed in the iL3 stage . Due to the difficulty of accessing human hookworm material the proteomes of Na and Ancylostoma spp . are still presently unavailable , but APR-1 RNA expression has been identified in the iL3 stage of both Na and the zoonotic hookworm A . caninum [20] . While it is currently accepted that hookworms are blood-feeding only from their intestinal stage ( L4 onwards ) , other proteins involved in the blood-feeding cascade , such as the saposin-like protein of A . caninum that allows the lysis of RBC in the parasite intestine , have been reported to be expressed in the iL3 stage [41] . Furthermore , it has been described that Na and Nb iL3 cultivated in vitro in chicken embryo extract develop a similar intestinal pigmentation to the one we report here after feeding Nb iL3 with haemoglobin [28 , 42] . Altogether this raises the possibility that hookworms may , in fact , be blood-feeding as early as the infective larval stage . Interestingly , the amino acid sequence for Nb-APR-1 shows homology to sequences for aspartic proteases found in other helminth species , including those known to be non-blood-feeding , such as the free-living nematode C . elegans and the gut-dwelling rodent helminth Heligmosomoides polygyrus , suggesting that these molecules have a general role in protein turnover regardless of the food source . Further experiments to assess the effects , if any , of anti-APR-1 on H . polygyrus larval development in vivo could elucidate its role in this helminth which is thought to graze on epithelial cells , and not use RBC as its primary food source [43] . The rationale for the design of an efficient vaccine against helminths includes combining antigens from both the infectious stage ( to limit establishment of the parasite ) and the adult stage ( to alleviate the pathology and create a reproductive bottleneck ) . Previous attempts at vaccination with ASP-2 , the lead antigen targeting hookworm iL3 stages , did not live up to expectations when clinically tested in humans [44 , 45] , thus highlighting the need for novel targets against this stage . We believe that vaccination with APR-1 or other proteins of the blood digestome could ensure the blockade of hookworm development at not only the reproductive stage , but also the establishment stage . Indeed , targeting blood-feeding is now the major strategy of the human hookworm vaccine initiative , with two blood-feeding antigens ( Na-APR-1 and Na-GST-1 ) now in human clinical trials [46–48] . Our study highlights the importance of further dissection of the molecular pathways in the blood-feeding cascade , using high-throughput approaches such as RNA-seq and proteomics , to discover new blood-feeding-targeting drugs or vaccines of therapeutic potential . One important extrapolation from our data is that quinolones , well-known for their anti-malarial activity , could also target the hemozoin-like pigment arising from the blood-feeding behaviour of human hookworm parasites . While chemotherapy is currently the treatment of choice to control helminth infection , of the four available treatments against soil-transmitted helminths , only albendazole produces satisfying protection [49] . More worryingly , such drugs have well-known limitations: single dose regimens are inefficient , re-infection occurs rapidly after treatment , and drug resistance can arise ( as shown in veterinary medicine ) [50–52] . Consequently , research to develop and maintain a pipeline of new anthelmintic drugs in addition to specific anti-hookworm vaccines to prevent/limit infection is indispensable . While quinolones cannot be candidates for direct use against helminths ( due to widespread multi-resistances developed by Plasmodium spp . ) [53 , 54] , their efficiency against the hookworm rodent model , N . brasiliensis , pinpoints a vulnerability in the parasite’s metabolism . Interestingly , a cross-epidemiological study of the effect of chloroquine treatment for malaria in hookworm-endemic areas previously established that treated patients presented a reduced egg burden and pathology [55] , confirming in situ the relevance of targeting the haem detoxification pathway in hookworm infection . Furthermore , co-infections with hookworms and schistosomes are common , and there are at least a dozen countries ( within both Africa , South America and Asia ) with more than five million infection cases by each [56] . Due to both the important overlap of endemic regions of these parasites and the synergistic effect of their blood-feeding on anemia , a multivalent vaccine targeting both parasites would be a solution of choice [56] . The remarkable convergence of the blood digestome and haem detoxification via a hemozoin-like pigment in distant species such as P . falciparum , S . japonicum and Nb in particular , points to new avenues of research for the identification of multivalent vaccine antigens . It also raises the possibility that a drug targeting the hemozoin-like formation could be used to treat three of the most widespread and debilitating human infections at once . In conclusion , our study describes the requirement for blood-feeding for the early development of gastrointestinal nematode larvae , which opens an opportunity to target the establishment of haematophagous helminths in the host through vaccination against the blood-feeding digestome , or chemotherapy such as drug administration of quinolones . This could potentially reduce the global impact of human blood-feeding nematodes such as hookworms and Schistosoma spp . Notably , such discoveries could also be transferred to veterinary medicine , helping to alleviate the economic and ecological burden of species such as Haemonchus contortus [57] .
C57BL/6J mice used in these experiments were bred by the Biomedical Research Unit , Malaghan Institute of Medical Research , Wellington , New Zealand . Mice were used at 6-10-weeks old , and age- and sex-matched . Nb was originally sourced from Lindsay Dent ( University of Adelaide ) and has been maintained by monthly passage through Lewis rats for 20 years . iL3 larvae were prepared from 2-week rat fecal cultures and viable larvae were recovered from lung or gut tissue , all as previously described [58] . Briefly , tissue was diced , placed on cheese-cloth , and suspended in a 50 mL centrifuge tube containing PBS at 37°C for at least 2 h . Viable worms migrated out and accumulated at the bottom of the tube before being counted on a gridded counting plate . Day 0 always refers to the day of infection with Nb . Subcutaneous infections were performed by inoculating mice in the scruff of the neck with 550–600 live iL3 worms in a volume of 200 μl . L4 were harvested at day 3 in the gut; adults at day 6 . For cross-protection experiments 100 iL3 of Na or Nb were administered intravenously 30 days before the subcutaneous challenge with 600 iL3 of Nb . Larvae were killed for some experiments by boiling them in the microwave for 5 minutes . For in vivo whole-blood labelling , mice were intravenously injected with 10 μl PKH26 cell membrane dye ( Sigma ) in 100 μl of diluent C ( as per manufacturer’s recommendation ) , two days before parasite harvest . For in vivo labelling of RBC , 10 μg of anti-Ter-119-APC antibody ( eBioscience ) in PBS was intravenously administered to mice two days before parasite harvest . For QND treatment , mice were injected intraperitoneally daily with either 25 mg/kg QND ( Sigma ) in 200 μl PBS containing 4% Tween 80 ( Sigma ) or vehicle alone , starting the day before infection with Nb . QND was made up fresh every two days . Haematocrit measurements were obtained by collecting blood directly into heparinised glass haematocrit capillary tubes ( Vitrex Medical , Denmark ) and centrifuging for 15 min at 1500 g . Packed cell volume was measured with a ruler to the nearest millimetre . Haematocrit was calculated as the percentage of packed red blood cells to the total length occupied by the packed red blood cells , the white blood cells and the plasma together . For vaccination with wild-type rNa-APR-1 protein [4] or rNa-GST-1 protein [22] ( both produced in a yeast expression platform , respectively lot A141112CJK-1 and lot G090513EMH-01 , from the Sabin Vaccine Institute , TX ) , 25 μg of protein combined with alum ( Alu-Gel-S , Serva , Germany ) was administered subcutaneously ( APR-1 ) or intraperitoneally ( GST-1 ) , each week for 3 weeks . Mice were allowed to rest for 15 days , before subcutaneous challenge with 600 iL3 of Nb . Whole worms were crushed in PBS and the homogenates centrifuged at 12 , 000 g at 4 °C for 30 minutes . The supernatants were taken , and protein concentration was quantified using a bicinchoninic acid assay . 20 μg of worm lysate and 0 . 1 μg of recombinant Na-APR1 protein were run on a 4–12% Bis-Tris gel . After transfer , membranes were probed with a polyclonal rabbit antibody generated against the recombinant Na-APR1 protein [5] overnight at 4 °C . For visualization , membranes were probed with a goat anti-rabbit HRP secondary antibody and visualized under luminescence with a Gel Logic 4000 Pro . iL3 were washed several times in PBS and incubated for 1 hr at 37°C in an antibiotic solution ( Penicillin/Streptomycin 10X ( Gibco ) , Gentamicin 3X ( Sigma ) in PBS ) . 1500 larvae were then cultured overnight in complete DMEM ( DMEM ( Gibco ) plus 10% FBS ( Gibco ) , 1% L-glutamine ( Gibco ) , 1% Penicillin/Streptomycin , 1% Gentamicin ) in 12 or 24 well plates . Supplements ( haemoglobin , myoglobin , transferrin , ferric citrate or haemocyanin ( all Sigma ) ) were prepared in complete DMEM and added to the culture medium at concentrations according to the experiment . Quinolines were made fresh for every experiment ( CLQ , QN , QND ( all Sigma ) ) at 100 mM in complete DMEM . RBC were collected by cardiac puncture or cheek vein bleeding the day of culture into Alsevers solution . They were then washed 3 times in RBC washing buffer [59] , which resulted in 98% purity ( as identified by Ter119 staining by flow cytometry ) . RBC were co-cultured in complete DMEM with 1500 iL3 at 1x108/mL unless otherwise specified . For in vitro blocking of larval pigmentation with the antibody to Na-APR-1 , Nb iL3 were co-cultured in complete DMEM with or without RBCs for 48 hours in the presence of increasing doses of monoclonal antibody to Na-APR-1 ( clone 11F3 ) , an isotype matched control antibody or polyclonal antibodies to Na-APR-1 [5] . For blocking of growth , larvae were cultured in cDMEM alone or with 15 mg/mL haemoglobin ( Sigma ) , with increasing doses of 11F3 for 5 days . Larvae were then washed , individual larvae imaged and measured to the nearest 0 . 001 μm using ImageJ . 50 larvae per treatment were measured . Adult Nb were obtained from the guts of rats 10–12 days after infection , washed several times with PBS , then incubated for at least 1 hour at 37 °C in an antibiotic solution ( Penicillin/Streptomycin 10X ( Gibco ) , Gentamicin 3X , Neomycin 3X ( Sigma ) in PBS ) . 100 males and 100 females per well were cultured in 6 well plates for 7 days in DMEM , 10% FBS , 1% Neomycin , 1% Penicillin/Streptomycin and 1% Gentamicin with or without quinolines . Egg output was then quantified following the salt flotation technique using a McMaster egg slide chamber . The monoclonal anti-Na-APR-1 antibody ( 11F3 ) was coupled to APC , using APC-Lightning Link ( Innovabiosciences ) according to the manufacturer’s instructions . An isotype-matched antibody was similarly coupled to APC . Prior to imaging , worms were transferred into complete DMEM for 1 hour to allow intestinal contents to be expelled . The fluorescence was evaluated by confocal microscopy . For RBC staining , aliquots of 1 million cells were stained with Ter119-APC or PKH26 ( Sigma ) according to manufacturer recommendations . White blood cells were obtained by passing mouse spleens through a 70 μm cell filter ( BD Falcon ) . After RBC lysis , the cells were extensively washed in PBS with 1% FBS to limit haemoglobin contamination . Aliquots of 1 million cells were then stained with CD45-APC ( BD Pharmingen ) and co-cultured with 1500 iL3 at 1x108/mL for 24 hours . Fluorescence in the gut of the larvae was evaluated by confocal microscopy 24 hours after the initiation of feeding . Fluorescence was recorded with an FV1200 confocal microscope ( Olympus , Japan ) . Samples were imaged through a 10x or 20x objective . For detection of fluorophores , samples were exposed to diode laser light at a wavelength of 559 nm for the excitation of PKH26 , and 633 nm for the excitation of APC . The fluorescence was detected through 540/100 and 680/100 filters respectively . DAPI was excited by a 405 nm laser and was detected through a 445/15 filter . The larval gut autofluorescence can be detected after 635 nm excitation through a 705/50 filter . The intracellular localisation of the pigment was observed by differential interference contrast ( DIC ) imaging on a confocal using 633 nm excitation . Images were analyzed and color channels were merged with ImageJ [60] . The following parasite features were analysed by DIC microscopy ( larvae were fixed in toto with 2% formaldehyde ( Sigma ) in cold PBS to avoid body shrinkage ) : i ) size of the larvae , ii ) size of the oesophagus , iii ) the length and width of the buccal capsule , iv ) the length of the intestinal cells . For pigment quantification larvae were observed live , as fixation destroys the pigment . For larval viability measurements , 20 larvae were homogenized using 1 . 1 mm tungsten carbide beads ( BioSpec Products , Inc . ) in 100 μl PBS . ATP levels in larval homogenates were analyzed by CellTiter-Glo Luminescent Cell Viability Assay ( Promega , Madison , WI ) as reported previously [61] . Pigment was extracted as described previously [62] , with the exclusion of the urea treatment step . Briefly , worms were homogenised for 2 minutes using 1 . 1 mm beads , ultrasonicated for 5 minutes and spun down to collect supernatant . This was spun at 12 , 000g for 10 minutes and the supernatant aspirated out . The pellet was washed in 2 . 5% SDS and 0 . 1 M NaCO3 and incubated with proteinase K at 20 mg/mL at 37 °C overnight . The next day , the pellet was washed 3 times in SDS and 3–6 times in MilliQ water . Finally , the sample was lyophilised overnight . Spectrophotometric absorbance of the pigment at 400 nm was evaluated on a pool of 100 iL3 dissolved for 24 hours in 0 . 1 M NaOH . All measurements were carried out in duplicate or in triplicate . Absorbance was read on a Tecan Infinite M1000 Pro plate reader . Extracted peptides were subjected to mass spectrometry analysis as subsequently described . Eluted peptides were injected onto a 300 μm × 5 mm Agilent Zorbax SB-C18 trapping column and peptides were subsequently resolved on a 1 . 8 μm , 2 . 1 x 50 mm column containing Silica C18 packing . BlastP ( v2 . 2 . 28 , https://www . ncbi . nlm . nih . gov/pubmed/18440982 ) was used to identify Nb protein sequences obtained from the L3 & “L5” adult secretomes [19] presenting high similarity to groups of selected target proteins of interest . BlastX ( v2 . 2 . 28 , https://www . ncbi . nlm . nih . gov/pubmed/18440982 ) was used to do the same for proteins translated from transcripts assembled from the iL3 transcriptome [19] . Top-scoring hits with alignments covering at least 95% of the Nb proteins were considered for further analysis . Two groups of target proteins of interest were defined: one including homologs of the Na-APR1 protein ( see list below ) , and one including homologs of Na-GST1 protein ( see list below ) . With these targets , 1 and 11 putative Nb protein homologs were manually identified from the Blast results , respectively . Proteins from each group were then aligned using Clustal Omega ( v1 . 2 . 3 , http://msb . embopress . org/content/7/1/539 ) with default settings for protein alignment . Multiple sequence alignments were visualised using the JalView 2 Desktop application ( http://bioinformatics . oxfordjournals . org/content/25/9/1189 ) . For the GST1 group , amino acids were visually coloured based on the ClustalX scheme . Molecular phylogenies were generated using Clustal Omega’s Phylogeny program using UPGMA clustering with distance correction ( http://bioinformatics . oxfordjournals . org/content/23/21/2947 . full ) , and were used to order sequences in the alignments . Finally , sequence features of interest were manually highlighted in the alignments . The choice of statistical tests was based on sample size and on Bartlett's test when normal distributions of the errors were expected . Data from separate experiments were pooled when possible . Total lung or gut worm numbers were analyzed by ANOVA ( one- or two-way ) or by t-test when only two groups were compared . Morphology parameters were analyzed using t-tests . Data were excluded only based on error of manipulation ( incomplete worm injection ) . Representation and data analysis were performed with GraphPad Prism 5 . Statistically significant values are indicated as follows: NS , P>0 . 05; * = P<0 . 05; ** = P<0 . 01; *** = P<0 . 001; **** = P<0 . 0001 All experimental procedures described in this study were approved under Protocol 2015R15 by the Victoria University Animal Ethics Committee in accordance with the the Code of Ethical Conduct for the use of Live Animals for Teaching and Research , Animal Welfare Act 1999 approved by the Ministry of Primary Industries , New Zealand . | Hookworm infections ( Necator americanus or Ancylostoma duodenale ) represent a major neglected tropical disease affecting approximately 450 million people worldwide and causing morbidity due to their need to feed on host blood resulting in severe anemia . New chemotherapy and vaccines are needed to combat hookworm infections . Using a rodent parasite model , we describe a new haem detoxification pathway that is a metabolic checkpoint for parasite development , survival and reproduction . This provides a starting point for the development of novel therapies against such metazoan blood-feeders . | [
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"digestive... | 2018 | A novel blood-feeding detoxification pathway in Nippostrongylus brasiliensis L3 reveals a potential checkpoint for arresting hookworm development |
Predicting the effects of mutations on the kinetic rate constants of protein-protein interactions is central to both the modeling of complex diseases and the design of effective peptide drug inhibitors . However , while most studies have concentrated on the determination of association rate constants , dissociation rates have received less attention . In this work we take a novel approach by relating the changes in dissociation rates upon mutation to the energetics and architecture of hotspots and hotregions , by performing alanine scans pre- and post-mutation . From these scans , we design a set of descriptors that capture the change in hotspot energy and distribution . The method is benchmarked on 713 kinetically characterized mutations from the SKEMPI database . Our investigations show that , with the use of hotspot descriptors , energies from single-point alanine mutations may be used for the estimation of off-rate mutations to any residue type and also multi-point mutations . A number of machine learning models are built from a combination of molecular and hotspot descriptors , with the best models achieving a Pearson's Correlation Coefficient of 0 . 79 with experimental off-rates and a Matthew's Correlation Coefficient of 0 . 6 in the detection of rare stabilizing mutations . Using specialized feature selection models we identify descriptors that are highly specific and , conversely , broadly important to predicting the effects of different classes of mutations , interface regions and complexes . Our results also indicate that the distribution of the critical stability regions across protein-protein interfaces is a function of complex size more strongly than interface area . In addition , mutations at the rim are critical for the stability of small complexes , but consistently harder to characterize . The relationship between hotregion size and the dissociation rate is also investigated and , using hotspot descriptors which model cooperative effects within hotregions , we show how the contribution of hotregions of different sizes , changes under different cooperative effects .
In the first part of this work , sets of hotspot descriptors are generated , where each set represents hotspot descriptors generated from a particular hotspot predictor . The hotspot predictors tested include; two hotspot prediction servers ( KFC2 [30] and Hotpoint [28] ) and also two hotspot predictors developed in this work ( RFSpot and RFSpot_KFC2 ) . The hotspot descriptors' ability to characterize changes in off-rate due to mutations is assessed on a set of 713 experimental off-rates taken from wild-type and mutated proteins in the SKEMPI database [41] . Experimental off-rates in the dataset cover a range of Δlog10 ( koff ) of −8 . 5 to 6 . 8 , with koff units of s−1 , and represent a diverse set of interactions as listed in the Supplementary Information ( Dataset S1 ) . As a relative performance measure , a benchmark set of 110 molecular descriptors ( Text S1 ) is also included in the analysis and compared to the performance of the hotspot descriptors . The molecular descriptor set consists of a complex and comprehensive set of structure related descriptors characterizing various aspects of protein-protein interactions and their energetics; a subset of which have already proven to be successful in our previous work on predicting wild-type protein-protein binding free energies and kinetic rate constants [19] , [42] and therefore serves as a thorough benchmark comparison . All descriptor analysis in the initial section is independent of any machine learning models trained on off-rate data . Rather , the aim here is to uncover the individual predictive power of each descriptor in estimating off-rate mutations . The Pearson's Correlation Coefficient ( PCC ) is used to evaluate fine-grain predictive ability , i . e . the ability to make numerical predictions . On the other hand , the Mann Whitney U-Test and several classification measures described in Materials and Methods are used to evaluate the coarse-grain ability to detect stabilizing mutations from neutral and destabilizing mutations . In the second part , the prediction of off-rates using machine learning models is investigated . Here , several models using both hotspot and molecular descriptors are built , and their prediction patterns and anomalies highlighted . In order to uncover similarities in their predictions , the 713 off-rate dataset is categorized into what we term as data regions . Such data regions represent mutations that have a common physical property , or come from a similar type of complex or region on the interface . Mutations within a data region in turn might hold different predictive difficulty than mutations in another . This data region analysis enables us to identify current strengths in the prediction of off-rates and conversely , mutations which are consistently harder to characterize . In the third part of this work , the use of specialized models specific to different data regions is investigated . By doing so we are able to identify descriptors of which their predictive value is highly specific to subsets of mutations , regions on the interface , or types of complexes . The specialized models are generated using a Genetic Algorithm running Feature Selection ( GA-FS ) with either linear ( Linear Regression , LR ) or non-linear ( Support Vector Machines , SVM ) learning models . In the latter sections , the effects of complex size and interface area on the distribution of stability regions at the interface are investigated . Issues related to cooperativity and conformational changes , in the determination of off-rates , are also highlighted .
One of the main motivations behind this work is to explore the use of currently available descriptors ( physics-based and knowledge-based potentials ) and design a new class of descriptors ( hotspot descriptors ) for describing changes in off-rates . On the design of a new class of descriptors , our proposition is that interface hotspots can be seen as the anchor points responsible for the stable longevity of a complex . Namely , changes in the number of hotspots , hotspot energies and their distribution across the interface brought upon by structural mutations directly relates to changes in off-rate . Our approach of using hotspot predictions and subsequently hotspot descriptors for characterizing off-rates is depicted in Figure 1 . First a pre-mutation alanine scan is performed; essentially this translates to using a hotspot predictor of choice on each residue at the interface . This generates a collection of single-point alanine ΔΔGs that are then employed differently depending on the hotspot descriptor in question ( See Table 1 ) . For example if we are using Int_HS_Energy , then this hotspot descriptor will sum all the energies of only the hotspot residues . After all the hotspot descriptors for the wild-type complex are calculated , the mutation in question is applied using FoldX [43] , such as the Arg to Leu mutation in Figure 1 . Then , using a hotspot predictor as in the wild-type scan , another computational alanine scan is performed on the mutated interface . Again , all single-point alanine ΔΔGs are then fed into the hotspot descriptors . Continuing with the example of Int_HS_Energy as a hotspot descriptor , here the ΔΔGs of only the hotspot residues on the mutated interface are summed , and the final descriptor value will be the change in the sum of the single-point ΔΔGs to alanine of all hotspot residues pre- and post-mutation . This value is then correlated to ΔkoffLeu→Arg . The motivations and calculation for each of the 16 hotspot descriptors is detailed in Materials and Methods . In summary ( See Table 1 ) ; Int_HS_Energy , is the difference in the sum all the energies of hotspot residues pre- and post-mutation . HSEner_PosCoop and HSEner_NegCoop are identical to Int_HS_Energy except that , in order to account for positive and negative cooperativity effects between hotspots within a hotregion , the hotspot energies are down-weighted and up-weighted accordingly to the size of hotregion they are in . CoreHSEnergy , RimHSEnergy and SuppHSEnergy , are similar to Int_HS_Energy , except that changes in hotspot energies are limited to the given region on the interface . Each of the 6 descriptors also has its coarse-grain counterpart ( No_HS , HS_PosCoop , HS_NegCoop , CoreHS , RimHS and SuppHS ) , where only hotspot counts instead of energies are used in the calculations . Other hotspot descriptors include the change in the size of the largest hotregion ( MaxClusterSize ) , the number of hotregions ( No_Clusters ) , the spread of the hotspots at the interface ( AVG_HS_PathLength ) and Int_Energy_1 that characterizes changes in all single-point alanine mutations at the interface . A number of hotspot predictors are investigated for the generation of hotspot descriptors , and in total 6 sets of hotspot descriptors are generated ( See Table 2 ) . These include hotspot descriptors generated from available hotspot prediction servers , KFC2a , KFC2b [30] , RFHotpoint1 and RFHotpoint2 [28] , along with the hotspot descriptors generated from hotspot prediction algorithms developed in this work ( RFSpot , RFSpot_KFC2 ) . Explanation of each hotspot prediction algorithm , its features , and performance comparisons can be found in Materials and Methods . In summary , KFC2a and KFC2b are SVM hotspot prediction models developed in [30] and use a combination of solvent accessibility and plasticity features . RFHotpoint1 and RFHotpoint2 are random forest models using the features from the original Hotpoint [28] hotspot predictor , but re-trained on a larger dataset from SKEMPI ( Table S16 in Text S4 ) . RFSpot is a random forest model that employs a large set of molecular descriptors and RFSpot_KFC2 adds to this feature set , features from the original KFC2a and KFC2b models . The use of multiple hotspot predictors enables us to probe consistencies and anomalies in the predictive abilities of the hotspot descriptors . Confirming that energy estimates of single point-alanine mutations can be used to describe the effects of off-rate changes of single- and multi-point mutations not limited to alanine , we assess whether the whole set of 16 hotspot descriptors from each hotspot prediction algorithm can be combined synergistically in a model for off-rate prediction to achieve even higher correlations . A separate Random Forest ( RF ) regression model is trained on the 713 off-rate mutant dataset using the descriptors generated by each hotspot predictor ( RFSpotOff-Rate , RFSpot_KFC2Off-Rate , RFHotpoint1Off-Rate , RFHotpoint2Off-Rate , KFC2aOff-Rate and KFC2bOff-Rate ) . In addition , models that add the set of 110 molecular descriptors to the hotspot descriptors ( RFSpot+MolOff-Rate , RFSpot_KFC2+MolOff-Rate , RFHotpoint1+MolOff-Rate , RFHotpoint2+MolOff-Rate , KFC2a+MolOff-Rate and KFC2b+MolOff-Rate ) are also built for comparison . Note that the ‘Off-Rate’ subscript is used to distinguish the off-rate predictor trained on hotspots , from the actual hotspot predictor generating the hotspot descriptors in question . The 20-Fold Cross-Validation ( 20-Fold CV ) results are concatenated to form of a set of 713 test predictions and their PCC with Δlog10 ( koff ) are shown in Figure 5A ( See Table S1 for list of predictions for each model ) . The best performing off-rate predictor ( RFSpot_KFC2Off-Rate , R = 0 . 79 , see Figure 6A ) combines the hotspot descriptors generated from RFSpot_KFC2 hotspot predictor and the molecular feature set . In general , the models which combine both hotspot and molecular descriptors achieve higher correlations to the hotspot descriptor models , though which on their own , the latter still achieve correlations of R>0 . 7 using only 16 hotspot descriptors . Off-rate models using hotspot descriptors ( Figure 6A and B ) , have more stabilizing mutations in the lower left quadrant , and hence such mutations tend to be less underestimated , than a model using molecular descriptors ( Figure 6C ) . Previous analysis has been performed using models trained on all the 713 off-rate mutations in the dataset , of which the predictions were then subdivided into data regions for separate analysis . Here , descriptors , which are specific to the prediction of mutations within each data region , are investigated . To do so , separate models are built for the different data regions of the dataset using a Genetic Algorithm for Feature Selection ( GA-FS ) as described in Materials and Methods . All 110 molecular descriptors and 16 hotspot descriptors generated from the RFSpot_KFC2 hotspot predictor are available for feature selection . The feature set size is set to 5 features to avoid over-fitting and both non-linear ( using Support Vector Machines , SVM ) and linear ( using Linear Regression , LR ) models are investigated . For every data region , 50 separate GA-FS runs are performed; an inner-cross validation loop is used for FS ( And SVM parameter optimization ) , whereas an outer-cross validation loop is used for testing the final model , of which the results are summarized in Figure 8E ( blue and red ) . The GA-FS models built on rim and support region mutations achieve markedly lower correlations than core region models , though a non-linear model increases the accuracy of the latter two models . There are no notable differences in the ability to model LIA and SIA complexes; however , multi-point mutations are markedly better predicted than single-point mutations . Polar and charged mutations show good correlation which decreases when considering hydrophobic residues . One advantage of using hotspot descriptors to estimate off-rates is the ability to localize interface regions of high stability and assess how mutations affect the distribution of stabilities , within these regions . The importance of the core interface region is implicated largely due to the tendency of hotspots to preferentially occur in this region [24] . On the other hand rim residues seem to play a more secondary role of solvent shielders by providing an ideal dielectric constant for better interactions at the core [24] . In this section we analyze hotspot energies at specific regions of the interface , namely the core , rim and support regions and evaluate whether complex stability can be effectively disrupted homogenously across the interface or preferentially in a particular region . More specifically the role of rim residues is re-investigated in the light of off-rate changes upon mutations on complexes of various sizes and interface-areas . CoreHSEnergy , RimHSEnergy and SuppHSEnergy represent the change in total hotspot energies limited to each region upon mutation . Effectively , the PCC of these descriptors with the off-rate expresses how well changes in the given region show themselves as changes in log10 ( koff ) - irrespective of changes in hotspot energies in any other region . Therefore , by assessing the relative PCCs of the three regions we can gauge whether a given region acts independently and dominates in its contribution to complex stability compared to other regions . Given that we have 6 instances of each hotspot descriptor , as generated per each hotspot predictor , the correlations for each descriptor shown are the mean of each descriptor's correlation under the 6 hotspot predictors . Hence results can be considered to be independent of the hotspot predictor generating the hotspot descriptors . From the PCCs of the three hotspot region specific descriptors ( CoreHSEnergy |R| = 0 . 48 , RimHSEnergy |R| = 0 . 20 and SuppHSEnergy |R| = 0 . 38 ) , it is observed that changes in the hotspot energies at the core affect the off-rate more significantly than the rim ( p<<0 . 01 ) and support region ( p<0 . 01 ) . Given that 355 mutations affect hotspot energies in the core region compared to 148 and 182 for rim and support regions respectively , results may however be biased . For example , if fewer events are observed at the rim region , there is less chance of the rim region playing a significant role in off-rate changes , when looking at it globally over a population of complexes as is done presently . To remove this potential bias , the subset of mutations , which affect all three regions simultaneously , is extracted and the PCC recalculated . The PCCs still suggest dominance from the core region ( |R| = 0 . 53 ) , more significantly than the rim region ( |R| = 0 . 22 p<<0 . 01 ) . In this work we have shown that indeed changes in the energies of hotspots upon mutations have a direct relationship with the off-rate . More so , changes at certain regions of the interface such as the rim may affect the off-rate differently depending on its size , whereas the core is a critical stability region for complexes of a wide range of size and interface areas . Hotspots tend to cluster into tightly packed regions and the conservation of this type of organization suggests that they are important for protein-protein association [37] . The aforementioned analysis however is not performed in relation to binding free energies or off-rates for protein-protein interactions . Therefore , it is still not clear to which extent , the presence , number and size of hotregions is advantageous to complex stability . Using the hotspot descriptors and the experimental off-rates , some insights into this can be gained . Predictions of off-rate models are analyzed separately for mutations on complexes which undergo significant backbone conformational changes . The subset of complexes for which the unbound crystal structures of the wild-type complex are available , were singled out and their I_RMSD values for backbone conformational rearrangements were extracted from [66] . This subset of complexes for which unbound crystal structures are available , amounts to 17 complexes and 332 mutations . 67 mutations on 4 complexes show significant conformational changes with ( I_RMSD >1 . 5 Å ) as defined in [66] , and if the threshold is lowered to ( I_RMSD >1 Å ) , this results in 119 mutations on 6 complexes . The PCCs for the off-rate model predictions with Δlog10 ( koff ) are shown under three conformational change categories ( Figure 11 ) . The PCC , for complexes which show little to no conformational change ( I_RMSD <1 . 5 Å ) , averaged over all prediction models , shows a correlation of R = 0 . 86 , which decreases to R = 0 . 58 at ( I_RMSD >1 Å ) and R = 0 . 28 at ( I_RMSD >1 . 5 Å ) . Though for the latter category , RFSpotOff-Rate achieves a correlation of R = 0 . 43 . Changes in the different models are more apparent at complexes with higher conformational changes , most notably is the discrepancy in PCC between Molecular and RFSpotOff-Rate off-rate prediction models . This discrepancy is minimal at complexes with little conformational changes , ΔR = 0 . 01I_RMSD <1 . 5 Å and increases to ΔRI_RMSD >1 Å = 0 . 11 and ΔRI_RMSD >1 . 5 Å = 0 . 24 for complexes with significant conformational changes . Reduction in the prediction accuracies for wild-type binding free energy prediction for complexes which undergo conformational change have also been noted [42] , constituting an important challenge . Several factors may contribute to this , for example , complexes that are natively unstructured/disordered in local regions , may still remain disordered even in the bound state [67] , [68] . Binding site variability has also been observed in certain complexes where the variability is not explained by experimental or procedural inaccuracies [69] and the off-rate may also be affected directly by the unbinding mechanism [70] . In all these examples , having a single snap-shot i . e . one conformational state for the complex we wish to calculate off-rate changes for , may not provide a picture comprehensive enough to predict off-rates . Methods for modeling conformational changes which in turn can be used to generate relevant snap-shots , are still one of the main limitations in current docking algorithms [71] . The generation of relevant snap-shots might also possibly involve the characterization of encounter complexes and their stability , where both the computational generation and experimental measurement of such states is still major challenge [50] . In this work we take a comprehensive look at the determinants of complex dissociation in relation to interface hotspot energies and organization . Though the ΔΔG of a mutation may manifest itself as change in the off-rate as well as the on-rate [72] , several lines of evidence suggest a dominant contribution from the off-rate [44]–[46] . Using experimental values on 713 mutations , in this work we also find evidence for a stronger relationship of ΔΔG with Δlog10 ( koff ) . More importantly , our investigations show that the change in the off-rate of a protein-protein interaction can be sufficiently explained by the re-distribution of hotspot energies caused by that mutation . Hence , the ΔΔG of single-point alanine mutations , and readily available hotspot predictors , can indeed be used as a starting point for the estimation of off-rate mutations to any residue type and also multi-point mutations . Given this , the novelty in our approach is in the way we quantify the effects of a mutation on the dissociation rate of a protein-protein interaction . Namely , instead of directly calculating a number of features pre- and post-mutation , a complete computational alanine scan is performed at the interface pre- and post-mutation . Using the single-point alanine energies from the scans we generate a set of hotspot descriptors which describe both local and global changes caused by the mutation in question . These include changes in the size and distribution of hotregions , cooperative effects within hotregions and changes in localized stability regions such as the core , rim and support regions . Using these sets of hotspot descriptors and a number of computational experiments , we are able to gain new insights into the determinants of protein-protein dissociation . The predictive ability of the hotspot descriptors , in estimating Δlog10 ( koff ) , is first assessed independent of a learning model . Emphasis is given , both to numerical estimation and detection of stabilizing mutations ( Δlog10 ( koff ) <−1 ) . As a benchmark comparison , the performance of the hotspot descriptors is compared to a diverse set of molecular descriptors , varying from physics-based energy terms to coarse-grain and atom-based statistical potentials . Here we find consistently higher predictive abilities for the hotspot descriptors , in estimating Δlog10 ( koff ) . The results suggest that both the synergistic and distributional information within hotspot energies may be exploited to uncover the more causative changes in complex stability . More importantly , it proposes an alternative way of modeling single-point and multi-point mutations to any residue type , which is that of mapping them to functions using only alanine ΔΔG energies . To assess the predictive abilities of hotspot descriptors when combined in learning models , several machine learning models trained on Δlog10 ( koff ) are also investigated . The best regression model , which combines both molecular and hotspot descriptors , RFSpot_KFC2Off-Rate+Mol , achieves a PCC of 0 . 79 with experimental off-rates . Model predictions are also assessed on different subsets of mutations defined as data regions . The data regions enable us to identify , classes of mutations which are consistently harder to characterize , data set biases and prediction patterns . We find that core and multi-point mutations are the most accurately predicted; however , mutations at rim regions are consistently harder to characterize . In terms of the prediction of stabilizing mutations , a pattern emerges where mutants to alanine which stabilize the complex are harder to detect . To uncover relationships between different subsets of off-rate mutations and descriptors , we develop linear and non-linear feature-selection models trained on data-regions . Descriptor-data region networks generated from these models , enable us to identify descriptors highly specific to certain classes of mutations and those which are broadly important to a number of different regions simultaneously . The results gained in this work are particularly useful from a computational design perspective . Off-rate classification models for stabilizing mutation prediction ( Δlog10 ( koff ) <−1 ) , achieve a MCC of 0 . 59 , which increases to 0 . 82 when neutral mutations are excluded . We find that hotspot descriptors which are able to capture the intricacies of off-rate changes related to the re-distribution of hotspot energies and positive cooperative effects play a key role in detecting such mutations . Secondly , we underline the importance of performing a computational alanine scan , if possible , before optimizing an interface . This presents a distributional context that one may exploit and apply mutations accordingly , and thus adopt a biomimetic design strategy mirroring that taken by evolution . For example , our results indicate that the distribution of the critical stability regions across protein-protein interfaces is a function of complex size . Though large-size complexes investigated here show more robustness to mutations than small-size complexes , here we show the insensitivity to mutations is not shared equally across all parts of the interface , as changes in the core can still significantly affect complex unbinding for large complexes . Conversely for small complexes , the increase in insensitivity to mutations is distributed homogenously across the interface , with hotspots in the rim region becoming jointly critical for complex longevity . This suggests that the accurate characterization of rim hotspots is important in the design of small complex interfaces . Further advances in characterization of off-rate mutations are likely to be achieved upon improved modeling of cooperative effects within hotregions and that of conformational changes .
Six hotspot prediction algorithms ( RFSpot , RFSpot_KFC2 , RFHotpoint1 , RFHotpoint2 , KFC2a and KFC2b ) are used for the generation of hotspot descriptors , which are subsequently used for the prediction of off-rates . The method is explained in Figure 1 and requires that the hotspot predictor in question generates a prediction for each residue at the interface , both pre- and post-mutation , akin to an alanine-scan . The energies from single-point alanine mutations of the pre- and post-mutation scans are then used to calculate a set of 16 hotspot descriptors . For each hotspot predictors , its own set of hotspot descriptors is generated . The hotspot descriptors enable us to use the energies from single-point alanine mutations , particularly those which are hotspots , in order to describe the effects of off-rate mutations to non-alanine mutations and also multi-point mutations . The prediction of hotspots is an active area of research and several hotspot prediction algorithms have been developed [25]–[35] . One short-coming of these algorithms is that they have been trained and tested on very limited alanine scanning databases , namely ASEdb [73] and BID [74] . The shortcoming of these datasets as benchmarks has been highlighted in [25] , [41] . To address these limitations we recently assembled the largest database of mutations to date , with 3047 experimentally determined structures and binding kinetics , including free energy changes , dissociation/association rates and enthalpies/entropies where available [41] . All single-point alanine mutations , limited to the complex interfaces , were extracted from the SKEMPI database . This totals to a set of 635 non-redundant mutations with experimental ΔΔG in 59 different complexes and 154 hotspot residues with ΔΔG > = 2 kcal/mol ( Table S16 in Text S4 ) . All hotspots represent the positive training examples and anything , which is not a hotspot ( ΔΔG <2 kcal/mol ) as negative training examples . As depicted in Figure 1 , for any given complex , a computational alanine scanning is first performed on the wild-type interface using a hotspot prediction algorithm . This enables calculation of the set of hotspot descriptors described in Table 1 . The respective single-point or multi-point mutation is then applied using FoldX [43] , and another computational alanine scan is performed on the mutated interface , again using the same hotspot prediction algorithm invoked for the wild-type scan , from which a new set of hotspot descriptors are calculated . The energetic value contributed by each hotspot descriptor is then the difference in its energetic value pre- and post-mutations: ( 5 ) The hotspot descriptors are calculated for a set of 713 mutations from the SKEMPI database [41] . Therefore , in total , for each hotspot prediction algorithm , we make 50 wild-type and 713 mutant computational alanine scans . To ensure that off-rate predictions are not made via hotspots models trained on the same examples , all 713 computational alanine-scans made by RFSpot , RFspot_KFC2 , RFHotspoint1 and RFHotspoint2 are strictly 20-Fold-test predictions for mutations common between the off-rate and hotspot datasets , and test predictions for the rest . Therefore , all hotspot predictions on which the hotspot descriptors are calculated are unbiased and not susceptible to over-fitting . Each mutation in the 713 off-rate mutant dataset has available the experimental wild-type and mutant off-rates and the respective PDB structure . This off-rate dataset is the largest assembled to date and experimental off-rates within this set range cover a range of Δlog10 ( koff ) of −8 . 5 to 6 . 8 , with koff units of s−1 and represent a diverse set of interactions as listed in ( Dataset S1 ) . Hotspots provide a very rich source of information , which can be exploited on many levels . Firstly , the occurrence of a hotspot is not limited to any particular physical phenomena . Instead hotspots are the result of the synergistic effect of different phenomena together . These may include evolutionary pressures , along with physicochemical and structural properties [76] . Thus , mapping all the critical points for each to an interface produces a complex distribution . However , the description of an interface though hotspots is conceptually much simpler . From a computational stand-point , the advantage is that one is able to represent an interface with a much smaller set of features without compromising accuracy , as the effects of several phenomena is still encompassed within the hotspots themselves . This reduction in feature set size is also particularly attractive in the context of learning algorithms . A second attractive attribute of hotspots is their distributional properties . Hotspots tend to cluster into hotregions , within which , hotspots are suggested to be energetically cooperative [37] , [65] . It has also been shown that hotspots tend to occur more at the core regions as opposed to the rims; however , low solvent accessibility is not a sufficient property for a residue to be a hotspot [24] . Understanding how these two aspects of hotspot structure and organization , relate to the off-rate of a complex , is critical for an accurate characterization of changes in the off-rate caused by mutations . The aim of the hotspot descriptors designed here ( Table 1 ) is therefore to present hotspots in different positional contexts , which may affect complex destabilization to differing degrees . The relevance of each descriptor to off-rate variation is then assessed with different feature importance measures and the key determinants of the dissociation process reported . The importance of the descriptors used in this work , in relation to the dissociation rates , is assessed using three methods . The first method is the global correlation of a given descriptor with the target variable , which in this case is the experimental off-rate Δlog10 ( koff ) . To calculate this , the Pearson's Correlation Coefficient ( PCC ) is used . A second method is the Mann-Whitney U-test , which checks whether a set of two independent observations have smaller or larger values than the other . The test is used to assess the coarse-grain predictive power of our descriptors in discriminating between stabilizing mutants from neutral to destabilizing mutants . Several other classification related measures are used for this same purpose also , namely: True-Positive-Rate ( TPR ) /Recall: False-Positive-Rate ( FPR ) : Specificity: Precision: Accuracy: Matthew's Correlation Coefficient ( MCC ) : F1-Score: where TP = True-Positive , FP = False-Positive , TN = True-Negative , FN = False-Negative . A third method used is an assessment of descriptor importance in the context of a learning model where several of the descriptors are combined together to make a prediction . For this the built-in Random Forest Feature Importance measure ( RFFI ) is used [75] . Note that unlike the PCC , Mann-Whitney U-test and the above mentioned classification measures , the RFFI calculates feature importance as a function of other features in the model . The 713 off-rate mutations from SKEMPI are also subdivided into the following data regions for analysis: Single-Point ( SP ) alanine mutations , 361; SP non-alanine mutations , 155; SP mutations , 516; Multi-Point ( MP ) mutations , 197; SP mutations to polar ( Q , N , H , S , T , Y , C , M , W ) residues , 39; SP mutations to hydrophobic ( A , I , L , F , V , P , G ) residues , 309; SP mutations to charged ( R , K , D , E ) residues , 68; mutations exclusively on core regions , 272; rim regions , 79; support regions , 114; mutations on complexes of Large-Interface-Area ( >1600 Å2 ) , 355 and Small-Interface-Area ( <1600 Å2 ) , 358 . The GA-FS algorithm runs feature selection on subsets of the off-rate mutation dataset defined as data regions . Two separate GA-FS runs are performed , one for Linear Regression models and another for Support Vector Machine ( RBF ) Regression Models ( using the LIBSVM package ) . Two separate 10-fold cross-validation loops are used . One to assess prediction accuracy on the off-rate mutations for the given data region and the second to derive the optimal feature set . A 10-fold inner-cross validation loop is used within the GA-FS fitness function to drive the feature selection process with reference to Pearson's Correlation Coefficients . After the GA has converged , the LR/SVM model is tested for its accuracy on the outer-loop fold . This process is repeated 10 times such that all 10 outer loop folds are used as a test set validation for the final model . Therefore , the accuracy of the final model is tested on data which is not used to derive the feature set . As an initial feature set available for selection , 110 molecular descriptors and 16 hotspot descriptors from the best performing off-rate prediction model RFSpot_KFC2 are available . A fixed feature set size of 5 is chosen so as to avoid overfitting on smaller sized data regions . Therefore , the genome size for the GS-FS ( LR ) is 5 whereas that for GA-FS ( SVM ) is 7 to also optimize the cost and gamma parameters of the RBF . Available cost parameters values are quantized into 111 bins ranging from 2−5 to 26 . Gamma parameter values are quantized into 1300 bins ranging from 2−8 to 25 . The GA's initial population size was set at 1000 individuals , and generated such that the initial population included at least one instance of each of the 126 features . Tournament selection was employed with a size of 8 individuals . Uniform random crossover was used with a crossover fraction set to 50% and a mutation rate that exponentially decreased as the number of generations applied increased . Note that for each data region 50 separate GA-FS runs were performed . To assess the discriminatory power of the hotspot and molecular descriptors , the 713 off-rate mutations are partitioned into ( Δlog10 ( koff ) <−1 ) , representing the stabilizing portion of the dataset , and ( Δlog10 ( koff ) >0 ) , representing the neutral to destabilizing portion of the dataset ( referred to as CDS1 –Classification Dataset 1 ) . The motivations behind the thresholds of CDS1 are two-fold . Firstly , previous error estimates show that experimental noise in the data can be as high as 2kcal/mol [41] , [42] . Experimental noise causes miscategorization errors when converting Δlog10 ( koff ) from continuous values to categorical bins , and therefore , the exclusion of data-points within [−1 , 0] should reduce sufficiently the number of miscategorization errors between stabilizing and neutral/de-stabilizing mutations . Secondly , being able to detect stabilizing mutations from neutral ones is an important aspect of interface design . As shown in Figure S1 , the range within [0 , 1] contains 43% of the data . Therefore , the removal of Δlog10 ( koff ) within the range [−1 , 0] still allows a sufficient amount of neutral mutations . This data subset , results in a dataset of 501 neutral to destabilizing mutations ( referred to as non-stabilizing mutations ) and 31 stabilizing mutations ( See Dataset S2 ) . To further investigate the discrimination ability of the descriptors , an additional threshold satisfying |Δlog10 ( koff ) | >1 is also investigated ( Dataset S3 ) . This dataset which removes most of the neutrals is referred to CDS2 . | Within a cell , protein-protein interactions vary considerably in their degree of stickiness . Mutations at protein interfaces can alter the interaction between protein pairs , causing them to dissociate faster or slower . This may lead to an alteration in the dynamics of the cellular networks in which these proteins are involved . Therefore , the calculation and interpretation of mutants , which affect the rate of dissociation , is critical to our understanding of complex networks and disease . A key characteristic of protein–protein interfaces is that a subset of residues are responsible for most of the binding energy , such residues are called hotspots and effectively represent the sticky points of the interaction . In this work , we exploit both hotspot energies and organization and use them for the calculation of off-rate changes upon mutations . The insights gained provide us with a clearer understanding of the critical regions of stability and how they change for complexes of different sizes . Moreover , we provide a comprehensive map of the key determinants responsible for the accurate characterization of different classes of mutations , complexes and interface regions . This paves the way for more intelligent computational-interface-design algorithms and provides new insight into the interpretation of destabilizing mutations involved in complex diseases . | [
"Abstract",
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"Results/Discussion",
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"and",
"Methods"
] | [] | 2013 | Characterizing Changes in the Rate of Protein-Protein Dissociation upon Interface Mutation Using Hotspot Energy and Organization |
Here , we investigated intrinsic spinal cord mechanisms underlying the physiological requirement for autonomic and somatic motor system coupling . Using an in vitro spinal cord preparation from newborn rat , we demonstrate that the specific activation of muscarinic cholinergic receptors ( mAchRs ) ( with oxotremorine ) triggers a slow burst rhythm in thoracic spinal segments , thereby revealing a rhythmogenic capability in this cord region . Whereas axial motoneurons ( MNs ) were rhythmically activated during both locomotor activity and oxotremorine-induced bursting , intermediolateral sympathetic preganglionic neurons ( IML SPNs ) exhibited rhythmicity solely in the presence of oxotremorine . This somato-sympathetic synaptic drive shared by MNs and IML SPNs could both merge with and modulate the locomotor synaptic drive produced by the lumbar motor networks . This study thus sheds new light on the coupling between somatic and sympathetic systems and suggests that an intraspinal network that may be conditionally activated under propriospinal cholinergic control constitutes at least part of the synchronizing mechanism .
Locomotion , or any other form of physical activity , mobilizes not only the motor nervous system but also the autonomic nervous system , which governs body homeostatic processes such as blood pressure control and respiratory frequency . These autonomic responses that confront metabolic expenditure during exercise rely mostly on a functional coupling between the sympathetic and somatic motor systems . However , the neural substrate for this coupling remains enigmatic . Although the crucial role of supraspinal structures in this adaptive process , such as the rostral ventrolateral medulla , the hypothalamus , and the nucleus of the solitary tract , has been established [1 , 2] , the involvement of intrinsic spinal mechanisms , albeit acknowledged , remains unclear ( for review , see [3] ) . Significantly , several studies have reported that an intraspinal coordination between sympathetic and motor outflow may still occur in newborn [4] and adult rats [5] and mice [6] after removal of supraspinal influences by spinal cord sectioning . The cells responsible for the spinal sympathetic outflow are the sympathetic preganglionic neurons ( SPNs ) , which in turn innervate postganglionic sympathetic neurons . Studies on SPN membrane properties have revealed that these cells can express spontaneous rhythmic burst activity at frequencies ranging from 0 . 1 to 10 Hz . Each of these frequencies has been related to a specific physiological rhythm such as the cardiac and respiratory rhythms or low frequency changes in blood pressure [7–9] . Intracellular recordings from in vitro preparations or anesthetized animals have also revealed that different SPN rhythmic activity patterns can be elicited under various conditions , relying mainly on neuromodulatory controlling influences [8 , 10–17] . Similarly , spinal motoneurons ( MNs ) also fire rhythmic bursts of action potentials when engaged in locomotor activity and , as for SPNs , this rhythmogenicity is initiated and regulated mainly by neuromodulatory pathways [18 , 19] . Other than extrinsic neuromodulatory influences originating from supraspinal centers , an intrinsic spinal neuromodulatory influence involving acetylcholine ( Ach ) has been shown to play an important role in controlling both the spinal motor network and sympathetic neuron activity [20–28] . In addition to SPNs and MNs that use Ach as their neurotransmitter , thoracic segments are enriched in cholinergic interneurons and show a dense expression of muscarinic cholinergic receptors ( mAchRs ) [29 , 30] . The question then arises as to the precise role of these Ach-releasing interneurons in the thoracic spinal cord region . In the present study , by using the in vitro isolated spinal cord preparation of newborn rats , we show that an activation of mAchRs unmasks rhythmogenic capabilities of the spinal thoracic segments and a common synaptic drive to SPNs and axial MNs . This thoracic mAchR-induced rhythmicity merges with the locomotor activity expressed in lumbar spinal networks and modulates the latter's expression . We therefore propose that thoracic networks sensitive to mAchR-mediated cholinergic influences may act as an important intrinsic substrate for the coupling between spinal motor and sympathetic activities .
To assess the effect of cholinergic system activation on thoracic segments , we used oxotremorine , a nonselective cholinergic muscarinic agonist . In a first series of experiments , oxotremorine was bath-applied on the whole thoracolumbar spinal cord . As previously described in such preparations , a mixture of N-methyl-D-aspartate ( NMDA ) and serotonin ( 5-HT ) -induced rhythmic locomotor-related activity was recorded from both lumbar and thoracic ventral roots ( Fig 1A1 , 2 ) , with a mean period of 3 . 2 ± 0 . 1 s , n = 31 ( Fig 1C ) [31 , 32] . This fictive locomotion was characterized by right-left and extensor-flexor alternations of bursts of action potentials monitored from the L2 and L5 ventral root bursts , respectively ( Fig 1E ) [33] . In contrast , the bath-application of 10 μM oxotremorine on the whole spinal cord elicited a slow rhythmic activity in the thoracic and lumbar region , with a mean period of 21 . 7 ± 1 . 3 s , n = 26 ( Fig 1B1 , 2 and Fig 1C ) . This oxotremorine-induced rhythm consisted of right and left slow alternating bursts of action potentials , but with no alternation between the flexor and extensor units in the majority of the preparations tested ( Figs 1E and 2D ) . Oxotremorine also was more potent in exciting thoracic segments than the NMDA/5-HT cocktail . Indeed , the amplitude of the thoracic bursts was significantly increased in the presence of oxotremorine compared to NMDA/5-HT , while L2 burst amplitudes were not significantly increased in the same conditions ( Fig 1D ) . This oxotremorine-induced rhythm was consistently observed at concentrations ranging from 0 . 5 to 100 μM ( Fig 2A1–5 ) . Increasing the oxotremorine concentration up to 10 μM progressively decreased the cycle period of thoracic motor bursts ( Fig 2B ) and increased their amplitude ( Fig 2C ) . Furthermore , there was a switch from an in-phase activity recorded from the left and right segments at the lowest concentrations ( 0 . 5 and 1 μM ) to an out-of-phase activity under 10 μM ( Fig 2D ) . Five micromolar oxotremorine appears as an intermediate concentration , with in-phase left and right motor bursts observed in 55% of the preparations tested and alternating motor bursts in the remaining ones ( n = 9 ) ( Fig 2D ) . Increasing oxotremorine up to 100 μM seemed to become less effective as the induced motor activity exhibited an increased period , compared to that with 5 and 10 μM oxotremorine ( Fig 2B ) , and a disorganized motor burst expression ( Fig 2D ) . The various patterns elicited when raising the oxotremorine concentration could be linked to the progressive increase in the excitation state of spinal networks and recruitment of spinal neurons [34 , 35] . It has been previously shown that activation of the cholinergic system by inhibiting acetylcholinesterase ( AchE ) , the enzyme responsible for Ach degradation with physostigmine , neostigmine , or edrophodium , elicits locomotor-like activities in lumbar ventral roots in the isolated spinal cord preparation from newborn rat [21 , 24 , 25] and that this rhythm is mediated through mAchR activation [27] . Therefore , in a next step , we compared the effects of oxotremorine ( Fig 3A ) with those of the AchE inhibitors neostigmine and physostigmine . In our experimental conditions , neither inhibitor mimicked the effects of oxotremorine ( Fig 3B and 3C ) . Rather , their superfusion transiently elicited episodes of fast locomotor-like rhythmicity , as previously described [24 , 25 , 27] , instead of the slow non-locomotor rhythm observed here in the presence of oxotremorine . The mean period computed in the presence of neostigmine or physostigmine was 3 ± 0 . 3 s ( n = 5 preparations tested in the presence of neostigmine and n = 2 in the presence of physostigmine ) . The results obtained when oxotremorine was bath-applied to the whole thoracolumbar spinal cord ( Fig 1D ) suggested that the thoracic and lumbar spinal levels may respond differentially to oxotremorine . To test whether the thoracic and lumbar segments exhibit similar responses to mAchR activation by oxotremorine , we used a partitioned spinal cord ( see Material and methods ) in which the thoracic and lumbar compartments could be independently superfused ( Fig 4A1 and 4B1 ) . Bath-application of oxotremorine to the thoracic segments alone induced a rhythmic burst pattern that was recorded from the various thoracic segments but also from lumbar roots ( Fig 4A2 ) . This thoracolumbar propagation was observed in 24 of 28 preparations tested . In contrast , and as previously shown [32] , the bath-application of NMDA/5-HT to the thoracic segments alone did not induce any rhythmic activity ( n = 10; Fig 4A3 ) in these experimental conditions . When the bath-application of oxotremorine was now restricted to the lumbar spinal cord , rhythmic bursting was recorded solely from the lumbar ventral roots , without any propagation to the thoracic segments ( n = 10; Fig 4B2 ) . The same lack of lumbo-thoracic propagation was observed when oxotremorine was replaced by NMDA/5-HT in the caudal compartment , with the recorded locomotor-like activity always restricted to the lumbar segments only ( n = 13; Fig 4B3 ) . It should also be noted that , using transverse sections of the thoracic spinal cord , we determined the number of thoracic segments sufficient to produce the oxotremorine-induced rhythm . In five preparations , we observed that , regardless of the thoracic level , spinal pieces of at least three thoracic segments were sufficient to generate bilateral alternating rhythmic activity in the presence of 10 μM oxotremorine . Altogether , these results suggest that the thoracic spinal cord exhibits a distinct sensitivity to oxotremorine compared to the lumbar spinal cord and that thoracic segments are endowed with specific rhythmogenic capabilities that can be unveiled by mAchR activation . In a next step , we assessed whether and which of the four classes of mAchR subtypes present in the spinal cord [36] were involved in the genesis of the oxotremorine-induced rhythm . To this end , after a first control bath-application of oxotremorine , receptor subtype antagonists [37] were preincubated on the in vitro spinal cord preparation before their co-application with oxotremorine . An example of the blockade of the oxotremorine-induced rhythm by the presence of the antagonist of the M1 receptor subtype ( pirenzepine 1–10 μM , n = 6 ) is provided in Fig 5A1 . A similar blockade was observed with antagonists of the M2 ( AF-DX 116 , 10 μM , n = 8; Fig 5A2 ) , M3 ( 4-DAMP , 0 . 5–1 μM , n = 4; Fig 5A3 ) and M4 ( tropicamide 1 μM , n = 2; Fig 5A4 ) receptors . At concentrations lower than 1 μM , M1 and M4 antagonists did not suppress the rhythm ( n = 4 ) . Lower concentrations of M2 antagonist also failed to suppress rhythmic activity ( AF-DX 116 , 0 . 5 μM , n = 2 and 1 μM , n = 2 ) . The effects of selective agonists for M1 and M2 receptors were also tested . M1 agonists ( xanomeline and McN-A343 ) bath-applied to the whole thoracolumbar spinal cord did not elicit any activity at concentrations ranging from 10 to 100 μM ( n = 2; Fig 5B1 , 2 ) . Bath-application of the M2 receptor agonist arecaidine only partly reproduced the oxotremorine effects and induced a weak rhythmic activity , even at high concentration ( n = 2 , Fig 5C1 , 2 ) . These data suggest that oxotremorine triggers motor activities in the spinal cord through the activation of the M1 , M2 , M3 , and M4 receptors ( see Discussion ) . Ventral roots not only carry axons of somatic MNs all along the spinal cord but also axons of SPNs from thoracic 1 to lumbar 3 segments in rats . Consequently , extracellular recordings from these latter ventral roots do not permit somatic and sympathetic axonal activity to be distinguished . To assess whether axial MNs and/or SPNs are activated during pharmacological activation of the thoracolumbar spinal cord with NMDA/5-HT or oxotremorine , intracellular recordings from these two cell types were then performed in whole cord preparations ( Fig 6A1 ) , in which the MNs ( Fig 6A2 ) or SPNs located in the IML ( intermediolateral sympathetic preganglionic neurons [IML SPNs]; Fig 6A3 ) were identified by ventral root antidromic stimulation . In the majority of recorded thoracic MNs , the bath-application of NMDA/5-HT elicited locomotor-related activity with action potentials superimposed on phasic depolarizations during locomotor cycles ( Fig 6B1 ) [32] . Wavelet transform analysis was conducted to assess whether the extracellular and intracellular activities recorded from the ventral roots and from MNs , respectively , were linked . The mixed cross-coherence map ( bottom panel , Fig 6B1 ) shows that the MN membrane potential oscillations were significantly correlated to the lumbar L2 activity in a frequency band of 0 . 4 ± 0 . 02 Hz . In the presence of oxotremorine on the whole thoracolumbar spinal cord , intracellular recordings revealed phasic depolarizations in MNs that were significantly correlated to the lumbar L2 activity ( n = 22/30 recorded MNs; Fig 6B2 ) . In this sequence , because of the irregular nature of the oxotremorine-induced activity , the frequency band ranged from 0 . 06 to 0 . 03 Hz . In contrast , in almost all the IML SPNs tested , no detectable variations in their membrane potential ( n = 16/18 recorded IML SPNs ) were observed in time with locomotor-like activity induced by the bath-application of NMDA-5-HT ( top panel , Fig 6C1 ) . The mixed cross-coherence map ( bottom panel , Fig 6C1 ) confirmed that the IML SPN activity was not correlated to NMDA/5-HT-induced fictive locomotion . In contrast , in the presence of oxotremorine , IML SPNs exhibited strong rhythmic burst activity associated with large membrane potential oscillations in correlation with the slow bursting activity recorded from both thoracic and lumbar segments ( n = 21/25 recorded IML SPNs , Fig 6C2 ) . Wavelet analysis confirmed that in the presence of oxotremorine , IML SPN activity was significantly correlated to the extracellularly recorded neuronal activity in the frequency band of 0 . 03 ± 0 . 01 Hz ( bottom panel , Fig 6C2 ) . In conclusion , almost all the MNs and IML SPN neurons tested in these different experimental conditions exhibited similar rhythmic behavior: MNs ( 73% ) were rhythmically activated by both NMDA/5-HT and oxotremorine pharmacological stimulation while IML SPNs ( 84% ) were rhythmically activated solely in the presence of oxotremorine . We call the synaptic command shared by both axial MNs and IML SPNs , in the presence of oxotremorine , the somato-sympathetic drive ( SSD ) . Because axial MNs and IML SPNs responded differently to the pharmacological conditions tested , we investigated whether the two subtypes are endowed with different electrophysiological properties that could account for their differing responses ( Fig 7 ) . In the absence of pharmacological stimulation , resting membrane potential ( RMP ) and action potential threshold ( AP Th ) values were similar between axial MNs ( RMP: −61 . 1 ± 1 . 2 mV , AP Th: −51 ± 0 . 7 mV , n = 36 ) and IML SPNs ( RMP: −60 . 4 ± 1 . 2 mV , AP Th: −49 . 3 ± 0 . 9 mV , n = 24 ) . However , IML SPNs had a significantly higher membrane input resistance ( Fig 7A ) and a significantly larger and longer after-spike hyperpolarization ( AHP ) compared to axial MNs ( Fig 7B1–3 ) . Using triangular current pulses and the functional categorization established by Button and collaborators [38] , we found that the frequency-current relationships of both axial MNs and IML SPNs could be classified as type 1 ( linear , Fig 7C1 , 2 ) or type 2 ( adapting , Fig 7C3 , 4 ) , with the proportions of these two firing patterns not being significantly different between axial MNs and IML SPNs ( Fig 7C5 ) . Finally , we found that both axial MNs and IML SPNs exhibited a sag potential and a post-hyperpolarization rebound in response to hyperpolarizing current steps ( Fig 7D ) . Altogether , these results thus indicate that axial MNs and IML SPNs exhibit basic membrane properties that are relatively similar . In a next series of experiments , we investigated the nature of the SSD received by thoracic neurons during the oxotremorine-induced rhythm . In the presence of NMDA and non-NMDA receptor antagonists ( 10 μM 2-amino-5-phosphonovalerate [AP5] and 10 μM 6 , 7-dinitroquinoxaline-2 , 3-dione [DNQX] , respectively ) , we observed the disappearance of oxotremorine-induced rhythmic bursting in both recorded ventral roots ( Fig 8A and 8B ) and intracellularly recorded axial MNs ( Fig 8A ) and IML SPNs ( Fig 8B ) . This suppressive effect , which was consistently observed in the 10 preparations tested , suggested that the spinal network activated by oxotremorine involves glutamatergic neuronal relays that are upstream from MNs and IML SPNs . We then assessed whether glycinergic or GABAergic synaptic inputs also participate in the SSD drive , because bursts of inhibitory postsynaptic potentials could be clearly observed ( and even reversed ) in axial MNs ( Fig 9A1 ) and IML SPNs ( Fig 9A2 ) during the oxotremorine-induced rhythm . However , the glycinergic and/or GABAergic nature of this synaptic drive was difficult to determine , because the addition of strychnine ( 1 μM ) and/or gabazine ( 1 μM ) ( antagonists of the glycinergic and GABAergic receptors , respectively ) to the oxotremorine-containing saline completely altered the pattern of the elicited rhythm ( n = 4 , Fig 9B ) and led to the expression of disinhibited , large amplitude bursting [39] . Nonetheless , together our data suggest that the SSD received by both axial MNs and IML SPNs following mAchR activation involves glutamatergic excitatory neurons and should also implicate inhibitory interneurons . The above data show that mAchR activation with oxotremorine constitutes a means to unmask the rhythmogenic capabilities of thoracic segments and to recruit both axial MN and IML SPN activity . The question then arises as to the functional consequences of this activation of thoracic networks during the expression of fictive locomotion in the lumbar part of the spinal cord . Fig 10A1 shows representative recordings from a compartmentalized preparation of typical locomotor-like activity induced by bath-applied NMDA/5-HT to the lumbar area only . The subsequent addition of oxotremorine specifically to the thoracic region ( Fig 10A2 ) triggered independent slow bursting activity in the thoracic ventral roots . This slower thoracic burst rhythm was also observed in lumbar ventral roots , where it could be seen to regularly disrupt the ongoing faster locomotor rhythm ( Fig 10A2 ) ( n = 4/5 tested preparations ) . The gray columns in Fig 10A2 overlay the motor bursts recorded from the right T8 ( rT8 ) ventral root , which alternated with left T10 bursts . As can be seen in the plots of Fig 10A3 , which display the cycle-by-cycle amplitudes of L2 locomotor-like bursts before ( blue dots; see Fig 10A1 ) and during the additional slower rhythm triggered by oxotremorine applied to the thoracic cord ( black dots; see Fig 10A2 ) , L2 burst amplitudes were strongly decreased throughout the occurrence of each slow burst in the ipsilateral thoracic segments ( see red ellipses in Fig 10A3 ) . In contrast , when oxotremorine was applied with NMDA/5-HT to the lumbar compartment only ( Fig 10B ) , the structure and regularity of the fictive locomotor rhythm remained unaltered , although in 9 out of 12 tested preparations , an overall increase in lumbar burst amplitudes consistent with a previously described direct depolarizing effect of oxotremorine on lumbar MNs was observed [37 , 40] . Moreover , whether oxotremorine was applied either to the thoracic or lumbar cord region , or to both , the mean cycle periods of the ongoing fictive locomotor bursting were not significantly modified ( Fig 10C ) . Because the axons of both MNs and IML SPNs are conveyed in ventral roots until the L3 segment , we wondered whether the two rhythms expressed in lumbar ventral roots in vitro could somehow be linked to the differential activation of these two neuronal subtypes in vivo . To address this possibility , we made intracellular recordings from thoracic MNs ( n = 7 ) under the different neuromodulatory conditions using the same experimental procedures as illustrated in Fig 6 . During bath-application of NMDA/5-HT to whole cord , as seen in Fig 6A1 , the membrane potential of a recorded thoracic MN underwent rhythmic fluctuations correlated with the locomotor-like bursts monitored extracellularly in the L2 ventral root ( Fig 11A; mean cycle period 2 . 4 ± 0 . 1 s , n = 30 cycles ) . After washout with normal saline in the same preparation , the bath-application of oxotremorine alone elicited slow rhythmic oscillations in membrane potential correlated with the slow extracellular activity now expressed in the L2 ventral root ( Fig 11B; see also Fig 6B2 ) . When both the locomotor-like and slow bursting pattern were simultaneously elicited by the conjoint bath-application of NMA/5-HT and oxotremorine , respectively , a synaptic merging of the two motor rhythms with the superimposition of both fast locomotor-like drive potentials and slow membrane potential oscillations was observed ( Fig 11C ) . Altogether , these data thus indicate that the oxotremorine-induced bursting generated by thoracic networks is able to impose its slower motor-related rhythm on the locomotor activity generated by lumbar networks , resulting in a combined functional coupling of the two motor patterns .
In the present report , we show that the thoracic segments of the newborn rat spinal cord exhibit a preferential sensitivity to mAchR activation compared to the rhythmogenic circuitry in the lumbar segments , leading to the expression of a slow and robust bursting activity . To the best of our knowledge , this is the first demonstration that thoracic networks are endowed with such an independent rhythmogenic capability . In contrast to the majority of known neuromodulatory systems capable of activating or controlling the motor spinal networks , the cholinergic system does not originate from extrinsic spinal sources but is thought to be completely intrinsic to the spinal cord . While the role of Ach in influencing spinal motor network activities is recognized [21 , 23 , 24 , 26–28 , 41] , its contribution to the generation of the mammalian locomotor rhythm is still debatable [42] . Some of the effects of the cholinergic propriospinal system were also observed in the isolated spinal cord of rodents through the use of AchE inhibitors ( such as edrophonium , neostigmine , and physostigmine ) that prevent Ach degradation and so enhance the spinal endogenous content of spontaneously released Ach [24 , 27 , 42] and increase the potency of bath-applied Ach [21] . In the in vitro spinal cord preparation , AchE inhibitors induce episodes of locomotor-like activity that can be blocked by M2 and M3 muscarinic antagonists [27] . In our experimental conditions with the newborn rat preparation , AchE inhibitors only triggered short bouts of locomotor-like activity . In addition , we observed that the bath-application of oxotremorine systematically and uniquely induced a slow motor rhythm devoid of extensor-flexor alternation , instead of a recognizable locomotor-like activity . Interestingly , this slow motor pattern has been reported in previous studies ( see , for example , Fig 1 in Jordan and colleagues , 2014; Fig 1B in Anglister and colleagues , 2008 ) . However , these authors did not describe this slow rhythm and did not test the effects of cholinergic muscarinic agonists . Why do cholinergic muscarinic agonists only partly reproduce the activating effects of AchE inhibitors ? Even if muscarinic antagonists abolish the action of AchE inhibitors [27] , it cannot be ruled out that either nicotinic receptors are involved or a more localized activation of mAchRs are required to induce locomotor-like activity . Alternately , this may be due to weak potency of available selective mAchR agonists ( Ishii and Kurashi , 2006 ) because the different agonists tested either partly reproduced oxotremorine action or failed to elicit any motor activity . Ach plays a ubiquitous role in the control of motor and sympathetic outflow . As a neurotransmitter utilized by both the MNs and SPNs , it is engaged in all autonomic ganglia , neuromuscular junctions , and also at many autonomically innervated organs . Furthermore , both MNs and IML SPNs are themselves targeted by cholinergic synapses arising from the cholinergic propriospinal system [20 , 22 , 24 , 29 , 37 , 41 , 43] . The data presented here suggest that the rhythmic activity observed following mAchR activation results from concomitant network and direct effects of oxotremorine on SPNs and MNs . Although the sympathetic nervous system is still maturing at the age studied here [44] , Zimmerman and Hochman ( 2010 ) previously demonstrated that the membrane properties of IML SPNs are specified as early as P3 and that the overall functional features of SPNs are already mostly in place in the neonate [45] . The present study is the first to provide a direct comparison of the membrane properties of MNs and IML SPNs in similar experimental conditions and reveal no striking differences between these two neuronal subtypes . MNs as well as SPNs must integrate inputs from both descending and sensory systems that shape the output of the somatic and sympathetic nervous systems . The classical view that has emerged over the past century is that the background activity of SPNs is controlled by supraspinal neuronal networks whose random and diffuse outputs were synchronized by rhythmic inputs arising from cardiovascular and respiratory origins ( for review , see [8] ) . This view was challenged from the late 1970s by the hypothesis that the various rhythmic sympathetic outputs were generated by central neural networks capable of rhythm generation , rather than by pools of interneurons driven by afferent inputs ( for review , see [8] ) . Interestingly , a similar debate emerged at the same time regarding the central generation of motor patterns [46 , 47] . Double retrograde labeling has identified neurons in the pontomedullary reticular formation , the pedunculopontine tegmental nucleus , and lateral hypothalamus that have been classified as central commanders of both autonomic and motor outflow [48 , 49] . Some reports have also postulated the existence of a decentralized control system that would reside in the spinal cord [3 , 5 , 6 , 50] . The first evidence for a coupling between somatic and sympathetic outflow in the thoracolumbar spinal cord was provided by Chizh and colleagues [6] . Using an arterially perfused trunk–hindquarter preparation of adult mouse , these authors found that at rest , when only tonic background activity was produced , or during NMDA-induced rhythmic activity , sympathetic and somatic motor output could become synchronized . Subsequently , Goodchild and colleagues ( 2008 ) showed that an independent supraspinal coupling exists between multiple sympathetic and motor outflows in the adult rat spinal cord in vivo . These authors proposed that this coupling allows a coordination of activity between the different outflows when hyper-excitation occurs [5] . Here , we propose that an intraspinal rhythmogenic network , whose activation is conditional upon the cholinergic propriospinal system , is likely to be responsible for this synchronizing process . The neuronal substrate of such a network remains elusive ( for reviews , see [3 , 51] ) . Our data indicate that glutamatergic neurons are part of the local network unmasked by oxotremorine . Potential candidates for the inhibitory component of the SSD is a subset of GABAergic interneurons located around the central canal , which has been previously shown to inhibit SPNs [51 , 52] . Therefore , further investigation will be needed to assess the different cholinergic , glutamatergic , and inhibitory neurons that compose the rhythmogenic network activated by oxotremorine . Here , we also found that mAchR activation leads to a superposition of the thoracic and lumbar rhythms induced by the presence of oxotremorine and NMDA/5-HT , respectively . Comparable reconfigurations of motor outputs have been observed in both invertebrate [53] and vertebrate [54] central pattern-generating systems that have been proposed as the basis for the functional flexibility of motor system output . Does the slow oxotremorine-induced rhythm have a specific physiological relevance ? In physiological conditions , SPNs are rhythmically active in a frequency band ranging from 0 . 1 to 10 Hz [55] . The slow sympathetic neuronal activities have been related to cardiovascular control [7] and rhythmic changes in arterial pressure ( corresponding to the ‘‘10-s rhythm” in humans ) . In the context of the present study , these so-called “Mayer waves” are of particular interest ( for review , see [56] ) . From their first description , these activity oscillations were referred to as vasomotor waves and were proposed to provide an indirect measure of efferent sympathetic nervous activity . In several species , including humans , Mayer waves are modulated in situations that result in sympathetic activation and parallel the mean level of sympathetic nervous activity [57] . The relationship between the Mayer waves and locomotor ( or respiratory ) rhythms has been recently explored in decerebrate cats under neuromuscular blockade [58] . These authors reported that the occurrence of Mayer waves was frequently related to the initiation of episodes of fictive locomotion and to variations in the extracellular locomotor burst amplitude . Furthermore , the occurrence of Mayer waves also matched episodes of entrainment between the respiratory and the locomotor rhythms . In the present study , we could not assess whether the intraspinal network revealed by oxotremorine is also involved in respiratory-related discharge concomitant with hind limb muscle activity ( as shown by Wienecke and colleagues , 2015 ) . This would require a new experimental paradigm because the isolated spinal cord does not allow us to monitor the respiratory activity that is generated in the brain stem [59] . The frequency range of the oxotremorine-induced rhythm found in the present study is compatible with the frequency of the Mayer waves because , in our experimental conditions , the cycle period of the oxotremorine-induced activity ranged from 13 to 30 s ( average period , 21 . 7 s; Fig 1D1 ) . In our in vitro conditions , however , it has been shown that the locomotor period decreases by more than 50% when the temperature is increased from 25°C ( the temperature at which recordings are made ) to 35°C [60] . On this basis , therefore , at such higher temperatures corresponding to those in the intact animal , the cycle period of the oxotremorine-induced rhythm would be expected to be less than 10 s . An important wider implication of the present findings relates to overcoming one of the major problems for people suffering from spinal cord injury ( SCI ) : severe cardiovascular disturbances that contribute to 40% of deaths [61] . The existence of an intraspinal coupling mechanism and the possibility to activate coordinated somatic and sympathetic activities through pharmacological means thus raises the prospect for mitigating vascular dysfunctions after SCI .
Experiments were conducted in vitro on isolated spinal cords from newborn Sprague Dawley rats of either sex , aged from postnatal day 0 ( P0 ) to P5 ( n = 108 preparations ) . All procedures were conducted in accordance with the local ethics committee of the University of Bordeaux and the European Committee Council Directive ( approval number 2016012716035720 ) . All efforts were made to minimize animal suffering and reduce the number of animals used . Rat pups were anesthetized with isofluorane until reflexes could no longer be elicited in response to tail or toe pinching . Animals were decapitated , and the skin of the back was removed before preparations were placed dorsal side up in a dissecting chamber . A laminectomy was performed to expose the spinal cord , which was carefully dissected free under a binocular microscope . Dissection and recording procedures were conducted under continuous superperfusion with artificial cerebrospinal fluid ( aCSF ) equilibrated with 95% O2/5% CO2 , adjusted to pH 7 . 4 at room temperature ( 24–26°C ) and containing the following ( in mM ) : 130 NaCl , 3 KCl , 2 . 5 CaCl2 , 1 . 3 MgSO4 , 0 . 58 NaH2PO4 , 25 NaHCO3 , and 10 glucose . Spinal cords were sectioned at the T1 level at the beginning of the experiment . In some experiments , the spinal cord was artificially partitioned using Vaseline walls , as previously described [62] , to restrict the bath-application of pharmacological agents to specific segmental regions . The watertightness of the barriers was systematically checked at the end of each experiment by observing the movements of methylene blue added to the bathing medium on one side of the Vaseline wall ( s ) . Whole cell patch-clamp recordings from neurons were obtained with a Multiclamp 700B amplifier ( Molecular Devices ) . Patch-clamp glass microelectrodes ( 4–7 MΩ ) were filled with the following solution ( in mM ) : 120 K-gluconate , 20 KCl , 0 . 1 MgCl2 , 1 EGTA , 10 HEPES , 0 . 1 CaCl2 , 0 . 1 GTP , 0 . 2 cAMP , 0 . 1 leupeptin , 77 D-mannitol , 3 Na2-ATP , pH 7 . 3 . Intracellular recordings from MNs and SPNs were made according to a protocol developed in a previous study [32] . To access thoracic neurons , a transverse section of the spinal cord was made at a chosen thoracic level using fine scissors ( MC-26B , Moria ) . To maintain the sectioned surface of the cord face-upwards and facilitate microelectrode positioning , the cut end of the spinal cord was positioned on a Sylgard wedge ( see Fig 6A1 ) . SPNs and MNs were targeted on the basis of their location in the transverse plane ( see Fig 6A ) and subsequently identified by recording their antidromic action potentials in response to electrical ventral root stimulation of the same segment ( see Fig 6A2–6A3 ) . A liquid junction potential of +12 mV was experimentally determined [63] and records were corrected for this potential . Series resistance was monitored throughout the experiments and was not compensated . Data were discarded if series resistance varied more than ±20% of the initial value . Motor activities were recorded extracellularly from various spinal cord ventral roots using glass suction electrodes . Recorded activity was amplified using custom-made amplifiers . The recordings were digitized using an analog-to-digital interface ( Heka Elektronik , Germany ) driven by Axograph software ( Axograph , Australia ) . Raw signals were high-pass filtered ( 50 Hz ) , rectified , and integrated before analysis . Locomotor burst parameters were computed using custom-made routines written in Matlab ( Mathworks , France ) . Mean cycle periods were computed using an L2 ventral root as the reference because it invariably exhibited the best signal-to-noise ratio . Wavelet transform analyses [64] were performed using a Matlab wavelet coherence package provided by Aslak Grinsted ( http://noc . ac . uk/using-science/crosswavelet-wavelet-coherence ) . Of particular interest was the extraction of the common power , correlation , and phase relationship between two simultaneously acquired signals of the cross wavelet transform and wavelet coherence [65 , 66] . A detailed explanation of the wavelet-based methodology used in the present work has been previously reported [32] . Episodes of locomotor-like activity were elicited by exogenous bath-application of a mixture of 7 . 5 μM NMDA and 15 μM 5-HT [67] . All drugs were obtained from ABCAM . AF-DX 116 ( M2 antagonist ) , 4-DAMP ( M3 antagonist ) , and tropicamide ( M4 antagonist ) were diluted in DMSO at concentration less than 0 . 1% [68] . All other drugs were prepared as stock solutions in aCSF . Drugs were bath-applied using a peristaltic pump ( flow rate 4 mL/min; recording chamber volume 4 mL ) . The effects of the drugs were monitored from 5–10 min after reaching the recording dish ( i . e . , the time estimated for a total replacement of the bathing saline and diffusion into the tissue ) . Statistical analyses of raw data were conducted using GraphPad Prism software . Because of some relatively small samples , nonparametric tests were used for all analyses performed . In the text , all data are expressed as means ± SEM . In the figures , the box plots display the distribution of data based on the minimum , first quartile , median , third quartile , and maximum . Asterisks in the figures indicate statistical significance ( p < 0 . 05 ) . Wilcoxon or Mann–Whitney tests were used when applicable . The coupling between left and right activities was analyzed using circular statistics , using the Oriana Software ( Kovach Computing Services ) . | Physical movements require mobilization of animals’ autonomic nervous system , in order to maintain stable bodily functions while matching the increasing physiological demand . These autonomic responses rely on a coupling between the sympathetic and somatic nervous systems , although how this coupling occurs remains unresolved . To address this issue , we used the in vitro isolated spinal cord preparation from newborn rats , which can be kept alive up to 12 h and generate locomotion-like activity . We found that the stimulation of the muscarinic cholinergic receptors specifically activates intraspinal neural networks generating a slow motor rhythm . During this slow rhythm , recordings from sympathetic neurons ( the cells responsible for the spinal sympathetic output ) and somatic motoneurons ( responsible for skeletal muscle activity ) reveal that both cell types receive a common synaptic input that results in a coupling of sympathetic and rhythmic locomotor activities . This shared somato-sympathetic drive could merge with and modulate the locomotor synaptic drive produced by the lumbar motor networks . We propose that the coupling mechanism we describe here underlies part of the vascular changes needed to maintain adequate muscle oxygenation during locomotion . | [
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"spina... | 2018 | Cholinergic-mediated coordination of rhythmic sympathetic and motor activities in the newborn rat spinal cord |
Acquisition of adaptive mutations is essential for microbial persistence during chronic infections . This is particularly evident during chronic Pseudomonas aeruginosa lung infections in cystic fibrosis ( CF ) patients . Thus far , mutagenesis has been attributed to the generation of reactive species by polymorphonucleocytes ( PMN ) and antibiotic treatment . However , our current studies of mutagenesis leading to P . aeruginosa mucoid conversion have revealed a potential new mutagen . Our findings confirmed the current view that reactive oxygen species can promote mucoidy in vitro , but revealed PMNs are proficient at inducing mucoid conversion in the absence of an oxidative burst . This led to the discovery that cationic antimicrobial peptides can be mutagenic and promote mucoidy . Of specific interest was the human cathelicidin LL-37 , canonically known to disrupt bacterial membranes leading to cell death . An alternative role was revealed at sub-inhibitory concentrations , where LL-37 was found to induce mutations within the mucA gene encoding a negative regulator of mucoidy and to promote rifampin resistance in both P . aeruginosa and Escherichia coli . The mechanism of mutagenesis was found to be dependent upon sub-inhibitory concentrations of LL-37 entering the bacterial cytosol and binding to DNA . LL-37/DNA interactions then promote translesion DNA synthesis by the polymerase DinB , whose error-prone replication potentiates the mutations . A model of LL-37 bound to DNA was generated , which reveals amino termini α-helices of dimerized LL-37 bind the major groove of DNA , with numerous DNA contacts made by LL-37 basic residues . This demonstrates a mutagenic role for antimicrobials previously thought to be insusceptible to resistance by mutation , highlighting a need to further investigate their role in evolution and pathoadaptation in chronic infections .
Cystic fibrosis ( CF ) is the most common lethal , heritable disease in the US and results from mutations in the gene encoding the CF transmembrane conductance regulator . One of the most concerning effects of this mutation is altered anion transport of the airway epithelial cells resulting in increased susceptibility to infections and enhanced innate immune responses ( reviewed in ref . [1] ) . The combinatorial effect of defects in the CF airway and chronic bacterial infections results in a hyper inflammatory environment dominated by an influx of polymorphonucleocytes ( PMNs ) . Chronic pulmonary infections with Pseudomonas aeruginosa are a leading cause of death in CF patients [2] , [3] . During the course of infection , P . aeruginosa often undergoes a phenotypic change from a non-mucoid to a mucoid appearance , which directly correlates with a worsening clinical prognosis [2] , [4] . Mucoid conversion is characterized by the overproduction of the polysaccharide alginate , which confers a selective advantage for P . aeruginosa in the CF lung by providing recalcitrance to currently available therapeutics and host antimicrobials ( reviewed in refs . [5] , [6] ) . Mucoid conversion occurs when the negative regulator of alginate synthesis , MucA , is genetically or physiologically disrupted [7] , [8] . Up to 84% of mucoid isolates from patients possess mutations within mucA resulting in constitutive overexpression of alginate [9]–[12] . Hyper inflammation in the CF lung environment generates an abundance of mutagenic factors , which may be responsible for directly inducing mucA mutations . For example , PMNs and hydrogen peroxide ( H2O2 ) elevate mucoid conversion in vitro by promoting mutagenesis [13]–[15] . However , the robust adaptive nature of the P . aeruginosa genome in chronic CF infections is evident and perpetuated by the appearance of mutator strains , which likely contribute to selection of mucoid variants ( reviewed in [16]–[19] ) . Therefore , it will be of therapeutic utility to determine if specific host factors in CF promote mucA mutagenesis and investigate if intervention at this initial stage would prove effective . This study aimed to examine the specific role of host inflammatory factors in promoting mucA mutations leading to mucoid conversion . PMNs derived from both healthy and CF individuals stimulated mucoid conversion independent of selection . Surprisingly , while reactive oxygen species ( ROS ) have the capacity to induce mucoid conversion in vitro , PMNs still efficiently promote mucoid conversion in the absence of an oxidative burst response . This led to the discovery that non-oxidative PMN components , including the antimicrobial peptide LL-37 , are important for promoting mucoid conversion in CF . Importantly , LL-37 also elevated spontaneous rifampin resistance in P . aeruginosa and E . coli , indicating a new role for LL-37 as a bacterial mutagen . LL-37-induced mutagenesis required the translesion DNA polymerase , DinB ( Pol IV ) ; whose error-prone replication is responsible for generating mucA mutations . Using several independent methods , it was determined that , at sub-inhibitory concentrations , LL-37 enters P . aeruginosa cells , interacts with DNA , and promotes mucA mutations . Finally , conversion of P . aeruginosa to the mucoid phenotype then protects the bacterial cells from killing by lethal concentrations of LL-37 . These data reveal a novel mechanism to describe how antimicrobial peptides interact with bacterial cells and demonstrate that LL-37 may promote mutations leading to persistence and chronic infection .
The study of mucoid conversion in the laboratory has been hampered by the difficulty in isolating rare mucoid variants that arise in a population ( ∼1×10−9 ) . To circumvent this problem , a system for selecting mucoid colonies was developed ( Figure S1 and Materials and Methods ) . A promoterless gene encoding chloramphenicol-resistance ( cat ) was placed under control of the promoter of the alginate biosynthetic operon ( algD ) [20] , [21] and integrated into the chromosome of non-mucoid , chloramphenicol sensitive P . aeruginosa strain PAO1 to generate PAO1algD-cat ( WFPA934 ) . Under this system , mucoid bacteria that are producing alginate and therefore transcribing algD will be chloramphenicol-resistant , allowing for selection of mucoid variants by growth on chloramphenicol-containing media ( Figure S1A ) . By comparing the numbers of colonies on non-selective versus chloramphenicol media the relative frequency of mucoid conversion was determined . To investigate the utility of this selection strategy , non-mucoid P . aeruginosa were exposed to a sub-inhibitory concentration ( 1/10 the minimum inhibitory concentration ( MIC; 0 . 1 µM ) ) of H2O2 for one hour , followed by overnight recovery in media alone . Cultures are then serially diluted and plated on non-selective media to determine the total number of bacteria ( ∼1×1010 ) and whole culture plated on chloramphenicol containing media to determine the number of mucoid colonies ( 0–300 depending on treatment ) . The mucoid conversion frequency is then determined by dividing the number of mucoid colonies by the total . There was no significant difference among treatments in the total number of bacteria after the one-hour treatment or after the overnight recovery period , therefore changes in mucoid conversion frequencies are a direct result of induction of mucoid variants . In agreement with previous studies [13] , [14] , [22] , treatment with H2O2 significantly increased the frequency of mucoid conversion ( Figure S1B ) . While non-mucoid chloramphenicol isolates were observed , the frequency was very low ( ∼1 . 9×10−10 ) and was not altered by the addition of H2O2 ( or any of the other mutagens used in this study , see below ) . Since the translesion DNA polymerase DinB ( Pol IV ) and defects in the mismatch repair protein MutS contribute to mucoid conversion [14] , [15] , [22] , the role of these proteins was also examined . As predicted , P . aeruginosa isolates lacking mutS exhibited increased mucoid conversion frequencies and dinB– deficient isolates had severely impaired mucoid conversion ( Figure S1B ) . Importantly , no reduction in growth was observed during the short treatment periods utilized and resulting mucoid variants were stable upon multiple passages , where approximately 80% possessed mutations in mucA ( Table S1 ) . Therefore , we were able to identify mucoid variants , which acquire mutations by direct induction versus selection based on resistance . This selection strategy now provides a unique opportunity to interrogate the CF factors that directly promote mutations leading to mucoid conversion . To examine the role of PMNs in mucoid conversion , opsonized PAO1algD-cat was incubated in the presence of PMNs derived from three sources: peripheral PMNs from healthy or CF human donors , or an inducible promyelocytic cell line ( HL-60 ) as described in Supporting Material and Methods ( Text S1 ) . Treatment with each cell type significantly increased the frequency of mucoid conversion ( Figure 1A ) . A trend was also observed for increased mucoid conversion in CF PMNs compared to healthy PMNs from multiple donors; however , this was not a statistically significant difference . Moreover , mucA from mucoid isolates treated with healthy human PMNs harbored mutations ( Table S1 ) , demonstrating that human PMNs can induce mutations promoting mucoid conversion . To investigate the mechanism ( s ) by which PMNs induce mucoid conversion , we first sought to determine if bacterial uptake is necessary . Phagocytosis was blocked by the addition of cytochalasin D ( Figure 1B; confirmed by microscopy ( Figure S2A ) ) , or by separating PMNs from the bacteria with transwells ( Figure 1B ) . Efficient mucoid conversion was maintained when phagocytosis was inhibited by either method , demonstrating the factors promoting mucoid conversion are released from the PMN . Since we hypothesize ROS generation is responsible for PMN-induced mucoid conversion , we sought to determine if inhibition of bacterial uptake affected the oxidative burst response . Surprisingly , both cytochalasin D and separation by transwells blocked the PMN oxidative burst ( Figure S2B , C respectively ) , suggesting PMNs can promote mucoid conversion in the absence of ROS . To confirm this , PMNs were specifically inhibited by pretreatment with the NADPH oxidase inhibitor diphenyleneiodonium ( DPI ) . While treatment of PMNs with DPI significantly inhibited ROS production ( Figure 1C ) , this did not lead to inhibition of mucoid conversion ( Figure 1D ) . Inhibition of oxidative burst was further confirmed with lucigenin and scopoletin , which measure O2• generation , and H2O2 , respectively ( data not shown ) . Collectively , these data demonstrate that PMNs deficient in generating ROS can still efficiently induce mucoid conversion . The above data reveal that ROS-independent PMN factors may be important determinants of mucoid conversion . Moreover , while ROS clearly promote mucoid conversion in vitro and likely contribute to pathoadaptation in vivo , oxygen independent mechanisms may predominate in CF due to the hypoxic nature of the mucopurulent masses in the lumen of patient airways , where P . aeruginosa microcolonies reside [23] , [24] . To test whether non-oxidative components are involved in mucoid conversion , PMN lysates that retained granular components but lack ROS were prepared ( see Text S1 . Supporting Materials and Methods ) . Treatment of PAO1algD-cat with PMN lysates or soluble components specifically isolated from granules increased the frequency of mucoid conversion compared to vehicle treated P . aeruginosa ( Figure 2A ) . Moreover , PMN lysates and granule fractions promoted mutations within mucA , indicating non-oxidative PMN components possess mutagenic capacity ( Table S1 ) . To investigate which granule component ( s ) are responsible for mucoid conversion , PAO1algD-cat was incubated with sub-inhibitory concentrations ( 0 . 25 µM ) of purified AMP including the human cathelicidin , LL-37 , beta defensins ( hBD ) 1 and 2 , and human neutrophil peptide 1 ( HNP1 ) . LL-37 and hBD2 treatment increased the frequency of mucoid conversion approximately four-fold and two-fold respectively compared to buffer controls , while hBD1 and HNP1 did not exhibit any effect ( Figure 2B ) . Together these data reveal a novel property for non-oxidative PMN granular components , including specific antimicrobial peptides , in promoting P . aeruginosa mucoid conversion . LL-37 is a human cationic host defense molecule elevated in CF sputum and found in the granules of PMNs and at mucosal surfaces [25] . In addition to possessing a broad range of antimicrobial action against bacteria , viruses , and fungi , LL-37 possess immunomodulatory and anti-biofilm activities at sub-inhibitory concentrations [26]–[28] . In PMNs , the propeptide form of LL-37 is stored within the specific granules and upon stimulation is released to the extracellular environment and cleaved to the mature form by serine proteases [29] . Since the PMN factors primarily responsible for mucoid conversion are released from the PMN into the extracellular environment ( Figure 1B ) and we were able to confirm the presence of mature LL-37 in PMN lysate preparations by immunoblot analysis ( Figure S3 ) , we hypothesized that LL-37 may promote mucoid conversion in CF and focused current studies on LL-37 . Despite the presence of elevated LL-37 in CF sputum , it has been argued that in the CF pulmonary environment some AMPs may not function optimally due to high salt concentrations [30] , [31] or may be sequestered by extracellular DNA and F-actin bundles [32] . Therefore , to determine if LL-37 mediated mucoid conversion is a process capable of functioning in the CF pulmonary environment , the impact of sputum isolated from CF patients on mucoid conversion was investigated . Sputum isolated from four different CF individuals induced an average mucoid conversion frequency of 3×10−9 ( Figure 2C ) . To determine if LL-37 contributes to sputum induced mucoid conversion , LL-37 was immune-depleted from each sputum sample . The mucoid conversion frequency decreased significantly upon depletion with antibodies directed toward LL-37 , compared to an isotype control antibody ( Figure 2C ) , demonstrating LL-37 contributes to mucoid conversion in CF sputum . However , immune-depletion of LL-37 did not completely abolish mucoid conversion . This is likely a combination of incomplete elimination of LL-37 from CF sputum , as well as potential contribution of other inflammatory factors in sputum to mucoid conversion ( oxidative and nitrosative stress , other AMP , etc ) . Moreover , after incubation of P . aeruginosa with CF sputum , no change in the overall viability of the bacteria was observed ( data not shown ) . These data suggest that antimicrobial factors present in CF sputum may not be present at bioavailable levels sufficient to control P . aeruginosa infections , but are sufficient to promote conversion to the mucoid phenotype . To investigate how LL-37 promotes mucoid conversion , we first interrogated the primary mechanism of mucoid conversion observed in CF P . aeruginosa isolates , mutagenesis of the anti-sigma factor encoding gene mucA . We subjected ten LL-37-derived mucoid isolates to mucA sequence analysis ( Table S2 ) . All isolates possessed mutations within mucA and 70% of these were frameshifts predicted to eliminate mucA function . Mucoid clinical isolates from CF patients possess a range of mucA alterations; however , frameshift mutations and C to T transitions are the most common [33]–[36] , a pattern represented here in LL-37 treated mucoid isolates ( Table S2 ) . To confirm the alterations in mucA were responsible for the mucoid phenotype , mucA was expressed on an arabinose inducible plasmid ( pHERD20TmucA ) in representative isolates with unique mucA alleles ( WFPA934 LL-37 1 . 1 ( ΔC at 184 ) , 1 . 2 ( C→A at 531 ) , and ( ΔT at 470 ) ) . The phenotype of each isolate harboring pHERD20TmucA was complemented to the non-mucoid phenotype upon growth on arabinose ( Figure 3A ) , indicating these mutations are directly responsible for LL-37-dependent mucoid conversion . To determine if LL-37 promotes global bacterial mutagenesis , the frequency of LL-37-induced rifampin resistance ( RifR ) was examined . Upon treatment of non-mucoid PAO1 with sub-inhibitory concentrations of LL-37 a moderate , but significant increase in RifR was observed compared to cells treated with buffer . The frequency of acquisition of RifR was similar to control treatments with H2O2 ( Figure 3B ) , which is known to promote global mutagenesis in a range of microorganisms [37] . Furthermore , LL-37 also elevated the acquisition of RifR in E . coli , demonstrating this mechanism of mutagenesis is not specific to P . aeruginosa ( Figure 3B ) . Collectively , these data demonstrate for the first time that LL-37 can function as bacterial mutagen and may contribute to the generation of mutations during chronic infection . To uncover the mechanism by which LL-37 induces mutations , we investigated the DNA repair enzymes MutS and DinB , due to their previously identified role in both P . aeruginosa and E . coli mutagenesis [14] , [15] , [22] , [38] . The mucoid conversion frequency of ΔmutS , ΔdinB and double ΔmutSΔdinB mutants upon treatment with LL-37 was determined ( Figure 3C ) . In the mutS mutant the spontaneous mutation frequency was increased nearly 1000-fold and this was further elevated with LL-37 treatment . Of notable significance , mucoid conversion was almost eliminated in a dinB-deficient strain , independent of the mutS status . These results illustrate an important role for DNA repair proteins in LL-37-induced mucoid conversion . Cell envelope stress has been linked to production of intracellular ROS , which induce mutagenesis and bacterial SOS response genes [39] . Since DinB is a member of the SOS regulon [15]; we initially hypothesized that LL-37 interactions with the P . aeruginosa cell envelope would result in membrane stress , leading to induction of SOS response genes , including dinB . Therefore , the expression of P . aeruginosa regulators of membrane and SOS responses , algT/U ( σE/22 ) , and lexA , respectively , were evaluated by quantitative real time PCR ( qRT-PCR ) following LL-37 exposure . However , treatment of non-mucoid PAO1 with sub-inhibitory concentrations of LL-37 did not significantly alter the expression of lexA or algT/U , whereas positive control treatments did ( mitomycin C and D-cycloserine , respectively ) ( Table S3 ) . Surprisingly , expression of dinB was also not altered by LL-37 treatment ( Table S3 ) . Combined with the observation that DinB is required for spontaneous generation of mucA mutations ( buffer treated , Figure 3C ) , these results suggest that basal levels of DinB present during normal replication are sufficient to promote mutagenesis leading to mucoid conversion . We propose that LL-37 elevates mutagenesis in P . aeruginosa by a mechanism independent of membrane or SOS stress responses . Bacterial DNA has been proposed to be an alternative target for a subset of AMPs , whereby peptides gain entry to the cytosol and bind bacterial DNA , resulting in subsequent disruption of DNA or protein synthesis [40]–[45] . Since membrane stress and SOS response pathways were not induced by LL-37 , we explored an alternative hypothesis that LL-37 may induce translesion DNA synthesis and mutagenesis by interacting directly with genomic DNA . The structure of LL-37 resembles classical cell-penetrating peptides and it has been postulated that LL-37 may enter both prokaryotic and eukaryotic cells . Lande et al demonstrated that LL-37 is capable of trafficking into dendritic cells , providing evidence that LL-37 can penetrate eukaryotic cells [46] . While multiple reviews have generalized this observation to include all cell types , it has yet to be formally determined if LL-37 can enter the cytosol of bacterial cells [27] , [45] . Therefore , the capacity of sub-inhibitory concentrations of LL-37 to enter the P . aeruginosa cytosol was examined utilizing fluorescent confocal- and transmission-electron microscopy ( TEM ) . Non-mucoid PAO1 was treated with LL-37 , fixed , and labeled with anti-LL37 . For confocal microscopy , the localization of anti-LL37 labeling of permeabilized PAO1 ( to allow antibody access to the cytosol ) was compared to non-permeabilized cells . Non-permeabilized cells showed a weak , diffuse signal on the surface , compared to permeabilized cells , which demonstrated a stronger , punctate signal in the center of the cell ( indicated by white arrows , Figure 4A ) . This was clearly visible only in the cytosol , when sequential images where taken along the extent of the Z plane of the cells ( shown in Movie S1 ) . With transmission electron microscopy ( TEM ) , LL-37 labeling was visible in the cytosol of approximately 28% ( white arrows ) of cells and in the membrane of 11% ( black arrows ) ( Figure 4B , center ) . These data reveal for the first time that LL-37 can gain entry to the cytosol of P . aeruginosa . However , intracellular LL-37 was visualized in only a subset of cells . Of note , 43% of cells with cytosolic LL-37 labeling , as visualized by confocal microscopy , appeared to be actively dividing ( Figure 4A ) . At lethal concentrations , LL-37 readily interacts with the septum of diving cells [47]; therefore active replication may also play a role in LL-37 gaining access to the P . aeruginosa cytosol at sub-inhibitory concentrations . Several models have been proposed to explain how AMPs interact with bacterial membranes , but accumulating evidence for LL-37 supports the Shai-Matsuzaki-Huang model [28] , [48]–[51] . In this model , peptides carpet the outer leaflet of the bacterium and integrate into the membrane . This is followed by a transient pore forming stage , where lipids and peptides are transported to the inner leaflet , resulting in collapse of membrane fragments and disruption of the membrane . In some cases , transient pore formation results in diffusion of peptides into the cytosol , where peptides can then interact with intracellular targets [48] . At sub-inhibitory concentrations , transient pore formation may occur without significant disruption of the membrane or loss of cell viability . To determine if bacterial DNA might be an intracellular target of LL-37 , TEM experiments were performed as described previously , with a second label added for double-stranded DNA . 76% of intracellular LL-37 was localized with P . aeruginosa DNA ( Figure 4B , bottom panel ) . Electrophoretic mobility shift assays ( EMSA ) were also performed , where LL-37 treatment resulted in a shift of all DNA tested with similar affinity ( apparent KD = 12 . 8 µM , Figure S4 ) , further demonstrating that LL-37 binds non-specifically to P . aeruginosa DNA . It was observed that upon the addition of increasing amounts of LL-37 a threshold concentration was reached where no migration of DNA was observed , instead of a step-wise migration , a phenotype that has been observed for other short DNA binding peptides [41] , [52] . To investigate how LL-37 may interact with DNA and whether DNA binding is necessary for mucoid conversion , a structure-based search of LL-37 revealed homology to eukaryotic transcription factors containing basic region leucine zipper ( bZIP ) motifs . A three-dimensional model of LL-37 bound to DNA suggested that the amino-terminal basic residues of LL-37 occupy positions suitable for interactions with the negatively charged phosphate groups of the DNA backbone ( Figure 5A ) . To investigate LL-37/DNA interactions , two synthetic peptides were generated: an LL-37 variant with the amino acid sequence randomly scrambled and a variant with the basic residues in the predicted DNA binding region replaced with glutamate residues ( K/R7-19E , Figure 5B ) . Scrambled LL-37 had a moderate loss of DNA binding , as well as a decrease in the ability to induce mucoid conversion ( Figure 5C and D ) . When basic residues within the putative DNA binding region were modified , a complete loss of both DNA binding and mucoid conversion was observed ( Figure 5C and D ) . These data suggest that LL-37/DNA interactions are required for LL-37 to promote mucoid conversion . Studies are currently underway to further define LL-37/DNA interactions , their impact upon DinB and/or the DNA replisome , and the precise mechanism of mutagenesis . Herein , we show that LL-37 induces mucoidy but it is unknown if this conversion provides P . aeruginosa adaptive protection from lethal concentrations of LL-37 . We observed a 10-fold increase in survival of two mucoid isolates derived from LL-37 treatment when compared with parental non-mucoid PAO1 ( Figure 6A ) . To determine if protection was dependent upon prior treatment with LL-37 , a H2O2-derived mucoid isolate , and a genetically engineered mucoid strain , PDO300 ( PAO1ΔmucA22 ) were examined and both showed enhanced resistance to LL-37 mediated killing ( Figure 6A ) . These data demonstrate that conversion to the mucoid phenotype provides P . aeruginosa protection from lethal concentrations of LL-37 , independent of prior exposure to LL-37 . Since mucA inactivation and subsequent algT/U induction controls an entire regulon , of which only a subset are dedicated to alginate production , it was necessary to determine if alginate overproduction is specifically responsible for LL-37 protection . FRDmucA22ΔalgD ( deficient in alginate production ) was 10-fold more susceptible to LL-37 compared to the isogenic mucoid parental strain FRD1 ( mucA22 , Figure 6B ) . These data demonstrate that alginate overproduction contributes to protection of P . aeruginosa from LL-37 killing and this might promote P . aeruginosa pathoadaptation in the CF pulmonary environment . Collectively , these data provide evidence for an additional role for LL-37 beyond the antimicrobial , anti-biofilm , and immunomodulatory functions previously described . We demonstrate that at sub-inhibitory levels , LL-37 promotes bacterial mutagenesis , which may contribute to evolution and pathoadaptation during chronic infections . Importantly , LL-37 induced mutations within mucA mimic what is observed in mucoid P . aeruginosa strains isolated from the CF airway and the sub-inhibitory conditions utilized may be representative of the level of bioavailable peptide present in the CF pulmonary environment . Furthermore , LL-37 induced mutagenesis of mucA leading to mucoid conversion was modulated exclusively by the error-prone polymerase DinB . We were intrigued by the stringent dependence upon DinB in this process , particularly since previous studies demonstrate DinB is not required for spontaneous or UV-induced RifR [15] . We observed that LL-37 can promote RifR in both P . aeruginosa and E . coli , suggesting perhaps the mechanism for acquisition of LL-37-induced mucA mutations and RifR occur via different pathways . Moreover , LL-37 did not increase the expression of DinB under these conditions demonstrating basal levels of DinB are sufficient to promote mutagenesis . Typical translesion DNA synthesis occurs when the replisome stalls upon encountering damaged DNA or a challenging template and low-fidelity polymerases like DinB will displace Pol III in order to perpetuate replication [53] . Since LL-37 interactions with DNA are required for LL-37-incuded mutagenesis we postulate that LL-37 presents a physical barrier that stalls Pol III , inducing a switch to DinB , whose error-prone replication promotes mutagenesis ( see Figure 7 for model ) . EMSAs suggest that LL-37 non-specifically interacts with DNA; however , peptides have been identified which specifically interact with DNA repair intermediates , such as Holliday junctions [54] , [55] . Therefore , an alternative hypothesis could be that LL-37 perturbs effective DNA repair by binding to repair intermediates . In this regard , Overhage et al performed microarray experiments with sub-inhibitory concentrations of LL-37 and identified changes in expression of genes involved in alginate regulation and DNA repair [26] . However , differences in the conditions utilized in these experiments and a lack of convergence upon a single pathway do not support any one hypothesis . Additionally , sub-inhibitory levels of LL-37 have been found to stimulate expression of the capsule synthesis operon in Group A Streptococci [56] . Together these studies suggest additional functions for sub-inhibitory levels of LL-37 impacting the expression of virulence genes . While the current study identified stable variants generated by mutagenesis , it is interesting to speculate how transient gene expression may impact their generation and selection in the host environment and further studies are clearly warranted . While data presented here demonstrate PMNs can promote mucoid conversion in the absence of an oxidative burst response , it is likely that mucoid conversion in the CF lung results from a combination of non-oxidative , oxidative , and nitrosative stresses . Moreover , other chronic pneumonias , such as COPD , are also characterized by elevated levels of PMNs , generally on the order of two to three-fold higher than healthy controls [57] , [58] . Mucoid P . aeruginosa have been isolated from patients with COPD , therefore , the mechanisms elucidated in this study may be important in other infections where chronic inflammation ensues [59] . Bronchial alveolar lavage fluid recovered from CF patients can have up to a 380-fold increase in PMNs recovered compared to healthy patients , even in patients without symptoms of an active infection [60] . Moreover , elevated levels of PMNs are detectable in CF newborns , which persist and undergo cycles of exacerbation throughout the life-time of the patient [61] . We therefore hypothesize that persistent exposure of P . aeruginosa to chronic inflammation for decades significantly contributes to microbial pathoadaptation in CF patients . Future investigation of how these inflammatory factors function in combination to promote P . aeruginosa pathoadaptation will be important for comprehensive understanding of these processes and the development of rational therapeutics for chronic infections . Aggressive antibiotic and anti-inflammatory use over the past decade has drastically improved the life expectancy and disease outcome for CF patients . However , increased acquisition of antibiotic resistance mechanisms is presenting a significant challenge for future treatment options [62] . Many are turning towards cationic antimicrobial peptides as a promising alternative for developing antimicrobials , as they are thought to be relatively insusceptible to the development of resistance mechanisms by mutations [63] . This study demonstrates that some antimicrobial peptides may instead act to promote mutagenesis and the acquisition of resistance at sub-inhibitory levels . These data reinforce how important it is to consider the impact of current and novel treatments and the host immune response on evolution of microbial communities during chronic infections .
Human PMNs and serum were obtained from healthy adult human donors according to the protocol approved by The Ohio State University Biomedical Sciences Institutional Review Board ( 2009H0314 ) , where informed consent was obtained from all donors . Sputum , PMNs and serum were obtained from CF patients ( adults and minors ) according to the protocol approved by The Nationwide Children's Hospital Institutional Review Board ( IRB12-00405 ) with informed consent obtained from all adult donors and from the parents/guardians of minors who participated . For children between the age of 9 and 18 an assent form was also obtained . Overnight cultures of WFPA934 ( PAO1algD-cat , Table S4 ) were diluted into fresh M63 media and grown to mid-log phase . 2×108 colony forming units ( CFU ) /ml were resuspended in Hank's buffered saline ( HBSS ) or 10 mM sodium phosphate buffer ( pH 6 . 2 , SPB ) , as indicated and treated with sub-inhibitory concentrations of antimicrobials for one hour . Reactive oxygen species were used at 1/10 the minimum inhibitory concentration ( MIC ) : hydrogen peroxide , 0 . 1 µM , hypochlorous acid , 0 . 1 µM , and paraquat to generate superoxide , 100 mM , antimicrobial peptides ( LL-37 , human beta-defensin 1 , human beta-defensin 2 and human neutrophil peptide 1 ) were used at 0 . 25 µM ( PeproTech , Rocky Hill , NY ) . Human PMNs and serum were isolated according to previously described protocols [64] . Mid-log phase P . aeruginosa suspensions were opsonized with 10% fresh human serum at 37°C for 30 min , added ( MOI of 50 ) to the wells and centrifuged at 100 g for 2 min at 4°C to synchronize the infection . PMN treatments were performed according to previously described protocols and are described in detail in Supporting Materials and Methods ( Text S1 ) . P . aeruginosa cells were then washed twice and resuspended in M63 media for an overnight growth recovery period . Cultures were then serially diluted for plating on non-selective Pseudomonas isolation agar ( PIA ) and incubated at 37°C overnight to determine the total CFU and plated straight onto PIA containing chloramphenicol ( 250 µg/ml ) and incubated at 37°C for 48 hours to determine the number of mucoid CFU . The mucoid conversion frequency was then determined by dividing the number of mucoid variants by the total number of CFU . CF sputum samples were collected by spontaneous expectoration from patients attending Nationwide Children's Hospital in Columbus , OH ( IRB12-00405 ) . The samples were diluted 1∶1 in buffer containing 140 mM NaCl , 10 mM Tris , 0 . 2 mM CaCl2 ( pH 7 . 4 ) and physically disrupted by pipetting up and down with decreasing sized serological pipettes , followed by pushing through decreasing sized needles until sample is easily pushed through a 27 gauge needle . Samples were then centrifuged ( 10 min; 15 , 500 g ) to pellet the remnant cells and bacteria . For immune-depletion of LL-37 , sputum was incubated with 1 . 6 µg/ml monoclonal anti-LL37 antibody ( Santa Cruz Biotechnology , Dallas , TX ) or mouse IgG1 isotype control antibody ( R & D Systems , Minneapolis , MN ) overnight at 4°C . The antibody/antigen complex was then pulled-down with Protein A/G Agarose Beads ( Thermo Scientific , Rockford , IL ) according to the manufacturer's instructions and confirmed by immunoblot analysis ( See Text S1 . Supporting Materials and Methods ) . Genomic DNA was harvested from the mucoid variants isolated in the mucoid conversion assay using the Wizard genomic purification kit ( Promega ) . PCR amplification was performed using mucA-specific primers mucAupF and mucAdnR ( Table S5 ) . After verification of PCR product by agarose gel electrophoresis , The Ohio State University Medical Center Nucleic Acid Core sequenced the PCR products using Sanger sequencing techniques with mucAupF , mucAdnR , and mucA1F21 . The sequence data produced for mucoid variants were then aligned with mucA gene sequence of the non-mucoid PAO1algD-cat parental to determine if and/or where mutations occurred in the mucA gene of the variants . The mucA allele was cloned into the shuttle vector pHERD20T [65] for complementation with gene expression driven by the pBAD arabinose-inducible promoter . P . aeruginosa strains were grown at 37°C on PIA plates supplemented with carbenicillin and 0 . 1% ( wt/vol ) arabinose for 24 h . Bacterial growth was removed from plates with phosphate-buffered saline ( PBS ) and the optical density at 600 nm ( OD600 ) of the bacterial suspension in PBS was measured . Alginate was isolated and measured by a standard carbazole assay as previously described [66] , [67] . Overnight cultures were diluted into fresh M63 media for P . aeruginosa or LB for E . coli and grown to mid-log phase . 2×108 CFU/ml were resuspended in 10 mM sodium phosphate buffer ( pH 6 . 2 ) ( SPB ) , and treated with sub-inhibitory concentrations of LL-37 or H2O2 ( 1 . 25 µM or for P . aeruginosa and 6 . 25 µM for E . coli . ) . Cells were then washed twice and resuspended in media for an overnight growth recovery period . Cultures were then serially diluted for plating on non-selective PIA or LB and plated straight onto media containing 100 µg/ml rifampin ( Rif ) and incubated at 37°C for 24 hours . The Rif resistance frequency was then determined by dividing the number of Rif resistant variants by the total number of CFU . For determination of localization of sub-inhibitory concentration of LL-37 by confocal microcopy , PAO1algD-cat was grown to mid-log phase and 1×106 cells were incubated with 0 . 25 µM LL-37 or SPB for one hour . Cells were washed , fixed in 4% paraformaldehyde for 10 min , permeabilized with 0 . 2% Triton-X for 1 min , blocked with 2% bovine serum albumin ( BSA ) and stained with anti-LL-37 antibody ( Santa Cruz ) ( 1∶100 ) conjugated directly to Alexa Fluor 647 ( Invitrogen ) . Bacteria were wet-mounted onto coverslips and visualized by confocal microscopy ( Olympus FV 1000 Spectral ) using a 100× oil objective . For quantification , 100 cells were chosen at random and intracellular and membrane-associated peptides counted and averaged by two readers blinded to the treatment conditions . For transmission electron microscopy , PAO1 was grown to mid-log phase and 1×106 cells were incubated with 0 . 25 µM LL-37 or SPB for one hour . Cells were washed and fixed in 4% paraformaldehyde for 12 h . Free aldehyde was quenched by the addition of 0 . 1 M glycine for 20 min and cells were resuspended in 0 . 2 M sucrose and incubated at 4°C overnight . Samples were embedded in LR white , cut into ultrathin sections ( Leica EMU 6 ultramicrotome ) ( 60–90 nm ) and collected into formvar-coated nickel grids . Sections were stained by anti-LL37 ( Santa Cruz ) antibody conjugated to Protein-G colloidal gold ( 20 nm ) ( 1∶500 ) ( EY Laboratories ) and/or anti-dsDNA ( Abcam ) conjugated to Protein-G colloidal gold ( 10 nm , 1∶100 , EY Laboratories ) . Sections were viewed by transmission electron microscopy ( FEI Tecnai G2 Spirit ) operating at 80 kV . Twenty images were chosen at random and intracellular and membrane-associated peptides counted and averaged by two readers blinded to the treatment conditions . A structural based homology search was performed using the DALI server . Homology modeling of LL-37 bound to B-DNA was performed manually on the basis of the backbone atomic coordinates of the homologous protein , sterol regulatory element binding protein , bound to DNA , whose crystal structure is known ( pdbid: 1am9 ) [68] . 2×108 CFU of mid-log phase P . aeruginosa strains were incubated with lethal doses of LL-37 ( 2 . 5 µM ) of LL-37 for 1 hour at 37°C washed , serially diluted and plated on PIA to determine the total CFU . Results of mutagenesis studies presented significant variation ( including spontaneous untreated controls ) in accordance with previous observations [69] . Therefore all mutagenesis studies ( including rifampin resistance ) were performed in triplicate on at least four independent occasions . For all statistical analyses , data were tested for normality using Prism Version 5 . 0b . Data determined to be normally distributed were analyzed for statistical significance using parametric unpaired two-tailed students t-test in Prism . Data not normally distributed were analyzed using non-parametric unpaired two-tailed Mann-Whitney test using Stata Version 10 . 1 . For experiments with multiple treatments ( Figure 3BC , 5D , and S1B ) ANOVA or Kruskal-Wallis ( for data not normally distributed ) with a Dunn's post-test yielded similar results as the indicated t-tests . Proteins discussed in this manuscript are listed followed by their corresponding UniProtKB ( Universal Protein Knowledgebase ) number: MucA ( MUCA_PSEAE ) , DinB ( DPO4_PSEAE ) , AlgT/U ( RPSH_PSEAE ) , and LexA ( LEXA_PSEAE ) . | Antimicrobial peptides ( AMPs ) are produced by the mammalian immune system to fight invading pathogens . The best understood function of AMPs is to interact with the membranes of microbes , thereby disrupting and killing cells . However , the amount of AMP available during chronic bacterial infections may not be sufficient to kill pathogens ( sub-inhibitory ) . In this study , we found that at sub-inhibitory levels , AMPs promote mutations in bacterial DNA , a function not previously attributed to them . In particular , we found that in the bacteria Pseudomonas aeruginosa , one AMP called LL-37 can promote mutations , which enable the bacteria to overproduce a protective sugar coating , a process called mucoid conversion . P . aeruginosa mucoid conversion is a major risk factor for those suffering from cystic fibrosis ( CF ) , the most common lethal , heritable disease in the US . We found that LL-37 is able to produce these mutations by penetrating the bacterial cell and binding to the bacterial DNA . DNA binding disrupts normal DNA replication and allows mutations to occur . Furthermore , we observed LL-37 induced mutagenesis in processes apart from mucoid conversion , in both P . aeruginosa and E . coli . This suggests that AMP-induced mutagenesis may be important for a broad range of chronic diseases and pathogens . | [
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] | 2014 | Cationic Antimicrobial Peptides Promote Microbial Mutagenesis and Pathoadaptation in Chronic Infections |
Analogously to chromosome cohesion in eukaryotes , newly replicated DNA in E . coli is held together by inter-sister linkages before partitioning into daughter nucleoids . In both cases , initial joining is apparently mediated by DNA catenation , in which replication-induced positive supercoils diffuse behind the fork , causing newly replicated duplexes to twist around each other . Type-II topoisomerase-catalyzed sister separation is delayed by the well-characterized cohesin complex in eukaryotes , but cohesion control in E . coli is not currently understood . We report that the abundant fork tracking protein SeqA is a strong positive regulator of cohesion , and is responsible for markedly prolonged cohesion observed at “snap” loci . Epistasis analysis suggests that SeqA stabilizes cohesion by antagonizing Topo IV-mediated sister resolution , and possibly also by a direct bridging mechanism . We show that variable cohesion observed along the E . coli chromosome is caused by differential SeqA binding , with oriC and snap loci binding disproportionally more SeqA . We propose that SeqA binding results in loose inter-duplex junctions that are resistant to Topo IV cleavage . Lastly , reducing cohesion by genetic manipulation of Topo IV or SeqA resulted in dramatically slowed sister locus separation and poor nucleoid partitioning , indicating that cohesion has a prominent role in chromosome segregation .
Chromosome dynamics studies in E . coli using either fluorescent in situ hybridization ( FISH ) or fluorescent repressor proteins bound to arrays of operator sequences ( FROS ) have shown that there is a significant time delay between passage of the replication fork and separation of replicated sequences into two visible foci [1]–[6] . Comprehensive surveys across the E . coli chromosome indicate that this delay is ∼10 minutes at most sites [3] , [6] , suggesting that a several hundred kilobase sliding window of sister “non-separation” ( i . e . , cohesion ) follows each replication fork . Superimposed on this brief and progressive cohesion program , three regions have been identified that exhibit much longer cohesion , including the replication origin , oriC and two broad domains on the right chromosome arm [3] , [6] . The two late-splitting right arm regions , which we term “snaps” , are further unique in that their cohesion is lost simultaneously and is accompanied by a major global nucleoid reorganization event that gives rise to a bilobed nucleoid morphology [6] . This abrupt transition involves significant nucleoid expansion [7] and comprises a sister individualization step in which each nucleoid lobe contains one partially replicated daughter chromosome [6] . These data led us to propose that snap regions promote efficient chromosome segregation by resisting global sister chromosome separation until an appropriate time in the cell cycle . In this light , snaps may be analogous to eukaryotic centromere elements , which provide essential tension for microtubule-assisted chromosome segregation ( Discussion ) . Although there is no known bacterial equivalent of the eukaryotic cohesin complex that holds sisters together by a covalent ring structure [8] , several lines of evidence suggest that colocalized sister regions in E . coli form a molecular complex . First , for the duration of the segregation delay , “cohered” regions remain within the resolution limit of fluorescence microscopy , ∼230 nm [6] . Subsequent separation is very rapid ( 1–2 µm in 1–3 min; [9] ) , implying that segregation tension is counteracted by covalent linkages during cohesion . Second , disruption of the oriC partitioning apparatus by eliminating MukB does not cause increased oriC cohesion [10] , as would be expected if newly replicated regions merely passively colocalized until acted upon by segregation machinery . Third , a critical component of cohesion in E . coli appears to be the decatenating enzyme topoisomerase IV ( Topo IV ) , suggesting that part or all of the basis for cohesion is entanglement of replicated DNA behind the fork [4] . Fourth , inter-sister recombination exchanges occur more frequently between cohered loci [11] , indicating that homologous sequences physically interact during the colocalization period , and are not merely in the same subcellular vicinity . Currently , the only known mediator of cohesion in E . coli is the well-conserved type-II topoisomerase , Topo IV . Inactivation of Topo IV via a temperature-sensitive mutation led to a reduction in sister separation near oriC [4] , [11] , and also within the terminus region [11] , implying that Topo IV modulates cohesion across the E . coli chromosome . Topo IV , which relaxes positively supercoiled DNA molecules by a double-stranded cut/passage/ligation mechanism [12] , was initially thought to act primarily in the terminus region , where converging replication forks generate maximal positive supercoiling . However , Topo IV is also present at the replisome continually during replication [13] , [14] , which suggests that positive supercoils frequently migrate behind the replication fork , causing nascent sister duplexes to wind around each other in a precatenane structure . Single molecule studies estimate that Topo IV , present at ∼1000 molecules per cell , has a total unlinking capacity of ∼6000 strand passages per second [15] , several orders of magnitude faster than the rate at which precatenanes are predicted to form [16] . In contrast , cohesion lasts at minimum ∼7 minutes and up to 30 minutes along snap regions [6] . Thus , it appears that either cohesion involves another molecular linking component besides precatenanes , or , that Topo IV is negatively regulated by an unknown factor . To investigate how sister cohesion is regulated in E . coli , we analyzed cohesion timing in a broad range of chromosome structure and segregation mutants . Candidate cohesion regulatory proteins included the SMC-like proteins MukB and RecN , the nucleoid associated proteins HU , IHF and Fis , the replication fork tracking protein SeqA and its binding regulator Dam , and Topo IV . MukB and RecN are the only E . coli proteins with structural similarity to eukaryotic cohesin [17] , and could potentially promote cohesion by forming protein bridges across sister chromosomes [1] . The “histone-like” proteins HU , IHF and Fis , and the abundant DNA binding protein SeqA , are important for maintaining nucleoid structure and supercoiling [18] , and could also modulate cohesion through bridging or by net effects on chromosome compaction [19] . SeqA in particular is well positioned to regulate cohesion because it binds strongly and specifically to newly replicated DNA [20] . As DNA exits the replication fork the newly synthesized strand is unmethylated for a period of 5–10 minutes , before remethylation by Dam methylase [21] . During this period of hemimethylation , GATC sequences are bound by SeqA , with potentially several hundred molecules bound behind each fork [19] , and SeqA-GFP fusions forming large foci near or adjacent to sites of DNA replication [19] , [22] , [23] . It may not be coincidence that hemimethylation and cohesion periods ( of typical non-snap loci ) are very near equal . Importantly , in addition to a direct ( bridging ) mechanism , any of these proteins could regulate cohesion indirectly by affecting the processing of DNA catenanes . Supporting this idea , both SeqA and MukB interact with Topo IV in vivo and have been shown to strongly affect Topo IV decatenase activity in vitro [24]–[26] .
To identify factors involved in the regulation of chromosome cohesion , we performed a candidate screen for mutants that have increased or decreased sister cohesion . Mutants were selected that displayed both a moderate to severe chromosome segregation phenotype and abnormal nucleoid shape or compaction ( Introduction ) . Cohesion in these strains was determined in exponential cultures at the well-characterized gln locus using our standard non-synchronized cell assay ( Figure 1A ) . gln copy number is determined by first measuring oriC copies per cell by rifampicin runoff flow cytometry , then measuring the relative ratio of gln copies to oriC copies by quantitative real time PCR ( qPCR ) . In parallel , gln foci per cell is determined by FROS in which a tandem array of tetO binding sequences is inserted into the chromosome and subsequently bound by a fluorescent TetR-YFP fusion protein . The duration of sister co-localization ( cohesion ) is then proportional to the difference in gln copy number and gln foci per cell . This assay requires high efficiency of fluorescence detection ( below ) and sufficient resolution of segregated loci . Because initial segregation velocities are rapid , about 0 . 4 µm/min in the current study with final positions 3–10 times greater than the resolution distance of light microscopy ( shown below ) , we estimate that sister loci appear as two fluorescent foci <30 seconds after loss of sister cohesion . Although this cohesion assay is valid under any growth rate , cells were grown in minimal media supplemented with alanine or succinate as indicated to minimize overlapping replication cycles , which simplified microscopy analysis . The gln locus , located on the right chromosome arm 130 kb from oriC , normally exhibits a 30 minute cohesion period under similar growth conditions [2] , [6] . In the present study , gln was present at 1 . 9 copies per cell in wild-type cells , indicating that most cells had one or two chromosomes and that replication initiation occurred relatively early in the cell cycle ( Figure 1B; Figure S1 ) . Mutant strains had similar copy numbers , ranging between 1 . 8 and 2 . 0 gln loci per cell . Average gln TetR-YFP foci per cell for wildtype , hupAB , ihfA , fis , recN and mukB , was ∼20% lower than the respective gln copy number ( Figure 1B ) . This suggests that a significant fraction of foci in these strains harbored two colocalized gln loci ( separated by <0 . 2 µm ) in a state of cohesion . Due to the inherent limitations of fluorescence labeling and imaging , the observed number of fluorescent tetO/TetR-YFP complexes per cell is slightly undervalued , leading to an overestimation of cohesion . To correct for this , efficiencies were determined for each FROS experiment ( 94%±4% ) , and raw focus counts were adjusted ( ≤+0 . 10 foci per cell; Materials and Methods ) . This method was verified by determining the number of gln foci in a population of non-replicating , and presumably cohesion-less , stationary phase cells ( Figure S2 ) . Resulting gln copies per focus values for wildtype and most mutants were 1 . 22±0 . 03 ( Figure 1C ) , indicating that most strains , including ΔmukBEF , had normal gln cohesion ( ∼30 min; [6] ) . In contrast to WT , ΔseqA cells contained only 1 . 09 gln copies per focus ( Figure 1C ) , indicating that gln cohesion is reduced ∼60% in the absence of SeqA protein . A dam mutant , which is defective in GATC methylation and thus does not target SeqA to newly replicated DNA , had nearly identical gln cohesion as ΔseqA as expected . An opposite effect on cohesion was seen in cells with reduced levels of Topo IV . Cells bearing a parE10 ( Ts ) mutation that produces a defective Topo IV protein at 42°C [27] , [28] showed a sharp increase in cohesion when incubated at the semi-permissive temperature of 37°C ( Figure 1C ) , indicating that Topo IV mediates cohesion at an arm locus in addition to its role at oriC and ter [4] , [11] . Although we infer from non-synchronized “batch” culture analysis that ΔseqA and parE10 cells have shorter and longer gln cohesion periods respectively ( Figure 1 ) , it is possible that these mutants produce mixed populations of cells ( with altered replication timing ) that could bias cohesion measurements . To address this , we examined the dynamics of gln cohesion during the cell cycle by synchronized cell analysis ( Figure 2 ) . Wild-type , ΔseqA , ΔmukBEF , and parE10 mutant cells were synchronized by the baby machine method , which results in 75–85% synchrony and cells that are unperturbed for rates of mass increase , DNA replication and cell division [29] . Synchronized cells were then assayed for gln replication and gln splitting for two hours after cell birth , the equivalent of one cell cycle for wild-type cells at 30°C . In wild-type cells at both 30°C and 37°C , gln copy numbers rose steeply after cell birth ( Figure 2A , top panels , gray ) , followed by an increase in gln foci per cell ∼30 minutes later ( black ) . Integrating the area under the raw data curves at each time point yields cumulative curves ( Figure 2A , dashed lines ) , which describe the percentage of cells among the synchronous fraction that have replicated or segregated over time [2] . Cohesion periods are thus defined as the time interval between the replication and segregation cumulative curves . For wild-type cells , gln cohesion lasted 31 minutes at 30°C and 26 minutes at 37°C in agreement with previous studies [2] , [6] . As was seen with wildtype , ΔseqA cells exhibited steep increases in gln copies and gln foci during the cell cycle . However , gln foci split much sooner after replication in ΔseqA cells ( Figure 2A , middle left panel ) . Cumulative curve analysis indicates that cohesion lasted about 12 minutes in ΔseqA cells , about 1/3 the normal duration of gln cohesion ( Figure 2B ) . Confirming results from our initial screen , mukB mutant cells exhibited ∼30 minutes of gln cohesion at 31°C , the highest temperature that supported steady state growth ( Figure 2A , middle right panel ) . Synchrony in mukB cells was relatively poor ( note shallow curves for gln copy number and foci per cell ) , but gln splitting was clearly delayed after replication to a similar extent as wildtype . We conclude that like oriC [10] , gln cohesion does not require MukB protein . In contrast , parE10 cells showed severely delayed gln splitting at the semi-permissive temperature of 37°C ( Figure 2A , lower right panel ) . Under this condition , gln cohesion lasted about 65 minutes , 2-fold longer than when cells were grown at 30°C ( left panel ) . Importantly , the segregation delay was not caused by indirect effects of temperature , as wild-type cells showed an even shorter cohesion period at 37°C compared to 30°C . Interestingly , both seqA and parE10 cells had significantly longer post-replication D periods than wild-type cells at the same temperature ( Figure 2B ) , indicating that cell division was delayed . This delay may stem from late sister segregation caused by improper cohesion , although indirect effects on segregation cannot be ruled out ( mukB cells also had extended D periods , Figure 2B ) . Synchronized cell analysis showed that gln cohesion , normally lasting ∼30 minutes , was reduced to ∼12 minutes in the absence of SeqA protein . To test whether SeqA mediates cohesion at sites other than gln , cohesion was measured in wild-type and ΔseqA cells at 5 chromosomal loci ( see map , Figure 3C ) : two late-splitting snap loci ( gln and psd ) , two fast-splitting non-snap loci ( dnaB and arcA ) , and oriC , which exhibits late-splitting but with much different timing than snap loci [6] . At oriC and both snap sites ( gln and psd ) , cohesion was ∼60% reduced in ΔseqA compared to wildtype ( Figure 3A ) . ΔseqA cells were slightly elongated ( 3 . 1 µm compared to 2 . 4 µm for WT ) with ∼2% anucleate cells ( Figure 3B , arrow ) , suggesting that nucleoid segregation is partially defective . Although it is possible that aberrant replication initiation causes these effects , under the current slow growth conditions the ΔseqA initiation phenotype is greatly suppressed as indicated by only a 5-minute advanced initiation timing ( Figure 2 ) , suggesting that segregation problems are due to reduced cohesion ( Discussion ) . Unlike snap cohesion , cohesion at the two non-snap loci dnaB and arcA was not measurably different in ΔseqA cells ( Figure 3A ) , although any subtle ( <20% ) changes in cohesion at these sites might be below our current level of detection . Subsequent experiments showing that an overabundance of SeqA causes prolonged cohesion at dnaB suggest that SeqA is able to promote cohesion at non-snap loci under some conditions ( below ) . To evaluate whether sites exhibiting higher cohesion are enriched in SeqA binding , we performed chromatin immunoprecipitation against HA-tagged SeqA protein , followed by site-specific analysis of immunoprecipitated DNA by qPCR ( ChIP-qPCR ) . As expected , oriC DNA bound much more SeqA than the non-snap locus dnaB ( 25-fold enrichment , Figure 3D ) . The snap locus gln also showed elevated SeqA binding ( 10-fold over dnaB ) , whereas two other non-snap loci , lac and ter , exhibited similar low levels of SeqA binding . The relatively high abundance of oriC DNA on the SeqA complexes , probably reflects a small but very dense cluster of GATC sequences within the origin itself ( known as the 13-mers , see [30] ) . To examine whether the above correlation between cohesion and SeqA binding extend to other sites on the chromosome , we compared cohesion at 15 characterized loci from our earlier study [6] to genomic SeqA binding data from two E . coli microarray ChIP-chip studies [31] , [32] as well as the frequency of GATC sequences ( Figure 3E ) . Several insights emerge from this analysis . First , large-scale SeqA binding trends ( 40-kb moving average shown ) from both ChIP-chip studies are quite similar , and generally reflect the density of GATC sequences , but not perfectly . This likely reflects the fact that cooperative SeqA binding is optimal when adjacent GATC spacing places them on the same helical face [19] , thus some GATC sequences do not bind SeqA well . Second , several prominent peaks and valleys are present in the SeqA binding plots , and these fluctuations correspond generally to locations of snaps and non-snaps , respectively . Third , a higher resolution analysis of the ChIP-chip data near our sites of interest ( 5-kb moving average , Figure S3 ) resulted in an improved correlation between SeqA binding and cohesion , suggesting that cohesion levels may be regulated by local variations in SeqA binding ( Discussion ) . To further evaluate the role of Topo IV in regulating cohesion , we measured copy number and foci per cell at a snap locus ( gln ) and a non-snap locus ( dnaB ) after inactivation of Topo IV via a temperature sensitive mutation ( Figure 4 ) . WT or parE10 cells were grown at 30°C in minimal succinate media to early log phase ( WT doubling time ∼90 min ) , shifted to the non-permissive temperature of 42°C , and assayed as described in Figure 1 . Cohesion at the gln locus increased steadily in parE10 cells after temperature upshift , reaching a maximum of ∼2 . 2 copies per focus by 4 hours ( Figure 4A , dark red symbols ) . Cohesion at the fast-splitting non-snap dnaB locus was also prolonged by depletion of Topo IV ( Figure 4B ) , reaching a maximum of ∼1 . 4 copies per focus by 4 hours ( dark blue symbols ) . Cohesion did not significantly change at either locus in par+ cells after temperature upshift ( Figure 4AB , light shaded symbols ) . Interestingly , in all four cases cohesion decreased during the first 30 minutes after temperature upshift , suggesting that high temperature induced conformational changes to DNA that facilitated cohesion loss . Increased cohesion in parE10 cells was not due to replication fork stalling , as shown by complete replication runoff after rifampicin treatment ( Figure S4A ) and continued DNA synthesis by radioactive thymidine incorporation ( Figure S4B ) . Although absolute cohesion levels were higher at gln than at dnaB under all conditions , the relative rate of increase in cohesion after Topo IV inactivation was similar for both loci ( Figure 4C ) . Thus , gln and dnaB were equally sensitive to a lack of Topo IV , further implying that all sequences experience similar levels of catenation . We postulate that higher observed cohesion at gln and other snap loci is caused by another mechanism at these sites , presumably mediated by SeqA , which either inhibits Topo IV and/or directly facilitates sister cohesion ( Discussion ) . Additionally , the present data provide insight into how sister chromosomes are arranged during development of the par phenotype . By 4 hours after Topo IV inactivation , cells appear elongated with large unsegregated nucleoids , usually with one or two closely spaced gln foci at midcell ( Figure 4D ) . This phenotype is maintained for longer 42°C incubations ( data not shown ) , and cells eventually arrest growth with multiple half-segregated chromosomes ( see two-color FISH labeling , Figure S5 ) . To determine the epistatic relationship between Topo IV and SeqA , we tested a parE10 ΔseqA double mutant for temperature sensitivity and cohesion . Single mutant parE10 cells exhibited partial growth at 38°C , with ∼25% reduction in colony forming units ( CFU ) compared to 30°C , and no growth at 42°C ( Figure 5A; 5B , green ) , while ΔseqA single mutants ( orange ) grew well at all temperatures . Double mutant parE10 ΔseqA cells ( purple ) showed intermediate growth at both 38°C and 42°C , indicating partial suppression of the parE10 Ts phenotype . Double mutant parE10 ΔseqA cells showed ∼40% decreased cohesion compared to parE10 alone , but suppression by ΔseqA was specific to gln ( Figure 5C right , red bars ) ; elevated dnaB cohesion in parE10 at 42°C was not significantly reduced by addition of ΔseqA ( blue bars ) . The relationship between SeqA and Topo IV was further examined by mildly overexpressing each protein from a low copy inducible expression vector and testing gln and dnaB cohesion ( Figure 5C left ) . Cells induced for Topo IV expression ( Topo IV-OE ) for one hour prior to observation had significantly reduced gln cohesion , to a level similar to that seen in ΔseqA ( red bars ) . Conversely , cells overexpressing SeqA protein ( SeqA-OE ) had the opposite phenotype , with >2-fold increase in gln cohesion and dnaB cohesion , similar to parE10 cells at 42°C . This phenotypic similarity also extended to cell morphology; ΔseqA and Topo IV-OE cells had poorly separated nucleoids and closely spaced gln foci , while SeqA-OE and parE10 cells were very elongated often with one mid-cell gln focus ( example , Figure S6 ) . Topo IV expression was normal in ΔseqA cells ( Figure S7 ) as shown previously [33] . Because the cohesion phenotype of a parE10 ΔseqA double mutant most closely resembled that of a parE10 single mutant , the simplest interpretation of the above results is that parE is epistatic to seqA ( SeqA acts upstream of Topo IV in a single pathway ) . Although this conclusion assumes complete penetrance of the parE10 mutation ( no partial or compensating activity at 42°C ) , it is supported by the fact that Topo IV overexpression was able to reduce cohesion levels well below wild-type , even in the presence of SeqA protein , indicating that all cohesion probably occurs via a precatenane mechanism ( Discussion ) . Cohesion along snap regions is apparently more complicated , where it is clear that SeqA has some Topo IV-independent function ( ΔseqA reduced gln cohesion ∼40% in a parE10 background ) . Such function could be direct bridging of sister chromosomes or negative regulation of compensating topoisomerases ( Gyrase or Topo III ) . Coordinated separation of gln and four other late-splitting snap loci on the right chromosome arm is accompanied by a 35% increase in nucleoid volume and deformation of the nucleoid into a bi-lobed mass with one copy of each replicated sequence positioned within each lobe [6] . This suggests that cohesion loss along these tightly cohered regions initiate and/or drive a key sister individualization step in E . coli chromosome segregation . We tested this hypothesis by measuring the rate of separation of segregating sister loci in cells with reduced cohesion after genetic manipulation of SeqA or Topo IV . Time-lapse analysis of gln segregation was performed by growing and imaging cells directly on agarose-coated slides . Cells bearing a tetO array at gln and a photostable TetR-mCherry fusion that allowed multiple ( 10–15 ) exposures were imaged every 10 minutes through one complete doubling time ( 2 h ) . Under these conditions , the majority of cells underwent a single round of replication per cell cycle and contained either one or two gln foci ( data not shown ) . Control cells exhibited abrupt gln separation with an average inter-gln distance of ∼1 µm immediately after appearance of two gln foci ( Figure 6A , left panel , t = 0 ) . Inter-gln distance continued to increase to ∼1 . 5 µm by 20 minutes after splitting , then gradually increased to a maximum of ∼2 µm before cell division . This corresponds to an initial separation speed of ∼0 . 15 µm/min , slowing to the rate of cell elongation ( ∼0 . 02 µm/min ) by 20 minutes after focus duplication ( Figure S8A ) . When images were acquired every 3 minutes , split gln foci still initially appeared ∼1 µm apart , indicating that the actual speed of focus separation likely exceeded 0 . 4 µm/min ( Figure S8B; Movie S1 ) . This estimation is in line [9] or slightly higher [34] than previous measurements . In cells overexpressing Topo IV for one hour before imaging , gln separation was much slower , with split gln foci initially appearing ∼0 . 4 µm apart ( Figure 6A , right panel ) and separation speeds ∼1/3 of that seen in non-overproducing cells ( Figure S8 ) . Supporting the time-lapse data , inter-gln distance after Topo VI overexpression in exponentially growing batch culture cells ( n = 500 ) was significantly reduced , with a wider distribution compared to vector control cells ( Figure 6C ) . Similarly to Topo IV overexpression , ΔseqA cells showed protracted gln segregation with ∼70% decrease in initial gln separation velocity compared to WT ( Figure 6B , right panel; Movie S2 ) . This finding implies that cohesion specifically along late-splitting snap regions is required for efficient chromosome segregation . An equally pronounced effect of Topo IV overexpression was seen on the distribution of inter-dnaB foci ( Figure 6D ) . The non-snap dnaB locus normally exhibits a bimodal distribution of inter-sister distances corresponding to times before and after snap separation [6] . This pattern , which was seen in vector control cells ( Figure 6D , left panel ) , implies that dnaB segregation occurs in two discrete steps: an initial separation to 0 . 6 µm apart , followed by a second larger separation event ( to 1 . 9 µm ) later in the cell cycle ( Illustrated in Figure 6E ) . Other non-snap loci behave similarly , and we have proposed that early separation of these loci is restrained by long-lived connections along snap regions [6] . After cohesion reduction by overexpression of Topo IV , this bi-modal positioning was lost , and sister dnaB loci had a ∼25% lower average inter-focus distance than vector control cells ( Figure 6D , right panel ) . We conclude that reduced cohesion causes inefficient segregation of both snap and non-snap loci .
We and others have previously showed that most chromosomal loci experience a 7–10 minute delay between passage of the replication fork and separation beyond a resolvable ( ∼230 nm ) distance [3] , [6] . During this period of colocalization , homologous sequences physically interact [11] , suggesting that similar to eukaryotic chromosomes , sisters are tightly juxtaposed during cohesion . At a fork speed of 700 nt/sec [6] , this means that a 300–400 kb sliding window of tight sister cohesion occur behind each replication fork . Superimposed on this progressive cohesion program , oriC and two broad >100 kb segments on the right chromosome arm remain cohered for 20–30 minutes [3] , [5] , [6] . Late-splitting right arm loci , or snaps , are further distinct from oriC and the rest of the chromosome in that they separate in unison and concomitantly with appearance of bi-lobed nucleoids [2] , [6] . Prior work by the Sherratt and Espeli labs indicated that segregation of oriC and ter sequences is modulated by Topo IV [4] , [11] . Theoretically , duplex tension generated by the replicative helicases can migrate back behind the fork twisting nascent sister chromatids around each other as originally proposed by Cozzarelli and colleagues [35] ( Figure 7A ) . Resolution of inter-sister twists , or precatenanes , requires a highly specific double strand cleavage , strand passage and ligation reaction that is mediated by the essential and highly conserved Topo IV protein [12] . Our current results extend the role of Topo IV to mediating cohesion of arm loci , including the late-splitting snap regions . Depleting Topo IV by shifting a parE10 mutant to non-permissive temperature caused an immediate block of sister separation at all loci tested , resulting in the classic par phenotype of large undivided nucleoids in elongated cells ( Figure 4 ) . Conversely , overexpression of Topo IV resulted in dramatically reduced cohesion at all loci ( Figure 5 ) . From these data , it can be argued that precatenanes are the fundamental basis of all cohesive linkages in E . coli . Importantly however , precatenanes do not readily explain the phenomenon of late-splitting snaps . Although snaps are cohered 2–3 times longer than non-snap loci , both loci responded identically to loss of Topo IV ( Figure 4C ) , indicating that high cohesion at snap loci is likely caused by another mechanism than Topo IV ( below ) . Plasmid studies and in vivo estimates of decatenation kinetics indicate that the abundant Topo IV protein likely has a cellular precatenane unlinking capacity equal to or even faster than the rate that they are formed [16] , [28] , suggesting that additional factor ( s ) exist in E . coli to impede sister separation . We propose that SeqA protein fulfills this role by binding to the same newly replicated DNA stretches acted on by Topo IV . Null mutants of seqA exhibited significantly reduced cohesion , and this effect was strongest at sites containing a high local concentration of GATC sites . As predicted by this model , late-splitting snap loci and oriC have higher than average local GATC frequency and bind ≥10-fold more SeqA than non-snap loci by ChIP-qPCR . When SeqA is overexpressed , cohesion time increases >2-fold at both snap and non-snap loci , with a cohesion and nucleoid phenotype indistinguishable from parE10 at 42°C ( Figures 5 , S6 ) . We conclude that SeqA is the primary timekeeper for sister cohesion , and is solely responsible for extended cohesion observed along snap regions . SeqA is uniquely suited to mediate sister cohesion due to its high specificity for hemimethylated DNA . Newly replicated strands are unmethylated for 5–10 minutes after passage of the replication fork before methylation by Dam [21] , and SeqA-GFP fusions form large , relatively immobile foci adjacent to the replisomes [19] , [22] , [36] . In the absence of Dam , SeqA does not form these complexes [22] , and in our study dam- cells exhibited a low cohesion phenotype identical to seqA− ( Figure 1 ) . Based on an average spacing of one favorable SeqA binding site per kb [19] , an estimated 100–200 SeqA dimers are continually bound behind each fork ( Figure 7A ) . Our data indicate that oriC and snaps bind several fold more SeqA than the more typical non-snap DNA , which may induce a higher order SeqA complex with increased stability [23] , [37] . Given that overexpression of SeqA delays remethylation of origin DNA [38] , and SeqA dimers can oligomerize into Dam-resistant RecA-like filaments along GATC-dense DNA fragments in vitro [23] , [37] , it is possible that SeqA binds as individual dimers along most of the chromosome and as a continuous or semi-continuous filament along snap regions . Two models can explain how SeqA modulates sister cohesion . SeqA could stimulate cohesion directly by forming protein-protein linkages across sister chromosomes , or it could promote cohesion indirectly by inhibiting the activity of Topo IV along precatenated junctions . The best evidence for a direct mechanism is that double mutant ΔseqA parE10 cells exhibited an intermediate cohesion phenotype to each of the single mutants . Although the parE10 mutation was partially epistatic to ΔseqA ( double mutants more closely resembled parE10 ) , it is clear that at least part of SeqA's ability to promote cohesion was independent of Topo IV , suggesting that these proteins reside in different pathways . In support , purified SeqA has been shown to physically tether hemimethylated oriC-containing molecules in an in vitro replication system [36] . Additionally , the binding characteristics of SeqA dimers suggest that oligomerization may be facilitated by individual subunits binding across opposing homologous GATC sites as they exit the fork [37] . It is unclear how SeqA nucleoprotein complexes are eventually disassembled , but SeqA molecules have an on/off rate that exceeds the hemimethylation period [39] , suggesting that another factor controls the lifespan of SeqA complexes , possibly Dam . If increased cohesion at snap loci is indeed due to inter-sister bridging by SeqA , then overexpression of Topo IV would be expected to have little effect on snap cohesion in a seqA+ strain . This was clearly not the case in our study: Topo IV overexpression resulted in a 3-fold reduction in cohesion at the gln snap locus , with cohesion and nucleoid phenotypes identical to ΔseqA . Similarly , SeqA overexpression phenocopied parE10 at 42°C . The simplest interpretation of these results is that SeqA and Topo IV reside in the same pathway , with SeqA inhibiting Topo IV decatenation . Observed partial synergism between ΔseqA and parE10 ( two pathway , above ) could result from SeqA inhibiting DNA gyrase or Topo III , which are known to partially compensate for Topo IV function at the replication fork [15] . How could SeqA inhibit precatenane removal ? If present in sufficient quantities , SeqA could conceivably physically block access of Topo IV to catenated structure . However this may be unlikely given that Topo IV binding is not sequence-specific , and typical DNA exhibiting ∼10 minutes of cohesion contain only sparse ( ∼one per kb ) SeqA binding sites [19] . Instead , we favor a topological-based mechanism in which SeqA binding temporarily sequesters positive supercoils behind the fork , preventing Topo IV from recognizing catenated DNA crossings . Normally in positively supercoiled DNA , duplex crossings ( inter or intra-molecular ) adopt a tight geometry with signature “hooked juxtapositions” ( Figure 7A ) that are recognized and cleaved by Topo IV and gyrase [40] . This mechanism may explain how Type-II cleavage , strongly cytotoxic if unregulated and used as a chemotherapeutic , is limited to only positively supercoiled regions [16] , [40] . SeqA binding , which is known to alter DNA twist or writhe by restraining supercoils [23] , might relax inter-sister junctions , preventing Topo IV-mediated decatenation ( Figure 7A ) . Further , SeqA has been shown to directly modulate Topo IV-mediated cleavage in vitro , inhibiting decatenation at high SeqA concentrations and favoring decatenation at lower concentrations [24] . We speculate that variable binding of SeqA along the E . coli chromosome results in a wide dynamic range of Topo IV regulation , and may explain the highly variable and “patchy” behavior of sister cohesion . An analogous mechanism may operate in eukaryotes , in which the ring-like cohesin complex retards decatenation of sister chromosomes by inhibiting topoisomerase II , the eukaryotic homolog of bacterial Topo IV [41] . In wild-type cells , sister snap segregation is very rapid , with foci appearing 1 . 5 µm apart within 20 minutes after splitting , and separating with an initial velocity of ≥0 . 4 µm/min . Repressing cohesion via a seqA deletion or overexpression of Topo IV , resulted in 30% reduced final inter-sister distances and 70% slower initial separation velocities ( Figures 6 , S8 ) . Although we hypothesize that the observed segregation defects in these strains were a direct consequence of reduced cohesion , it is possible that they were caused instead by effects on cell cycle timing or nucleoid compaction . For example , ΔseqA cells initiate prematurely [20] , which could potentially advance segregation timing beyond its normal cell cycle window . They also exhibit over-condensed nucleoids , which might reflect some inability to separate newly replicated regions from the replisome [19] . However , replication timing defects in ΔseqA cells are suppressed under slow growth conditions [20] , and replication initiation was only five minutes earlier than WT in our experiments ( Figure 2A ) . Moreover , Topo IV overexpression , which has no known effect on the timing of DNA replication , resulted in slowed sister segregation that was indistinguishable from ΔseqA . In sum , we conclude that poor segregation in these strains was a direct result of reduced sister cohesion . These findings provide direct supporting evidence for a previously proposed model in which snaps mediate a key mid-replication chromosome reorganization event ( Figure 7B; [6] ) . This event involves the following coordinate chromosome transformations: 1 ) simultaneous release of inter-sister linkages along both snap regions , 2 ) conversion of the nucleoid from unilobed to bilobed morphology , 3 ) 35% increase in total nucleoid volume , 4 ) further dramatic separation of replicated non-snap loci , and 5 ) placement of one copy of each thus-far replicated sequence in each daughter nucleoid lobe . The net effect of these changes are conversion of the nucleoid from a highly condensed mixed state to a relaxed pre-divisional state with spatially individualized sister chromosomes ( Figure 7B ) . Recent work from the Kleckner lab has shown that E . coli progresses through four chromosome expansion stages ( T1–T4 ) , with the above described T2 transition being the most prominent in terms of sister separation [7] . How could holding sisters together promote their separation ? Cohesion at eukaryotic centromeres directs sister chromatid segregation by providing counter tension between opposing microtubule assemblies . Similarly , it is possible that snaps resist global separation of replicated E . coli chromosomes until they are acted on by an ‘external’ segregation mechanism such as MukB [42] , FtsK or MreB . Or , in theory , pushing forces generated between highly confined snap segments during cohesion , and their simultaneous release , could drive sister separation without outside influence [7] . Such cycles of restraint and programmed release of DNA confinements are proposed to be a general basis for chromosome movements observed in eukaryotes [43] . In fact , release of cohesion along chromosome arms in prometaphase is required for the generation of compact side-by-side sister chromatids long before microtubule involvement ( [43] and references therein ) . Given that identified snap regions comprise only a small fraction of the total genome , we speculate that snap splitting is a triggering mechanism for a global nucleoid reorganization event that relies on a combination of internally and externally derived forces . Despite dramatically slowed sister separation velocities in the absence of cohesion , most cells were eventually able to complete chromosome segregation , with moderate cell elongation and production of anucleate cells ( e . g . , Figure 3B ) . Thus , the significance of cohesion in the greater E . coli chromosome segregation program remains somewhat clouded . It is logical to assume that segregation defects in cohesion-less cells observed under the current slow growth conditions are compounded during multi-forked replication , which is in agreement with the rich media sensitivity of ΔseqA strains [20] , [30] . SeqA plays a prominent role in nearly every phase of genome duplication and inheritance . First discovered in a screen for mutations that allowed replication of a hemimethylated oriC plasmid [44] , SeqA binds and sequesters oriC immediately after replication starts for about one third of the replication period , during which oriC is refractory to further initiations [21] , [44] . There is also evidence that SeqA stabilizes replication fork progression: seqA mutants grown in rich medium exhibit stalled replication forks after rifampicin runoff [20] , and they are hypersensitive to the replication elongation inhibitors hydroxyurea ( HU ) or azidothymidine ( AZT ) [45] . SeqA's ability to organize replication forks ( or at least the DNA created by forks ) into so-called “hyperstructures” is well documented [19] , [22] , [36] . This activity has been hypothesized to improve fork progression by concentrating replication proteins to a central location [36] , [46] and even to drive chromosome segregation by continually condensing daughter nucleoids on either side of the replisome [19] . Our current work shows that SeqA promotes sister cohesion , and that extended cohesion along snap regions is involved in a global chromosome reorganization event that is important for efficient chromosome segregation . Through its capacity to indefinitely cohere DNA , SeqA may also mediate cell cycle blockage during the stringent response , as indicated by a requirement of SeqA for nutritional deprivation-induced chromosome segregation blockage , independently of its function at oriC [45] . Logically , cohesion in E . coli may also drive homologous recombination dependent DNA repair by co-localizing sister molecules immediately after replication , presumably when double strand breaks are created . Supporting this model , seqA mutations result in mild SOS induction [33] , [47] , and are synthetically lethal with recA mutations in rich medium [47] .
The genetic background for all strains is DB81 , a derivative of CM735 ( metE46 trp-3 his-4 thi-1 GalK2 lacY1 , lacZ4 mtl-1 ara-9 tsx-3 ton-1 rps-8 , or rps-9 supE44 lambda ) [48] containing the Ptac-fliCst synchronization allele [29] . Gentamycin-marked tetO array insertion strains were previously described [6] . Gene deletion or disruption alleles were obtained from the following sources: ΔseqA in-frame deletion [44]; dam13::Tn9 [49]; parE10 and parE1215 [27]; hupA::cat and hupB::kan [50]; mukBEF::kan [51]; fis767::kan [52]; and ihfA::cat [53] . Marked alleles were introduced into DB81 by P1 transduction selecting for antibiotic resistance or in the case of parE10 and parC1215 reversion of methionine auxotrophy , ΔseqA was introduced by the gene replacement vector pBIP [44] . Cells were grown in AB minimal media supplemented with 0 . 2% alanine and 20 µg/ml each of tryptophan , histidine , methionine and thiamine or 0 . 2% succinate and 0 . 1% casamino acids , as indicated . These media resulted in doubling times for DB81 at 30°C of 126 minutes and 83 minutes , respectively . Cell synchronization was carried out as previously described [6] . All images were acquired with a Zeiss AxioImager Z1 microscope equipped with a Hamamatsu EM-CCD camera , and FROS and FISH data was analyzed using a custom Matlab image analysis program , FocusCounter ( http://www . bcm . edu/genetics/bateslab ) . Raw foci/cell values were adjusted for focus detection inefficiency , determined empirically for each experiment based on the frequency of cells with zero foci ( Figure S2 ) . Detection inefficiencies ranged between 0 . 9% and 3 . 6% ( avg . 1 . 4% , ±0 . 8% ) , resulting in final corrections of only +0 . 06 to +0 . 15 foci/cell . This method was validated by accurately calculating foci/cell in a control experiment with cohesion-less stationary phase cells ( Figure S2 ) . FROS was performed as previously described [6] . Cells carrying the TetR-YFP expression plasmid pDB316 were grown to OD 0 . 2 with 50 ng/ml ampicillin , induced with 0 . 02% arabinose for 1 hour , then imaged directly without fixation . pDB316 is a derivative of pWX6 [54] that carries a deletion of the LacI-CFP gene and a spontaneous mutation that weakens expression . For time-lapse experiments , TetR-mCherry was expressed from pDB317 , a derivative of the salicylate-inducible nahG promoter vector pKG110 that provides highly tunable expression at sub-micromolar concentrations of inducer . Cells were grown to OD 0 . 2 with 25 µg/ml chloramphenicol and 50 µg/ml anhydrotetracycline ( to reduce TetR binding ) , induced with 0 . 5 µM sodium salicylate for one hour , placed onto agarose-coated slides ( liquid media with 2% SeaKem ME low melting agarose ) and imaged in a controlled temperature 37°C environment . Unless otherwise noted , ∼1000 cells are analyzed per sample for all experiments . An absence of replication pausing or blockage at the array site was confirmed by qPCR analysis for all FROS experiments ( Figure S9AB ) . Such blocks can occur under high TetR expression and was observed with the original TetR expression plasmids pLAU53 or pWX6 ( [54]; Figure S9 ) . Additionally , when cohesion was analyzed in cells without a tetO array by FISH , copy number and foci per cell in both WT and parE10 strains were very similar to values obtained by FROS ( Figure S10 ) . For FISH , DB81 cells ( parE10 and parE+ derivatives ) without tetO array were grown to exponential phase in minimal succinate medium and fixed with 2 . 5% paraformaldehyde . Three kilobase gln and dnaB probes were amplified by PCR ( Table S1 ) and labeled with PromoFluor-500 or -594 , respectively by nick translation ( Promokine , Germany ) . In situ hybridization was performed as previously described [6] . The number of gln or dnaB loci per cell was determined by measuring the relative ratio of gln or dnaB loci to oriC loci by qPCR as previously described [6] . These ratios were then multiplied by the total number of oriC loci per cell determined by Rifampicin runoff analysis of duplicate cell samples ( Figure S1 ) . To exclude any error caused by possible rifampicin-resistant initiations in mutant cells ( e . g . , [53] ) , gln and dnaB copy numbers were verified by absolute quantification qPCR in which cell samples were spiked ( 1∶1 ) with a calibrator strain containing a unique sequence that was used to generate a standard curve of DNA copies per cell ( values were ±0 . 08 of those shown in Figure 1 ) . Real time qPCR was performed in 384-well plate in ABI Prism 7900HT Thermal Cycler using KAPA SYBR Fast qPCR reagent ( Kapa Biosystems , USA ) and analyzed with ABI-prism software ( primers in Table S1 ) . Cohesion timing at a given locus in exponential cultures is measured as the ratio of locus copy number to foci per cell ( Figure 1A ) , thus cell cycle determinations are not required . In synchronized cell experiments ( Figure 2 ) , cohesion duration is determined directly by measuring the timing of locus replication and segregation [6] . Locus replication time is equal to the point at which 50% of cells have duplicated locus copy number by qPCR ( the replication cumulative curve ) , and locus segregation time is the point at which 50% of cells have duplicated the number of foci per cell ( the segregation cumulative curve ) . Similarly , the timing of replication initiation and termination are equal to the point at which 50% of cells duplicate the oriC and ter loci , respectively . Resulting B , C and D periods ( Figure 2B ) are generated from the above replication timing and generation time . For Topo IV overexpression , the parC and parE open reading frames were amplified from the chromosome by PCR with EcoRI and HindIII restriction tails at 5′ and 3′ ends ( Table S1 ) and cloned into pBAD322-kan [55] , a low copy arabinose-inducible vector designed to express genes that are toxic at high levels . The resulting plasmid , pDB332 , modestly overexpressed Topo IV after two hours induction with 0 . 02% arabinose ( 6-fold over WT; Figure S7 ) , did not impede growth or cause cell filamentation after many generations of growth , and completely suppressed temperature sensitivity of both parE10 and parE1215 alleles ( data not shown ) . This suggests that Topo IV overexpression did not block chromosome segregation or create DSBs , which could bias chromosome segregation analyses . For SeqA overexpression , the seqA ORF-containing BssHII fragment ( excluding the downstream pgm gene ) was cloned into the expression vector pGC2 under Plac promoter control and containing the lacIQ fragment to reduce leaky expression , resulting in pDB338 . Induction of pDB338 containing cells with 25 nM IPTG for 2 hours did not exhibit decreased DNA synthesis by flow cytometry ( data not shown ) , as can occur under high SeqA expression [38] . Three copies of the haemagglutinin ( HA ) epitope ( TACCCATACGACGTCCCAGACTACGCT ) were cloned onto the 3′ end of seqA , and integrated into the endogenous DB81 seqA locus via pBIP gene replacement . The resulting SeqA-HA3 protein exhibits a seqA+ phenotype as shown by normal growth rate and synchronous replication initiations ( Figure S11 ) . Chromatin immunoprecipitation was performed essentially as in [32] . Briefly , DB81 seqA-HA3 cells were grown in AB alanine media to early log phase , formaldehyde cross-linked , lysed , and sonicated to fragment DNA . Triplicate samples of cross-linked SeqA-HA3-DNA were immunoprecipitated with monoclonal 12CA5 anti-HA antibody ( Roche ) . Samples were washed , cross-links were reversed , and DNA was purified . Total DNA was also prepared from identical control “input” samples not subjected to immunoprecipitation . The relative abundance of five different sequences of bound DNA was determined by qPCR using specific primer pairs previously described [6] . For each input and IP DNA sample , qPCR was performed in triplicate and amplification Ct values were averaged . Fold-enrichment of bound DNA at each site was determined by the ΔΔCt method , where ΔΔCt equals the difference in amplification of IP DNA and input DNA for each site relative to dnaB ( the locus showing lowest abundance in IP samples ) . Thus , ΔΔCtsitex = ( CtIP−Ctinput ) sitex− ( CtIP−Ctinput ) dnaB , and fold-enrichment = 2−ΔΔCt . | Sister chromosome cohesion in eukaryotes maintains genome stability by mediating chromosome segregation and homologous recombination-dependent DNA repair . Here we have investigated the mechanism of cohesion regulation in E . coli by measuring cohesion timing in a broad set of candidate mutant strains . Using a sensitive DNA replication and segregation assay , we show that cohesion is controlled by the conserved DNA decatenation enzyme Topo IV and the abundant DNA binding protein SeqA . Results suggest that cohesion occurs in E . coli by twisting of replicated duplexes around each other behind the replication fork , and immediate resolution of cohered regions is blocked by SeqA . SeqA binds to a sliding 300–400 kb window of hemimethylated DNA behind the fork , and regions binding more SeqA experience longer cohesion periods . An analogous decatenation inhibition function is carried out by the cohesin complex in eukaryotes , indicating that cells mediate pairing and separation of replicated DNA by a conserved mechanism . In both cases , mismanaged cohesion results in failed or inefficient chromosome segregation . | [
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"... | 2013 | Regulation of Sister Chromosome Cohesion by the Replication Fork Tracking Protein SeqA |
Considerable progress has been made in identifying the targets of plant microRNAs , many of which regulate the stability or translation of mRNAs that encode transcription factors involved in development . In most cases , it is unknown , however , which immediate transcriptional targets mediate downstream effects of the microRNA-regulated transcription factors . We identified a new process controlled by the miR319-regulated clade of TCP ( TEOSINTE BRANCHED/CYCLOIDEA/PCF ) transcription factor genes . In contrast to other miRNA targets , several of which modulate hormone responses , TCPs control biosynthesis of the hormone jasmonic acid . Furthermore , we demonstrate a previously unrecognized effect of TCPs on leaf senescence , a process in which jasmonic acid has been proposed to be a critical regulator . We propose that miR319-controlled TCP transcription factors coordinate two sequential processes in leaf development: leaf growth , which they negatively regulate , and leaf senescence , which they positively regulate .
In plants , microRNAs ( miRNAs ) regulate target genes through miRNA-guided cleavage or translational repression of mRNAs that have highly complementary motifs to the regulatory miRNA . Because of the high sequence complementarity that is apparently required in most cases for miRNA target interaction , computational target identification is much more simple and much less ambiguous than in animals [1–3] . Although translational repression may be more widespread than previously thought by those not familiar with the field [4] , much of the available evidence suggests that the sequence requirements for regulation by mRNA cleavage and translational repression are very similar [5–7] . In general , the phenotypes of plants in which target genes have been inactivated by knockout mutations closely resemble those in which the corresponding miRNAs are overexpressed . In addition , even closely related miRNAs can have a unique spectrum of target genes , without evidence for cross-regulation at the level of mRNA cleavage or translational repression [8] . One of the few exceptions appears to be an engineered mutation in a microRNA 398 ( miR398 ) target gene that prevents efficient mRNA cleavage but still allows translational repression [9] . Many miRNAs that are conserved throughout flowering plants target transcription factor genes that control various aspects of development ( recently reviewed in [10–12] ) . Several of these in turn modulate the response to hormones , such as the miR159-regulated GAMYB ( GIBBERELLIC ACID MYB ) genes [13–15] , or the miR160- and miR167-regulated ARF ( AUXIN RESPONSE FACTOR ) genes [16–19] . Another set of ARF genes is controlled by TAS3 ( TRANS-ACTING SIRNA LOCUS 3 ) , which encodes trans-acting small interfering RNAs ( siRNAs ) [20–23] . Finally , miR393 regulates a group of related auxin receptors that includes the F-box protein TIR1 ( TRANSPORT INHIBITOR RESPONSE 1 ) [24] . While the vast majority of plant miRNAs have been found by large-scale sequencing [25–31] , the first described plant miRNA mutant , jaw-D , overexpresses an miRNA , miR319a , that had not been previously identified by deep sequencing [32] . In addition , the major targets of miR319a , a series of related TCP transcription factor genes , were also the first targets that were identified experimentally , rather than through computational predictions . The TCPs constitute a plant-specific group of transcription factor genes . Although the conserved TCP domain does not share sequence similarity with other characterized DNA-binding domains , it has been predicted to adopt a basic helix-loop-helix ( bHLH ) structure . Teosinte Branched1 ( TB1 ) from maize , CYCLOIDEA ( CYC ) from Antirrhinum , and the PCNA promoter binding factors ( PCF1 and PCF2 ) from rice are the founding members of the TCP family [33 , 34] . TB1 , CYC , and its close homolog DICHOTOMA ( DICH ) control various aspects of plant form , and the mutant effects suggest that they negatively regulate growth [35–37] . The PCFs are also implicated in growth control because they bind to promoter motifs that are essential for the expression of the cell cycle regulator PCNA [33] . The Arabidopsis genome encodes at least 24 TCPs , which fall into two major groups , classes I and II [34 , 38] . In contrast to the class II factors including TB1 and CYC/DICH , class I factors such as TCP20 are positive regulators of growth , and it has been suggested that competition on similar DNA binding sites between class I and class II factors is very important in shaping shoot morphology [38 , 39] . The five miR319-regulated TCPs in Arabidopsis belong to class II . This group of TCP genes is represented in Antirrhinum by CINCINNATA ( CIN ) [40] . Like jaw-D , in which mRNA levels of TCP2 , TCP3 , TCP4 , TCP10 , and TCP24 are all strongly reduced , cin loss-of-function mutants have highly crinkled leaves [32 , 40] . A detailed developmental analysis showed that CIN is required for the arrest of cell division in the peripheral regions of the leaf . In cin mutants , derepressed growth in the periphery leads to a change from the wild-type form with zero leaf curvature to negative leaf curvature , which is expressed as crinkles that cannot be flattened without cutting the leaf [40] . Conversely , reduced leaf size is seen in Arabidopsis as well as tomato plants in which miR319 control of TCP genes is impaired [32 , 41] . Finally , experiments with dominant-negative versions have indicated that all class II TCPs , including those that are not regulated by miR319 , have similar effects on plant growth [42] . Leaf history starts with the recruitment of founder cells at the flanks of the shoot apical meristem , which develop into leaf primordia ( reviewed in [43] ) . Cell division in the leaf is terminated by a front of mitotic arrest moving from the distal to the proximal part , after which the leaf gains size by cell expansion . The expanded leaf transforms from a metabolic sink into a source for carbon assimilation . The last stage in the life of a leaf is senescence , during which nutrients are coordinately exported to sink tissue , photosynthesis decreases , and chlorophyll is degraded , visible in the change of leaf color from green to yellow . Finally , the cells die [44 , 45] . The senescence program includes the differential expression of many hundreds of genes [46–49] . Several signaling molecules are involved in leaf senescence , including salicylic acid , and the plant hormones ethylene , cytokinin , and jasmonic acid ( JA ) [49–52] , although the specific mechanisms by which these hormones control senescence are not well understood . Here , we reveal a new role of miR319-regulated TCP genes , which links leaf morphogenesis with other processes , including JA biosynthesis and senescence . We propose that the miR319/TCP regulatory module coordinates and balances different events that are important for leaf development and physiology .
We have previously shown that the jaw-D mutant phenotype , with epinastic cotyledons and conspicuously crinkled leaves , is caused by the overexpression of miR319a [32] . To determine the contribution of different miR319 targets to this phenotype , we identified insertional alleles for TCP2 , TCP4 , and TCP10 . Loss-of-function alleles for all three genes had slightly epinastic cotyledons and slightly enlarged leaves ( Figure 1 ) . Loss of TCP4 function in addition caused plants to produce , on average , seven additional leaves before flowering ( Figure S1 ) , similar to the delay observed in jaw-D mutants [32] . tcp2 tcp4 double mutants showed a further increase in leaf size , with some signs of crinkling . tcp2 tcp4 tcp10 triple mutants had the most obvious defects , but were still less strongly affected than jaw-D plants were ( Figure 1 ) . Interestingly , among plants that overexpressed miR319a from a constitutive 35S promoter , weak lines had bigger , but not crinkly leaves , similar to the tcp single knockout plants ( Figure S2 ) . In summary , the similar phenotypes of tcp loss-of-function mutants and miR319 overexpressers confirmed the conclusion from microarray and other analyses , that the TCP genes are the major targets of miR319 [3 , 8 , 32] . On the other hand , that all single mutants were only weakly affected indicated partially redundant function of the different TCP genes in wild type . These general conclusions are in broad agreement with defects reported for plants expressing dominant negative alleles of TCP genes , which mimic many phenotypes of jaw-D plants [42] . We also prepared plants that expressed a mutant form of TCP4 linked to GREEN FLUORESCENT PROTEIN ( GFP ) sequences under the control of TCP4 regulatory sequences ( rTCP4:GFP ) . In these plants , TCP4 mRNA escapes regulation by miR319 due to synonymous changes that reduce sequence complementarity to miR319 [32] . rTCP4:GFP plants have a similar , but generally milder phenotype than rTCP4 plants [32] . Because many more survive to adulthood , we were able to analyze the effects of increased TCP levels beyond the seedling stage . Several phenotypic aspects of these plants are opposite to those seen in tcp loss-of-function or jaw-D mutants . For example , their cotyledons are hyponastic ( bent upwards ) and hypocotyls are longer than those of wild-type plants ( Figure S3A ) , contrasting with the shorter hypocotyls of jaw-D ( Figure S3B ) . The rosette leaves of rTCP4:GFP plants were smaller , more rounded , and often darker green than those of wild type ( Figure S3C ) , which contrasts with the larger leaves of jaw-D mutants . In summary , these results indicated that a variety of leaf sizes and shapes can be obtained by manipulating the levels of miR319 and its targets , the TCP genes , similar to what has been reported for the tomato homologs [41] . To identify potential target genes of the miR319-regulated TCPs , we analyzed the results from several microarray experiments ( Table S1 ) . We separately compared leaves and shoot apices of wild-type plants with jaw-D plants , which have increased miR319a activity and therefore reduced TCP activity . In a third comparison , we analyzed apices from tcp2 tcp4 double mutants and rTCP4:GFP plants , which have increased TCP activity . We focused on genes that are likely to be positively regulated by TCPs , as indicated by reduced expression in jaw-D or tcp2 tcp4 plants , or increased expression in rTCP4:GFP plants . Because only nine genes were significantly down-regulated in tcp2 tcp4 double mutant apices , and only two of these were not detected in one of the other three comparisons , we omitted this dataset from further analyses . The weak transcriptional effects seen in tcp2 tcp4 double mutants are consistent with the weak morphological defects when compared with those of jaw-D plants , in which three additional TCP genes are strongly suppressed . To obtain first insights into the potential role of the TCP-responsive genes during development , we made use of a developmental microarray dataset [53] . The averaged relative expression levels of the gene sets identified as differentially expressed in each experiment were highly similar , even though there was only partial overlap between them ( Figures 2A and 3A ) . In rTCP4:GFP plants , more genes are changed in their expression than in jaw-D . One explanation could be that in rTCP4:GFP plants , the TCP4 expression domain is expanded and hence more cells and tissues are affected than in plants with reduced TCP activity . In addition , overall TCP activity is merely attenuated in jaw-D plants , because of the incomplete clearing of TCP transcripts by miR319 , and because of the partial redundancy between miR319-targeted and nontargeted TCP genes , all of which have similar expression patterns ( Figure S4 ) and similar dominant-negative effects [42] . Main sites of expression of TCP-responsive genes were leaf-like organs , including cotyledons , rosette leaves , cauline leaves , and sepals , consistent with the tissues affected especially in jaw-D plants [32] . That expression of potential TCP targets persisted in leaves throughout senescence suggested that the miR319/TCP regulatory module might not only be important early in leaf development , but also during later stages . Using stringent criteria ( logit-T per-gene variance p < 0 . 025 , common variance > 2-fold ) , only a single gene , LIPOXYGENASE2 ( LOX2 ) , was identified as being affected in the different microarray comparisons ( Table S2 ) . LOX2 was the second most suppressed gene in our original analysis of jaw-D plants , after TCP4 itself [32] . The opposite effects observed in plants with reduced and increased TCP activity , respectively , indicated that TCPs are important determinants of LOX2 expression levels in the absence of other stimuli known to affect LOX2 expression , such as wounding [54 , 55] . LOX2 encodes a chloroplast-localized lipoxygenase that catalyses the conversion of α-linolenic acid ( 18:3 ) into ( 13S ) -hydroperoxyoctadecatrienoic acid , the first dedicated step in the biosynthesis of the oxylipin JA [56] . Apart from LOX2 , the Arabidopsis genome encodes three other lipoxygenases that are predicted to be chloroplast-localized , LOX3 , LOX4 , and LOX6 [57] . The expression of LOX3 and LOX4 could not be detected by microarray analysis , but more sensitive reverse transcription followed by real-time PCR showed that expression of both genes is reduced in jaw-D plants , and increased in rTCP4 plants as well ( Figure 2B ) . Since JA is regulated through a positive feedback loop , with JA inducing the expression of its own biosynthetic genes [55 , 56 , 58] , we examined the effect of miR319/TCP on the entire biosynthesis pathway for JA and other oxylipins , for which 19 genes have been described in Arabidopsis . The first steps in JA biosynthesis occur in the chloroplast , and only the JA precursor OPDA ( or its coenzyme A [CoA] ester ) are transported into the peroxisome , where several rounds of β-oxidation are carried out , leading to the final product , JA [59 , 60] . We plotted the average expression level of the JA biosynthesis genes against the different genotypes that were subjected to microarray analysis . The average expression of JA biosynthetic genes was approximately 2-fold reduced in jaw-D plants compared to wild type , and approximately 4-fold increased in rTCP4:GFP plants ( Figure 2C ) . We also analyzed the pathway for the hormones cytokinin , gibberellic acid , and auxin , all of which have been implicated in leaf development or leaf physiology . None of the other three pathways showed as great a contrast between wild-type , rTCP4:GFP , and jaw-D plants as the JA pathway ( Figure 2C , Table S1 ) . When we analyzed publicly available microarray data for JA response , we found the data to be consistent with an effect of miR319-regulated TCPs on endogenous JA levels , since several genes that are either down-regulated in jaw-D and tcp2 tcp4 plants or up-regulated in rTCP4:GFP plants are induced in wild-type plants treated with methyl jasmonate ( MeJA ) ( Figure 2D ) . These include genes known from the literature to be responsive to MeJA , such as PDF1 . 2 and COR1 [61 , 62] ( Table S3 ) . To understand how miR319a regulates the JA and oxylipin biosynthesis pathways through the TCP transcription factors , we turned again to the microarray data that we had obtained from the different tissues and genotypes with altered miR319/TCP activity , and we searched for genes that appeared to be positively regulated by TCPs . With slightly relaxed parameters ( logit-T per-gene variance p < 0 . 05 ) , we identified a set of 117 genes with consistently changed expression ( down in jaw-D and up in rTCP4:GFP ) in at least two of the three analyzed tissues ( Figure 3A ) . In the promoters of this set , the most common motifs were GGACCA and its complement , TGGTCC , which were present at least once in 49 genes ( Figure 3B and Table S1 ) . In parallel , we identified the preferred binding site of TCP4 by in vitro selection [63] . Of 27 clones obtained after ten rounds of selection , 25 contained a variant of the consensus motif gGGaCCAC , which includes as a core the GGACCA motif found in the promoters of TCP-response genes ( Figure 4A and Figure S5 ) . Competition experiments with unlabeled oligonucleotides confirmed the specificity of the TCP4 binding site ( Figure 4B ) . Electrophoretic mobility shift assays ( EMSAs ) with oligonucleotides that contained single base pair mutations indicated some flexibility in the ability of TCP4 to bind its preferred site in vitro ( Figure 4C ) , which may explain why the motif deduced from in silico promoter analysis is only a submotif of the one identified by stringent binding site selection . The complement of the gGGaCCAC motif is related to a sequence , G ( T/C ) GGNCCC , that is preferentially bound by PCF5 , a protein encoded by an miR319-targeted TCP gene from rice [38] . In plants , metabolic pathways are often coordinately regulated by the same transcription factors [64] , and we found the TCP motif GGACCA in the promoters of eight out of 19 oxylipin biosynthesis genes ( Table S2 ) . Only two promoters were expected to have this motif by chance , using the promoters of all Arabidopsis genes to determine the background distribution of the GGACCA motif . No such overrepresentation was found in the promoters of 13 auxin , 13 cytokinin , and 15 GA biosynthetic genes , with the GGACCA motif being present in the promoters of only two GA biosynthetic and one auxin biosynthetic genes , and missing in the promoters of cytokinin biosynthesis genes ( Table S2 ) . To investigate whether the TCP binding sites were indeed required for promoter activity of JA biosynthetic genes , we focused on the LOX2 promoter , which has four sites with at most one mismatch to the motif GGACCAC . Using double-stranded oligonucleotides covering the potential TCP binding sites in the context of the LOX2 promoter , we performed EMSAs . The in vitro studies confirmed that TCP4 can bind strongly to at least two of the consensus motifs ( Figure 5A ) . To assess the requirement for these binding sites in planta , we constructed two LOX2:GUS ( β-glucuronidase ) reporters , one with the wild-type sequence and one in which the four consensus motifs were mutated . In untreated plants , the wild-type reporter had strong GUS activity throughout leaves , similar to what has been reported [55] , whereas the mutant reporter had very little activity ( Figure 5B ) . Moreover , the wild-type reporter was less active in tcp2 tcp4 tcp10 triple mutants ( Figure S6 ) , confirming that TCPs positively regulate LOX2 promoter activity . Together , our findings suggest that the miR319-targeted TCPs directly regulate expression of LOX2 . LOX2 strongly responds to wounding or treatment with MeJA [55 , 56] . We tested if the mutated LOX2 reporter lacking TCP binding sites was still responsive to these stimuli . Wounding of rosette leaves or external treatment of plants with MeJA led to strong activation of reporter activity within 45 min ( Figure 5B ) , indicating that TCPs are not involved in these two responses , but rather regulate the developmental aspect of LOX2 expression . The strongly reduced expression of JA biosynthetic genes in jaw-D plants prompted us to ask whether this is accompanied by a reduction in levels of endogenous JA . We measured the concentration of JA in planta by using gas chromatography followed by mass spectrometry [65] . JA levels are low in resting tissue of both wild-type and jaw-D plants ( Figure 6 ) . A difference between wild-type and jaw-D plants was most obvious in response to wounding , which strongly induces JA biosynthesis . Near the peak of JA induction in wild type , 90 min after wounding [66] , JA levels had substantially increased in both wild-type and jaw-D plants , but were about four times lower in jaw-D plants . This result is consistent with the observation that wounding can still activate a LOX2 promoter lacking TCP4 binding sites . For the microarray analyses , we had used primary rTCP4:GFP transformants with relatively strong phenotypes , and found an increase in the expression of JA biosynthetic genes ( Figure 2 ) . Unfortunately , only plants with weak , almost wild-type–like phenotypes provided sufficient material for JA measurements , because rTCP4:GFP plants with strong phenotypes stay small , have a shorter life span than wild type , and do not produce seeds . No clear differences in JA levels were seen in rTCP4:GFP plants with mild leaf phenotypes ( unpublished data ) . Neither allene oxide synthase ( aos ) mutants , which appear to be completely devoid of jasmonate [66] , nor oxophytodienoate reductase3 ( opr3 ) mutants show an obvious defect in an induced senescence assay ( Figure S8 ) . On the other hand , it is well known that exogenously applied MeJA can accelerate the final stage of leaf development , senescence ( e . g . , [67] ) , and several JA biosynthetic genes , including LOX2 , are transiently induced during developmental senescence [49] . Thus , JA likely plays a role in the control of senescence , but is not essential for it , as pointed out before [47] . We had noticed that positively regulated TCP targets tend to be expressed at higher levels in older leaves of wild-type plants ( Figure 2A ) . An opposite pattern was seen for genes that were down-regulated in rTCP4:GFP plants ( Figure S7 ) . Considering that the rTCP4:GFP samples analyzed consisted of apices with small , developing leaves , this observations suggested that the developmental age of rTCP4:GFP leaves is advanced relative to that of wild-type leaves . The up-regulated genes include several genes encoding WRKY transcription factors , so named after the first four amino acids of the conserved motif WRKYGQK , which is the hallmark of this protein family . One of these genes , WRKY53 , is an important positive regulator of senescence [68 , 69] , which is induced more than 30 times in rTCP4:GFP plants , although it lacks TCP4 consensus binding motifs in its promoter ( Table S4 ) . The precocious activation in rTCP4:GFP of genes that are normally expressed only later during leaf development is consistent with the role of the snapdragon TCP gene CIN as a regulator of the mitotic arrest front during early stages of leaf growth [40] , and suggests a more general role for TCPs during leaf aging . This in turn led us to examine the hypothesis that rTCP4:GFP plants might show a premature onset of senescence , and that jaw-D plants show a delay in senescence . Obvious effects were seen in jaw-D plants grown under long days; in these plants , leaf senescence was delayed by about a week ( Figure 7A ) , which is similar to the effects seen in the senescence mutant oresara9 [70] . In rTCP4:GFP plants , senescence was slightly accelerated ( Figure 7A ) , consistent with these rTCP4:GFP plants examined having only relatively mild morphological defects . Incubation of detached leaves in the dark induces senescence within days , and the onset of senescence can be accelerated by treatment with exogenous MeJA [57 , 67] . Although there are differences between induced and developmental senescence ( e . g . , [49] ) , we could confirm the delay observed in on-plant senescence with the in vitro assay . We monitored chlorophyll degradation and maximum efficiency of photosystem II ( PSII ) photochemistry ( Fv/Fm ) in detached jaw-D leaves incubated in the dark , and found a delayed decline in both these indicators of healthy leaves ( Figure 7B and 7C ) . We used the in vitro assay also to determine whether the delayed senescence in jaw-D plants is potentially caused by a lack of JA or a defect in JA signaling . When we compared jaw-D to wild type , we found that treatment with exogenous MeJA restored the senescence response ( Figure 8 ) , consistent with our previous results that TCPs regulate JA biosynthesis , rather than the JA response . The findings that MeJA was sufficient to restore senescence in jaw-D plants , but that JA on its own is apparently not essential for senescence , suggests that JA acts redundantly with other pathways during the control of senescence . One candidate is salicylic acid ( SA ) signaling , which often antagonizes the effects of JA [71] . However , jaw-D plants appeared to be largely normal in their SA response , as deduced from induction of the marker gene PR1 ( Figure S9 ) .
Using a combination of microarray meta-analysis , in vitro DNA binding experiments and reporter gene studies , we identified the LOX2 gene , which encodes an enzyme catalyzing a key step in JA biosynthesis , as being likely to be directly regulated by TCPs in vivo . The transcriptional response of other genes in the JA biosynthesis pathway and the overrepresentation of a TCP DNA binding motif in this pathway suggest that TCPs directly control additional JA biosynthetic genes . This strategy , coordinated control of metabolic pathways by the same set of transcription factors , is commonly used in plants [64] . Several previous analyses of JA biosynthetic genes , including LOX2 , have focused on regulatory elements and upstream factors mediating the effects of wounding or MeJA treatment [55 , 56] . Mutation of the TCP binding sites in the LOX2 promoter strongly reduced its activity in the absence of stimulation by wounding or MeJA , but it did not abolish the inducibility of the promoter . Our results highlight the importance of developmental control of LOX2 , and of the fact that developmental regulation can be at least partially uncoupled from transcriptional induction by wounding or JA treatment . This finding is consistent with JA playing not only a role in pathogen and stress response , but also in many developmental processes . Expression of the TCP genes themselves is not wound- , pathogen , or MeJA-responsive , as deduced from publicly available microarray data ( http://www . weigelworld . org/resources/microarray/AtGenExpress/ ) [72 , 73] , supporting the conclusion that the TCPs represent a pathway of JA regulation that is linked to the developmental program of the plant rather than to environmental responses . Plants with lower JA levels due to reduced activity of the enzyme encoding genes DONGLE ( DGL ) and OPR3 have been reported to be larger than wild type , while plants that overexpress DGL are smaller , similar to plants treated with JA [74 , 75] . DGL shares overlapping activity with a homolog , DEFECTIVE IN ANTHER DEHISCENCE 1 ( DAD1 ) , in stamen maturation [76] . DAD1 in turn is a direct target of the homeotic transcription factor AGAMOUS ( AG ) , which regulates both organ identity during early flower development and organ development during later stages [77] . In light of these related findings , the observation that TCP transcription factors and JA have parallel effects on leaf growth suggests that the oxylipin pathway potentially acts downstream of TCPs in affecting growth . Importantly , several links between JA and cell cycle progression as well as growth have previously been demonstrated ( e . g . , [78 , 79] ) . Although it is well known that exogenously applied MeJA can accelerate senescence ( e . g . [67] ) , there have been no reports that plants with mutations in the JA biosynthetic pathway are deficient in the senescence program [59 , 66 , 76 , 80–84] , which we have confirmed for aos and opr3 mutants using an induced senescence assay ( Figure S8 ) . Nevertheless , a bona fide effect of JA on leaf senescence can be deduced from the observation that exogenously applied MeJA fails to induce senescence in the coronatine insensitive1 ( coi1 ) mutant , which is defective in JA signal transduction [57 , 75 , 85] . The coi1 mutant on its own , however , does not show a senescence defect either . Analyses of biosynthetic mutants as well as the coi1 signaling mutant therefore both suggest that endogenous JA is not limiting for natural senescence . There is thus an interesting contrast between JA biosynthetic and signaling mutants on the one hand , and jaw-D , which has decreased JA levels due to reduced expression of JA biosynthetic genes , on the other hand . We initially considered the possibility that the TCP4 target LOX2 might be required for the production of additional metabolites that prevent senescence , and that LOX2 might thereby directly affect chloroplast stability . There are no reports that LOX2 catalyzes processes other than the conversion of α-linolenic acid ( 18:3 ) into ( 13S ) -hydroperoxyoctadecatrienoic acid , but the LOX2-catalyzed step can lead to end products other than JA [66] . The functions of these other oxylipins are not well known , and it cannot be ruled out that LOX2-catalyzed products are involved in JA-independent processes that delay senescence . An observation that speaks against such a scenario is that lipoxygenase activity is almost undetectable in leaf extracts of a recently isolated loss-of-function lox2 mutant . Similar to other JA biosynthetic mutants , these lox2 mutants do not show obvious changes in their senescence program ( L . Dubugnon and E . E . Farmer , unpublished data ) . We therefore propose an alternative scenario , namely that miR319-regulated TCPs control leaf senescence by regulating not only JA biosynthesis , but also a second , as-yet unidentified pathway that suppresses senescence in wild-type plants . We speculate that inactivation of the endogenous JA pathway alone is not sufficient to delay precocious senescence , due to such a second , redundantly acting pathway . Because of their parallel effects on senescence , JA alone should be , however , sufficient to induce senescence , both in wild-type plants and in jaw-D plants , which presumably lack activity of both pathways . Many genes activated in rTCP4:GFP plants are progressively up-regulated during leaf development , including WRKY53 , an important positive regulator of senescence [68 , 69] , suggesting perhaps a more general role for TCPs during leaf aging . Most conserved plant miRNAs affect transcription factor genes with important roles in development [11 , 12] , but in vivo targets that mediate the effects of these transcription factors are largely unknown . Our identification of targets of miR319-regulated TCPs thus provides an important advance in the understanding of small RNA–controlled regulatory networks . In addition , it demonstrates that the function of miRNA-controlled transcription factors is not limited to the modulation of downstream hormonal responses [13–16 , 18] , but that miRNAs may , in addition , regulate development through effects on hormone biosynthesis .
Plants were grown at 23 °C . All experiments were done under long days ( 16 h light ) , except for microarray analyses , which were with plants grown under short days ( 8 h light ) . Regular illumination was 125 μmol m−2 s−1 . For low light conditions , intensity was reduced to 15 μmol m−2 s−1 . Origin of tcp mutants and gene identifiers are given in Tables S5 and S6 . Wild type was Columbia ( Col-0 ) , unless stated otherwise . Microarray analyses using the Affymetrix ATH1 platform were performed as described [86] . For the collection of apices ( including the youngest leaf primordia ) , plants were dissected under a stereomicroscope , and all leaves with visible petioles were removed and discarded . Tissue was harvested directly into liquid N2 . Differentially expressed genes were identified with a combination of per-gene variance ( calculated using logit-T [87] ) and common variance based on expression estimates using gcRMA ( http://www . bioconductor . org ) , a modification of the robust multi-array analysis ( RMA ) algorithm [88] . Accession numbers for microarray experiments are GSE518 ( jaw-D ) [32] , and E-MEXP-469 ( rTCP4:GFP and tcp2 tcp4 ) . Microarray data for hormone treatment were downloaded from http://www . arabidopsis . org; TAIR accession numbers are 1007965964 ( JA ) , 1007965859 ( auxin ) , and 1007966175 ( GA ) . Six to eight–nucleotides-long overrepresented motifs were identified using a routine implemented in Genespring GX 7 . 3 . 1 ( Agilent Technologies , California ) . Promoters were defined as 800 nucleotides upstream of the initiation codon , and exact matches among positions −800 to −10 were considered . The frequency of each individual motif in the 117 genes that changed in at least two conditions was compared to the frequency of the same motif in promoters of other , randomly chosen genes . The Ath1_02_04 annotation was used . Real-time RT-PCR using the Opticon Continuous Fluorescence Detection System ( BioRad ) was performed as described [86] . GUS staining was carried out as described [89] . The TCP4 expression construct pRSETC-TCP4-1 , designed to express the amino-terminal , 224–amino acid fragment of TCP4 including the DNA-binding domain , was transformed into the Escherichia coli strain BL21 ( DE3 ) pLysSpSBET . 100ml LB containing 100μg/ml ampicillin was inoculated with 1 ml of overnight culture and grown at 37 °C to mid-log phase . Recombinant protein expression was induced with 1 mM isopropyl β-L-thiogalactoside ( IPTG ) . Cells were harvested after 3 h of induction . Cells were lysed by sonication in 2 ml of lysis buffer ( 50 mM NaH2PO4 , 300 mM NaCl , 10 mM imidazole , 1 mg/ml lysozyme ) . The lysate was centrifuged and the supernatant was loaded onto a Ni-NTA spin column ( Qiagen ) . Recombinant protein was eluted in 150 μl volume containing 500 mM imidazole . Eluted protein was dialyzed against 50 mM NaH2PO4 , 300 mM NaCl and 10% glycerol for 6 h . Purification was monitored by protein blot using anti-His HRP conjugate antibodies ( Qiagen ) . Methods described earlier [63] were used . The double-stranded oligonucleotide targets ( R704 ) , which contained random 18-mer sequences flanked by 19 bp defined sequences on both ends , were prepared by annealing oligonucleotides R704 ( GGAAACAGCTATGACCATG [N]18 ACTGGCCGTCGTTTTAC ) and 704 ( GTAAAACGACGGCCAGT ) followed by primer extension with Klenow fragment . Recombinant protein was incubated with 3 . 6 μg of double stranded R704 in 15 μl of 1 X binding buffer containing 0 . 1 M KCl , 10 ng of salmon sperm DNA , and 10 μg of bovine serum albumin ( BSA ) . The DNA-protein complex was separated by polyacrylamide gel electrophoresis , bound oligonucleotides were eluted from the gel and dissolved in 20 μl of TE . The recovered DNA was amplified by 14 cycles of PCR with primers 703 ( GGAAACAGCTATGACCATG ) and 704 . The PCR product ( 30 μl ) was extracted with phenol/chloroform and ether , and 10 μl was subjected to next round of selection . With each round of selection , the number of PCR cycles was reduced by one cycle to avoid generation of high–molecular weight PCR products . The DNA from the tenth round of selection was amplified by PCR , purified on a 20% polyacrylamide gel and cloned into the pGEM-T Easy vector ( Promega ) for sequencing . Double-stranded DNA probes were generated by annealing oligonucleotides and primer extension with [α-32P]-dCTP using Klenow enzyme . The binding reaction was carried out in a total volume of 10 μl containing ∼10 fmol of oligonucleotide probe , 1 X binding buffer ( 20 mM HEPES-KOH , pH 7 . 8 , 100 mM KCl , 1 mM EDTA , 0 . 1 % BSA , 10 ng herring sperm DNA , and 10% glycerol ) and 5–100 ng of recombinant protein . The mixture was incubated for 30 min at room temperature and loaded on 6% native polyacrylamide gel . Electrophoresis was conducted at 4 V/cm for 45 min in 0 . 5 x TBE electrophoresis buffer at room temperature . The gels were autoradiographed using a phospho-imager . Mutagenesis was carried out using the QuikChange Multi Site-Directed Mutagenesis Kit ( Stratagene ) , according to the manufacturers instructions . Primer sequences are available on request . The four sites and positions of mutations ( with ATG as +1 ) are: ( 1 ) position −1173 to −179 , mutated at −1174 , −1176; ( 2 ) position −944 to −949 , mutated at −944 , −946 , −949; ( 3 ) position −432 to −437 , mutated at −432 , −434; ( 4 ) position −300 to −305 , mutated at −303 , −305 . Protocol 2 of Mueller and colleagues [65] with an oxygen-18 labeled internal standard was used . | Short , single-stranded RNA molecules called microRNAs ( miRNAs ) regulate gene expression by negatively controlling both the stability and translation of target messenger RNAs that they recognize through sequence complementarity . In plants , miRNAs mostly regulate other regulators , the DNA-binding transcription factors . We investigated the downstream events regulated by five TCP ( TEOSINTE BRANCHED/CYCLOIDEA/PCF ) transcription factors that are controlled by the microRNA miR319 in Arabidopsis thaliana . The miR319-regulated TCPs were previously known to be important for limiting the growth of leaves . By applying a combination of genome-wide , biochemical , and genetic studies , we identified new TCP targets that include enzymes responsible for the synthesis of the hormone jasmonic acid . Our analysis of leaf extracts from plants with increased activity of miR319 confirms that altered expression of the biosynthetic genes leads to changed jasmonic acid levels . These plants show also an altered senescence behavior that becomes more normal again when the plants are treated with jasmonate . We propose that the miR319-regulated TCP factors thus coordinate different aspects of leaf development and physiology: growth , which they negatively regulate , and aging , which they positively regulate . | [
"Abstract",
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] | [
"genetics",
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] | 2008 | Control of Jasmonate Biosynthesis and Senescence by miR319 Targets |
The serine-rich repeat glycoprotein Srr1 of Streptococcus agalactiae ( GBS ) is thought to be an important adhesin for the pathogenesis of meningitis . Although expression of Srr1 is associated with increased binding to human brain microvascular endothelial cells ( hBMEC ) , the molecular basis for this interaction is not well defined . We now demonstrate that Srr1 contributes to GBS attachment to hBMEC via the direct interaction of its binding region ( BR ) with human fibrinogen . When assessed by Far Western blotting , Srr1 was the only protein in GBS extracts that bound fibrinogen . Studies using recombinant Srr1-BR and purified fibrinogen in vitro confirmed a direct protein-protein interaction . Srr1-BR binding was localized to amino acids 283–410 of the fibrinogen Aα chain . Structural predictions indicated that the conformation of Srr1-BR is likely to resemble that of SdrG and other related staphylococcal proteins that bind to fibrinogen through a “dock , lock , and latch” mechanism ( DLL ) . Deletion of the predicted latch domain of Srr1-BR abolished the interaction of the BR with fibrinogen . In addition , a mutant GBS strain lacking the latch domain exhibited reduced binding to hBMEC , and was significantly attenuated in an in vivo model of meningitis . These results indicate that Srr1 can bind fibrinogen directly likely through a DLL mechanism , which has not been described for other streptococcal adhesins . This interaction was important for the pathogenesis of GBS central nervous system invasion and subsequent disease progression .
The serine-rich repeat ( SRR ) glycoproteins are a large and diverse family of adhesins found in Gram-positive bacteria [1] , [2] . Each SRR protein is encoded within a large locus that also contains genes encoding proteins responsible for glycosylating the SRR protein , as well as an accessory Sec system that is dedicated to the export of the adhesin . The SRR proteins have a highly conserved domain organization , including a long and specialized signal sequence , two extensive serine-rich repeat regions that undergo glycosylation , and a typical LPXTG cell wall anchoring motif [3] , [4] . The N-termini also contain a binding region that varies considerably , both in terms of structure and adherence properties ( Figure 1 ) . Among the best-characterized is GspB of Streptococcus gordonii , which binds human platelets through its interaction with sialyl-T antigen on the platelet receptor GPIbα [2] , [5] . This appears to be an important event in the pathogenesis of infective endocarditis , since disruption of Siglec-mediated binding results in reduced virulence , as measured by an animal model of endocardial infection [3] , [4] . A number of other SRR proteins have been shown to contribute to virulence , including SraP of Staphylococcus aureus , PsrP of Streptococcus pneumoniae , and the two SRR proteins ( Srr1 and Srr2 ) of GBS [6]–[11] . However , the molecular basis for binding by these other adhesins is less defined . Their binding regions have no homology to that of GspB , indicating that they are not Siglec-like adhesins . Although SraP mediates binding to platelets , the receptor for this SRR protein has not been identified [6] . PsrP binds cytokeratin 10 in vitro , which appears to be important for binding to pulmonary epithelial cells and subsequent pneumonia [12] . Expression of Srr1 or Srr2 by GBS has been shown to contribute to virulence in models of meningitis [7] , [8] . Srr1 mediates binding to several types of human epithelial cell lines , as well as human brain microvascular endothelial cells ( hBMEC ) [7] , [13] . Binding of these cells appears to be important for both colonization and invasion . In vitro studies have indicated that one ligand for Srr1 is human keratin 4 , which may facilitate attachment to cervical , vaginal , and pharyngeal cells [13] , [14] . We now report , however , that Srr1 also binds human fibrinogen directly through its interaction with the Aα chain of the heteromultimeric protein . This interaction mediates the binding of GBS both to fibrinogen and to hBMEC , and appears to be important for virulence in the setting of meningitis .
We first measured the adherence of GBS strain COH31 ( a serotype III clinical isolate ) to a variety of host plasma and matrix proteins . As shown in Fig . 2A , GBS adhered to immobilized human fibrinogen at levels ( mean: 16±2 . 8% of inoculum ) that were significantly higher than those seen to with the negative control , casein ( <1% ) . Low levels of binding ( <2% ) were observed with thrombin , fibronectin , laminin , plasminogen , collagen IV , and fetuin . Binding was significantly inhibited by pretreatment of immobilized fibrinogen with anti-fibrinogen IgG , indicating that the interaction between GBS and fibrinogen was specific ( Figure 2B ) . We also examined eight additional GBS isolates , representing a range of capsular types , all of which were found to bind immobilized fibrinogen . As was seen with the COH31 strain , binding of all GBS strains tested was significantly reduced during treatment with IgG specific for fibrinogen . These data indicate that GBS can adhere specifically to immobilized fibrinogen and adherence to fibrinogen is a general property of GBS . To better characterize the GBS surface components responsible for fibrinogen interaction , we examined the binding of soluble human fibrinogen to GBS cell wall proteins by Far Western blotting . Although the GBS cell wall extracts contained numerous proteins ( Figure 2C , left panel ) , fibrinogen binding was restricted to a group of high MW bands ( 300–400 kDa ) ( middle panel ) . Probing the membranes with WGA revealed binding of the lectin to one or more proteins of similar size , indicating that they were glycosylated ( right panel ) . Since the serine-rich repeat protein Srr1 of GBS is a high MW glycoprotein , we next assessed the impact of deleting srr1 on WGA and fibrinogen binding . When cell wall extracts of COH31Δsrr1 ( PS954 ) were probed with WGA or fibrinogen , no binding was observed , confirming that the glycoprotein bound by fibrinogen was Srr1 . To examine the impact of Srr1 expression on bacterial binding to fibrinogen , we tested the ability of GBS strains COH31 and NCTC 10/84 , and Δsrr1 variants to bind to immobilized fibrinogen . As shown in Figure 3A , deletion of srr1 markedly reduced GBS binding to fibrinogen . Similar results were observed with additional GBS strains H36B and 515 ( data not shown ) . To confirm the role of Srr1 expression in fibrinogen binding by GBS , we next assessed whether binding by COH31 and NCTC 10/84 to fibrinogen was inhibited by rabbit anti-Srr1 IgG ( Figure 3B and Figure S1 ) . In control studies , co-incubation of either strain with rabbit IgG had no effect on fibrinogen binding . In contrast , co-incubation of GBS with anti-Srr1 IgG significantly reduced binding to fibrinogen . The level of inhibition was concentration-dependent , with 100 µg/ml of anti-Srr1 IgG being sufficient to reduce WT GBS binding to levels comparable to those seen with GBSΔsrr1 . Complementation of the srr1 mutation in trans restored fibrinogen binding by NCTC 10/84 Δsrr1 ( Figure S2 ) , thereby demonstrating that the loss of binding observed with srr1 disruption was not due to polar or pleiotropic effects . These results indicate that GBS binding to immobilized fibrinogen is mediated by the surface expressed Srr1 protein . The attachment of GBS to human brain microvascular endothelial cells ( hBMEC ) is thought to be important for the invasion of the central nervous system by this organism [15]–[17] . Previous studies indicate that binding of GBS to brain endothelium is mediated by Srr1 [7] . To assess whether fibrinogen contributed to this interaction , we assessed the role of fibrinogen in Srr1-mediated binding of GBS to hBMEC . Fibrinogen was detectable on the surface of washed hBMEC , as measured by immunofluorescence microscopy ( Figure 4A ) . Exposure of the cells to exogenous human fibrinogen ( 20 µg/ml ) , markedly increased the amount of the protein on the cell surface , indicating that hBMEC are capable of binding fibrinogen . Strain NCTC10/84 and an isogenic Δsrr1 variant ( PS2645 ) were incubated with hBMEC in tissue culture wells . After 30 min , WT GBS efficiently adhered to these cells , whereas the Δsrr1 mutant was significantly reduced in binding ( p<0 . 01 ) ( Figure 4B ) . Preincubation of bacteria with purified human fibrinogen ( 20 µg/ml ) enhanced the binding of the WT strain to hBMEC , but had no effect on binding of the Δsrr1 mutant strain . The ligand binding site of the SRR proteins characterized to date has been localized to the region bridging the two serine-rich repeat domains ( Figure 1 ) [1]–[3] , [6] , [9] . To confirm that the putative binding region of Srr1 ( Srr1-BR ) interacts with fibrinogen , we assessed the binding of the purified FLAG tagged binding region ( FLAGSrr1-BR ) with fibrinogen . In control studies , no significant binding by FLAGSrr1-BR to immobilized casein blocking regent was detected . In contrast , FLAGSrr1-BR showed significant binding to fibrinogen , which increased in direct proportion to the amount of protein applied ( Figure 5A ) . No fibrinogen binding activity was detected by either the N-terminal of Srr1-BR ( AA303–479 ) or C-terminus ( AA480–641 ) alone , indicating that entire region is required . To determine the apparent KD for the binding of FLAGSrr1-BR to fibrinogen , we analyzed data from six independent ELISA-based binding assays , as described previously . The calculated mean KD was 7 . 51×10−8 , which is within the range reported for staphylococcal fibrinogen binding proteins [18] . To validate these findings , we also examined the inhibition of this interaction with either anti-fibrinogen IgG or unlabeled Srr1-BR ( Figure 5C and D ) . When immobilized fibrinogen was pretreated with anti-fibrinogen IgG , the binding of FLAGSrr1-BR to the protein was subsequently reduced ( Figure 5C ) . In addition , when FLAGSrr1-BR was co-incubated with unlabeled ( non-tagged ) Srr1-BR , subsequent binding was effectively blocked ( Figure 5D ) . These findings indicate that the fibrinogen binding domain of the Srr1 is indeed located in the binding region ( AA 303–641 ) . We next sought to characterize the region within fibrinogen responsible for Srr1-BR binding . Fibrinogen is a complex protein consisting of two subunits , each containing three polypeptide chains ( Aα , Bβ and γ ) . When separated by SDS-PAGE under reducing conditions , fibrinogen appeared as three bands corresponding to the Aα , Bβ , and γ chains ( Aα = 63 . 5 kDa , Bβ = 56 kDa , γ = 47 kDa ) having the expected masses ( Figure 6B ) . When transferred to nitrocellulose and probed with purified FLAGSrr1-BR , the Aα chain was readily detected , with low levels of binding seen to the Bβ and γ chains ( Figure 6B ) . We also assessed the binding of Srr1-BR to recombinant forms of each chain , expressed as MalE fusion proteins . In this case , FLAGSrr1-BR was found to bind the MalE:Aα chain , while no binding was seen to the MalE:Bβ and MalE:γ chains ( Figure S3 ) . We next sought to identify the domains within the Aα chain bound by Srr1-BR , by examining the binding of Srr1-BR to a series of recombinant Aα chain truncates ( Figure 6A and 6C ) . Far Western blot analysis showed that binding of FLAGSrr1-BR was localized to subdomains containing residues 283–410 , which correspond to the tandem repeat region of the Aα chain ( Figure 6C ) . To confirm that this region was the Srr1-BR binding site , we assessed by ELISA the interaction of FLAGSrr1-BR with the immobilized fibrinogen Aα subdomains ( Fig . 6D ) . As was observed with the Far Western analysis , we found no significant binding of FLAGSrr1-BR to immobilized MalE:Aα198–282 or MalE:Aα ( 198–282+411–610 ) . However , FLAGSrr1-BR exhibited levels of binding to MalE:Aα283–410 that were comparable to recombinant full length Aα chain ( MalE:Aα1–610 ) , indicating that the Srr1-BR binding site is indeed the 13 AA tandem repeat region within the Aα chain of fibrinogen . Next we examined whether fibrinogen binding by GBS was mediated by the interaction of Srr1-BR with Aα283–410 . GBS strains COH31 and NCTC 10/84 , and their respective Δsrr1 mutants ( PS954 and PS2645 ) were incubated with either immobilized MalE:Aα283–410 or MalE:Aα198–282 ( Fig . 7A and B ) . The Δsrr1 mutant strains exhibited low levels of binding to both Aα chain truncates . In contrast , WT GBS strains had high levels of binding to MalE:Aα283–410 , as compared with MalE:Aα198–282 . In addition , we found that GBS binding to immobilized fibrinogen was subsequently reduced during co-incubation with MalE:Aα283–410 ( Figure S4 ) , suggesting that Srr1-BR binds fibrinogen specifically within AA 283–410 of the Aα chain , and that this interaction is important for GBS fibrinogen binding . To gain a better understanding of the structural determinants present within the binding region of Srr1 , bioinformatic analysis was performed on the predicted binding region sequence ( AA 303–641 ) . Interestingly , PSI-BLAST analysis identified this region to be related to the fibrinogen binding domain of the staphylococcal adhesins SdrG and ClfA ( sharing 22% and 23% identity respectively ) . Structure prediction analysis using PHYRE -[19] , Swiss-Model [20] , and HHPRED [21] algorithms also identified the binding region of Srr1 as having structural similarity to the fibrinogen-binding region of SdrG ( HHPred; 100% probability , e = 4 . 5e−51 ) and ClfA ( HHPred; 100% probability , e value = 9 . 2e−51 ) ( Fig . S5 ) . The binding regions of ClfA and SdrG are composed of two domains ( N2 and N3 ) ( Figure 1 ) , each of which adopts an IgG-like fold [22]–[24] . This domain architecture enables fibrinogen binding through a “dock , lock , and latch” mechanism ( DLL ) [24] , in which fibrinogen engages a binding cleft between the N2 and N3 domains . As the ligand “dock” , the flexible C-terminal extension of the N3 domain ( the “latch” ) changes conformation , so that it “locks” the ligand in place , and forms a β strand complex with the N2 domain [24] . Bacterial adhesins that are structurally related to Clf-Sdr family are able to bind fibrinogen using this mechanism , which appears to represent a general mode of ligand-adhesin binding [24]–[28] . Collectively , our bioinformatic analysis suggests that the binding region of Srr1 structurally resembles the binding region of the Clf-Sdr family proteins ( SdrG , ClfA , ClfB ) and may have a similar binding mechanism . Using structure prediction searches ( HHPRED ) [21] , we did not identify a latch-like sequence in C-terminal end of the Srr1-BR . However , a highly homologous TYTFTDYVD-like “latching cleft” sequence between the D1 and E1 strands was identified at AA 412∼420 ( TYTWTRYAS ) ( Figure S5 and Table S3 ) . To investigate whether the C-terminal end of Srr1-BR contained a functional latch-like domain , we generated a variant of Srr1-BR , in which the C-terminal 13 AA had been deleted ( FLAGSrr1-BRΔlatch ) . As shown Figure 8A , this mutation abolished the binding of the Srr1-BR . Moreover , untagged Srr1-BRΔlatch ( 100 µg/ml ) failed to inhibit the binding of FLAGSrr1-BR binding to immobilized fibrinogen ( data not shown ) . The Srr1-BR protein readily bound to hBMEC and this interaction was increased by preincubating hBMEC with fibrinogen ( 20 µg/ml ) ( Figure 8B ) . In contrast , the Srr1-BRΔlatch protein exhibited lower levels of binding to hBMEC compared with the Srr1-BR protein , which were not enhanced by fibrinogen . To exclude the possibility that this deletion had produced changes in the secondary structure of the protein that might account for the reduction in fibrinogen-binding activity , we analyzed Srr1-BR and Srr1-BRΔlatch proteins by circular dichroism ( Fig . S6 ) . The two proteins had a similar CD profile , with a maximum at less than 200 and a minimum at 216–218 , resembling previously determined CD spectra for ClfA [29] . These results indicate that the Srr1-BR mediates Srr1 binding to fibrinogen , and that the C-terminal end of Srr1-BR contains a latch-like domain . We next generated an isogenic variant of strain GBS NCTC 10/84 in which the latch-like domain of the Srr1-BR had been deleted . Of note , deletion of this region did not affect surface expression of Srr1 ( Figure 8C ) . We then examined the impact of this mutation on GBS binding to fibrinogen and brain endothelium . As shown in Fig . 8D and E , deletion of the latch region significantly reduced GBS binding to fibrinogen and hBMEC , as compared with the parent strain . These results strongly suggest that GBS binding to fibrinogen is mediated by Srr1-BR via the “dock , lock , and latch” mechanism . To investigate the role of Srr1–mediated binding to fibrinogen in the pathogenesis of experimental meningitis , we compared the relative virulence of NCTC 10/84 with its isogenic latch-deficient variant . CD-1 mice were infected intravenously with either the WT or the Δlatch mutant strain . Twenty-four hours after challenge , the levels of GBS detected in the blood of each group were essentially identical ( Figure 9A ) . Despite their initial similarities in establishing a high-grade bacteremia in the mouse , infection with the WT GBS strain resulted in significantly higher mortality ( p = 0 . 017 , Log Rank test ) . By 54 h , 50% of mice infected with NCTC10/84 had died . In contrast , all animals infected with GBSΔlatch were alive at 78 h ( Figure 9B ) . At the time of death ( or upon euthanasia at 78 h ) , blood and brain were harvested from each mouse for quantitative bacterial culture . Mice infected with the WT strain exhibited significantly higher final bacterial loads and penetrated into the brain more frequently than the Δlatch mutant ( Figure 9C ) . Histologic examination of brain tissue from mice infected with the Δlatch mutant showed normal brain morphology with no signs of inflammation or injury ( Figure 9D ) , whereas mice infected with WT GBS showed meningeal thickening , tissue destruction and neutrophil infiltration ( Figure 9E and 9F ) .
The SRR proteins of GBS are thought to be important both for colonization of the female genital tract , and for the pathogenesis of invasive diseases , such as sepsis and meningitis . Expression of Srr1 has been shown to enhance the attachment of bacteria to vaginal and cervical epithelial cells in vitro , and to facilitate genital colonization in mice [30] . These interactions may be mediated in part by the binding of Srr1 to cytokeratin 4 on the surface of these epithelial cells . Studies in vitro indicate that the Srr1 interacts with cytokeratin 4 to promote bacterial attachment to the cell surface [14] , [30] . However , binding can be blocked by sWGA , suggesting that the glycosylated serine-rich domains may also be involved in the interaction of Srr1 with cytokeratin 4 [14] . Strains expressing Srr1 are also more virulent in animal models of meningitis , as compared with their isogenic , srr1-deleted variants [7] , [8] . Expression of Srr1 enhances GBS binding to hBMEC , which is likely to be an essential step for initiating central nervous system invasion and meningitis [7] . Our results now demonstrate that Srr1 promotes the adherence of GBS to human fibrinogen , and that this process is likely to be important for the pathogenesis of meningitis . Binding occurs via the interaction of Srr1-BR with the C-terminus of the fibrinogen Aα chain . This appears to be a specific event , requiring the entire Srr1-BR , and amino acids 283–410 of the Aα chain . Although Srr1 has limited primary sequence similarity to other known fibrinogen binding proteins , our secondary structure analyses indicate that Srr1-BR is likely to have a conformation resembling that of ClfA and possibly other related proteins , such as SdrG of Staphylococcus epidermidis . These and a number of other Gram-positive bacterial adhesins are thought to bind fibrinogen through a “dock , lock , and latch” ( DLL ) mechanism [24]–[26] , as described above . Deletion of the predicted latch-like domain of Srr1 significantly reduced fibrinogen binding by the recombinant protein , as well as by bacteria , suggesting that Srr1 binding occurred by a comparable mechanism . If so , this would be the first example of a streptococcal DLL adhesin . Notwithstanding these similarities , there are some notable differences between Srr1 and its staphylococcal counterparts . For example , while Srr1 binds the Aα chain of fibrinogen , ClfA recognizes the C-terminus of the γ chain , and SdrG binds the N-terminus of the β chain [24] , [25] , [27] . Although both Srr1 and ClfB bind the C-terminus of the Aα chain , their binding sites on fibrinogen appear to differ [27] , [28] , [31] . A recombinant peptide representing the Aα chain binding site for ClfB ( AA283–347 ) did not inhibit Srr1-BR binding to fibrinogen ( Figure S7 ) . Conversely , a peptide containing Aα chain residues 348–410 effectively blocked Srr1-BR binding , but no effect on ClfB binding to fibrinogen . These findings suggest that , while the binding of Srr1 to the Aα chain has some features in common with ClfB , the interactions of these adhesins with fibrinogen must also differ significantly . Further understanding of the precise basis for Srr1 binding to fibrinogen , and whether it occurs via a DLL mechanism , will require solution of its crystal structure . Srr1 binding to fibrinogen was also important for the attachment of GBS to hBMEC in vitro . Binding of GBS to brain endothelium was reduced by deletion of the putative latch domain of Srr1 , and was significantly enhanced by adding human fibrinogen , at concentrations ( 20 µg/ml ) well within those found in whole blood ( 2–4 mg/ml ) [32] . These findings indicate that the Srr1-fibrinogen binding is a relevant process for CNS invasion , and indeed we found that in mice with experimental meningitis , the latch deletion was also associated with significantly reduced levels of bacteria , mortality , and inflammation within the CNS . Of note , levels of the bacteria within the bloodstream were not altered by the above mutation , further indicating that the virulence properties associated with Srr1 and fibrinogen binding are specific to CNS infection . FbsA and FbsB are two additional fibrinogen binding proteins of GBS that have been characterized [33] , [34] . These proteins appear to be structurally unrelated to Srr1 or other known fibrinogen binding proteins . FbsA and FbsB can bind fibrinogen directly in vitro , although their binding sites on fibrinogen have not been identified . FbsA can also enhance the attachment of GBS to hBMEC [35] . However , FbsA alone is not sufficient for cell invasion , but appears to require FbsB for this process [36] . The contribution of FbsA and FbsB , and their interactions with fibrinogen to virulence is not well-defined . Neither protein has been examined for its role in the pathogenesis of meningitis . Deletion of fbsA was associated with decreased virulence in an animal model of septic arthritis and septicemia [37] . However , neither active nor passive immunization with FbsA or FbsA-specific antibodies resulted in protection against subsequent infection [37] , suggesting that the virulence properties of FbsA may be unrelated to fibrinogen binding . Two other GBS proteins ( the fibronectin binding protein Fib and a predicted ABC transport protein SAG0242 ) have been shown to bind fibrinogen , but neither the mechanisms for protein binding , nor the biologic importance of these interactions , have been addressed [33] . In summary , our results show that Srr1 mediates the binding of GBS to fibrinogen , and that this interaction is likely to occur via a DLL-like mechanism , involving the C-terminus of the fibrinogen Aα chain . It is the first streptococcal adhesin for which this type of binding has been identified , indicating that DLL binding may be a generalized mechanism for attachment by Gram-positive organisms . In addition , Srr1-fibrinogen binding appears to be important for the adherence to brain endothelium and the development of meningitis Given that Srr1 or its homolog Srr2 appear to be expressed by most clinical isolates of GBS , this interaction may prove to be a promising candidate for novel therapies targeting bacterial virulence .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved by the Institutional Animal Care and Use Committee of San Diego State University ( Animal Welfare Assurance Number: A3728-01 ) . All efforts were made to minimize suffering of animals employed in this study . Purified human fibrinogen was obtained from Haematologic Technologies . Rabbit anti-fibrinogen IgG was purchased from Aniara . Rabbit anti-Srr1 IgG was generated using purified Srr1-BR protein ( NeoPeptide ) . The bacteria and plasmids used in this study are listed in Table S1 and S2 . S . agalatiae strains were grown in Todd-Hewitt broth ( Difco ) supplemented with 0 . 5% yeast extract ( THY ) . All mutant strains grow comparably well in vitro ( data not shown ) . Escherichia coli strains DH5α , BL21 and BL21 ( DE3 ) were grown at 37°C under aeration in Luria broth ( LB; Difco ) . Appropriate concentrations of antibiotics were added to the media , as required . Genomic DNA was isolated from GBS NCTC 10/84 , using Wizard Genomic DNA purification kits ( Promega ) , according to the manufacturer's instructions . PCR products were purified , digested , and ligated into pET28FLAG to express FLAG-tagged versions of Srr1-BR ( amino acids [AA] 303–641 ) , the amino terminus of Srr1-BR ( AA 303–479 ) , the carboxy terminus of Srr1-BR ( AA480–641 ) or the latch deletion of Srr1-BR ( AA 303–628 ) . Untagged Srr1-BR and Srr1-BRΔlatch were cloned into pET22b ( + ) ( Novagen ) . The plasmids were then introduced to E . coli BL21 ( DE3 ) for over-expression . Proteins were purified by either Ni-NTA ( Promega ) or anti-FLAG M2 agarose affinity chromatography ( Sigma-Aldrich ) , according to the manufacturers' instructions . cDNAs encoding the Aα- , Bβ- and γ-chains of human fibrinogen were generously provided by Professor Susan Lord ( University of North Carolina at Chapel Hill ) [38]–[40] . The full length and truncated forms of chains were amplified and cloned into pMAL-C2X ( New England Laboratory ) to express MalE-tagged versions of the chains . Plasmids were then introduced to E . coli BL21 by transformation . All recombinant proteins were purified by affinity chromatography with amylose resin , according to the manufacturer's instructions ( New England Biolabs ) . Purified human fibrinogen and recombinant fibrinogen chains were separated by electrophoresis through 4–12% NuPAGE Tris-Acetate gels ( Invitrogen ) and transferred onto nitrocellulose membranes . The membranes were treated with casein-based blocking solution ( Western Blocking Reagent; Roche ) at room temperature , and then incubated for 1 h with FLAG-tagged Srr1-BR ( 0 . 5 µM ) suspended in PBS-0 . 05% Tween 20 ( PBS-T ) . The membranes were then washed three times for 15 min in PBS-T , and bound proteins were detected with mouse anti-FLAG antibody ( Sigma-Aldrich ) . Purified fibrinogen ( 0 . 1 µM ) was immobilized in 96-well microtiter dishes by overnight incubation at 4°C . The wells were washed twice with PBS and blocked with 300 µl of a casein-based blocking solution for 1 h at room temperature [41] , [42] . The plates were washed three times with PBS-T , and FLAGSrr1-BR , FLAGSrr1-BR-N , FLAGSrr1-BR-C or FLAGSrr1-BRΔlatch in PBS-T was added over a range of concentrations . The plates were then incubated for 1 h at 37°C . Unbound protein was removed by washing with PBS-T , and the plates were incubated with mouse anti-FLAG antibodies diluted 1∶4000 in PBS-T for 1 h at 37°C . Wells were washed and incubated with HRP-conjugated rabbit anti-mouse IgG diluted 1∶5000 in PBS-T for 1 h at 37°C . The dissociation constant KD for Srr1 binding was calculated using Prism software v . 4 . 0 ( GraphPad ) . For inhibition assays , the wells containing immobilized with fibrinogen ( 0 . 1 µM ) were pretreated with rabbit anti-fibrinogen or rabbit IgG for 30 min , followed by washing to remove unbound antibody prior to the addition of FLAGSrr1-BR . In addition , FLAGSrr1-BR was coincubated with anti-Srr1 IgG or purified untagged Srr1-BR proteins on the wells immobilized with fibrinogen . After washing out unbound proteins , bound FLAGSrr1-BR was then assessed as described above . hBMEC were fixed with 4% paraformaldehyde and fibrinogen was stained with rabbit anti-fibrinogen IgG ( 1∶1000 ) and Alexa Fluor 488 conjugated goat anti-rabbit IgG ( Invitrogen ) . Coverslips were mounted on glass slides using Vectashield ( Vector labs ) and visualized with a confocal laser scanning microscope ( Leica Microsystems ) . Overnight cultures of GBS were harvested by centrifugation and adjusted to a concentration of 106 CFU/ml in PBS . Purified fibrinogen ( 0 . 1 µM ) was immobilized in 96-well microtiter plates as described above , and then incubated with 100 µl of GBS suspension for 30 min at 37°C . The wells were then washed to remove unbound bacteria , and then treated with 100 µl of trypsin ( 2 . 5 mg/ml ) for 10 min at 37°C to release the attached bacteria . The number of bound bacteria was determined by plating serial dilutions of the recovered bacteria onto THB agar plates as previously described [41] . The human brain microvascular endothelial cell line ( hBMEC ) was developed and kindly provided by Kwang Sik Kim ( Johns Hopkins University ) [43] , [44] and cultured as previously described [45] . Bacterial adherence assays were performed as described [46] . In brief , bacteria were grown to mid-log phase and then added to confluent hBMEC monolayers at a multiplicity of infection ( MOI ) of 0 . 1 . After 30 min incubation , monolayers were washed 6 times with PBS to remove non-adherent bacteria , lysed and plated on THB agar to enumerate the bacteria . Bacterial adherence was calculated as ( recovered CFU/initial inoculum CFU ) ×100% . In indicated experiments exogenous fibrinogen ( 20 µg/ml ) was added directly to bacteria and incubated 1 . 5 hours with rotation at 37°C prior to addition to hBMEC monolayers . GBS cell wall extracts were prepared by treatment with spheroplasting buffer ( 500 units/ml mutanolysin , 20 mM Tris , 10 mM MgCl2·6H2O , and 0 . 5 M raphinose ) , as described previously [47] , [48] . Proteins were separated by SDS-PAGE with 3–8% Tris-Acetate gels ( Invitrogen ) under reducing conditions and then were transferred to nitrocellulose membranes . After blocking with casein based blocking reagent ( Roche ) , the membranes incubated with either 1 ) anti-Srr1-BR IgG ( 1∶3000 ) following by incubation with anti-rabbit IgG ( 1∶10 , 000 ) ; or 2 ) biotin conjugated wheat germ agglutinin ( WGA; Vector Labs ) ( 0 . 2 µg/ml ) followed by incubation with HRP conjugated streptavidin ( 0 . 2 µg/ml ) . A murine model of hematogenous GBS meningitis has been described previously [46] . Outbred 6- to 8-week old male CD-1 mice ( Charles River Laboratories; 10 mice per group ) were injected via the tail vein with 5×107 CFU WT GBS ( NCTC 10/84 ) or GBSΔlatch mutant . At 24 h post GBS injection , blood was collected via tail vein ( 20 µl ) and plated on THB agar to determine the bacterial load in the bloodstream . Mouse survival was accessed over time . At the time of death , or at 78 h post infection , blood and brain tissue were collected aseptically from mice after euthanasia . Bacterial counts were in blood and tissue homogenates were determined by plating serial 10-fold dilutions on THB agar . Brain sections were also embedded in paraffin and stained with hematoxylin and eosin ( H&E ) . Amino acid similarity was compared using PSI-BLAST and secondary structure was determined by the prediction servers ( PHYRE and HHPRED ) [19] , [49] , [50] . Data were expressed as means ± standard deviations and were compared for statistical significance by the unpaired t test . | Streptococcus agalactiae ( Group B streptococcus , GBS ) is a leading cause of meningitis in newborns and infants . This life-threatening infection of the brain and surrounding tissues continues to result in a high incidence of morbidity and mortality , despite antibiotic therapy . A key factor in disease production is the ability of this organism to invade the central nervous system , via the bloodstream . We now report that a GBS surface protein called Srr1 binds fibrinogen , a major protein in human blood . This interaction enhances the attachment of GBS to brain vascular endothelial cells , and contributes to the development of meningitis . A mutation in Srr1 that specifically disrupted binding to fibrinogen significantly reduced GBS attachment to brain endothelium , and markedly reduced virulence in an in vivo model of GBS disease . These studies have identified a new mechanism by which Srr1 contributes to GBS invasion of the central nervous system and may provide a basis for novel therapies targeting Srr1 binding . | [
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] | 2012 | Binding of Glycoprotein Srr1 of Streptococcus agalactiae to Fibrinogen Promotes Attachment to Brain Endothelium and the Development of Meningitis |
Rickettsiosis is a re-emergent infectious disease without epidemiological surveillance in Colombia . This disease is generally undiagnosed and several deadly outbreaks have been reported in the country in the last decade . The aim of this study is to analyze the eco-epidemiological aspects of rickettsial seropositivity in rural areas of Colombia where outbreaks of the disease were previously reported . A cross-sectional study , which included 597 people living in 246 households from nine hamlets in two municipalities of Colombia , was conducted from November 2015 to January 2016 . The survey was conducted to collect sociodemographic and household characteristics ( exposure ) data . Blood samples were collected to determine the rickettsial seropositivity in humans , horses and dogs ( IFA , cut-off = 1/128 ) . In addition , infections by rickettsiae were detected in ticks from humans and animals by real-time PCR targeting gltA and ompA genes . Data was analyzed by weighted multilevel clog-log regression model using three levels ( person , household and hamlets ) and rickettsial seropositivity in humans was the main outcome . Overall prevalence of rickettsial seropositivity in humans was 25 . 62% ( 95%CI 22 . 11–29 . 12 ) . Age in years ( PR = 1 . 01 95%CI 1 . 01–1 . 02 ) and male sex ( PR = 1 . 65 95%CI 1 . 43–1 . 90 ) were risk markers for rickettsial seropositivity . Working outdoors ( PR = 1 . 20 95%CI 1 . 02–1 . 41 ) , deforestation and forest fragmentation for agriculture use ( PR = 1 . 75 95%CI 1 . 51–2 . 02 ) , opossum in peridomiciliary area ( PR = 1 . 56 95%CI 1 . 37–1 . 79 ) and a high proportion of seropositive domestic animals in the home ( PR20-40% vs <20% = 2 . 28 95%CI 1 . 59–3 . 23 and PR>40% vs <20% = 3 . 14 95%CI 2 . 43–4 . 04 ) were associated with rickettsial seropositivity in humans . This study showed the presence of Rickettsia antibodies in human populations and domestic animals . In addition , different species of rickettsiae were detected in ticks collected from humans and animals . Our results highlighted the role of domestic animals as sentinels of rickettsial infection to identify areas at risk of transmission , and the importance of preventive measures aimed at curtailing deforestation and the fragmentation of forests as a way of reducing the risk of transmission of emergent and re-emergent pathogens .
Rocky Mountain spotted fever ( RMSF ) is a neglected disease without epidemiological surveillance in Colombia . This disease accounted for several deadly outbreaks in the northwest and the center of the country with a case fatality rate of 26 to 75% [1–3] . Fatal cases were related to delay in doxycycline treatment , the recommended therapy when RMSF is clinically suspected in endemic areas [4] . Most of the RMSF cases in Antioquia department ( i . e . , state ) have been detected by research projects [5] . Previous studies in regions where outbreaks occurred have shown the presence of rickettsial antibodies in the human population as well as in domestic and wild animals [5–8] . However , factors associated with pathogen transmission are largely unknown because RMSF cases are usually undiagnosed or included as fevers of unknown origin in hospital surveillance records . Furthermore , the disease is similar to other febrile syndromes such as Dengue and Leptospirosis [9 , 10] . Although Rickettsia rickettsii and Rickettsia parkeri have been isolated from ticks of the Amblyomma genus collected from dogs and cattle in endemic areas of Colombia [5 , 11 , 12] , there is limited information in this country about the species of ticks infesting humans and the rickettsial infection status of the identified tick species . Surveillance studies that focus on human infestation and tick infection are useful to reduce the number of cases and case fatality rate in endemic areas , as have been shown by surveillance programs implemented in United States [13] . In addition , the inclusion of domestic animals as sentinels of the disease is an essential component of surveillance studies . In Brazil , several reports have shown higher rickettsial infection rates by Rickettsia in domestic animals in endemic areas compared to non-endemic areas [14 , 15] . Regions affected by RMSF outbreaks in northwest Colombia had high levels of unmet basic needs and are affected by violence perpetrated by illegal armed groups [16] . These factors cause migration , changes in land use , deforestation and forest fragmentation near to urban areas , which increase interactions among humans , wild animals and disease vectors [17 , 18] . Studies of infectious diseases with a complex life cycle should include human populations , agents , amplifying hosts and vectors to better understand the ecological relationships governing pathogen transmission . Furthermore , individual and cluster variables are required for a comprehensive analysis of factors affecting the presence of these diseases . Consequently , the aim of this study is to analyze eco-epidemiological aspects of rickettsial seropositivity in two municipalities in northwest Colombia where previous RMSF outbreaks have been reported . The study tests the hypothesis of whether ecological factors ( human , wild and domestic animals relationships ) and social factors ( occupation , sex , age , land use , household characteristics ) influence the prevalence of rickettsial seropositivity in humans .
A cross-sectional study from November 2015 to January 2016 was conducted in nine hamlets from two localities of northwestern Colombia , Alto de Mulatos in the municipality of Turbo ( 8°08'12 . 5"N 76°33'01 . 7"W ) , and Las Changas in the municipality of Necoclí ( 8°32'52 . 5"N 76°34'23 . 7"W ) . Inclusion criteria were: persons of all ages and residents from the hamlets selected in the study , who agreed to participate in the study , and who signed the informed consent . People suspected to be affiliated with illegal armed groups were excluded from the study . Participants were recruited by house-to-house visits in each hamlet . Nine hamlets were selected for convenience: five in Alto de Mulatos and four in Las Changas . Hamlet selection was based on ease of access to the hamlets , shorter distance to the urban center , public safety , number of households and ecological conditions favorable for rickettsiae transmission , such as the presence of domestic animals , opossums , wild or synanthropic rodents , and previous reports of humans bitten by ticks . A finite probabilistic complex sample was designed , in which the sample units were households within the nine hamlets and analysis units were people inhabiting the households . To obtain the sampling frame a population census was conducted in the nine hamlets and information about the number of people , sex and presence of domestic animals in each house was collected . A total of 461 households inhabited by 1915 people were registered in the census . The sample size calculations indicated that 208 households inhabited by 865 persons would suffice to detect associations with 95% level of confidence , considering 5% error and 41% expected prevalence of infection in humans [7] . Sample size was calculated in Epidat 4 . 0 [19] . Households within each hamlet were selected using probability proportional to size sampling . In addition , non-probability sampling of domestic animals was done in the nine hamlets to study the role of canines and equines as sentinels of infection . Blood samples from humans and animals were taken for the serological diagnosis of rickettsial seropositivity by indirect immunofluorescence assay ( IFA ) . Slides with R . rickettsii antigens were used to detect IgG against rickettsiae . The main outcome of the study was rickettsial seropositivity in humans defined as positive titers at 1/128 as previously recommended in endemic areas [20] . Rickettsial seropositivity in domestic animals was also defined as a positive titer at 1/128 . In addition , antibody titers against several rickettsial antigens were investigated in animals to determine the potential species circulating in the study area . Slides containing specific antigens for Rickettsia amblyommatis , R . rickettsii and R . parkeri were used . The potential species of Rickettsia was determined when the IgG titer against that antigen was at least four serial dilutions higher than the IgG titer of other species evaluated . Positive controls were obtained from patients and domestic animals diagnosed by Laboratorio de Ciencias Veterinarias-Centauro , Universidad de Antioquia and Laboratorio de Microbiología , Pontificia Universidad Javeriana . Ticks were collected from December 2015 to May 2016 . Study participants were asked to collect ticks attached to their skin or attached to clothes and place them in containers with 70% ethanol . Ticks were removed directly from dogs and equines during three minutes of collection per animal and were placed in containers with 70% ethanol . Ticks collected from humans and domestic animals were identified using the morphological key by Barros-Battesti et al . [21] . DNA extraction of ticks from all stages was done using Thermo GeneJet DNA purification kit . The DNA of each individual tick or 5-ticks pools ( when ticks were not engorged ) was extracted . Engorged ticks ( >10 mg ) were cut in sterile petri dishes using a sterile scalpel blade and pools were prepared according to the same stage , sex or individual host . To confirm the absence of PCR inhibitors amplification of the 12S mtDNA gene in all collected ticks was performed [22] . In addition , species of ticks infected by rickettsiae were confirmed by sequence analysis of the 12S mtDNA . To detect infection by Rickettsia of the Spotted Fever Group in ticks a real-time PCR amplifying a 146 bp of gltA gene was designed ( gltA-Forward 5’- GCTCTTCTCATCCTATGGCT-3’ , gltA-Reverse 5’- AGACATTGCAGCGATGGTAG-3’ and 5’-56FAM-TGCGGCTGTCGGTTCTCTTGCGGCA-3BHQ_1–3’ . ) . Reactions were done using FastStart Universal Probe Master Mix Roche in LightCycler 96 Real Time-PCR System . Positive samples by Real Time-PCR were confirmed using primers to detect larger fragments of gltA ( 401pb ) and ompA ( 631pb ) [23 , 24] . Positive control used in PCR was DNA extracted from Rickettsia rhipicephali , donated by Dr . Marcelo Labruna , Laboratório de Doenças Parasitárias of Universidade de São Paulo , Brazil . Forward and reverse sequences of 12S mtDNA from ticks and gltA and ompA from rickettsiae were analyzed in Geneious 8 . 1 . 5 [25] . Sequences were aligned to reference sequences from tick species of Rickettsia of the Spotted Fever Group , and the best model for nucleotide substitution was selected by Bayesian Information Criteria in jModelTest 2 . 12 [26] . Bayesian phylogenetic analysis was performed using Markov Chain Monte Carlo ( MCMC ) sampling implemented in MrBayes 3 . 2 . 6 [27] . The exposures of interest were occupation , age ( years ) , time of residence in the area ( years ) , travel routes to the place of work , education level , previous exposure to ticks and previous episodes of fever . Occupation was measured as previous occupation ( within the last five years ) and recent occupation ( at the time of the administration of the questionnaire ) . The variable was categorized as outdoors ( farmers , ranchers and day laborers ) and indoors ( the remaining occupations ) . Attitudes and practices related to rickettsioses were investigated with survey data . Heads of households ( male or female ) were interviewed with a structured questionnaire . The questions related to those practices common among family members . Household variables included: types of floors , walls and roofs , vegetation in the area surrounding the house , location of the house , proximity among households , previous tick infestations , presence of domestic animals ( canines , felines , birds , pigs , donkeys , mules and horses ) , presence of rodents and opossums . As well , there were questions related to household members involved in deforestation and forest fragmentation for agricultural purposes in the area . In addition , attitudes and practices regarding use of white clothes when working outdoors , use of long-sleeved shirts , protection against rodents , dog bathing , and personal cleaning of tick infestation . Proportion of rickettsial seropositivity in domestic animals ( canines and equines ) was estimated in each hamlet by dividing the number of infected animals over the total number of tested domestic animals . Proportions were categorized as less than 20% , between 20 to 40% and more than 40% . Categorization was guided by reports from Brazil , where more than 40% of animals were seropositive in endemic areas for human rickettsioses and less than 20% were seropositive in non-endemic areas [14 , 28] . Median and interquartile ranges were used for the description of quantitative variables . The linearity assumption was confirmed to include quantitative variables in bivariate and multivariate models . Absolute and relative frequencies were used for the description of qualitative variables ( dichotomous and polytomous variables ) . To estimate risk factors for rickettsial seropositivity a weighted multilevel clog-log regression model was used . The model included three levels: individuals within households , households within hamlets ( model random effect ) and hamlets ( model random effect ) . The association between the main outcome ( rickettsial seropositivity in humans ) and variables at each level was evaluated ( household- and hamlet level ) . Variables included in the multivariate model were those with p<0 . 25 in bivariate analysis . The multivariate analysis was done using the stepwise method . The multilevel model of risk factors for rickettsial seropositivity in humans was weighted by the inverse probability of animal selection because animal sampling was not proportional in the hamlets . Confounders were evaluated in multivariate models and the best model explaining the outcome was selected according to Bayesian and Akaike’s Information Criteria ( BIC and AIC , respectively ) . All analyses were performed in SAS 9 . 04 . 01 [29] . Prevalence ratio was calculated using the following formula [30]: P1P0= ( 1−e−e ( b0+b1+∑j=2k−1bjXj ) / ( 1−e−e ( b0+∑j=2k−1bjXj ) Where b0 is the model intercept , b is the regression coefficient ( b1; b2;…bj ) , X are covariables ( X1; X2;…; Xk-1 ) , and e is the base of Napierian logarithms . Prevalence Ratios ( PR ) , 95% confidence intervals and p-values are reported in the models . The Committee of Ethics in Research ( meeting of May 22 , 2014 ) and the Committee for Animal Experimentation ( meeting of June 10 , 2014 ) of the Universidad de Antioquia approved the participation of humans and animals in this study , respectively . All adult participants ( ≥18 years ) signed the informed consent . Children ( <18 years ) were enrolled in the study after parents or guardians signed the informed consent on their behalf . The animal protocol used in this study adhered to the Colombian Law 84 of 1989 regulating the protection of animals against suffering and pain in the Colombian territory . The nucleotide sequences of 12S mtDNA , gltA and ompA genes have been deposited in the GenBank database under the accession numbers MF004422 , MF004423 , MF004424 MF034492 , MF034493 , MF034494 , MF034495 , MF034496 and MF034497 .
A total of 597 people inhabiting 246 households participated in the study . House sampling coverage was 100% with 18 . 27% over-sampling . The sampling coverage of people was 69 . 01% ( 597/865 ) . The overall prevalence of rickettsial seropositivity in humans was 25 . 62% ( 153/597 ) ( 95%CI 22 . 11–29 . 12 ) . The prevalence was 30 . 41% ( 104/342 ) in Las Changas and 19 . 22% ( 49/255 ) in Alto de Mulatos . Prevalence in hamlets varied from 5 . 88% ( 1/17 ) to 37 . 29% ( 22/59 ) . Females accounted for 60 . 8% ( 363/597 ) of the participants . Median age was 29 . 7 years ( IQR 15 . 3–46 . 1 ) and the median of the residence time in the hamlets was 11 years ( IQR 6–19 ) . In addition , 27 . 30% ( 163/567 ) of the population had worked in the last five years or had recently worked outdoors . Most of the participants were bitten by ticks in the previous days or years from the date of sampling ( 92 . 29% , 551/597 ) . Other characteristics of the study population are presented in S1 Table . 46 . 34% of the households had a least one seropositive person . From 246 households , 44 . 72% ( 110/246 ) had thatched roofs ( palm or cane ) , 69 . 92% ( 172/246 ) had complete or partial zinc roofs , 4 . 07% had complete or partial tiled roofs and 5 . 69% had complete or partial wood roofs ( S1 Table ) . Complete or partial dirt soil was present in 72 . 36% ( 178/246 ) of households and 4 . 47% ( 11/246 ) had complete or partial tile floors . In 88 . 21% ( 217/246 ) of households walls were made totally or partially of wood and 31 . 71% ( 78/246 ) of households had complete or partial brick walls . Other household characteristics are presented in S1 Table . The overall proportion of rickettsial seropositivity in canines and equines was 34 . 03% ( 81/238 ) . Hamlets from Las Changas had a higher proportion of seropositivity ( 48 . 52% , 66/136 ) compared to hamlets from Alto de Mulatos ( 14 . 71% , 15/102 ) . The proportion of seropositivity in equines was 30 . 88% ( 42/136 ) and 38 . 23% ( 39/102 ) in canines . Antibodies titers against R . rickettsii antigens in domestic animals from both localities were variable , ranging from 1/128 to 1/16384 . Analysis of titers against rickettsial antigens showed R . amblyommatis probably infected two horses , one donkey and one dog from Las Changas . R . rickettsii probably infected six donkeys and two horses from Las Changas and two mules from Alto de Mulatos . Finally , R . parkeri probably infected one dog from Alto de Mulatos ( Table 1 ) . A total of 458 ticks were collected , 49 . 78% ( 223/458 ) were Amblyomma nymphs , 19 . 65% ( 90/458 ) Amblyomma larvae , 16 . 16% ( 74/458 ) Amblyomma cajennense s . l . adults , 1 . 53% ( 7/458 ) Amblyomma ovale adults , 1 . 09% ( 5/458 ) Amblyomma dissimile adults , 8 . 30% ( 38/458 ) Dermacentor nitens nynphs , 2 . 40% ( 11/458 ) Rhipicephalus microplus and 0 . 66% ( 3/458 ) Rhipicephalus sanguineus adults ( Table 2 ) . Two ticks collected from humans in Las Changas were infected by Rickettsia . One tick biting a person was closely related to Amblyomma varium in the Bayesian phylogenetic analysis of 12S mtDNA with 100% posterior probability support . Phylogenetic analysis of rickettsial gltA showed a close relationship to Rickettsia honei ( 99% posterior probability support ) , while ompA showed close a relationship to R . amblyommatis ( 98% posterior probability support ) . The second tick was attached to the clothes of one person and it showed a close relationship to A . dissimile by 12S mtDNA ( 100% posterior probability support ) ( Fig 1 ) . Sequence analysis of gltA and ompA showed high similarity to Candidatus Rickettsia colombianensi ( 99 and 100% posterior probability support , respectively ) ( Figs 2 and 3 , respectively ) . A total of 433 ticks from Amblyomma genus were collected in Alto de Mulatos and Las Changas in January 2016 . Identification of ticks revealed 133 ( 30 . 72% , 133/433 ) A . cajennense adults , 68 ( 15 . 70% , 68/433 ) A . ovale adults , 137 ( 31 . 64% , 137/433 ) nymphs and 95 ( 21 . 94% , 95/433 ) larvae ( Table 2 ) . Rickettsial infection was detected in three pools ( two , three and five individuals ) of A . ovale ( 100% posterior probability support in Bayesian analysis of 12S mtDNA ) infesting dogs from Las Changas ( Fig 1 ) . The Bayesian phylogenetic analysis of gltA and ompA showed a close relationship to Rickettsia sp . strain Atlantic Rainforest with more than 90% posterior probability support ( Figs 2 and 3 , respectively ) . The individual level variables included in multivariate analyses were occupation , sex , age and time of residence in the area . The household level variables were deforestation and forest fragmentation for agriculture use and presence of opossum in peridomiciliary areas . Hamlets level variable was proportion of seropositivity in domestic animals ( Table 3 ) . A comparison of BIC and AIC criteria showed the best model explaining the outcome included the variables sex , age , occupation , deforestation for agriculture use , presence of opossum in peridomiciliary area and proportion of seropositivity in domestic animals ( Model 3 , Table 4 ) . Household and hamlets random effects significantly explained the variability in the outcome in models 1 and 2 , and only household random effect significantly explained the variability in outcome in model 3 ( Table 4 ) . Male sex and age in years were risk markers for rickettsial seropositivity . The prevalence of seropositivity was 1 . 65-fold higher in men compared to women ( PR = 1 . 65 95%CI 1 . 43–1 . 90 ) . For each additional year of age , the prevalence of seropositivity increases by 1% ( PR = 1 . 01 95%CI 1 . 01–1 . 02 ) . Working outdoors had 1 . 20-fold increase in the prevalence of seropositivity compared to working indoors ( Model 3 , Table 4 ) . Sex and age in years confounded the association between working outdoors and rickettsial seropositivity ( PRadjusted ( sex and age ) = 1 . 22 95%CI 1 . 04–1 . 43; PRcrude = 1 . 77 95%CI 1 . 33–2 . 14 ) ( Model 1 in Tables 4 and 3 , respectively ) . In addition , the prevalence of seropositivity was 1 . 75-fold higher in people using deforested lands for agriculture compared to people who did not deforest ( PR = 1 . 75 95%CI 1 . 51–2 . 02 ) and 1 . 56-fold higher in households where opossums were reported in the peridomiciliary area compared to households without opossum reports ( PR = 1 . 56 95%CI 1 . 37–1 . 79 ) ( Model 3 , Table 4 ) . Interestingly , the proportion of seropositivity in domestic animals was an indicator of the serological status in humans . The prevalence of seropositivity in humans was 2 . 28-fold higher in hamlets where the proportion of seropositivity in domestic animals was 20% to 40% compared to hamlets where the proportion was less than 20% . Similarly , the prevalence of seropositivity in humans was 3 . 1-fold higher in hamlets where the proportion of seropositivity in domestic animals was more than 40% compared to hamlets with less than 20% ( PR20-40% vs <20% = 2 . 28 95%CI 1 . 59–3 . 25 y PR>40% vs <20% = 3 . 14 95%CI 2 . 43–4 . 07 ) ( Model 3 , Table 4 ) .
Our results showed a lower prevalence of rickettsial seropositivity in humans ( 25 . 62% ) than in a previous study conducted in the same region ( 35–41% ) [7] . This study had been conducted only in urban centers from both localities , while in our study hamlets were also included . High proportions of seropositivity in humans and domestic animals were found in La Union , La Salada and El Cativo hamlets in Las Changas . These results were similar to previous reports in endemic areas of Brazil [14] . Our study identified several factors associated with rickettsial seropositivity . Among individual-level variables , working outdoors was a risk for rickettsial infection due to people having greater exposure to ticks infesting woodland and grasses . Of note , participants from Las Changas worked outdoors more frequently ( 33 . 92% ) than participants from Alto de Mulatos ( 18 . 43% ) , and consequently this population was more exposed to tick infestation . Similarly , in North Carolina in United States , it has been reported that people working outdoors had a higher risk of tick infestation and rickettsial infection [31] . However , tick infestation could be also determined by factors influencing household infestation , e . g . , household materials , presence of domestic animals and rodents , among others [32] . Most of the study participants ( 92 . 29% ) had been bitten by ticks during their lifetime , either recently or several years prior to the time of the inclusion in the study ( S1 Table ) . These findings suggest that the risk of infestation exists in peri and intra-domiciliary areas . We also found male sex and age were risk markers for rickettsial seropositivity . In these hamlets , men usually have outdoor occupations ( e . g . as farmers , ranchers or day laborers ) , which increases the probability of being exposed to ticks as aforementioned . Accordingly , 75 . 50% of males worked outdoors and 74 . 42% of females worked indoors . In addition , older people were more frequently infected than younger participants . Older people had been exposed to rickettsiae for a longer period of time , which increased the opportunity of becoming infected . Consequently , seropositive people had a median age of 37 . 1 years ( IQR 19 . 3–52 . 8 ) , compared to median age of 27 . 5 years ( IQR 14 . 4–44 . 4 ) for seronegative participants . Likewise , participants that once worked outdoors had a median age of 44 . 3 years ( IQR 29 . 7–57 . 1 ) , while participants that work or worked indoors had a median age of 22 . 7 years ( IQR 13–41 . 4 ) . These results are consistent with reports from Cameroon , where age was also a risk marker for infection with R . africae , a bacterium causing a clinical syndrome similar to R . parkeri . In this previous study , the odds of infection increased by 80% in people aged 36–45 years compared to 16–25 years [33] . Among household-level variables , deforestation for agricultural use and the presence of opossums in peri-domiciliary areas were risk factors for rickettsial seropositivity . Frequent deforestation and fragmentation as observed in both areas facilitates human-vector interactions in the forest edge zones . This phenomenon was also observed in Lyme disease on Rhode Island , where edge zones presented a higher risk of tick infestation and consequently a higher risk of pathogen transmission [34] . Recently , the association between forest fragmentation and rickettsial infection in canines and RMSF in humans was investigated in São Paulo . The study reported that areas impacted by urban settlements or farming had a higher number of human RMSF cases [35] . Forest fragmentation significantly reduces both diversity and species composition of mammals subsequently reducing their interactions . In such cases , ticks parasitize available hosts ( canines or humans ) , influencing the risk of human exposure to rickettsiae [36–39] . For instance , Amblyomma aureolatum , whose main host is Cerdocyon thous , a carnivorous mammal present in non-fragmented forests [40] , can parasitize canines when forests are fragmented and competition among mammals decreases [41–43] . In such cases , A . aureolatum , one of the main vectors of R . rickettsii in Brazil , could infest dwellings and peridomiciliary areas [35 , 44 , 45] , and become a risk for rickettsial infection for humans . Similarly , our results showed that canines and equines were infested with A . cajennense s . l . , a tick species frequently found infesting humans with a potential role in the transmission of rickettsiae in the study area . Exposure to opossums increased the prevalence of rickettsial seropositivity suggesting a possible role in pathogen transmission . However , as the presence of opossums in peri-domiciliary was reported by the participants , information bias could exist . The role of marsupials as amplifying hosts of rickettsiae in Colombia is obscure , yet in Brazil the opossum Didelphis aurita can have rickettsemia for long periods of time and therefore can become a source of infection for several tick species [46] . Recently , a high proportion of seropositives were detected in opossum Didelphis marsupialis in a previous study conducted in the Urabá region , supporting the role of these mammals in the life cycle of rickettsiae [47] . In addition , at the hamlets-level proportion of seropositivity in equines and canines were associated to the prevalence of rickettsial seropositivity in humans . This information suggests equines and canines could be sentinels for rickettsial infections , although this role is still debated in Brazil spotted fever endemic zones [8 , 14 , 48–50] . Consequently , studying prevalence of seropositivity in domestic animals could detect new areas of transmission where RMSF have not been diagnosed or are undiagnosed because of the lack of surveillance systems . In addition , high antibody titers in asymptomatic sentinels such as equines could indicate recent infections [51] . In our study , high antibody titers ( 1/16 , 384 ) detected in domestic animals suggesting that recent infections are occurring in the region . Additionally , antibody titers also revealed the circulation of R . rickettsii , R . amblyommatis and R . parkeri in domestic animals . Furthermore , two of these species ( R . amblyommatis and R . parkeri ) were also detected in ticks infesting canines and humans , indicating the potential transmission of these rickettsiae among hosts . Similarly , previous studies in Colombia have detected ticks infected by R . amblyommatis , R . parkeri and R . rickettsi [11 , 12 , 52] , and the latter species also have been reported circulating in canines and equines in Brazil [8] . Of note , only RMSF cases by R . rickettsii have been reported in the region and therefore the potential role of other rickettsiae species detected in ticks in our study should be investigated . In our study , most of the ticks infesting humans were from Amblyomma genus . Accordingly , most of households had bushes ( 91 . 46% , 225/246 ) , pasture ( 51 . 22% , 126/246 ) or trees ( 86 . 18 , 212/246 ) in the peridomiciliary area , which are the sites preferred by Amblyomma ticks [32] . Importantly , humans were infested mainly by nymphs , which are known to be the most anthropophilic stage . In Brazil the majority of rickettsioses cases occur in winter and spring , seasons related to peaks of nymph abundance and infestation , mainly of A . cajennense s . l . [32] . Unexpectedly , R . amblyommatis was detected in one nymph of A . varium . This finding could explain why new clinical cases caused by R . rickettsii have not been diagnosed in the Urabá region . It has been suggested that in the United States the case fatality rate of RMSF decreased because the expansion of Amblyomma americanum , the vector of R . amblyommatis . The disease caused by R . amblyommatis is usually mild and the antigenic cross-reaction with other rickettsiae from the spotted fever can protect against virulent species , such as R . rickettsii [53] . In this study , Candidatus R . colombianensi was detected in ticks potentially infesting humans . This finding is of acaralogical and epidemiological interest since the status of this species as pathogen is unknown . In previous studies , Candidatus R . colombianensi was detected in ticks collected from iguanas ( Iguana iguana ) [54 , 55] and in one nymph of Amblyomma collected in spiny rat ( Proechimys semiespinosus ) [5] . In addition , R . parkeri strain Atlantic Rainforest was the only species detected in ticks A . ovale from canines . This species has been implicated in human clinical cases in rural regions of Brazil , characterized by eschar , fever , rashes on the legs and arms , and muscular and joint pain [56] . In our study , A . ovale infested humans in both localities , suggesting the potential role of R . parkeri as a cause of disease . This study has several limitations . First , we had a lower coverage of sampling in humans . Because not all the people in the selected households agreed to participate , a high risk of selection bias is present in the study . Gaining people’s confidence to participate in the research project was difficult due to the increasing violence , presence of illegal armed groups and migration in the Urabá region . Second , information regarding variables at the household level should be measured using questionnaires in each age group to confirm the information retrieved at this level . However , in this study it was considered that behaviors and habits are similar among household members as well as within hamlets and regions . Likewise , risk perception and health care are also shared among people depending where they have lived or grown-up [57] . Fourth , although the collection of ticks in humans was voluntary , information retrieved about species infesting humans is essential to gain knowledge of species involved in rickettsiae transmission . Of note , our results showed a rich diversity of tick species infesting humans , including ticks commonly infesting canines , equines , bovines , turtles and iguanas . The moderate to high prevalence of rickettsial seropositivity in humans and animals suggests circulation of rickettsiae in both populations . To obtain a better insight on circulation of rickettsiae among humans , it is necessary to diagnose clinical cases of the disease or at least to estimate the incidence of infection in areas where RMSF cases have been reported . Most of the studies on rickettsiae in Colombia are descriptive or cross-sectional , which makes it difficult to interpret the temporality of the effects estimated [58–60] . In conclusion , our study showed that working outdoors is a major factor associated to rickettsial seropositivity , and is influenced by age and sex . In addition , our results highlighted the role of domestic animals as sentinels of disease in areas with circulation of rickettsiae . Furthermore , preventive measures to avoid deforestation and forest fragmentation should be taken in the region to decrease the risk of pathogen transmission . Finally , new studies that seek to assess the strength of the evidence related to factors associated to RMSF cases are necessary to ensure the appropriate diagnosis and timely treatment of this neglected disease and consequently to decrease the case fatality rate caused by this disease . | Rocky Mountain spotted fever is one of the main diseases transmitted by tick bites in Colombia . Studies examining rickettsial seropositivity in humans , potential vectors and amplifying hosts in regions where previous outbreaks occurred are necessary to highlight this disease in the differential diagnosis of febrile syndromes and to implement epidemiological surveillance programs . This study reveals several factors associated with rickettsial seropositivity , including working outdoors , practices related to deforestation and forest fragmentation , and the potential contact between humans and wild animals , such as opossums , that could be involved in the transmission cycle . In addition , it reveals the importance of domestic animals as sentinels of infection as well as the tick species acting as potential vectors of rickettsiae in human and domestic animals . | [
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... | 2017 | Eco-epidemiological analysis of rickettsial seropositivity in rural areas of Colombia: A multilevel approach |
Trypanosomiasis induces a remarkable myenteric neuronal degeneration leading to megacolon . Very little is known about the risk for colon cancer in chagasic megacolon patients . To clarify whether chagasic megacolon impacts on colon carcinogenesis , we investigated the risk for colon cancer in Trypanosoma cruzi ( T . cruzi ) infected patients and rats . Colon samples from T . cruzi-infected and uninfected patients and rats were histopathologically investigated with colon cancer biomarkers . An experimental model for chemical myenteric denervation was also performed to verify the myenteric neuronal effects on colon carcinogenesis . All experiments complied the guidelines and approval of ethical institutional review boards . No colon tumors were found in chagasic megacolon samples . A significant myenteric neuronal denervation was observed . Epithelial cell proliferation and hyperplasia were found increased in chagasic megacolon . Analyzing the argyrophilic nucleolar organiser regions within the cryptal bottom revealed reduced risk for colon cancer in Chagas’ megacolon patients . T . cruzi-infected rats showed a significant myenteric neuronal denervation and decreased numbers of colon preneoplastic lesions . In chemical myenteric denervated rats preneoplastic lesions were reduced from the 2nd wk onward , which ensued having the colon myenteric denervation significantly induced . Our data suggest that the trypanosomiasis-related myenteric neuronal degeneration protects the colon tissue from carcinogenic events . Current findings highlight potential mechanisms in tropical diseases and cancer research .
Over a century ago the Brazilian physician Carlos Chagas described the American trypanosomiasis , named after him the “Chagas disease” [1] . Fritz Koberle , one of the very founders of our department , investigated for decades the Chagas disease hypothesizing the “neurogenic theory” [2] . While Chagas had previously observed that the brainstem undergo inflammatory degeneration due to the Trypanossoma cruzi ( T . cruzi ) infection [1] , Koberle proposed the T . cruzi-related degeneration of the peripheral nervous system promote the “gross enlargement of the oesophagus , colon , and heart” [2] . A careful reading of the original Chagas’ manuscript reveals he might catch a glimpse of the relationship between the trypanosomiasis-induced neuronal degeneration and Chagas heart disease , which , in fact , was proven by Koberle [1 , 2] . Major health concern lies on the poor living conditions of those 8 to 10 million American trypanosomiasis-infected patients . Regardless of the fact that neither all infection cases show symptoms nor all acute patients develop the chronic disease , about 12 , 500 people die annually by Chagas disease in Latin America [3 , 4] . Great efforts have been launched to control new cases of the Chagas disease in the whole American continent , from which “The London Declaration on Neglected Tropical Diseases” is the most recent initiative [5] . Although T . cruzi-infection is a life-threatening condition , scientific research has shown new potential avenues for cancer therapy understanding this parasite activity on the immune system [6 , 7] . A 47-kDa chaperone that has been named T . cruzi calreticulin ( TcCRT ) binds to the C1 complement element and the mannan-binding lectin ( MBL ) modulating immune response and angiogenesis [7 , 8] . Interestingly , T . cruzi-related modulatory activity on immune system has been reported to decrease not only tumor growth , but also cancer-related angiogenesis [8 , 9] . For the last fifteen years , our research group has been exploring the why Chagas patients do not develop colon tumors within the megacolon segment [10–14] . In two chagasic megacolon patients , we previously reported adenocarcinomas in the nondilated transverse portion of the colon [13] . Analyzing 894 chagasic megacolon cases , we found neither tumors nor preneoplastic lesions , like polyps , within the colonic dilated region [14] . Furthermore , our previous experiments revealed T . cruzi-infected rats develop less colon tumors than its control group [11] . Chemical myenteric neuronal ablation was also shown protective against the development of colon tumors [15] . Recently , neurons have been suggested to promote prostate cancer , as they enhanced tumor invasion and metastasis [16] . Liebl et al have however shown no great impact of neural invasion on colon cancer [17] . These authors then revealed a migration of Schwann cells toward colon tumors occur since early malignant stages [18] . Recently , we discussed how the neuronal activity might promote the development of colon carcinogenesis [19] . Colon carcinogenesis is indeed a complex malignancy and the third most common cancer worldwide [20] . Over 1 . 2 million patients endure colon cancer in the USA , while prospective data suggest about 150 thousand people will be newly diagnosed per year [21] . Colon carcinogenesis develops throughout a multi-stepped sequences of changes [22] , from which early lesions transit towards tumors [22–24] . Thus , genetic mutations turn first into detectable preneoplastic lesions before tumors are detected [24] . Bird identified these histological changes in the colon of carcinogen-exposed rodents , naming them aberrant dysplastic crypts ( ACF ) [23] . Carcinogen-induced changes thus happen in single crypts , which will show altered structure ( as width , height , thickness , and luminal opening ) due to genomic instability and aberrant cell growth . Researchers have suggested large ACF numbers enhance the risk for colon cancer [25–27] . Current investigation aimed to clarify the why chagasic patients do not develop colon tumors within the megacolon region . We reveal here the myenteric neurons are pivotal elements for the initiation of colon carcinogenesis since its early stages in humans and rats .
In spite of potential T . cruzi effects modulating the immune system have been suggested to reduce other tumors besides colon cancer [7–9] , how the major T . cruzi effect on the colon , that is the degeneration of myenteric neurons [11] , impacts on colon carcinogenesis are not fully understood yet . Indeed , our latest hypothesis suggests the enteric nervous system play a pivotal role in the colon carcinogenesis [19] . Here , histopathological analysis confirmed a significant myenteric neuronal denervation in chagasic megacolon patients ( Fig 1A and 1B ) . Such condition was however associated with high-epithelial cell proliferation in the colon ( Fig 1C ) . Also , hyperplastic crypts were largely found in chagasic megacolon patients ( Fig 1D ) . Analyzing the AgNOR staining within the cryptal bottom revealed however reduced risk for colon cancer in Chagas megacolon patients ( Fig 2A and 2B ) . Next , another analysis was performed to enumerate the number of AgNOR dots per cell nucleus . Here , we found as much dots a cell had , as much rare it was found in chagasic megacolon ( Fig 2C and 2D ) . Ascertaining the relationship between myenteric neuronal activity and colon carcinogenesis , in vivo experiments were performed . Rats were weekly exposed to a carcinogen after 86-days from T . cruzi infection . Following those 12-wks of carcinogenic exposure , T . cruzi-infected rats developed significant less colon preneoplastic lesions than their uninfected control group ( Fig 3A; DMH , 2 . 89 ± 2 . 1 vs T . cruzi+DMH , 1 . 05 ± 0 . 94 T . dysplastic lesions per mm2; p<0 . 04 ) . Interestingly , a significant colonic myenteric neuronal denervation was observed in that carcinogen-exposed and T . cruzi-infected group ( Fig 3B; DMH , 0 . 15 ± 0 . 04 vs T . cruzi+DMH , 0 . 05 ± 0 . 01 M . myenteric neurons per T . crypts; p<0 . 0001 ) . No preneoplastic lesions were observed in rats that did not undergo carcinogenic exposure . Then , we set out to investigate whether this modulation on myenteric neuronal density plays a pivotal role during the early steps of colon carcinogenesis . A well-established chemical denervation model was applied in carcinogen-exposed rats [34] . Puzzling , preneoplastic lesions were found decreased from the 2nd wk onward ( Fig 3C ) , which ensued having the colon myenteric denervation significantly induced ( Fig 3D ) . Yet , Hartmann's procedure was performed to clarify the activity of myenteric neurons aside from the colonic fecal content . With low mortality rates in humans [35] , such procedure results in an end colostomy and colonic stump closure . Having the carcinogen exposure before surgery , preneoplastic lesions develop without any potential effects from the fecal content on them . Again , colon preneoplastic lesions developed significantly less in myenteric denervated rats ( Fig 3E and 3F ) . Our collective data demonstrate myenteric neurons are a key-factor for the development of early carcinogenic lesions in the colon . Neuronal activity has been previously shown essential for the colonic homeostasis . For instance , NSE-noggin and Hand2+/- mice ( transgenic models with more or less enteric neural innervations than normal , respectively ) were exposed to dextran sulfate sodium to induce intestinal inflammation . Hand2+/- mice showed much lower inflammatory signals than wild-type littermates , which did not occur in NSE-noggin mice [36] . However , colitis was previously observed in chagasic megacolon patients [37] , as well as such condition is a well known risk factor for colon cancer [38] . Interestingly , we found here no tumors within the megacolon segment . T . cruzi-infected patients undergo deep and complex modulations throughout the development of megacolon . For instance , an acute transitory immunosuppression has been found in T . cruzi-infected patients [39 , 40] , in spite of colonic enteric ganglia undergo unremitting cytotoxic T cell invasion for the chronic development of chagasic megacolon [41] . Considering that regardless of infectious agents chronic inflammation promotes colon cancer [42] , the most stable factor known to promote colon cancer but missing in chagasic megacolon is the myenteric neurons . This indeed supports our previous hypothesis that colonic neurons exposed to a carcinogen might enhance the development of carcinogenic initiated colonocytes into preneoplastic lesions [19] . Despite a neural invasion has not been observed in colon tumors [17] , as reported in prostate cancer [16] , the role of neuronal elements in the colon carcinogenesis cannot be ruled out [18] . Puzzling , reports have shown neuronal denervation leading to megaesophagus did not protect chagasic patients from the development of malignancies at this gastrointestinal upper region [43 , 44] . Despite such data appear controversial at first it actually supports the idea of a neuronal activity specifically promoting preneoplastic lesions in the colon . Actually , a specific stimulus could induce distinct effects in different body tissues . For instance , human antigen R ( HuR ) has different activities in azoxymethane-exposed small bowels and colon tissues; whereas it enhances prosurvival gene expression in the small bowels , colonic epithelial HuR downregulates specific proapototic RNAs in carcinogen-exposed mice [45] . Thus , we also showed myenteric neurons promote the development of colon tumors in two different experimental models [11 , 15] . Considering our latest hypothesis that epithelial cells and neuronal elements might undergo carcinogenic changes in the colon tissue [19] , current investigation demonstrate that the neuronal activity is essential for the development of early colonic carcinogenic events . Taken together , chagasic megacolon seems a natural model to study how the enteric nervous system impacts on colon tissue . We thus believe myenteric neurons promote the colon carcinogenesis since its early stages . This might clarify the why tumors do not develop within the chagasic megacolon region . It seems reasonable to suggest that besides the direct effects of a carcinogen on colonic epithelial cells , neuronal activity is also required for the full development of colon preneoplastic lesions . | The myenteric neuronal activity on colon carcinogenesis is a matter of debate . Chagas disease ( a trypanosomiasis-related chronic infection ) induces megacolon damaging myenteric neurons . Puzzling , tumors have been rarely reported in chagasic megacolon patients . We reveal here hyperplasia-related high-proliferation occurs in chagasic megacolon , although the risk for colon cancer is reduced . Having carcinogen-exposed rats infected with Trypanosoma cruzi reduced the numbers of myenteric neurons and colon preneoplastic lesions . An experimental model for chemical myenteric denervation was applied in carcinogen-exposed rats revealing that myenteric neurons promote the development of colon preneoplastic lesions . Yet , activity of the fecal content had to be secluded from the myenteric neuronal activity on colon carcinogenesis . Hartmann’s surgical procedure enabled that . This was applied together with carcinogenic exposure and myenteric neuronal denervation ensuring that the neuronal activity is associated with enhanced development of colon carcinogenesis . Taken together , we believe colon tumors are not found within the chagasic megacolon region because the myenteric neuronal density is impaired . These observations shed lights on novel potential cell to cell interactions promoting the colon cancer development . | [
"Abstract",
"Introduction",
"Results",
"and",
"Discussion"
] | [] | 2015 | Trypanosomiasis-Induced Megacolon Illustrates How Myenteric Neurons Modulate the Risk for Colon Cancer in Rats and Humans |
The epigenetic modification of chromatin structure and its effect on complex neuronal processes like learning and memory is an emerging field in neuroscience . However , little is known about the “writers” of the neuronal epigenome and how they lay down the basis for proper cognition . Here , we have dissected the neuronal function of the Drosophila euchromatin histone methyltransferase ( EHMT ) , a member of a conserved protein family that methylates histone 3 at lysine 9 ( H3K9 ) . EHMT is widely expressed in the nervous system and other tissues , yet EHMT mutant flies are viable . Neurodevelopmental and behavioral analyses identified EHMT as a regulator of peripheral dendrite development , larval locomotor behavior , non-associative learning , and courtship memory . The requirement for EHMT in memory was mapped to 7B-Gal4 positive cells , which are , in adult brains , predominantly mushroom body neurons . Moreover , memory was restored by EHMT re-expression during adulthood , indicating that cognitive defects are reversible in EHMT mutants . To uncover the underlying molecular mechanisms , we generated genome-wide H3K9 dimethylation profiles by ChIP-seq . Loss of H3K9 dimethylation in EHMT mutants occurs at 5% of the euchromatic genome and is enriched at the 5′ and 3′ ends of distinct classes of genes that control neuronal and behavioral processes that are corrupted in EHMT mutants . Our study identifies Drosophila EHMT as a key regulator of cognition that orchestrates an epigenetic program featuring classic learning and memory genes . Our findings are relevant to the pathophysiological mechanisms underlying Kleefstra Syndrome , a severe form of intellectual disability caused by mutations in human EHMT1 , and have potential therapeutic implications . Our work thus provides novel insights into the epigenetic control of cognition in health and disease .
Modification of chromatin structure regulates many aspects of cell and developmental biology . Epigenetic regulators are known to affect complex neuronal processes such as learning and memory [1]–[3] and contribute significantly to the occurrence of cognitive disorders , such as schizophrenia and intellectual disability ( previously referred to as mental retardation ) [4] , [5] . However , little is known about the “writers” of the neuronal epigenome that lay down the basis for proper cognition . Are these chromatin writers required to safeguard neuronal homeostasis/fitness by influencing the expression of a large and heterogeneous group of genes , such as house-keeping and non-neuronal genes ? Or do they lay down specific epigenetic programs to regulate neuronal genes that are directly involved in determination of connectivity , plasticity , learning , and memory ? The euchromatin histone methyltransferases ( EHMTs ) are a family of evolutionarily conserved proteins that write part of the epigenetic code through methylation of histone 3 at lysine 9 ( H3K9 ) [6]–[10] . In mammals , two EHMT paralogs exist , EHMT1/GLP and EHMT2/G9a . Heterozygous mutations or deletions of the human EHMT1 gene cause Kleefstra Syndrome ( OMIM #610253 ) , a neurodevelopmental disorder that is characterized by autistic-like features and severe intellectual disability [11]–[14] . Studies in mice have shown that Ehmt1/GLP and Ehmt2/G9a form a heterodimeric complex [8] , and that loss of either protein resulted in nearly identical phenotypes , such as early embryonic lethality , reduced H3K9 dimethylation ( H3K9me2 ) , and inappropriate gene transcription [7] , [8] . Furthermore , mice with neuronal ablation of Ehmt1/GLP and Ehmt2/G9a in adulthood show defects in fear conditioned learning . A gene expression study in different brain areas of these mice has led to the suggestion that EHMT proteins act as repressors of non-neuronal genes in neuronal tissues [15] . Here , by using classic Drosophila genetics , extensive neurodevelopmental and behavioral phenotyping , expression profiling , and genome-wide mapping of EHMT target loci by H3K9me2 profiling , we uncover EHMT as a key epigenetic regulator of neuronal genes and processes .
We set out to study EHMT/G9a in Drosophila . In contrast to mammals , which have two EHMT genes , flies possess a single ortholog [6] , [10] that we will subsequently refer to as EHMT throughout our manuscript . Phylogenetic analysis of the EHMT protein family in Drosophila , human , and mouse shows that Drosophila EHMT is equally similar to both EHMT1 and EHMT2/G9a ( Figure 1a ) . We first examined expression and subcellular localization of the EHMT protein in the fly nervous system using an anti-EHMT antibody [10] . In the adult brain EHMT staining is widely abundant , in a pattern resembling nuclear DAPI staining ( Figure 1b ) . Analysis at single cell resolution in the ventral nerve cord of third instar larvae demonstrates that EHMT is localized in the nuclei of neurons as revealed by colocalization with the neuronal nuclear marker elav ( Figure 1c ) [16] . Weaker EHMT staining colocalized with repo ( Figure 1d ) , a nuclear glial marker [17] . EHMT staining was also observed in the nuclei of the larval multiple dendrite ( md ) sensory neurons of the peripheral nervous system labeled using the 109 ( 2 ) 80-Gal4 driver [18] to express memGFP ( Figure 1e ) . In addition , EHMT staining was observed at low levels in non-neuronal tissues such as the muscle and epidermis ( Figure S1c ) . The anti-EHMT immunolabeling is specific , as it is absent in EHMT mutant embryos , md neurons , adult brains , and larval body-walls ( Figures 2c and S1 ) . These data reveal that EHMT is widely expressed in the Drosophila nervous system . In order to uncover the functions of EHMT we generated deletions in the EHMT gene by excision of a P-element , KG01242 , located in the 5′ UTR . We screened 80 independent excision lines and identified two downstream deletions ( DD ) resulting from imperfect excisions of KG01242 . Both deletion strains are viable to adulthood , which is consistent with a viable EHMT knock-out allele generated by homologous recombination in Drosophila [19] . EHMTDD1 and EHMTDD2 lack 870 and 1473 base pairs of DNA downstream of the original P-element insertion site , respectively , including the EHMT translational start site ( Figure 2a ) . We also isolated a precise transposon excision line that represents the same genetic background as our deletion lines and served as a control in all subsequent experiments ( referred to as EHMT+ ) . Western blot analysis revealed a band of the expected size ( 180 kDa ) in EHMT+ embryonic protein extracts , which was absent in extracts from both deletion lines ( Figure 2b ) . No extra bands were detected by the C-terminally directed EHMT antibody [10] that would point to expression of an N-terminally truncated protein . EHMT protein was also undetectable by immunohistochemistry in EHMT mutant embryos , md neurons , and adult brains , while showing a nuclear staining pattern in EHMT+ animals ( Figures 1 , 2c , and S1 ) . Expression of the neighbor gene , CG3038 , was not affected by the deletions ( Figure S2 ) . These data show that EHMTDD1 and EHMTDD2 are strong and specific loss of function mutants , most likely complete null alleles . Next , we examined several aspects of neuronal development in EHMT mutant flies . Analysis of adult mushroom body architecture , synaptic morphology of the larval neuromuscular junction , and adult photoreceptor function ( assessed by electroretinography ) ( Figure S3 ) as well as analysis of embryonic nervous system integrity ( unpublished data ) did not reveal significant differences in mutant versus control conditions , indicating that general nervous system development and neuronal function is not affected . Loss of EHMT did however result in altered dendrite development in multidendrite ( md ) neurons , which are sensory neurons that tile the larval body wall . We specifically examined dendritic arbors of type 4 md neurons , which were highlighted using the 477-Gal4 driver [20] and the UAS-Gal4 system [21] ( Figure 3a and 3b ) . These neurons are highly stereotyped in their number , position , and morphology , thus allowing for quantitative analysis of dendritic arbors of identical neurons in different animals and genotypes . While the basic organization of these arbors is maintained in EHMT mutants ( primary branches labeled I , II , III , and IV in Figure 3a and 3b ) , reduction of higher order branching resulted in dendritic fields of appreciably reduced complexity ( Figure 3a , 3a' versus 3b , 3b' ) . We quantitatively assessed this defect by counting the number of dendrite ends per standardized field of view in stacked confocal images . This analysis confirmed that EHMTDD1 and EHMTDD2 had a consistent and statistically significant decrease in the total number of dendrite ends , showing 16 and 17 . 5 percent reduction , respectively , when compared to EHMT+ ( Figure 3c ) . To address whether this phenotype results cell-autonomously from EHMT deficiency in neurons , we generated UAS-EHMT transgenic flies and performed cell-specific rescue experiments ( Figure 3c ) . Re-expression of EHMT in mutant type 4 md neurons ( using 477-Gal4 ) did indeed rescue dendrite branching towards wild-type levels ( Figure 3c , red and orange bars ) . This reversal is specific , since expression of EHMT in the EHMT+ genetic background did not increase branching ( Figure 3c , black bars ) . Rather , EHMT overexpression appeared to reduce dendrite branching as compared to controls expressing Gal4 and GFP in the absence of UAS-EHMT , although this reduction was not statistically significant ( Figure 3c , black bars ) . These data show that EHMT is cell-autonomously required in type 4 md neurons to establish normal dendrite complexity . Drosophila md neurons are important in the regulation of larval locomotion behavior [22]–[24] . We therefore examined larval locomotory patterns during the early third instar using an established larval foraging assay ( Figure 4 ) [25] . Larval crawling paths were analyzed for total path length over a 5 min period and for specific crawling patterns , such as branched versus straight paths . The total path length covered by foraging larvae was not different between mutants and EHMT+ controls ( Figure 4b ) , indicating that crawling ability is not hindered in these larvae . However , striking differences in larval locomotory patterns were observed between mutant and wild-type . Foraging EHMT mutant larvae often stop , retract , and turn , causing increased branching in their crawling paths ( Figure 4a ) . Quantitative analysis of the length of the resulting side branches revealed an increase of approximately 4-fold and 2-fold , respectively , in EHMTDD1 and EHMTDD2 ( Figure 4c ) . Thus , the dendrite phenotype of EHMT mutant larvae is associated with an altered crawling behavior . In contrast , other innate behaviors , such as adult phototaxis and negative geotaxis , were normal in EHMT mutants ( Figure S4 ) . To address whether the dendrite phenotype of type 4 md neurons alone is sufficient to cause the abnormal crawling pattern , we attempted to rescue this phenotype by re-expression of UAS-EHMT in type 4 md neurons using 477-Gal4 . This was not sufficient to restore normal larval locomotor behavior , indicating that dendritic defects in type 4 md neurons and abnormal locomotory behavior might arise independently . Next , we analyzed the role of Drosophila EHMT in learning . Habituation is a form of non-associative learning where an initial response to a repeated stimulus gradually wanes [26] . In the light-off jump reflex habituation assay [27] flies were exposed to a sudden light-off pulse and measured for a jump response over the course of 100 trials with a 1 s inter-trial interval . Figure 5a and 5b show the proportion of flies that do show a jump response over the course of 100 trials . Hemizygous EHMT mutant males ( genotypes: EHMTDD1/Y and EHMTDD2/Y ) and transheterozygous EHMT mutant females ( genotype: EHMTDD1/EHMTDD2 ) both displayed a drastically slower response decrement during the habituation procedure as compared to wild-type EHMT+ flies ( Figure 5a and 5b ) . Individual flies were deemed to have habituated when they failed to jump in five consecutive trials ( no-jump criterion ) . Habituation was scored as the number of trials required to reach the no-jump criterion ( trials to criterion ) . The mean number of trials to criterion for mutants , EHMTDD1/Y , EHMTDD2/Y , and EHMTDD1/EHMTDD2 , was significantly higher ( 12- , 8- , and 6-fold , respectively ) than for EHMT+ wild-type flies ( p<0 . 001 ) ( Figure 5c ) . These experiments establish a role for EHMT in regulating non-associative learning . Having established a role for EHMT in habituation , a simple learning process , we asked whether EHMT is also involved in more complex forms of learning and/or memory using the courtship conditioning paradigm . This assay is based on the conditioning of male courtship behavior by exposure to a non-receptive female , which in presence of normal learning and memory capacities results in suppression of courtship [28] . Male flies were paired with a non-receptive pre-mated female for appropriate time intervals ( see Experimental Procedures ) and tested for courtship suppression immediately following the training period , after 30 min or after 24 h , to assess learning , short- , and long-term memory , respectively . The mean Courtship Index ( CI , the percentage of time spent on courtship during a 10 min interval ) of trained males and of socially naïve males was assessed to calculate a Learning Index ( LI ) , which is defined as the percent reduction in mean courtship activity in trained males compared to naïve males; LI = ( CInaive − CItrained ) /CInaive . We found that EHMT mutant flies are perfectly capable of this form of learning , as they efficiently suppressed courtship immediately following the training period ( Figure 5d ) . Strikingly , the Learning Index of EHMTDD1 males was reduced by 50% at 30 min after training ( STM-short term memory ) , and even more dramatically , to 17% of the wild-type value at 24 h after training ( LTM-long term memory ) ( Figure 5d ) . These results indicate that EHMT is dispensable for courtship learning but necessary for both short- and long-term courtship memory . To provide evidence for the specificity of the courtship conditioning phenotype and to roughly map where EHMT is required to control learning and memory in this paradigm , we performed rescue experiments in the EHMTDD2 background using tissue specific expression of UAS-EHMT and short-term memory ( 30 min after training ) as a read-out . The elav-Gal4 driver was used to express EHMT in all neurons , and the 7B-Gal4 promoter for more selective expression . Indeed , pan-neuronal expression of EHMT in the mutant background restored the Learning Index to normal levels ( Figure 5e , orange bars , pan neuronal versus EHMT mutants ) , providing evidence that EHMT is required cell-autonomously in neurons to achieve normal memory . Elav-driven expression of EHMT in the EHMT+ genetic background had no significant effect on Learning Index ( Figure 5e , black bars , pan neuronal versus EHMT mutant ) . 7B-Gal4 is predominantly expressed in the mushroom bodies of adult brains but is also expressed and at lower levels in some other brain regions , including the antennal lobe ( Figure S5 ) [29] . Expression of EHMT with this driver in the EHMT mutant background was able to rescue the Learning Index ( Figure 5e , orange bars , 7B-Gal4 versus EHMT mutant ) , revealing that EHMT function in 7B-Gal4 neurons is sufficient for normal memory . We also observed that overexpression of EHMT using 7B-Gal4 in the EHMT+ background significantly reduced the Learning Index ( Figure 5e , black bars , 7B-Gal4 versus EHMT mutants ) . Since the Learning Index was normal in EHMT mutants containing both 7B-Gal4 and UAS-EHMT , we conclude that there is no deleterious effect due to the expression of Gal4 or the 7B-Gal4 P-element insertion itself . We therefore asked whether the presence of endogenous EHMT could make a significant difference to the absolute protein levels in the mushroom body upon 7B-Gal4-mediated overexpression . We observe a very high and uniform EHMT staining in all mushroom body Kenyon cells upon UAS-EHMT expression with 7B-Gal4 in the EHMT+ background ( Figure S6 ) . A similar staining pattern was observed using this driver in the EHMT mutant background , although staining intensity was noticeably lower , likely due to the absence of endogenous EHMT ( Figure S6 ) . In contrast , the elav-Gal4 driver resulted in a non-uniform staining pattern , with high EHMT levels in only a small proportion of Kenyon cells ( Figure S6 ) . Thus , overexpression of EHMT in 7B-Gal4 neurons appears to be deleterious when above a certain threshold . These results suggest that appropriate levels of EHMT in the Drosophila nervous system are critical for courtship memory and indicate that the requirement for EHMT in this process is confined to 7B-Gal4 positive neurons . Taken together with the defect in jump reflex habituation , these data reveal an important role for EHMT not only in a simple form of learning but also in a more complex cognitive process such as courtship memory . Recently , it has been reported that postnatal loss of Ehmt1 and G9a in mice causes cognitive defects in the absence of obvious developmental abnormalities [15] . We therefore asked whether the memory defects of EHMT mutants in the courtship conditioning paradigm can be rescued by expression of EHMT in adulthood . Indeed , induced expression of EHMT using hs-Gal4 after eclosion ( see Experimental Procedures ) completely restored memory defects shown by siblings of the same genotype that had not undergone the heat-shock procedure ( Figure 5f ) . This demonstrates that EHMT is required for memory in adult flies and highlights that cognitive defects are reversible in EHMT mutant flies . The reversible memory defects in EHMT mutant flies suggest a critical role for EHMT in neuronal function in addition to its role in dendrite development . We therefore wanted to determine the molecular mechanisms through which EHMT regulates neuronal processes . EHMT proteins mediate histone 3 lysine 9 dimethylation ( H3K9me2 ) in euchromatic regions of the mammalian and fly genomes [6] , [7] , [10] . Therefore , we investigated EHMT target sites by generating genome-wide H3K9me2 profiles for EHMT mutant and wild-type larvae using chromatin immunoprecipitation ( ChIP ) with an H3K9me2 antibody followed by massive parallel sequencing of the co-precipitated DNA ( ChIP-seq technology ) . Mapping of the sequenced tags to the Drosophila genome revealed a genome-wide profile that is consistent with known H3K9me2 patterns [30] , [31] . High H3K9me2 is a known characteristic of heterochromatin [31] . Accordingly , we find high H3K9me2 levels in both wild-type and EHMT mutant strains in annotated heterochromatic regions that are contiguous with the assembled euchromatic chromosome arms ( Chr2Lh , Chr2Rh , Chr3Lh , Chr3Rh , and ChrXh ) ( Figure S7 ) [32] . This was expected , since EHMT is known to have no effect on heterochromatin formation and heterochromatic H3K9me2 levels are known to be unaffected by loss of EHMT/G9a in fly and mouse [7] , [15] , [19] , [33] . The generated H3K9me2 profiles also follow expected patterns in euchromatin . H3K9me2 is known to dip immediately before the transcriptional start site ( tss ) and near the polyadenylation site ( polyA ) of genes [30] , [34] either due to nucleosome depletion or decreased H3K9me2 in these regions . We indeed observe a dip in H3K9 dimethylation in these regions ( Figure 6d and 6e , left panels , black lines ) , thus demonstrating the accuracy and reliability of our H3K9me2 profiles . Since the global pattern of H3K9me2 appeared to be normal in EHMT mutants , we reasoned that EHMT must affect discrete regions within the genome . To identify these regions we divided the euchromatic genome into 300 bp bins and compared the number of sequenced tags per bin in wild-type versus mutant samples . For each of the 384 , 944 bins in the euchromatic genome we calculated a methylation ratio by dividing the number of tags in wild-type by the number of tags in the mutant . Thus , a ratio greater than 1 identifies regions where methylation is decreased in EHMT mutant flies . We have plotted the log of these ratios ( log ( 2 ) wt/mt ) in a histogram , in which Loss of Methylation Bins ( LOMBs ) are found in the area of positive log values . The histogram roughly follows a normal distribution but is asymmetric , with 19 , 258 bins outside two-times the standard deviation of the mean on the positive side , while only 50 bins outside two-times the standard deviation on the negative side ( Figure 6a ) . The 19 , 258 LOMBs constitute about 5% of the euchromatic genome and provide an unbiased confirmation for the role of EHMT in H3K9 dimethylation . Loss of methylation ( LOM ) can also be visualized in the USCS genome browser as areas where H3K9me2 levels are depleted in the mutant but remain high in wild-type ( two examples given in Figure 6b ) . Interestingly , we find that LOMBs are not randomly distributed in the genome but are enriched in the areas 1 kb upstream of the tss and 1 kb downstream of the polyA site by 1 . 6-fold and 3 . 3-fold , respectively ( Figure 6c ) . As mentioned above , we observe a local depletion of H3K9me2 in these regions in wild-type animals ( Figures 6d and 6e , left panel , black line ) . In EHMT mutants , this local depletion is strongly augmented both upstream of the tss and near the polyA site ( Figures 6d and 6e , left panel , orange line ) , providing further evidence that EHMT deposits H3K9me2 marks in discrete euchromatic loci , with a bias towards the 5′ and 3′ ends of genes . H3K9me2 is a marker for condensed , transcriptionally repressive chromatin [35] , however the modification itself does not strongly correlate with transcription levels on a genome wide scale as is seen for some other histone modifications , like H3K4me3 and H3K27me3 [30] . To determine whether H3K9me2 can contribute to transcriptional repression in Drosophila , we performed microarray expression analysis to compare mRNA levels in EHMT wild-type and mutant larvae . We then analyzed H3K9me2 levels upstream of the tss and downstream of the polyA site for genes that were up- and downregulated in EHMT mutants . Genes that are activated by EHMT ( i . e . greater that 2 . 5-fold downregulated in mutants , Table S1 ) showed no difference in H3K9me2 profiles upstream of the tss or downstream of the polyA site when comparing EHMT wild-type and mutant strains ( Figure 6d and 6e , middle panels ) . In contrast , genes that are repressed by EHMT ( i . e . greater that 2 . 5-fold upregulated in mutants , Table S2 ) have a clearly augmented dip in methylation both at the tss and polyA sites ( Figure 6d and 6e , right panels ) when compared to the wild-type profile and to the average methylation profiles of all genes . These data indicate that EHMT-mediated H3K9 dimethylation immediately up and downstream of genes can affect transcriptional repression in Drosophila . Next , we investigated which genes were affected by loss of methylation in EHMT mutants by associating each LOMB with its closest gene . In total , LOMBs were found in or near 5 , 136 genes; 1 , 229 genes had LOMBs upstream of the tss ( upstream LOMB ) and 1 , 712 genes had LOMBs downstream of the polyA site ( downstream LOMB ) ( Table S3 ) . The two groups overlap by 255 genes ( Figure 7a ) . To assess the function of LOMB-associated genes , we analyzed their gene ontology for enrichment of specific biological processes using GOToolBox [36] . Genes associated with LOMBS are highly enriched in terms related to the nervous system ( Figure 7b , for lists of genes associated with selected terms see Table S4 ) . The broad term nervous system development , associated to more than 350 LOMB genes , reaches the highly significant p value of 2 . 3×10−28 and is the most enriched tissue-specific term . Consequently , the term is highly depleted from the pool of genes with unaltered H3K9me2 in EHMT mutants; i . e . in genes that are not associated with LOMBs ( Figure 7b , no LOMBs ) . Strikingly , all GO terms that describe EHMT mutant phenotypes ( e . g . short- and long-term memory , non-associative learning , dendrite morphogenesis , and larval locomotory behavior ) show significant enrichment when considering all LOMB-associated genes and genes associated with downstream LOMBs ( Figure 7b , observed phenotypes ) . Other neuronal terms that show high enrichment are also shown ( Figure 7b , neuronal terms ) . Signal transduction is also amongst the most strongly enriched terms , with a p value of 6 . 2×10−48 . Many specific signaling pathway terms are highly overrepresented amongst LOMB-associated genes , with G-protein coupled receptor protein signaling pathway and small GTPase mediated signal transduction being the top terms ( Figure 7b , signaling pathways ) . We also note significant enrichment of pathway terms that directly relate to EHMT mutant phenotypes , such as cAMP signaling , a major pathway involved in learning and memory . Notably , there is a stark contrast in enriched terms when comparing genes associated with either upstream or downstream LOMBs ( Figure 7b , Downstream LOMBs and Upstream LOMBs ) . Downstream LOMBs are associated with genes that are enriched for neuronal terms , signaling pathways , and terms describing observed EHMT mutant phenotypes , while upstream LOMBs are associated with genes involved in biological processes requiring a high transcriptional plasticity , such as stress response and actin cytoskeleton remodeling ( Figure 7b , enriched in upstream LOMBs ) . The contrast between these two groups in their gene ontology illustrates the importance of H3K9me2 position at target gene loci and provides further support as to the biological relevance of these data . Finally , genes involved in regulatory processes such as translation , chromatin assembly/disassembly , and chromosome organization are highly depleted from LOMB-associated genes ( Figure 7b , depleted ) , which contrasts the striking enrichment of nervous system and phenotype-relevant terms amongst LOMB-associated genes .
The EHMTs are an evolutionarily conserved family of proteins that regulate H3K9 methylation at euchromatic DNA [6] , [7] , [10] , [15] . Previous studies have shown that EHMTs affect transcription through H3K9 dimethylation in the promoters of certain genes [8] , [37]–[39] . Our study provides the first genome-wide overview of EHMT function with respect to its role in post-translational histone modifications . We provide evidence that Drosophila EHMT induces H3K9 dimethylation at a proportion ( about 5% ) of the euchromatic genome , with a preference for discrete regions at the 5′ and 3′ ends of genes ( Figure 6 ) . Genes with differential H3K9me2 levels at the 5′ end ( within 1 kb upstream of the transcriptional start site ) are predominantly involved in biological processes related to stress response ( e . g . heat shock response and actin cytoskeleton remodeling ) , which require rapid and frequent changes in transcription . This observation is consistent with studies in yeast and humans , which show that chromatin structure immediately upstream of transcriptional start sites directly correlates with transcriptional plasticity [40] . In contrast , genes that are differentially methylated at the 3′ end are highly enriched in genes that control neuronal processes that are disrupted in EHMT mutants ( Figure 7 ) . The general view is that gene expression is regulated through interactions at the promoter , or 5′ end . However , recent studies have revealed that 3′ gene ends also play an important and complex role in the regulation of transcription by: ( 1 ) mediating gene looping [41]–[45] , which is necessary for transcriptional memory , i . e . the altered transcriptional responsiveness of genes after a previous cycle of activation and repression [43] , [46] , [47]; ( 2 ) serving as an initiation site for antisense transcripts [41]; and ( 3 ) regulating transcript termination , a process that also affects transcript levels [48] . Currently there is no evidence linking H3K9me2 to any of these processes , however it is conceivable that differential histone methylation at the 3′ end of neuronal genes may act as a mechanism to control their expression . In line with this idea a recent study has reported that the DNA methyltransferase , Dnmt3a , also targets neuronal genes in “non-promoter” regions , including 3′ ends [49] . Thus , it appears that epigenetic alterations to non-promoter regions is emerging as a general theme for the regulation of neuronal gene expression . EHMT mutants show a decrease in dendrite branching in sensory neurons of the Drosophila peripheral nervous system ( Figure 3 ) . Type 4 md neurons are known to provide the sensory input that they receive via their dendrites as an essential functional component to the neuronal circuitry governing larval movement [23] . Our analysis of larval locomotion in EHMT mutants revealed a behavioral phenotype characterized by an increased performance of stops , retractions , and turns ( Figure 4 ) . It has been reported that such a phenotype can directly arise from dysfunction of type 4 md neurons [22] , [24] , which raised the possibility that decreased dendrite branching and altered locomotory behavior are connected traits . Re-expression of EHMT in type-4 md neurons did , however , not rescue larval locomotion defects , suggesting that larval locomotion and type 4 md neuron development are controlled independently by EHMT . Thus , this lack of rescue may be due to requirements for EHMT in additional peripheral or central neurons relevant to the crawling pattern . We can also not exclude unspecific secondary effects or that precise levels of re-expressed EHMT may be crucial for turning behavior . Ultimately , the relevance of EHMT in both dendrite development and crawling is illustrated by the observation that EHMT mutants show loss of H3K9me2 at 65 of 147 genes annotated to be involved in dendrite development and 15 of 16 genes involved in larval locomotory behavior ( see Table S4 for gene IDs ) . We have shown that EHMT is required for light-off jump reflex habituation ( Figure 5a–5c ) , a simple form of non-associative learning that is known to require classic learning and memory genes such as rutabaga [27] . In this paradigm a sequence of leg extension and flight initiation is induced by sudden darkness . This behavioral response is mediated by the giant fiber interneurons , which receive sensory input from the visual system in the brain and relay this information through the thoracic ganglion where efferent neurons descending from the giant fiber to thoracic muscles are stimulated [50] , [51] . Only a few genes are known to control jump reflex habituation and most of these are ion channels , or are involved in cAMP and cGMP second messenger signaling pathways [50] . EHMT is the first histone modifying enzyme to be implicated in this simple form of learning . Jump-reflex habituation is not an official gene ontology term , but significantly , seven of the eight genes known to be involved in jump-reflex habituation [50] show loss of H3K9 dimethylation in EHMT mutants ( Table S4 ) . We have also identified a role for EHMT in courtship memory ( Figure 5d–5f ) . This is a complex form of memory that allows male flies to discriminate between receptive and non-receptive females , presumably to optimize the energy that they spend on courtship . We demonstrate that loss of EHMT leads to impaired short- and long-term memory while the learning capacity of the EHMT mutants was unaffected ( Figure 5d ) . Moreover , we show that normal courtship memory is restored upon re-expression of EHMT in the whole nervous system and in a subset of neurons labeled by 7B-Gal4 , which is predominantly expressed in the mushroom body neurons of the adult brain ( Figure 5e ) . Although further work is required to map the specific circuits required for EHMT-dependent courtship memory , the mushroom body is known to be crucial for courtship memory , but not learning [52] , pointing towards a deficit in this area of the brain . Significantly , EHMT affects histone methylation in 22 of 36 genes that were annotated at the time of our analysis to be involved in memory ( Table S4 ) . Other relevant memory genes , such as Orb2 [53] ( Figure 6b ) , nemy [54] , and ben [55] , that were not yet included in gene ontology databases are also affected by loss of EHMT . Together , these data suggest that EHMT targets two-thirds of all currently known memory genes . Importantly , we were able to fully restore memory deficits by re-expression of EHMT during adulthood ( Figure 5f ) . Thus , although EHMT can affect neuronal hardwiring ( dendrite development in the peripheral nervous system; Figure 3 ) , it appears that adult cognitive defects do not arise from neurodevelopmental defects occurring prior to eclosion . This is consistent with a recently reported impairment in fear conditioning that has been observed in mice with postnatal loss of Ehmt1 in the brain [15] and with our observation that mushroom body morphology appears unaffected in EHMT mutant flies . Thus , EHMT-mediated H3K9 dimethylation of specific loci is required in adult post-mitotic neurons to consolidate or retrieve consolidated memories . Interestingly , other epigenetic regulators , such as the DNA methyltransferases Dnmt1 and Dnmt3a , are also required in post-mitotic neurons for normal memory [56] . These studies support the idea that the process of learning induces reprogramming of the neuronal epigenome , which crucially underlies memory [1]–[3] . Such “stable” chromatin modifications , including DNA and histone methylation , appear to be good candidates for “writing” long-term memory , however these marks must also remain dynamic allowing for memories to be modified . Our understanding of this stable versus dynamic state of epigenetics in neurons and its consequences are highly limited . It will thus be important to dissect the extent of epigenetic plasticity during the different phases of learning , memory consolidation , and memory retrieval , and to determine how these alterations to the epigenetic landscape translate into transcriptional changes required for information processing and storage . A recent study of mRNA levels in mice with brain region-specific loss of Ehmt1 has identified 56 genes that are consistently misregulated in the mutant mouse brain [15] . Of these 56 genes , 18 are non-neuronal , which led to the interpretation that EHMT proteins control cognition through repression of non-neuronal genes in neuronal tissues . In contrast to this view , our data show that Drosophila EHMT mediates H3K9 dimethylation at more than 350 neuronal gene loci with proven critical roles in nervous system development and function . Does this apparent discrepancy reflect evolutionary differences ? Of the 56 differentially expressed genes identified by Schaefer et al . [15] , 30 are conserved in flies and 20 show loss of H3K9me2 in EHMT mutants ( Table S4 ) . This correlation is very unlikely to occur by chance ( p<2 . 9×10−4; hypergeometric test ) , suggesting that EHMT target genes are , at least in part , evolutionarily conserved . The great number of highly enriched neuronal genes amongst Drosophila EHMT targets , their striking match with EHMT mutant phenotypes , and the reversibility of cognitive defects argue that EHMT orchestrates an epigenetic program that directly regulates a battery of neuronal players underlying the molecular basis of cognition . It is also noteworthy that EHMT targets include fly orthologs of NF1 , FMR1 , FMR2 , CNTNAP2 , GDI , DLG3 , and of many more genes underlying syndromic and non-syndromic forms of intellectual disability . Also , the major signaling pathways known to underlie intellectual disability , Rho and Ras GTPase pathways [57] , [58] , are highly enriched in our ontology analysis ( GO term: small GTPase mediated signal transduction ) . Our study complements a number of reports on post-embryonic rescue of cognitive phenotypes in disease models of intensively studied disorders such as Fragile X syndrome , Neurofibromatosis I , Tuberous sclerosis , Rubinstein-Taybi , Angelman , and Rett syndrome [59] . The growing number of such examples provides an argument for reappraisal of the traditional view that genetic forms of intellectual disability are largely due to irreversible neurodevelopmental defects , findings which open prospects for therapeutic intervention . Currently , clinical trials are underway to treat Fragile X patients with compounds that have initially been identified to rescue phenotypes in fly models of Fragile X syndrome [60]–[63] . The EHMT mutant fly has provided novel insights into the epigenetic regulation of cognition and will be a valuable tool to work further towards such translational approaches . Furthermore , a better understanding of the epigenetic mechanisms regulating cognitive processes is relevant to the wider medical community , considering the increased awareness of the epigenetic contributions to neurodevelopmental and psychiatric disorders in general [4] , [5] .
Flies were reared on standard medium ( cornmeal/sugar/yeast ) at 25 degrees and 45%–60% humidity with a 12-h light/dark cycle . All fly stocks were obtained from the Bloomington Drosophila stock center ( Indiana University ) ( see Text S1 for stock descriptions ) except for EHMT deletion strains and UAS-EHMT strains , which were generated according to standard procedures ( see Text S1 and Figure 2 ) . Tissues were dissected and fixed using standard methods . Rabbit-anti-EHMT antibodies were a gift from Dr . A . Lambertson [10] and were used at a 1/100 . Rat-anti-elav ( 1/500 ) , mouse-anti-repo ( 1/500 ) , mouse-anti-DLG ( 1/100 ) , and mouse anti-dac ( 1/100 ) antibodies were obtained from the Developmental Studies Hybridoma Bank ( University of Iowa ) . Nuclei were visualized using the fluorescent nuclear dye DAPI . For imaging of type IV md neurons expressing a membrane targeted mCD8-GFP fusion protein ( memGFP ) we used a rat-anti-mCD8 antibody ( Invitrogen ) at 1/100 . Secondary antibodies were conjugated to either alexa-fluor-568 or alexa-fluor-488 ( Invitrogen ) . Images were acquired using either a Leica DM-IRE2 confocal microscope ( Leica Microsystems ) or a Zeiss Axioimager Z1 fluorescent microscope equipped with an ApoTome ( Carl Zeiss B . V . ) . Where possible , colocalization was shown in a color-blind-friendly manner using photoshop to copy red or blue signals into both the red and blue channel to produce magenta . Proteins were extracted from 0–3 h embryo collections as previously described [64] and subjected to Western blot analysis according to standard procedures using the Bio-Rad electrophoresis system ( Bio-Rad ) ( see Text S1 for details ) . We have analyzed the morphology of the solitary type 4 md neuron in the ventral cluster called vdaB ( ventral dendritic arborization neuron B ) [18] . For the visualization of dendritic arbors we used the type 4 md neuron-specific driver 477-Gal4 [20] to drive expression of memGFP . Details of crosses , confocal microscopy , and quantification of dendrite ends are provided in Text S1 . Larval crawling was assayed as described previously ( refer to Text S1 for details ) [25] . Approximately 30 individuals per strain were tested per day over a 5-d period , resulting in a total of approximately 150 larvae per genotype and experiment . Experiments were performed at least twice . Quantification of path lengths was performed using Adobe Photoshop and Image J . Male flies of the genotypes EHMT+/Y , EHMTDD1/Y , EHMTDD2/Y , and female flies of the genotypes EHMT+/EHMT+ and EHMTDD1/EHMTDD2 were tested for light-off jump reflex habituation in a modified assay that was previously described by Engel and Wu [27] . Details of this high throughput assay are described Text S1 . Flies were tested for learning and/or memory at 4 d of age using the courtship conditioning assay as previously described [53] . For induced EHMT expression via hs-Gal4 , flies were incubated at 37 degrees for 45 min on days 1–3 , with the final heat shock treatment taking place 24 h before training and testing on day 4 . Chromatin immunoprecipitation was performed using standard methods with anti-H3K9me2 antibodies ( 07-441 , Upstate ) and Prot A/G beads ( Santa Cruz ) to capture antibody bound chromatin ( for details see Text S1 ) . Massive-parallel sequencing was performed using the Illumina Genome Analyzer IIx according to standard protocols of the manufacturer ( Illumina ) ( for details see Text S1 ) . All sequence analyses were conducted using the BDGP Release 5 genome assembly ( DM3 ) and the release 5 . 12 annotations provided by FlyBase . To compensate for differences in sequencing depth and mapping efficiency among the two ChIP-seq samples , the total number of unique tags of each sample was uniformly equalized relative to the sample with the lowest number of tags ( 7 , 043 , 913 tags ) , allowing for quantitative comparisons . For association of individual bins with genes , we determined the distances from the middle of the bin to the nearest tss or polyA site using the Pinkthing tool ( http://pinkthing . cmbi . ru . nl/cgi-bin/index50 . pl ) . The ChIP-Seq data from this study are available at the NCBI Gene Expression Omnibus ( http://www . ncbi . nlm . nih . gov/geo/ ) under series accession no . GSE22447 . Total RNA was isolated in triplicate from third instar larvae using the RNeasy Lipid Tissue Midi kit ( Qiagen ) . RNA quality was evaluated using spectrophotometry and integrity was confirmed using gel electrophoresis of glyoxal-denatured samples . Total RNA samples were labeled using the ‘indirect’ method [65] . Superscript II reverse transcriptase ( Invitrogen ) was used to produce cDNA incorporated with aminoallyl-dUTP ( Fermentas ) . Reactive fluorescent dyes ( Alexa647 or Alexa555; Invitrogen ) were conjugated to the individual samples . Two differently labeled samples , whole larvae from EHMT mutant versus EMHT+ wild-type , were pooled and co-hybridized to the 14K long oligo array from the Canadian Drosophila Microarray Centre ( www . flyarrays . com ) according to previously described methods [66] . Images of the hybridized microarrays were obtained using a ScanArray 4000 scanner ( Perkin-Elmer ) and were quantified using QuantArray 3 . 0 software ( Perkin-Elmer ) . Data were normalized using lowess sub-grid normalization using Genetraffic Duo ( Stratagene ) analysis software . Normalized data were exported and analyzed using the one-class test available in the Statistical Analysis of Microarrays ( SAM ) software package . The false discovery rate of the one-class test was adjusted such that the expected number of false positive results was less than one . Gene lists generated in SAM were filtered to include only those genes that displayed at least a 2 . 5-fold increase or decrease in abundance with respect to the wild-type sample and whose coefficient of variance was less than 100% . For all data , normal versus non-normal distribution was assessed using the Shapiro-Wilk test and by visual examination of histograms . For comparison of more than two variants with a normal distribution , one-way ANOVA analysis was used to determine the probability that there were differences between the variants . In the cases that ANOVA indicated that there was a significant differences between variants ( p<0 . 05 ) we performed post hoc pair-wise comparisons using the Bonferroni correction , which takes into account that multiple comparisons are being made and therefore increases the stringency of the test . This method was applied for normally distributed data in Figures 3c , 5c , S2e , S2f , S3 , and S4b . For comparison of more than two variants with a non-normal distribution , the Kruskal-Wallis test was used to determine if there were significant differences between any of the means . For data sets in which there was a significant difference ( p<0 . 05 ) , we subsequently performed pair-wise comparisons using the Mann-Whitney test , a post hoc test that can be used to compare two means with non-normal distributions . The combination of Kruskal-Wallice and Mann-Whitney was used for data sets which were not normally distributed ( Figures 4b–4c and 5d–5f ) . All of the above tests were performed using SPSS software ( SPSS Inc . ) . To test whether the number of LOMBs in a given genomic position ( e . g . gene body ) was significantly enriched compared to the distribution of all bins in the genome , we applied a hypergeometric test using an online tool ( http://stattrek . com/Tables/Hypergeometric . aspx ) . This test was performed for all genomic regions defined in Figure 6c—Upstream >1 kb , Upstream <1 kb , Gene Body , Downstream <1 kb , Downstream >1 kb , and Distant—to obtain individual p values . Since we performed this test for six genomic regions , p values were corrected using the Bonferroni method to account for multiple comparisons using the same data set . Enrichment of gene ontolology terms was analyzed using GOToolBox [36] to perform a hypergeometric test with Benjamini & Hochberg correction . | Epigenetic regulators can affect gene transcription through modification of DNA and histones , which together form chromatin . The importance of such regulators for cognition is increasingly appreciated , but only few key factors have been identified so far . Excellent candidates are histone modifiers that are involved in intellectual disability , such as EHMT1 , implicated in Kleefstra Syndrome . Here , we characterized the neuronal function of EHMT in Drosophila . Flies that lack EHMT are viable but show highly selective defects in specific aspects of neuronal development and function , including learning and memory . Genome-wide analysis of EHMT-mediated histone methylation revealed that EHMT targets the majority of all currently known Drosophila learning and memory genes . It also targets genes known to be involved in the other aspects of behavior and neuronal development that are compromised in EHMT mutants . Remarkably , EHMT mutant memory deficits can be reversed in adulthood , suggesting that epigenetic influences on cognition are not always permanent . Our results provide novel insights into the epigenetic control of cognition in health and disease . | [
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"neuroscience/... | 2011 | Epigenetic Regulation of Learning and Memory by Drosophila EHMT/G9a |
The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes . Predicting orthology relationships between species is a vital component of comparative biology . Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence , phylogenetic history , and functional interaction with progressively increasing accuracy . A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools , resulting in improved prediction performance . Here we present WORMHOLE , a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs ( LDOs ) between 6 eukaryotic species—humans , mice , zebrafish , fruit flies , nematodes , and budding yeast . Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence . In this study we demonstrate the performance of WORMHOLE across each combination of query and target species . We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species , expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs . We present extensive validation , including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity , and discuss future applications of machine learning in ortholog prediction . A WORMHOLE web tool has been developed and is available at http://wormhole . jax . org/ .
Comparative biology has become a central strategy in the study of human biology and disease . The availability of powerful genetic tools and our ability to control experimental conditions in model organisms often allows a much more detailed examination than directly studying a process of interest in humans . In diverse areas of biology—aging , development , stem cell differentiation , behavior—highly conserved molecular features have been described in model systems , even highly evolutionarily divergent organisms , and translated into useful interventions in humans . For example , the ability to delay aging by inhibition of the Target of Rapamycin ( TOR ) kinase was first discovered in the single-celled budding yeast Saccharomyces cerevisiae , and much of the work to characterize TOR signalling has been carried out in this system ( reviewed by Loewith and Hall [1] ) . Reduced TOR signalling has since been demonstrated to increase lifespan in a range of model systems from worms to mice ( reviewed by Cornu et al . [2] ) . Rapamycin and other drugs targeting this system are now in clinical trials for cancer [3 , 4] and show promise for other age-associated diseases , including Alzheimer’s disease [5] . Aging is a particularly salient example demonstrating the power of comparative biology . Lifespan studies are much shorter , much less expensive , and therefore much more tractable in invertebrate species than in vertebrates , allowing aging studies to be carried out more rapidly , on a larger scale , and in greater molecular detail for the same resource investment . To reap the practical benefits of invertebrate models in studying the genetics of human health , it is crucial to translate molecular results from invertebrates into vertebrates . A vital step in this translation is the identification of the gene or protein that fills the functionally equivalent role in the target vertebrate species . Since functionally equivalent proteins ( FEPs ) are difficult to predict directly , the most commonly used surrogate is orthology . Orthologs are genes that derive from the most recent common ancestral gene by speciation ( in contrast to paralogs; genes that derive from the most recent common ancestral gene by duplication ) [6] . Because orthology is defined by speciation , the evolutionary history separating orthologous genes may include other categories of evolutionary event , such as duplication , deletion , and de novo mutation in one or both lineages after the defining speciation event . In addition to simple one-to-one mappings , these evolutionary processes allow for one-to-many and many-to-many mappings between genes that define an orthologous group in different species . The boundaries between orthologs and non-orthologs can be difficult to discriminate based on readily measured features of genes , such as sequence composition , leading to a difficult bioinformatics problem . A subset of all orthologs are the least diverged orthologs ( LDO ) , defined as the pair of genes within an ortholog group for two species that have accumulated the fewest mutations after speciation and duplication-post-speciation events ( i . e . have ‘diverged the least’ ) [7] . The identification of LDOs is a sub-problem of the ortholog identification , but its solution has many desirable properties . In particular , the gene pair in an ortholog group with the least sequence divergence is the most likely to have been functionally conserved by evolution [8 , 9] . More divergent gene pairs are more likely to have developed novel function , particularly in gene families that have undergone numerous duplication events . In this study we focused specifically on the identification of LDOs . The idea that orthologous genes tend to be more functionally similar than non-orthologous genes is called the “ortholog conjecture” , which states specifically that orthologs are more functionally similar than paralogs . There has been recent debate surrounding this conjecture . Contrary to the ortholog conjecture , Nehrt et al . [10] found that paralogs within either humans or mice were more predictive of gene function than orthologs between humans and mice based on comparison of microarray and gene ontology ( GO ) data , suggesting that cellular context , rather than shared sequence , may be the primary driver of functional evolution . However , bias in GO annotations tends to favor functional similarity between paralogs [11] , and subsequent studies using RNA-seq data [8] or bias-corrected GO annotations [9] support the ortholog conjecture . Specifically , Chen and Zhang [8] found that gene expression similarity between orthologs is significantly higher than between paralogs across multiple tissue types , while Altenhoff et al . [9] found that functional GO annotation similarity was higher between orthologs than paralogs , and increased weakly , but significantly , with decreased sequence divergence , even across large evolutionary distance , when the GO annotations were controlled for common biases . Thus , while orthologs and FEPs are conceptually distinct , the preponderance of evidence suggests that they are related , and in particular that identifying an ortholog as a first step toward identifying an FEP is warranted . Because protein sequence ultimately determines function , the LDO—the ortholog with the least divergence in sequence—is therefore a strong estimate of an FEP . Likewise , observing high functional similarity between genes in different species provides evidence for , but does not guarantee , shared evolutionary history . The past decade has seen an explosion of new methodologies and tools designed to predict orthologous genes between two or more species . The majority use one of two approaches: graph-based or tree-based ortholog prediction . Graph-based algorithms begin with pairwise alignments between all protein sequences from two species to estimate evolutionary distance between each protein pair , followed by orthology prediction made using a range of clustering criteria: reciprocal best hit ( e . g . OMA [12] , OrthoInspector [13] , and InParanoid [14] ) , reciprocal smallest distance ( e . g . Roundup [15] ) , best triangular hit ( e . g . COG [16] and EggNOG [17] ) , or Markov clustering ( e . g . OrthoMCL [18] ) . Tree-based systems take advantage of our understanding of evolutionary relationships between species , using simultaneous alignment of sequences from many species to build phylogenetic trees and infer orthology relationships based on tree structure . Variations on this approach are employed by many popular ortholog prediction tools: Ensembl Compara [19] , metaPhOrs [20] , OrthoDB [21] , PANTHER [22] , and TreeFam [23] . Other strategies ( e . g . HomoloGene [24] and Hieranoid [25] ) combine aspects of both graph- and tree-based systems , progressively applying graph-based methods at the nodes of a species tree to generate more accurate ortholog predictions while maintaining the computational efficiency inherent to tree-based methods . A further alternative strategy is to directly identify genes in a target system that fills a functionally equivalent role . For example , the Isobase algorithm infers FEPs using both sequence information and functional information encoded in protein-protein interaction ( PPI ) networks . Each prediction algorithm uses a different methodology , producing overlapping but distinct sets of predicted orthologs or FEPs and displaying different strengths and weaknesses in terms of performance for the particular objective of that algorithm . Several groups have combined predictions from multiple sources in “meta-tools” to improve prediction performance . Shaye and Greenwald [26] created OrthoList , a set of human-worm orthology relationships , by simply combining the predictions from four commonly used ortholog prediction tools ( InParanoid , OrthoMCL , Homologene , and Ensembl Compara ) to produce a system with high recall ( i . e . low false negative rate ) while maintaining precision ( i . e . low false positive rate ) when tested on a manually curated set of human-worm ortholog pairs . MetaPhOrs was constructed by collecting phylogenetic trees from seven independent sources ( PhylomeDB , Ensembl , TreeFam , Fungal Orthogroups , EggNOG , OrthoMCL , and COG ) and applying a common algorithm to select orthologs between species , allowing improved ortholog prediction accuracy based on cross-tree comparison [20] . The Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool ( DIOPT ) reports predictions from eight ortholog databases ( Ensembl Compara , Homologene , InParanoid , OMA , OrthoMCL , PhylomeDB , RoundUp , and TreeFam ) and one functional database ( Isobase ) between six species ( human , mouse , zebrafish , fruit fly , nematode , and budding yeast ) and includes a confidence score based on the number of algorithms predicting each pair , and a weighted score that takes into account functional similarity based on GO term comparison [27] . The recently published Multiple Orthologous Sequence Analysis and Integration by Cluster optimization ( MOSAIC ) combines ortholog predictions generated by four methods ( Multiparanoid , Threshold Block Aligner ( TBA ) , six-frame untranslated BLAST-like alignment tool ( BLAT ) , and OMA ) and applies a filtration process to optimize pairwise alignment between members of each ortholog cluster [28] . Pereira et al . developed Meta-Approach Requiring Intersections for Ortholog predictions ( MARIO ) to aggregate four ortholog prediction methods ( reciprocal best hit , InParanoid , OrthoMCL , and Phylogeny [29] ) to identify high-specificity ortholog groups that were then analyzed by multiple sequence alignment and hidden Markov models to predict novel orthologs [30] . In each case , the meta-tool is shown to improve prediction performance when compared to the individual input algorithms . To date , all of these methods use the number of algorithms that predict an ortholog as a heuristic to determine the confidence of a given prediction . However , while some use sophisticated post-processing to improve performance , none take into account the individual performance of each input algorithm when assigning confidence levels to aggregate predictions . Here we present a novel strategy in this final category of meta-tools . The WORM-Human OrthoLogy Explorer ( WORMHOLE ) predicts LDOs between species by employing machine learning to differentially weight the output of 17 ortholog prediction strategies . WORMHOLE falls into a subcategory of meta-tools that do not predict orthology de novo ( others in this category include OrthoList and DIOPT ) , but rather integrate information from multiple sources to refine and extend predictions . Originally developed to identify orthologous genes between humans and nematodes , we have expanded the method to include six species: Homo sapiens ( humans ) , Mus musculus ( mice ) , Danio rerio ( zebrafish ) , Drosophila melanogaster ( fruit flies ) , Caenorhabditis elegans ( nematodes ) , and Saccharomyces cerevisiae ( budding yeast ) . WORMHOLE considers the patterns of ortholog calls of the 17 constituent algorithms and identifies signature patterns that correspond to likely LDOs . Specifically , WORMHOLE uses the genome-wide predictions of LDOs from PANTHER ( PANTHER LDOs ) as a set of high-confidence examples to train machine learning classifiers . PANTHER makes de novo predictions of LDOs based on evolutionary relationships . We expect that rigorous statistical criteria used by any de novo method will necessarily miss some true LDOs , particularly in edge cases with difficult-to-parse evolutionary history or patterns of sequence divergence ( e . g . duplication-post-speciation events in both lineages ) . Machine learning provides a principled method to extend de novo predictions with new data . We used the PANTHER LDOs to define positive and negative examples , but reserved judgment on genes for which PANTHER does not identify an LDO . The machine learning classifier then identified a “signature” of LDO vs . non-LDO status from the PANTHER LDO examples that can be used to infer LDO status for previously unclassified genes . WORMHOLE provides rigorous confidence scores based on how strongly the pattern corresponds to the known PANTHER LDOs . We present six findings: 1 ) The patterns of ortholog calls by the 17 constituent algorithms contain sufficient information to strongly predict LDOs in the reference set . This is non-trivial because , as discussed below , none of the input algorithms are designed to explicitly predict LDOs . Nevertheless they encode LDO status in the patterns of their respective ortholog predictions . 2 ) The use of support vector machine classifiers ( SVMs ) strongly improves LDO prediction over simple voting , a baseline method used in other meta-tools . 3 ) This enhanced prediction depends on the evolutionary distance between organisms with greater improvement for distant comparisons , e . g . between vertebrates and invertebrates . 4 ) The WORMHOLE SVMs expands the number of LDOs relative to the PANTHER LDO training set . The novel LDOs maintain a similar evolutionary distance distribution and Basic Local Alignment Search Tool protein ( BLASTp ) alignment score to the PANTHER LDO training set , indicating that the novel predictions are indeed LDOs . 5 ) The WORMHOLE models trained on one pair of species generalize well to other species pairs , suggesting that the WORMHOLE models are identifying information about orthology in general , and not just between particular species pairs . 6 ) The WORMHOLE predictions have high functional specificity by several criteria , while making significantly more LDO calls than the PANTHER LDOs used to train the models . This indicates that WORMHOLE has extracted functionally relevant information from the constituent algorithms that is complementary to the PANTHER LDOs .
Most novel ortholog prediction strategies seek to increase performance by expanding the scope or improving the quality of the underlying sequence data , or through application of a new algorithm . The wealth of ortholog prediction strategies now available opens the possibility of a two-layer prediction model . To conceptualize this model , consider the individual pieces of underlying biological and genetic information—gene and protein sequences , gene and protein interactions , phylogenetic relationships between species—as first-order features ( Fig 1A ) . Each of the established ortholog prediction algorithms ( Ensembl Compara , EggNOG , etc . ) uses different combinations of these first-order features to generate predicted ortholog relationships , forming the first layer of prediction ( Fig 1B ) . These algorithms generate a pool of candidate ortholog predictions , and hence candidate LDOs , that can be considered novel second-order features ( Fig 1C ) . In WORMHOLE , we apply a second layer of prediction to refine these candidate ortholog predictions to directly predict LDOs ( Fig 1D ) . This refinement is accomplished by generating a confidence score for each gene pair based on the pool of predictions and considering only those pairs that meet a minimum confidence threshold . This multilayer approach requires three ingredients: ( 1 ) genome-wide candidate ortholog predictions ( i . e . second-order features ) between the species of interest generated by a selected set of first-layer algorithms , ( 2 ) a second-layer algorithm to classify each gene pair as either an LDO or not based on the second-order features , and ( 3 ) a training dataset ( reference set ) composed of well-defined examples of both LDO and non-LDO gene pairs , which is used to train and test the second layer algorithm . To generate a genome-wide candidate pool ( ingredient 1 ) , we collected all ortholog predictions from 17 constituent algorithms between the selected species , representing a wide array of different prediction strategies . There are more than 30 databases that predict orthologous or functional relationships between species using different methodologies . In selecting algorithms to include in WORMHOLE , we sought to sample as wide a variety of prediction strategies as possible . We examined each database that we were able to locate and access online and included the 13 data sources that met the following criteria: ( 1 ) the availability for download of complete genome-wide ortholog predictions , ( 2 ) current ortholog prediction data ( updated since 2010 ) , and ( 3 ) demonstrated performance in published literature . This set includes 5 graph-based strategies , 5 phylogeny-based strategies , 2 hybrid graph- and tree-based strategies , and 1 PPI network-based strategy ( Table 1 ) . Because some projects identify multiple categories of orthologs ( e . g . EggNOG-COGs and EggNOG-KOGs ) , these 13 sources resulted in 17 predicted ortholog datasets ( constituent algorithms ) . We assembled these predictions into a common database ( the WORMHOLE database ) and call these predicted orthologous gene pairs candidate LDOs ( cLDOs ) . For a second layer algorithm ( ingredient 2 ) , we trained SVMs using the predictions of the constituent algorithms . SVMs are machine learning classifiers that take as input a set of labelled examples and a set of ‘features’ describing the examples and builds a mathematical model of each class based on the relevant information within the features . In our case , we trained SVMs on known LDO and non-LDO pairs using the orthology predictions of the 17 constituent algorithms as features . To the SVM classifier , each cLDO is represented as a signature vector of binary calls by the constituent algorithms ( e . g . ‘00011011101010110’ ) with each digit representing the prediction made by a specific algorithm ( 1 = predicts orthology; 0 = does not predict orthology ) . The SVMs require a reference set of known LDOs and non-LDOs to use as training data ( ingredient 3 ) . A well-defined reference set should: ( 1 ) be representative of the entire set of “true” LDOs between the species considered , ( 2 ) include only high-confidence examples , and ( 3 ) include examples of both LDO and non-LDO gene pairs . We selected the PANTHER LDO dataset as the reference set for training the SVMs . PANTHER identifies orthologous gene pairs based on species structure within algorithmically constructed phylogenetic trees . PANTHER LDOs include all one-to-one orthologs and the single least divergent gene pair in one-to-many and many-to-many ortholog groups within the broader PANTHER ortholog dataset . PANTHER LDOs consistently perform well , generating conservative predictions ( i . e . fewer , more closely related ortholog pairs ) when compared to other ortholog datasets using the orthology benchmarking service provided by Quest for Orthologs ( QfO ) , a consortium that provides community standards for developing and testing orthology prediction methodology ( http://questfororthologs . org/ ) [31] . Because the PANTHER LDO set is conservative , we anticipate that it contains strong positive examples of LDOs and that we can identify gene pairs that appear “LDO-like” with additional information not available to PANTHER . We grouped each cLDO in the WORMHOLE database into one of three classes: 1 ) Known LDOs are cLDOs that are contained in the PANTHER LDO set . 2 ) Known non-LDOs are cLDOs for which one or both genes in the pair has a predicted ortholog in the PANTHER LDO set that is not the other gene in the cLDO pair ( i . e . is a multiple mapping for which the cLDO is not the least diverged pair ) . 3 ) Unclassified cLDOs are cLDOs for which neither gene in the pair has a known LDO . We trained the SVMs using only the known LDOs and known non-LDOs and reserved the unclassified cLDOs for possible novel LDO identifications . These unclassified cLDOs are exactly the edge cases where PANTHER can potentially be extended . We trained an independent SVM for each pair of query and target species using the predictions made by the 17 constituent algorithms as features and the PANTHER LDOs as a reference set for classification . As a baseline aggregation strategy to benchmark the SVM performance we used simple voting—a straightforward tally of the number of constituent algorithms that predicted a cLDO—and ranked cLDOs by their vote counts . We employed nested cross-validation to ensure that the SVM models were not overfitting the training data ( see Materials and Methods ) . A summary of the number of genes , number of ortholog pairs , and genes with multiple ortholog mappings across species is provided in Table 2 , and for each species combination in S1 Table . As expected , the SVM models always outperformed the constituent algorithms and simple voting at predicting PANTHER LDOs in terms of precision ( P , the fraction of predicted LDOs that are known LDOs ) and recall ( R , the fraction of known LDOs that are contained in the predicted LDOs ) ( Fig 2A and S1 Fig ) . This is because none of the constituent algorithms were designed to directly predict LDOs . The constituent algorithms display a wide range of performance at predicting PANTHER LDOs and none achieve as high performance as WORMHOLE at predicting PANTHER LDOs . While each algorithm performs well at the prediction task for which it was designed ( e . g . prediction of orthologs from direct comparison of sequence , prediction of functional equivalence , identification of ortholog group with respect to a specific most recent common ancestor ) , the performance at predicting PANTHER LDOs depends on the similarity between PANTHER LDOs and the algorithm-specific design goal . PANTHER LDOs are a particularly conservative subset of ortholog predictions , and we observe that more conservative algorithms ( e . g . Roundup ) tend to achieve high precision and recall ( Fig 2A and S1 Fig ) , while more permissive algorithms ( e . g . eggNOG-KOGs; clusters of orthologs defined with respect to the most recent common ancestor , MRCA , for all eukaryotic species ) tend to display high recall at the cost of low precision at PANTHER LDO prediction . PANTHER , by definition , has perfect recall of PANTHER LDOs ( Fig 2A ) . The range of performance represented among algorithms is important , providing the SVM classifiers with a diverse set of features from which to discern “LDO-like” gene pairs and optimize LDO-prediction performance . The improved performance of WORMHOLE at predicting PANTHER LDOs demonstrates that WORMHOLE is able to consistently learn such structure , despite none of the constituent algorithms being designed to predict LDOs per se . Identifying LDOs is of particular importance in distantly related species where evolutionary time has resulted in greater sequence divergence between orthologs , obscuring the lineal relationship between genes . In Fig 2 we examine the behavior of the SVMs as a function of the evolutionary distance between organisms . The set of species compared in WORMHOLE includes three vertebrate species ( humans , mice , and zebrafish ) and three invertebrate species ( fruit flies , nematodes , and yeast ) . The three vertebrate species are substantially more closely related to each other than any vertebrate species to any invertebrate species , or any of the invertebrate species to one another . This allows LDO predictions to be grouped into those between closely related species ( vertebrate-vertebrate ) and more distantly related species ( invertebrate-invertebrate and vertebrate-invertebrate ) . Fig 2A presents the performance of the SVM at predicting known LDOs as compared to each constitutive algorithm and simple voting . For each comparison the SVM has higher precision at every value of recall than simple voting or any of the constituent algorithms . Vertebrates are closely related evolutionarily; as a consequence the constituent algorithms already perform well and simple voting or the SVM yield only marginal improvement . This is ultimately due to the clarity of orthology relationships in closely related species; most orthologs are one-to-one mappings with relatively little sequence divergence . In contrast , the invertebrate species are each distantly related from each other and from the vertebrate species and the PR-curves show dramatic improvement in classification by the SVMs over voting and the constituent algorithms . In order to normalize the outputs to make comparisons between groups , we scaled the output scores of the SVMs to the interval [0 , 1] so that 0 and 1 represent the extremes of low and high prediction confidence , respectively ( see Materials and Methods ) . We term the scaled confidence score the WORMHOLE Score . To allow direct comparison to our selected baseline , we similarly scaled the number of votes received by each algorithm to the Vote Score . A WORMHOLE or Vote Score of 0 . 5 is the point where the harmonic mean of precision and recall ( F ) is maximized . This point occurs at the “shoulder” of the PR-curve ( Fig 2A ) and denotes a convenient threshold of simultaneously high precision and recall . Fig 2B presents the range of F-values achieved by each constituent algorithm , simple voting , and the SVMs across species combinations . While simple voting generally outperforms the constituent algorithms , specific algorithms achieve greater performance in some cases , particularly when predicting LDOs between yeast and other species ( S1 Fig ) . Indeed , the median F achieved by OrthoMCL between invertebrate species is 1 . 7% higher than simple voting ( Fig 2B and S2 Table ) . In the vertebrate-vertebrate and vertebrate-invertebrate comparisons , simple voting achieves a median F 5 . 6% and 4 . 5% higher than the nearest constituent algorithm , respectively . In contrast to simple voting , the SVMs consistently outperform all constituent algorithms and simple voting , displaying median F 22 . 3% , 11 . 3% , and 1 . 4% higher than the nearest constituent algorithm at predicting LDOs between invertebrate-invertebrate , vertebrate-invertebrate , and vertebrate-vertebrate species , respectively ( Fig 2B and S2 Table ) . The ability of the SVM models to improve performance relative to voting appears dependent on the range of precision and recall represented in the underlying first-layer algorithms for a given species combination . Species combinations with little variation in recall in particular ( e . g . human-to-zebrafish predictions , S1E Fig ) , result in little or no improvement in SVM performance over voting , while combinations with wide variation in both performance metrics see a much larger improvement from the SVM classifiers ( e . g . human-to-worm predictions , S1E Fig ) . As a measure of the generalizability of the WORMHOLE SVMs , we examined the ability of a model trained on one pair of species ( e . g . human-worm ) to predict orthologs between each other pair of species . While optimum performance was achieved when a model was trained and tested on the same species pair , performance was surprisingly consistent across species combination ( Fig 3 and S3 Table ) . Two species combinations were an exception to this pattern . Models trained on human-mouse and , to a lesser extent , mouse-zebrafish reference LDOs displayed reduced performance relative to the other models when applied to predict LDOs in other species combinations . Humans and mice are the most closely related species examined and have the best annotated and least divergent ortholog datasets . We speculate that the relatively poor performance of human-mouse trained SVM models at predicting LDOs in other species is a result of the limited diversity in human-mouse ortholog prediction among constituent algorithms ( S1E and S1F Fig ) , limiting the information available to the SVM classifiers about general orthology . To further examine the relationship between models trained on different pairs of species , we next examined the variation in model parameters across species combinations . Each SVM is parameterized by a set of weights assigned to predictions made by each constituent algorithm that define the classifier ( see Materials and Methods ) . While the weights differ across species pairs , the weight vectors are correlated ( mean Pearson coefficient = 0 . 54 , standard deviation = 0 . 21 , Fig 4A ) , indicating that there are global trends for particular constituent algorithms to have high or low weight across species combinations . This trend is shown in Fig 4B . As expected , PANTHER receives the highest median weight . While the constituent algorithms were developed independently , all work from similar source data and many employ related strategies to predict orthologs . As a consequence , predictions between specific tools can be highly correlated . Providing prediction data from correlated algorithms introduces redundant information that results in over-representation in the case of simple voting . The SVMs respond to correlation by proportionally reducing the weight given to the predictions from correlated algorithms . For example , predictions made by Homologene and OMA are correlated ( Jaccard index = 0 . 46 , S4 Table ) . We speculate that this results in OMA receiving relatively low , sometimes even negative , weight , particularly in species combinations where Homologene/OMA predictions are not well suited to predicting PANTHER LDOs . Along the same lines , WORMHOLE considers predictions from metaPhOrs , which itself is a meta-predictor incorporating sequence data from several of the other WORMHOLE constituent algorithms . As expected , metaPhOrs predictions correlate well with most of these tools , including Ensembl Compara ( Jaccard index = 0 . 37 ) , TreeFam ( Jaccard index = 0 . 35 ) , and EggNOG-NOGs ( Jaccard index = 0 . 29 ) , while less strongly with others ( OrthoMCL; Jaccard index = 0 . 13 ) ( S4 Table ) . Higher weight is given to metaPhOrs than any of the three highly-correlated algorithms that represent metaPhOrs source data ( Fig 4B ) , indicating that the WORMHOLE SVMs are accounting for the correlation in assigning weights . WORMHOLE builds an image of what an LDO “looks like” by examining the PANTHER LDOs from the perspective of the amalgamated calls of the constituent algorithms . It then scans the collection of all cLDOs to identify novel gene pairs that fit that learned image . When applied across the genomes in question , we expect WOMRHOLE to capture an expanded set of LDOs that includes the majority of the PANTHER LDOs , as well as novel gene pairs . This is indeed what we observe ( Fig 5A , Table 3 ) . Importantly , WORMHOLE excludes a large portion of the broader PANTHER database that is not included in the PANTHER LDO set , removing the majority of the one-to-many and many-to-many gene-combinations . Importantly , the WORMHOLE classifier considers only the predictions made by the 17 constituent algorithms and is blind to the number of cLDOs corresponding to a specific query gene . As a consequence , WORMHOLE can generate multiple LDO predictions for a single query gene if there is sufficient evidence from the constituent algorithms . The number of query genes that generate multiple LDO predictions within a target species decreases as the WORMHOLE score threshold is increased ( Fig 5B ) . Using a threshold of 0 . 5 , WORMHOLE produces multiple LDO predictions for 12 . 4% of genes ( Fig 5B and Table 2 ) . Within the subset of genes with multiple LDO predictions , PANTHER LDOs receive higher WORMHOLE scores than gene pairs not in the PANTHER LDOs ( Fig 5C ) , indicating that WORMHOLE predicts known LDOs with higher confidence than non-LDOs or novel LDOs . To generate a high-confidence subset of the WORMHOLE LDOs that more closely matches the strict definition of an LDO , we identified WORMHOLE reciprocal best hits ( RBHs ) . A WORMHOLE RBH is a predicted LDO with a WORMHOLE Score of at least 0 . 5 for which each gene in the pair receives the highest WORMHOLE Score when the other gene is queried ( analogous to BLASTp RBHs ) . WORMHOLE RBHs are similar to PANTHER LDOs in that each gene in one organism will map to a single gene in the other organism . Comparing WORMHOLE RBHs to PANTHER LDOs , the WORMHOLE RBHs reproduce 81 . 7% of original PANTHER LDOs , but expand the total number of predicted LDOs by 17 . 7% ( Table 3 ) . This trend is reproduced for each comparison between vertebrates and invertebrates ( Table 3 ) . Note that in a small number of cases , multiple LDOs are predicted for a single query gene with identical WORMHOLE scores , preventing WORMHOLE from distinguishing a single RBH ( Table 2 ) . In these few cases , both predicted genes are included in the RBH category . When applied to predict PANTHER LDOs , the WORMHOLE RBHs produce similar performance to the unmodified WORMHOLE SVMs with a WORMHOLE Score of 0 . 75 or greater ( Fig 2A ) . By definition , the evolutionary divergence between genes in an LDO pair should be less than that between each gene in the pair and all other genes in the target genome . To evaluate the divergence of WORMHOLE LDOs and RBHs relative to PANTHER LDOs , we calculated evolutionary distance between all gene pairs for each species combination . We further examined alignment quality for each gene pair by calculating BLASTp bit scores . The set of all WORMHOLE LDOs and the set of novel LDOs predicted by WORMHOLE but not present in the PANTHER LDO training set both produce a similar distribution of evolutionary distance and bit score to the PANTHER LDOs ( Fig 6 ) . While the WORMHOLE SVMs are trained to predict LDOs based on the PANTHER LDOs , a subset of the PANTHER LDOs are excluded by the WORMHOLE SVMs . Gene pairs in this set of excluded PANTHER LDOs had markedly higher evolutionary distance and lower BLASTp bit scores than the WORMHOLE or PANTHER LDOs ( Fig 6 ) , indicating that the WORMHOLE SVMs specifically trimmed distantly related , low-confidence gene pairs from the PANTHER LDO dataset . A similar pattern was observed for WORMHOLE RBHs ( S2 Fig ) . The percentage of WORMHOLE RBHs and PANTHER LDOs that identify the least evolutionarily distant gene is similar ( Table 2 , S1 Table ) . As expected , this percentage is lower for the broader category of all WORMHOLE LDOs that receive a minimum WORMHOLE Score of 0 . 5 , which includes multiple LDO mappings for some genes ( Table 2 , S1 Table ) . Orthology is an evolutionary concept and does not necessarily imply that a pair of genes will be functionally related . However , orthologous genes , and in particular LDOs , are often functionally similar or equivalent , and ortholog prediction is commonly used as a starting point for identifying the gene or genes in a new species that fill an equivalent functional role as a gene in another species where the role is known . To assess the ability of WORMHOLE to identify functionally-related ortholog pairs , we measured the performance of the WORMHOLE SVMs at predicting Functional Orthologs from Swissprot Text Analysis ( FOSTA ) FEP pairs . The FOSTA database contains high confidence FEPs based on text analysis of Swiss-Prot annotations and thus represents an assessment of functional equivalence at a high level of manual curation by experts [32] . Voting improves prediction of FOSTA FEPs relative to the constituent algorithms , with SVMs giving an additional improvement in precision , recall , and harmonic mean of precision and recall ( Fig 7 ) . As observed in the ortholog reference dataset , WORMHOLE adds almost no benefit to FEP predictions between closely related species , while performance is greatly improved in more distantly related species ( Fig 7B and S3 Fig ) . In FEP prediction between vertebrate species ( Fig 7A ) , and predictions between humans and mice in particular ( S5E and S5F Fig ) , many of the first-layer algorithms produce nearly perfect performance , leaving no room for improvement . In contrast , prediction of FEPs between invertebrate species , or between vertebrates and invertebrates , receives a substantial benefit from the SVM models relative to simple voting , improving both precision and recall by more than 5% in most cases and more than 10% for certain species combinations ( S3 Fig ) . Performance statistics for WORMHOLE , voting , and each constituent algorithm at predicting FOSTA FEPs is provided in S5 Table . The QfO consortium provides a set of tools for benchmarking ortholog prediction datasets . One of these tools calculates gene ontology ( GO ) term conservation between gene pairs , an established metric of functional relatedness [33] . We used this service to assess the average functional relatedness between WORMHOLE-predicted LDOs as compared to predictions made by each of the constitutive algorithms and to PANTHER LDOs across the six examined genomes . WORMHOLE consistently maintained a similar level of functional relatedness between predicted gene pairs , but identified more gene pairs , as compared with the PANTHER LDOs ( Table 4 and Fig 8 ) . In invertebrate-invertebrate comparisons , WORMHOLE achieves nearly identical GO term conservation scores to PANTHER LDOs . In the vertebrate-vertebrate and vertebrate-invertebrate comparisons , WORMHOLE functional conservation is slightly decreased relative to PANTHER LDOs , but is higher than all methods that call a similar number of pairs . A similar result holds when comparing enzyme classification numbers ( EC ) , which depend strictly on the catalyzed chemical reaction , between enzyme LDO pairs ( Table 4 , S4 Fig ) . The WORMHOLE RBHs receive similar functional relatedness and enzyme conservation scores to the PANTHER LDOs–and higher mean scores in invertebrate comparisons–while generating substantially more LDO predictions ( Table 4 , Fig 8 , S4 Fig ) . A third measure evaluates the discordance between species and gene phylogenetic trees based on uploaded ortholog pairs [33] . Similar to GO term conservation , WORMHOLE expands the number of represented gene trees while maintaining low species-gene tree discordance and limiting the number of gene trees that do not match the phylogenetic structure of the species tree ( S5 Fig ) . The combined ability of WORMHOLE to improve FEP prediction and expand the pool of LDOs while maintaining functional relatedness shows that , despite non-one-to-one mapping of genes , WORMHOLE predictions are well tuned to gene function . This is demonstrated by the more restricted WORMHOLE RBHs , which maintain identical , or slightly better , functional relatedness to PANTHER LDOs while generating a larger pool of predicted LDOs . This implies that the WORMHOLE SVMs are sensitive to gene function . To illustrate the type of LDO predicted by WORMHOLE in difficult “edge cases” , we manually inspected a set of human-to-worm LDO predictions . Specifically , we examined genes that the WORMHOLE SVMs strongly selected ( WORMHOLE RBHs with WORMHOLE Score > 0 . 75 ) but were missed by simple voting ( Votes < 7 , Vote Score < 0 . 25 ) ; 17 genes fit these criteria ( Table 5 ) . As a metric of sequence conservation , we conducted protein BLASTp for each query gene against the target genome , and each target gene against the query genome ( Table 5 ) . Of the 17 human genes queried , 5 had PANTHER LDOs in worm . In all five cases , WORMHOLE predicted the same worm gene as PANTHER . Four of these genes also were the BLASTp RBH between human and worm . In the remaining case ( human gene CPLX2 ) , both WORMHOLE and PANTHER identify the worm gene cpx-1 , while a BLASTp of cpx-1 against the human genome points to CPLX1 . In addition to the five gene pairs that the PANTHER LDOs called , WORMHOLE identified 12 novel LDOs that were not PANTHER LDOs ( Table 5 ) . Of these novel LDOs , 9 represent the BLASTp RBH for the query gene examined . In one of the three remaining cases , the human gene queried , RP11-343C2 . 11 , overlaps nearly completely with another human gene , VPS4A . VPS4A is a paralog to the BLASTp RBH , VPS4B . This overlap suggests that RP11-343C2 . 11 may be an artifact in the human genome used by the constituent algorithms predicting the gene pair . In another remaining case ( human gene TNNI1 ) , multiple duplication-post-speciation events have occurred between human and worm , and WORMHOLE identified one member of a closely related group of genes ( tni-4 ) instead of another that is the BLASTp RBH ( unc-27/tni-2 ) . We next examined evolutionary distance for each gene pair . In the case of human CPLX1/2 and worm cpx-1/2 , CPLX2 is less evolutionarily distance from cpx-1 than CPLX1 , despite the failure of BLASTp to identify this pair as an RBH , suggesting that WORMHOLE is opting for the least divergent gene pair in this case . In contrast , the worm gene heh-1 is identified as the WORMHOLE RBH , the PANTHER LDO , and the BLASTp RBH , but not the least evolutionarily distant gene ( Table 5 ) . Similarly , only 3 of the 12 novel WORMHOLE LDOs represent the reciprocal least evolutionarily distant gene between humans and worms . Which metric is “correct” in these cases is unclear , and phylogenetic reconstruction often does not provide additional insight . Many of these edge cases represent phylogenetic trees where gene duplication has occurred in both species more recently than the orthology-defining speciation event ( e . g . CPLX2/cpx-1 and TNNI1/tni-4 ) . When this occurs , a single gene in one lineage will always be evolutionarily closer to all genes in the other lineage from the perspective of sequence divergence . For example , the CPLX2 sequence has diverged less from both cpx-1 and cpx-2 than CPLX1 . Other gene pairs belong to families with an even more complex and difficult to interpret evolutionary history with multiple speciation and duplication events ( e . g . HACD3/R10E4 . 9 ) . While the two genes in these complex families with the least sequence divergence are technically the LDO , the relationship between other family members , particularly when attempting to infer functional relationships from orthology , is ambiguous . In these cases , direct experimental examination is necessary to confirm functional relationships between orthologs . By considering consensus predictions from multiple prediction strategies , WORMHOLE provides a disciplined strategy for selecting genes prior to these analyses . Taken together , these examples suggest that , with the PANTHER LDOs as reference and the additional information provided by the constituent algorithms , the WORMHOLE SVMs add clarity to difficult-to-distinguish edge cases where orthology is ambiguous based solely on an examination of available ortholog prediction strategies or voting-based meta-tools . They also help define the limits of the current SVM models around gene families with complex evolutionary history involving multiple speciation and duplication events that are not clearly resolved by current phylogenetic models . To provide convenient access to WORMHOLE LDO predictions , we developed a web tool that can be accessed publicly at http://wormhole . jax . org/ . The web tool allows users to rapidly query the WORMHOLE database for LDO predictions between the six species , including options to manually define the WORMHOLE score threshold , exclude all but the highest scoring predicted LDOs for genes with multiple mappings , and select only WORMHOLE RBHs . Genome-wide ortholog predictions between each pair of species are also available for download .
In developing WORMHOLE we have taken a supervised machine learning approach to LDO prediction that combines and augments current methods by adding a second layer that intelligently aggregates the predictions of many ortholog predictors into a compound LDO prediction . Multilayer methods are standard in machine learning and were originally biologically inspired . For example , the visual cortex of primates is organized into a hierarchy of neuron layers that successively capture higher order features of the visual field as the stimulus travels deeper into the brain . The earliest layers of the visual cortex capture relatively simple features of a scene like spots of relative brightness or darkness , intermediate layers aggregate these low-level features into object boundaries , while the highest layers relate these boundaries to semantic object categories stored elsewhere in the brain allowing for object recognition . The multilayer structure of WOMRHOLE is analogous . In the case of WORMHOLE , the primitive features ( e . g . bright and darks spots in the visual field ) are represented by prior biological knowledge , such as sequence similarity , physical interactions between the protein products of genes , evolutionary distance between sequences , and known mutation rates as a function of taxonomy . The first layer of WORMHOLE—the 17 constituent algorithms—transforms these primitive features into intermediate features consisting of preliminary predictions of orthology between pairs of genes ( analogous to the object boundaries in the visual cortex ) . These intermediate features individually are not always sufficient to distinguish LDOs , indeed the constituent algorithms do not intend to make such a prediction ( see below ) , but each is a unique assessment of the many biological features that are relevant for such predictions . The second-layer aggregation operation integrates these preliminary predictions of the individual algorithms as input features for SVM classifiers , using the patterns in these features to recognize true LDOs ( as the visual cortex recognizes objects from object boundaries ) ( Fig 1 ) . This second layer is separated from the raw input data ( genetic sequence ) by the orthology predictions made by the constituent algorithms , combining them in an intelligent way to make LDO predictions . We stress that the constituent algorithms do not intend to explicitly predict LDOs . Rather , they predict orthology by applying various statistical criteria to input data including phylogeny , sequence alignment , and/or functional annotation that are algorithm-specific . WORMHOLE uses the orthology calls of each algorithm as features that may be relevant to predicting LDOs . Indeed , LDOs are a specific and rather small subset of all orthologs . The extent to which any constituent algorithm’s ortholog or FEP predictions align with the PANTHER LDO reference set is a function of the methodology and the orthology definition used by that algorithm . Nevertheless , we can treat the orthology calls of the constituent algorithms as predictions of LDOs . If this assumption is not valid for a specific algorithm , the SVM will simply assign a low weight to that algorithm based on the observed poor performance of that algorithm at predicting PANTHER LDOs ( e . g . Isobase , Fig 5A ) . From this point of view the constituent algorithms display wide variation in their precision and recall on the reference set; some are very conservative and precise , while others have high recall at the cost of calling many non-LDOs . On this basis we suspected that a simple voting strategy would be a useful heuristic for capturing likely LDOs by aggregating over a range of predictions and filtering out pairs that result from algorithm-specific errors or an overly broad orthology definition . Indeed , this voting strategy is enriched for LDO prediction compared to the constituent algorithms as it improves precision and recall over the constituent algorithms when predicting the PANTHER LDO set ( Fig 2 ) . More directly , the vote counts of PANTHER LDOs are significantly higher than non-LDOs ( S6 Fig ) , demonstrating that voting is a discriminative criterion for LDO identification . While the performance improvement is species-dependent , voting achieves higher precision at a fixed value of recall ( and vice versa ) in nearly all cases . The variation in precision and recall of the constituent algorithms demonstrates that giving each algorithm equal weight in the vote count is not optimal . Conservative algorithms that predict fewer orthologs but more often identify LDOs should be given higher weight . This raises the question of how to apportion weights to algorithms . One strategy would be to try to identify commonalities directly and construct weights “by hand” , but this runs the risk of incorporating our personal biases . Instead , we learned the weights from a training set of examples of true and false LDOs using the SVM algorithm ( see Materials and Methods ) . The SVMs clearly outperform the simple voting by learning which algorithms are more trustworthy and giving them higher weight . In any machine learning application , the scope is defined exclusively by the training dataset . We trained our models on the PANTHER LDOs , a set of high quality LDO predictions . Because PANTHER LDOs represent a conservative set of closely related genes pairs , and because there exist edge cases for which evolutionary information becomes difficult to parse , we anticipated that the PANTHER LDOs were not comprehensive in identifying all true LDOs . Indeed , these edge cases increase in frequency for distantly related genomes that contain many duplication-post-speciation events in both lineages . PANTHER LDOs are very likely true positive LDOs , have high functional conservation , and they are more or less representative of true LDOs . However , because PANTHER LDOs are conservative , they are not comprehensive , making them a suitable reference set for predicting a larger set of LDOs . The central assumption of WORMHOLE is that we can learn a signature identifying true LDOs by inspecting the PANTHER LDOs . Our predictions are thus “PANTHER-LDO-like” as far as the input features to the SVM are concerned . We have employed four strategies to ensure that the WORMHOLE predictions are sensible: 1 ) nested cross-validation , which prevents overfitting on the training data , 2 ) estimation of evolutionary and sequence divergence between predicted LDOs , 3 ) prediction of known functionally equivalent proteins using a distinct set of high confidence FEPs ( the FOSTA database ) , and 4 ) assessment of functional relatedness by measuring GO term conservation between predicted LDO gene pairs ( using the community standard benchmarking service provided by QfO ) . Our results on the evolutionary and sequence divergence between WORMHOLE LDOs and RBHs are a direct test of “least divergence” between the predicted ortholog pairs . WORMHOLE LDOs and RBHs improve the PANTHER LDOs on these measures by: 1 ) expanding to a larger set of predicted LDOs without compromising the small divergence between predicted LDOs , and 2 ) excluding a subset of PANTHER LDOs that have significantly higher divergence than is typical of the PANTHER LDOs . The tests of functional conservation and equivalence provide a completely independent assessment of the WORMHOLE predictions , but their results have to be interpreted with caution . First , as noted above , orthology is related , but not identical , to functional equivalence . Second , functional annotation of proteins is much less complete than predicted orthology . This is because sequence data are much more readily available than functional data and orthology can often be inferred with high confidence independent of any functional information . The SVMs perform better than voting and the constituent algorithms in predicting the FOSTA FEPs ( S3 Fig ) . This relative comparison is what is important . The PR-curves for the SVMs tested on the FOSTA FEPs must be understood in light of the fact that many FEPs are likely to be missing from FOSTA . Likewise , when considering the conservation of functional annotations provided by QfO , there are many “missing” functional annotations , so performance has to be considered in a relative sense . The WORMHOLE RBHs have comparable functional similarity scores to the PANTHER LDO reference set , but WORMHOLE makes a substantial number of novel calls ( Table 3 , Fig 8 and S4 Fig ) . These novel calls are particularly important in distant species comparisons , where the methodology used to identify PANTHER LDOs is conservative . WORMHOLE employs complementary information not available to the PANTHER algorithm to improve confidence and expand the number of LDOs predicted . The functional cross-validation results suggest that WORMHOLE-predicted LDOs are sensible candidates . Many of the WORMHOLE predictions are not one-to-one mappings , as required by the strict definition of an LDO . This can be interpreted simply as the expected “dead weight loss” of the machine learning strategy; the final model cannot reasonably be expected to perfectly predict the known LDOs and non-LDOs . An alternative interpretation is available when we observe that some LDOs will be less divergent from their non-LDO orthologs than others . Indeed , some genes will have multiple orthologs that are highly similar in both sequence and function , and selecting the LDO will amount to making an extremely fine distinction . These LDOs will be more difficult to separate using our strategy , but also much more functionally similar . The functional cross-validation shows that this is exactly what happens . Among the non-PANTHER LDOs ( genes pairs in the WORMHOLE database , but not part of the PANTHER LDO dataset ) , a significant fraction lies within the larger PANTHER database ( Fig 5A ) . These are the false positives that could not be reliably distinguished from true LDOs by the SVM during training . The functional cross-validation directly compares the WORMHOLE predictions to both the PANTHER LDOs and the full PANTHER set . The WORMHOLE predictions retain comparable scores to PANTHER LDO while calling many more pairs and producing better scores than other methods that call similar numbers of pairs . Simultaneously , WORMHOLE has higher performance than the full PANTHER set . We stress that this occurs purely as a side benefit of training an SVM to recognize LDOs from non-LDOs and not because WORMHOLE has explicitly included additional functional information beyond that contained in the first-layer algorithms . Depending on the purposes of user , these functionally similar multiple mappings may be useful per se . A limitation inherent to the strict definition of an LDO as the single least diverged gene pair in an ortholog group is that it will necessarily fail to identify cases where a lineage-specific duplication results in redundant genes that are both functionally equivalent to the gene in the other species . Our functional data suggests that this is not a rare occurrence , as WORMHOLE predicts many multiple mappings that are enriched for functional conservation near the same level as the LDOs . However , there are two filters that a WORMHOLE user can use to sift through multiple hits to potentially identify the true LDO . First , within a family of hits the pair with the highest WORMHOLE score is likely to be the LDO ( Fig 5C ) . An even stricter criteria is to select the gene pair with the reciprocal highest score ( i . e . the WORMHOLE RBH ) , should it exist . However , some instances of multiple hits arise because the candidates have the exact same vote patterns , and hence the same WORMHOLE score . A second filter when considering multiple mappings is to use auxiliary criteria , e . g . highest-quality sequence alignment , independent of WORMHOLE to identify the LDO , which is beyond the scope of the WORMHOLE web tool . A priori , a highly tuned model to predict LDOs in one species pair might not have any predictive power for unrelated species . However , we find that a model trained on one species pair does perform well when applied to predict LDOs between other species pairs ( Fig 3 and S3 Table ) . This strongly suggests that the WORMHOLE SVMs are identifying patterns in the constituent algorithm predictions that are indicative of LDO status in general and not just in the species pair used to train the model . This property points to broader applicability of the supervised machine learning framework and suggests that LDOs can be inferred in a species-independent manner . This is an intriguing prospect for future work . An examination of novel LDO predictions made by WORMHOLE in gene pairs with ambiguous orthology status ( Table 5 ) suggests that the WORMHOLE SVMs are able to parse non-intuitive information provided by the voting patterns in the constituent algorithms to provide clarity in distinguishing orthology . WORMHOLE identifies a number of novel LDOs in this realm , picking the BLASTp RBH in most cases . A few cases of disagreement between WORMHOLE and PANTHER or BLASTp indicate that there remains room for improvement by adding additional information or updating reference LDO sets in future iterations of WORMHOLE . WORMHOLE is the first machine learning meta-tool developed for the problem of predicting LDOs . We demonstrate the ability to improve LDO prediction using SVM classifiers . A key advantage to our approach is that it is a “meta-heuristic” , meaning that , in principle , any set of input algorithms can be used in Layer 1 and any user-preferred reference set and classification algorithm can be used in Layer 2 . As more data become available and ever more sophisticated ortholog prediction tools are developed , the multilayer machine learning approach can grow to accommodate such innovations in the field . This work represents a starting point for several potential lines of future work . While WORMHOLE considers only the predictions of other orthology prediction methods , machine learning classifiers can accept any form of relational data for a given pair of potential orthologs that can be appropriately represented as input , allowing for consideration of information not implicitly captured in the constituent algorithms . In principle , future adaptations of WORMHOLE may include direct information about sequence similarity ( e . g . alignment statistics ) , functional comparison ( e . g . GO term conservation scores ) , or even more obscure biological information ( e . g . relative expression levels in specific tissues ) . Beyond model systems , our results show that training a model on examples from one species pair generalizes well to other species pairs ( Fig 3 ) . It should be possible to use this property to make predictions in species not included in the design of WORMHOLE . Many current ortholog prediction projects make predictions for very large numbers of species . In principle , the machine learning framework can augment these predictions by , for example , training SVM models on a set of well-characterized and relevant models systems and using the predictions of the SVM models for less-characterized species . Some meta-tools ( e . g . MOSAIC and MARIO ) already use voting as a pre-processing step prior to sophisticated sequence-based analyses . Replacing simple voting with trained SVMs could supply candidates for sequence analysis at both a high level of sensitivity and specificity . The scope is only limited by availability of data and computational resources .
Ortholog and FEP datasets were acquired from the online repositories of each source database , in OrthoXML format when available . Web addresses , access dates , and version numbers for the 17 ortholog prediction datasets used to train WORMHOLE SVMs are provided in Table 1 , and for all other source data in S6 Table . In building models , we were able to simply include all predictions generated by each tool under default settings in most cases . For EggNOG and Isobase , tool-specific considerations motivated additional effort . As a first-order assessment of confidence in a given predicted ortholog pair we employ simple voting , a straightforward tally of the number of constituent algorithms that predict that pair . To improve upon simple voting , we applied machine learning to differentially weight the influence given to each algorithm based on its performance in predicting the reference LDOs . Specifically , let c denote a cLDO and let xc ϵ {0 , 1}17 denote the 17-dimensional binary vector of ortholog calls from each of the 17 constituent algorithms for c ( i . e . xic denotes the 0/1 prediction of the ith constituent algorithm ) . An SVM assigns a weight , wi , to predictions made by each constituent algorithm based on the individual performance of that algorithm at reproducing the PANTHER LDOs and defines a score for each cLDO , c: rawSVMscore ( c ) =∑i=117wixic−b where the sum is taken over the 17 constituent algorithms , wi is the weight assigned to the ith algorithm , and b is an offset that defines the boundary between positive and negative predictions . The parameters {wi , b} are learned from a set of labeled training examples . Note that if the offset is zero and all weights equal to one , then the SVM formula corresponds exactly to simple voting . Thus , the SVM is a weighted voting scheme where the weights are tuned to the training data . We fit the SVM classifiers using the R package “e1071” , which is available on the Comprehensive R Archive Network ( http://cran . r-project . org ) . In machine learning a key issue in model fitting is “overfitting” , i . e . setting the model parameters in such a way that the model performs well on the training data but fails to generalize to new data . The SVM has a single hyperparameter ( i . e . a parameter that defines the fitting of the model , but not the model itself ) , called C that can be tuned to prevent overfitting . The parameter C defines the penalty for misclassifications and balances the fit to the data against generalizability [39] . For each combination of query and target species , we employed nested 10-fold cross-validation ( nested CV ) [40] to choose C . Nested CV splits the model selection process into an inner CV to select model parameters and an outer CV to estimate performance of the selection procedure . The outer CV , first randomly separates the data into 10 equal parts , trains the model on 9 parts , and tests the performance of the resulting model on the withheld part . The process is then iterated , withholding each 10% of the data once for testing . During each iteration of the outer CV , the model-training step is carried out using an inner CV . The 90% of the data used for training is further subdivided into 10 parts for standard CV . Within each inner CV iteration , C was chosen from a logarithmic vector , C ϵ {4−2 , 4−1 , 40 , 41 , 42} , to maximize the average testing accuracy ( fraction of correct classifications ) on the 10th inner CV parts . The inner-CV-tuned model is then tested on the 10% of the data that were held out for the outer CV . All assessments of generalization performance ( precision , recall , harmonic mean ) were estimated by their mean and standard error of mean over the 10 outer CV iterations . In this way , the inner CV ensures that the model parameters are not overfitting the idiosyncrasies of the training data , while the outer CV provides an estimate of the robustness of the model selection procedure when applied to novel data that were completely unseen by the model selection procedure during training . Segregation of training data into training and test parts for cross validation requires care to ensure that each part is truly independent . Because there were many more negative examples than positive , we partitioned the two classes separately so that each part had the same proportion of positive and negative examples . We further ensured that all candidate ortholog pairs for a given query gene were assigned to a single part . We examined the effect of stratifying ortholog pairs into folds by gene family; however , while family-wise stratification resulted in a small increase in the variance of precision and recall across folds in the outer cross-validation , it did not affect either the overall quantitative performance of WORMHOLE or the qualitative conclusions reached in this work . Because the two strategies were qualitatively indistinguishable , we proceeded with the simpler , gene-wise stratification . To compare SVM models between species-pairs ( Fig 5B ) , we computed the Pearson correlation between the weight vectors , w , for each model . This correlation encodes whether or not the weight vectors assign high weight to the same set of constituent algorithms . For highly similar models this correlation is close to one , whereas for highly dissimilar models this correlation is close to zero . As primary metrics of performance we evaluated recall ( R ) and precision ( P ) . Recall is the fraction of the total number of correct ortholog pairs that are predicted by an algorithm , formally defined as: R=TPTP+FN while precision is the fraction of the total number of predictions made by an algorithm that are correct: P=TPTP+FP where TP is the number of true positives , or the number of correct ortholog pairs predicted by an algorithm , FN is the number of false negatives , or the number of correct ortholog pairs not predicted by an algorithm , and FP is the number of false positives , or the number of incorrect ortholog pairs predicted by an algorithm . These values are calculated by comparing the pairs of orthologs predicted by each algorithm to the reference ortholog dataset for a given pair of query and target species . A single performance metric is often useful for comparing a large number of predictions . In these cases we used a related metric , the harmonic mean of precision and recall ( F ) , defined as: F=2PRP+R F provides a single measure that balances precision and recall . The harmonic mean weighs P and R simultaneously and equally to summarize classification performance . A more flexible family of measures are the β-harmonic means defined by: Fβ= ( 1+β2 ) PRβ2P+R The β-harmonic means are a family of measures that depend on a parameter , β , which balances the importance of recall relative to precision . The measure F1 is simply the harmonic mean ( defined above ) . The measure F0 . 5 gives recall half the priority of precision , while F2 gives recall twice the priority of precision . The raw confidence values returned by each SVM model ( or voting ) cannot be compared across species pairs because the assigned SVM weights are specific to each species pair . To allow such comparisons , we normalized the raw SVM scores to a scale that is directly linked to the performance of the model . Specifically , we identified the thresholds T within the raw scores for which the precision and recall at T maximizes Fβ for β ϵ {0 . 125 , 0 . 25 , 0 . 5 , 1 , 2 , 4 , 8} . These thresholds are mapped onto the confidence scores 0 . 9375 , 0 . 875 , 0 . 75 , 0 . 5 , 0 . 25 , 0 . 125 , and 0 . 0625 , respectively . We then interpolated that the map from raw SVM scores to confidence scores using monotonic Hermite cubic spline interpolation [41] , which is implemented in the R function ‘splinefun’ . Thus , the vote and SVM scores are scaled in such a way that applying a threshold of 0 . 5 ( i . e . selecting all ortholog pairs with scores greater than or equal to 0 . 5 ) maximizes F for balanced precision and recall . Doubling β halves the distance toward 0 . 0/1 . 0 in the confidence scores . For example , a confidence threshold of 0 . 75 gives precision twice the weight of recall and a threshold of 0 . 875 gives precision four times the weight of recall . Conversely , a confidence threshold of 0 . 25 gives recall twice the weight of precision . We term the confidence score that scales simple voting the Vote Score and the confidence score that scales the raw SVM scores the WORMHOLE Score . In a few cases , the score given to an ortholog pair differs depending on which species is used as query and which is used as target ( e . g . a pair consisting of a human and worm gene may receive a different score if a human-to-worm query is made than when a worm-to-human query is made ) . This is a result of the way WORMHOLE is constructed , with a different SVM model used for each combination of query and target species . In order to harmonize scores with respect to direction of inquiry , the score given to each pair of orthologs by each Layer 2 WORMHOLE method was averaged between directions . Evolutionary distance between genes was estimated for the longest protein encoded by each gene in a pair . Genome-wide protein sequences were obtained from Ensembl BioMart for each species [19] . All protein pairs between species were aligned using the pairwiseAlignment ( ) function in the R package “Biostrings” [42] , which implements quality-based alignment as described by Malde [43] . Evolutionary distance was calculated for each alignment using the dist . ml ( ) function in the R package “phangorn” [44] using the BLOSUM62 substitution matrix . Both R packages are available on the Comprehensive R Archive Network ( http://cran . r-project . org ) . BLASTp bits scores and RBHs were determined by aligning each protein sequence against each target genome with NCBI BLAST+ ( acquired from http://www . ncbi . nlm . nih . gov/blast; Table 1 ) using the following command options: blastp-queryxxyy . fa-subjectxx . fa-outxx-yy-2 . txt-outfmt6-max_hsps1-evalue1e-4 where “-query” and “-subject” specify input files in FASTA format , “-out” specifies the output file in text format , “-outfmt 6” requests BLASTp hits to be reported in a pairwise table with BLAST statistics , “-max_hsps 1” limits the output to a single report per matched protein pair , and “-evalue 1e-4” sets a maximum threshold on E-value for reported matches . The placeholders “xx” and “yy” indicated two letter abbreviations for species names ( e . g . “ce” abbreviates Caenorhabditis elegans ) . The QfO Benchmarking Service tool accepts lists of predicted ortholog pairs and returns several measures of performance . We used this service to compare predictions made by WORMHOLE to those made by the PANTHER LDOs and the constituent algorithms for three performance criteria described by Altenhoff and Dessimoz [33] , which are described briefly below . QfO provides several publicly available datasets for comparison , many of which are included as constituent algorithms in WORMHOLE . To minimize potential bias introduced by differences in the version of each dataset used in QfO vs . WORMHOLE , we independently ran each QfO performance metric on the set of ortholog pairs predicted in each constituent algorithm , as included in the WORMHOLE database . Each ortholog dataset ( WORMHOLE , PANTHER LDOs , and constituent algorithms ) was mapped to the QfO reference proteome and uploaded to QfO for analysis . | Identifying functionally equivalent proteins between species is a fundamental problem in comparative genetics . While orthology does not guarantee functional equivalence , the identification of orthologs—genes in different organisms that diverged by speciation—is often the first step in approaching this problem . Many methods are available for predicting orthologs . Recent approaches combine methods and filter candidate predictions by “voting”—assigning confidence to ortholog pairs based on the number of predictions by independent methods . Although voting is a heuristic , it maintains precision while increasing recall . Here we employ machine learning to optimize voting by learning which methods make better predictions and , in essence , giving those methods more votes . We present a new tool called WORMHOLE that predicts a strict subclass of orthologs called least diverged orthologs ( LDOs ) with a high level of functional specificity by learning features of orthology that are encoded in the patterns of predictions made by 17 constituent methods . We validate WORMHOLE using multiple measures of evolutionary divergence and functional relatedness , including community standards provided by the Quest for Orthologs consortium . WORMHOLE’s particular strength lies in predicting LDOs between distantly related species , where orthology is difficult to identify and is of critical importance for comparative biology . | [
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"molecular",... | 2016 | WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning |
Management of vector population is a commonly used method for mitigating transmission of mosquito-borne infections , but quantitative information on its practical public health impact is scarce . We study the effectiveness of Ultra-Low Volume ( ULV ) insecticide spraying in public spaces for preventing secondary dengue virus ( DENV ) cases in Porto Alegre , a non-endemic metropolitan area in Brazil . We developed a stochastic transmission model based on detailed entomological , epidemiological and population data , accounting for the geographical distribution of mosquitoes and humans in the study area and spatial transmission dynamics . The model was calibrated against the distribution of DENV cluster sizes previously estimated from the same geographical setting . We estimated a ULV-induced mortality of 40% for mosquitoes and found that the implemented control protocol avoided about 24% of symptomatic cases occurred in the area throughout the 2015–2016 epidemic season . Increasing the radius of treatment or the mortality of mosquitoes by treating gardens and/or indoor premises would greatly improve the result of control , but trade-offs with respect to increased efforts need to be carefully analyzed . We found a moderate effectiveness for ULV-spraying in public areas , mainly due to the limited ability of this strategy in effectively controlling the vector population . These results can be used to support the design of control strategies in low-incidence , non-endemic settings .
Dengue virus ( DENV ) causes a considerable burden to public health worldwide , consisting of 60 to 100 million symptomatic infections , 14 , 000 to 20 , 000 deaths per year , and of a global annual cost of about 9 billion dollars [1 , 2] . Four distinct DENV serotypes exist; infection by one of them confers life-long immunity to that serotype and temporary cross-immunity to others . However , secondary dengue infections with different serotypes are more likely to cause severe illness because of an immune process known as antibody-dependent enhancement ( ADE ) , where pre-existing cross-reacting antibodies do not neutralize but rather enhance viral replication [3] . In Southern America , continental control efforts in the 1970s had come close to eliminate Aedes aegypti , the main vector mosquito , but in the last two decades arboviruses have strongly increased their circulation [4] thanks to the intensification of international travels [5] , international trade [6] and urbanization [7] and to climatic adaptation of mosquitoes and viruses [8] . In particular , DENV is expanding its geographic range to areas that were previously free from autochthonous transmission and are now prone to multiple outbreaks each year . A DENV vaccine has been recently licensed , with an overall efficacy of about 60% against its four serotypes [9]; however , after deployment in 10 different countries , increased hospitalization rates in children were reported , raising suspects that the currently available vaccine may predispose individuals who were seronegative before vaccination to ADE [10] . Therefore , modeling studies and the World Health Organization recommend the vaccine for high-transmission settings only where the proportion of seronegative vaccinees is very low [11 , 12] . In non-endemic settings , the main strategy for DENV control consists in the management of vector populations via insecticide spraying as a reactive intervention to ongoing local transmission . Despite its popularity , this practice has been rarely evaluated in terms of its impact on DENV transmission in real-life settings [13 , 14] . Such evaluation is made difficult by the inherently complex interactions between dynamics of mosquito populations , viral transmission and insecticide uptake in structured spaces and time-varying environmental conditions [15] . Furthermore , DENV transmission and control interventions typically occur over geographic scales of a few hundred meters [16–18] , making it necessary to consider spatial heterogeneities explicitly and at a high resolution [19] . To estimate the effectiveness of insecticide spraying on DENV containment , we developed a novel mechanistic computational framework , coupling a model for mosquito population dynamics and a disease transmission model over a high-resolution spatial grid . Mechanistic models are widely used to study mosquito-borne pathogens such as DENV , for instance to investigate outbreaks in a previously unaffected area [20] , assess vaccine effectiveness [11 , 21 , 22] or to compare transmission of different viruses in the same area [23] . Many of the available models do not include explicitly the mosquito immature stages [24] , which need to be considered to accurately reproduce the dynamics of recovery of the vector population after adulticide treatment . Spatial transmission models are another common epidemiological tool that has been used to study DENV control in different parts of the world [21 , 22 , 25–27] . Here , we combine these approaches with recent insights on the spatiotemporal dynamics of DENV [16] , to estimate the proportion of cases avoided by Ultra-Low-Volume ( ULV ) insecticide spraying in Porto Alegre , a Brazilian metropolis characterized by a subtropical climate , low DENV incidence and negligible pre-existing immunity .
Porto Alegre ( 30°01′40″S , 51°13′43″W ) is the capital of Rio Grande do Sul , the southernmost state of Brazil , with a population of 1 , 400 , 000 inhabitants [28] spread over an area of 500 km2 and characterized by a subtropical humid climate ( Cfa ) [29] . Local transmission of DENV has been recorded only since 2010 [30] and epidemics with more than 1 , 000 suspected cases have been recorded in 2013 and 2016 . In these years , the effective reproduction number exceeded the epidemic threshold between January and mid-March , with a peak value for of 1 . 5 [16] . Over 70% of all transmission events were estimated to occur within 500m from the residence of the infector [16] . To face the emergence of DENV , an integrated surveillance and prevention protocol has been put in place that includes entomological , virological , and active epidemiological components [31] . The reactive control policies implemented to reduce DENV transmission consisted in ULV insecticide spraying in public spaces such as roads and parks ( i . e . , not indoor or in private gardens ) , within an area of radius 200m around the residence of the patient triggering the intervention . The decision to treat an area was made depending on lab-confirmation of a case through ELISA immunological tests , previous treatment in the same area , and availability of resources at the time of decision . Table 1 reports the resulting observed frequency of treatment initiation for imported ( qI ) and locally transmitted ( qA ) cases , as classified by epidemiological investigations . Treatments were not systematic for confirmed cases because of limited resources , especially in cases when further transmission was not considered to be likely by public authorities , e . g . when a treatment had already been performed in the previous days in the same area . We considered DENV transmission in 42 central neighborhoods of Porto Alegre , over the period between December 1 , 2015 and June 30 , 2016 . We used human population data from the Worldpop database [32] spatially disaggregated at 100m resolution over the considered study area ( 99 . 2 km2 , about 20% of the total city area ) . The total population was 605 , 260 inhabitants ( 43% of the total city population ) , with human density values ranging from 5 . 3 to 102 . 5 persons per hectare ( Fig 1A ) . Temperature data for the whole city were obtained from the Brazilian National Institute of Meteorology ( INMET ) [33] . The mean daily temperature for the study period ranged between 7 . 4 and 29 . 5°C , with an average of 21 . 1°C ( Fig 1C ) . Data on female adult Ae . aegypti mosquitoes were previously collected using MosquiTRAP ( Ecovec LTDA , Belo Horizonte , Brazil ) sticky traps [34] containing a synthetic oviposition attractant ( AtrAedes ) that lures gravid Ae . aegypti . Overall , 776 geolocalized sticky traps were set on fixed outdoor positions at a distance of 250m between each other and inspected weekly ( Fig 1C ) . The Health Secretary of Porto Alegre provided notification data for autochthonous and imported dengue cases . 891 suspected cases with symptom onset between December 2015 and June 2016 were reported in the study area ( Fig 1B ) . Fig 2 shows a schematic representation of the computational framework adopted in this analysis . Aedes aegypti capture data were used to estimate the spatiotemporal distribution of mosquito abundance in the study area and period ( Fig 2A ) . Data were aggregated across all traps to obtain an overall time series of Ae . aegypti captures in Porto Alegre ( Fig 1C ) , which was used to calibrate a mosquito population model ( “entomological model” ) and provide an estimation of the daily total mosquito abundance M ( t ) . The entomological model adapts a previously published approach [35] representing the biology of Ae . aegypti life stages via temperature-dependent parameters . Larval carrying capacity was calibrated to weekly capture data with a Monte Carlo Markov Chain approach . For each trap , the total number of captures over time was used as an estimate of the local abundance of mosquitoes; these estimates were interpolated throughout the study area via standard kriging techniques , obtaining the relative abundance of mosquitoes , α ( x , y ) , over a spatial grid of 9919 geographic cells ( 100m x 100m ) covering the study area . The mosquito abundance over time and space , Q ( t , x , y ) , was finally obtained via the combination of these two estimates: Q ( t , x , y ) =M ( t ) ⋅α ( x , y ) . The calibrated entomological model was used to mechanistically simulate the effect of ULV spraying and the ensuing recovery of mosquito populations ( Fig 2B ) . Given a time of intervention tULV and a mosquito-induced mortality ρ , we computed the relative reduction Δρ ( t ) of the mosquito abundance after treatment MULV ( ρ , t ) , compared to the baseline: Δρ ( t ) ={M ( t ) −MULV ( ρ , t ) M ( t ) , t≥tULV0 , t<tULV When a treatment is initiated in any cell i , the mosquito abundance of all cells within a distance r ( the radius of the ULV-treated area ) from i is rescaled by a factor 1-Δρ ( t ) . This equation allows us to consider that: i ) ULV treatment causes the sudden death of a proportion ρ of adult mosquitoes; ii ) following ULV spraying , the mosquito population recovers as new adults emerge from pre-existing immature stages ( which are not affected by adulticides ) , as well as from newly deposed eggs from surviving mosquitoes , as predicted by the entomological model . The spatiotemporal DENV dynamics was implemented by considering a standard SEIR-SEI epidemiological model [11 , 20 , 23] where human-to-vector and vector-to-human virus transmission is regulated by temperature-dependent parameters and can occur across cells via a previously estimated distance-dependent kernel [16] ( Fig 2C ) . Given the negligible pre-existing immunity ( only one positive individuals over 422 tested was found in a seroprevalence study in 2015; C . Marquez Toledo , personal communication , November 2017 ) and cross-serotype ecological interactions , we modelled infection independently of DENV serotype and we assumed that the human population was fully susceptible to the infection . Asymptomatic individuals were assumed to be unable to transmit the virus; however , in a sensitivity analysis we allowed for asymptomatic transmission with different rates [11] . The epidemiological model is applied to simulate single transmission clusters originating from one infectious individual imported at coordinates ( x0 , y0 ) and time t0 . A transmission cluster is defined as the set of all human infections directly and indirectly generated by the index case until stochastic fadeout of the chain of transmission [36] ( i . e . when the number of exposed and infectious mosquitoes and humans is zero ) and the cluster size is defined as the number of secondary symptomatic infections [16] . We modeled interventions according to implemented control protocols: each symptomatic case had a probability of being lab-confirmed and , in such case , a probability to trigger vector control interventions after a delay since symptom onset τ , sampled from a Normal distribution , truncated to positive values . To reflect control protocols implemented in Porto Alegre , we assigned different probabilities of treatment to imported and autochthonous cases . DENV transmissibility in the model , mediated by parameter ψ , and the ULV-induced mosquito mortality , ρ , were calibrated to reproduce the size distribution ( average Ω¯ ) of the 76 clusters occurring in the study area and period [16] . Finally , the calibrated computational framework was used to evaluate the effectiveness of ULV spraying by comparing the number of symptomatic DENV cases obtained with and without treatment ( Fig 2D ) . In addition , to evaluate the effect of different control protocols , we explored the impact of different values of r , τ and ρ on the relative reduction of DENV cases . To ensure the robustness of results , we simulated for each considered scenario 20 , 000 transmission clusters by sampling the index case’s coordinates ( with probability proportional to the local population density ) and time ( uniformly between December 1 , 2015 and April 30 , 2016 ) . For clusters with at least one secondary transmission , the cluster duration was defined as the number of days necessary for stochastic fadeout since symptom onset of the index case , and the cluster radius as the maximal distance between the location of the index case and all other cases in the cluster . Full details on implementation of different components of the modeling framework are reported in the S1 Appendix; parameter values for the epidemiological model are reported in Table 1 . For all estimates , we computed 95% confidence intervals of the average using the Student’s t-test .
In order to reproduce the observed distribution of cluster sizes in Porto Alegre [16] , the mortality of mosquitoes due to ULV treatment in public spaces was estimated at about 40% ( see S1 Appendix ) , which is in good agreement with previous experimental estimates [37–40] . In absence of control interventions , our model estimated that the 76 clusters observed in the study area and period would have caused 1055 secondary symptomatic cases ( 95%CI: 995–1113 ) , which , compared to the observed 815 , implies that 240 cases ( 95%CI: 180–298 ) were avoided by the implemented protocols . The simulated average cluster size in absence of intervention was 11 . 9 ( 95%CI: 10 . 9–12 . 9 ) cases , with a peak of 32 . 1 ( 95%CI: 24 . 9–39 . 3 ) for importations occurring in the second week of January , falling to less than one for cases imported at the end of April ( Fig 3A ) . ULV treatment was able to moderately reduce the average cluster size to 9 . 1 ( 95%CI: 8 . 3–9 . 8 ) , i . e . by 23 . 9% ( 95% CI: 17 . 5–30 . 2% ) ( Fig 3B ) ; the difference in cluster size distributions was significant according to a Wilcoxon-Mann-Whitney test p-value<0 . 001 . Treatment was most effective during the months of highest transmissibility , with a peak reduction of symptomatic cases by 38 . 0% ( 95%CI: 36 . 8–40 . 4% ) for clusters initiated at the end of December . Treatment reduced only marginally the average probability of symptomatic local transmission , i . e . the probability that an imported case caused at least one secondary symptomatic case . The estimated probability of symptomatic local transmission from our model , including treatments , was similar to previous estimates in Porto Alegre for 2016 [16] and had a peak of 64% ( 95%CI: 62–67% ) at the end of February ( Fig 4A ) . On the other hand , the probability of clusters of size larger than 100 was more markedly reduced by treatment ( from 2 . 1% on average in the case of no intervention , to 1 . 6% , Fig 4B ) . Treatment was responsible of an almost negligible reduction of the average outbreak duration ( from 13 . 6 weeks , 95%CI: 13 . 5–13 . 7 , without ULV spraying , to 13 . 1 weeks , 95%CI: 12 . 9–13 . 2 ) and of the average cluster radius ( from 853m , 95%CI 827-878m , to 806m , 95%CI: 781-831m , see Fig 4C ) . Outbreak duration and cluster radius were strongly correlated ( Spearman correlation coefficient 0 . 62 , p-value<0 . 001 , both with and without treatment ) . We found that the proportion of avoided DENV cases changes significantly when varying the radius of the treated area ( see Fig 5A ) : from 10 . 6% ( 95%CI: 3 . 1–18 . 0% ) with a radius of 100m to 37 . 1% ( 95%CI: 32–42 . 2% ) for a radius of 300m and 50 . 0% ( 95%CI: 46 . 4–53 . 6% ) with a radius of 500m . Treatment effectiveness improved when reducing the average delay after symptom onset to 5 days ( Fig 5B ) , with a proportion of avoided cases of 32 . 8% ( 95%CI: 27 . 2–38 . 3% ) , while increasing the average delay to 25 days would reduce the average effectiveness to 16 . 1% ( 95%CI: 9–23 . 2% ) . Changes in the proportion of mosquitoes killed by ULV resulted in proportional reductions of secondary symptomatic DENV cases , up to an average maximum of 52% ( 95%CI: 48 . 2–55 . 7% ) when all existing mosquitoes are killed by treatment under current intervention protocols ( Fig 5C ) . Our estimates are robust with respect to the assumption on the relative transmissibility of DENV by asymptomatic individuals . In scenarios where asymptomatic individuals have a transmission rate of 50% and 100% compared to symptomatic patients [11] , the estimated average relative reduction of cases , assuming the same ULV induced mosquito mortality ( ρ = 40% ) , was 23 . 3% ( 95%CI: 15 . 1–31 . 4% ) and 22 . 1% ( 95%CI: 13 . 9–30 . 2% ) respectively ( see S1 Appendix ) .
Using a spatial stochastic model informed with geolocated capture data on Ae . aegypti female adults and with previous estimates on the size of dengue clusters in Porto Alegre , we estimated the effectiveness of implemented control interventions in terms of the reduction of symptomatic dengue cases . To the best of our knowledge , the impact of ULV outdoor spraying on the reduction of DENV transmissibility has never been estimated using observational data in non-endemic areas [13] . A theoretical assessment of different containment procedures including adulticide spraying has been previously suggested [26] , while others [25 , 27] evaluated indoor spraying effectiveness in regions with high DENV circulation . We found that ULV insecticide spraying in public places avoided approximately one fourth of all secondary symptomatic DENV cases , corresponding to roughly 240 cases in an area of about 100km2 over a full epidemic year . The performance of the intervention was negatively affected by the low estimated efficiency in killing existing mosquitoes in the treated area ( about 40% ) . This value is compatible with field experiments showing that the majority of Ae . aegypti rest within households [37]; furthermore , other studies measured the mortality of mosquitoes resting in sheltered locations [38] or in the vegetation [39 , 40] at values between 30% and 50% , indicating a moderate effect of ULV treatment on mosquito populations . To improve the effectiveness of control , a larger area might be treated , but the trade-off between increasing effort and increasing benefits needs to be taken into account . We estimate that increasing the intervention radius to 300m around the triggering case would result in about 143 additionally avoided cases ( an increase by 60% ) compared to the current protocol but requires treating a total area that is 2 . 25 times larger . An alternative way to improve the current control strategy would be to target private gardens and/or indoor spaces . An increase in the proportion of killed mosquitoes from 40% to 60–70% would improve the overall effectiveness by a similar amount than that allowed by expanding the treated radius to 300m . A similar efficacy could also be achieved by increasing the frequency of treatment for confirmed cases from 60% ( qI ) and 46% ( qA ) to 100% , which would avoid 38 . 6% ( 95%CI: 33 . 6–43 . 7% , see S1 Appendix ) of the expected cases . Alternative approaches towards the reduction of DENV transmission might consider routine preventive interventions , rather than reactive ones , such as deploying larvicides in city areas with highest mosquito abundance [41] . A potential source of uncertainty in our study is the role of asymptomatic individuals in viral transmission . Duong et al . have shown that mosquitoes can be infected by asymptomatic and pre-symptomatic children [42] , but key parameters such as their transmission rate to mosquitoes , the transmission rate to humans by mosquitoes infected by asymptomatic individuals and the asymptomatic infectious period , remain unknown . Since the study by Duong et al . , asymptomatic transmission has been included in most recent transmission models for dengue as a reduced human-to-mosquito transmission rate arbitrarily fixed between 0% and 50% of the value for symptomatic individuals [11 , 23] . Here , we assumed as a baseline that asymptomatic individuals do not transmit the infection; however , we run two alternative scenarios where asymptomatic transmission occurred as the symptomatic one and also at a relative transmission rate of 50% and we found that in both cases the relative reduction of DENV cases granted by ULV treatment would be similar ( see S1 Appendix ) . Among further potential limitations , we acknowledge that large temperature fluctuations may have a negative impact on Ae . aegypti biology [43] . However , in the absence of sufficient data to parametrize these effects , we modelled mosquito population dynamics by considering only average daily temperatures , similarly to previously published modelling studies ( e . g . [20 , 25] ) . Furthermore , our temperature data came from a single weather station for the whole city . The effect of within-day and local variability of temperature may be a source of bias for our estimates , whose impact is very difficult to assess . We did not consider the potential effect of treatment on mosquito capture data for the estimation of the mosquito population . This assumption was based on two observations: first , the limited capture rate of sticky traps , combined with the coarse ( weekly ) temporal resolution of captures and the small ULV-induced mosquito mortality , make it difficult to detect a significant effect of treatment on capture data ( see S1 Appendix for details ) ; second , treatment at any given time included a very limited proportion of the study area , so that the expected effect on the total abundance in the area is marginal . As a proof , we re-computed the mosquito abundance by simulating treatments at the time and sites where they were actually administered during 2016 , assuming an effectiveness ρ = 40%; the maximum difference in the total mosquito population compared to the no-treatment scenario was less than 10% at all times . In the absence of a safe vaccine for DENV in non-endemic settings , the prevention of dengue transmission will continue to rely on the management of mosquito populations . Quantifying how ULV mosquito control translates into a mitigation of the disease burden is a critical but still unanswered public health question [13 , 14] . This study provides a quantitative estimate of the effectiveness of ULV treatment in a non-endemic setting where dengue transmission has established only recently , based on recent insights on the spatiotemporal dynamics of dengue and on high-resolution entomological , population , clinical and treatment data . Our results can be used to support the design and implementation of future interventions in areas at the margins of the geographical range of DENV ( e . g . in Southern Europe , USA , subtropical South America and Australia ) which are undergoing a similar epidemiological transition , or are expected to do so in the next future . | Dengue is a mosquito-borne infection that causes millions of symptomatic infections and thousands of deaths per year . This pathogen is expanding its geographic range to areas that were previously free from autochthonous transmission thanks to the intensification of international travels , urbanization and to climatic adaptation of mosquitoes and viruses . Usually interventions against dengue transmission consist in insecticide spraying aimed at killing adult mosquitoes , but the impact of this practice has been rarely evaluated in real-life settings . In this work , we estimate the proportion of dengue cases avoided by Ultra-Low-Volume insecticide spraying in public areas in Porto Alegre ( Brazil ) . This city is characterized by a subtropical climate , negligible pre-existing immunity and low dengue incidence . The low incidence makes this region unsuitable for deployment of the currently licensed vaccine , which is only recommended by the WHO for high-transmission areas . We found that insecticide spraying avoided approximately one fourth of all symptomatic cases . The performance of the intervention was negatively affected by the low treatment-induced mosquito mortality , as we estimated that only 40% of Ae . aegypti are killed by the insecticide . Control outcomes could be improved by increasing the targeted area and including private premises , but trade-offs against increased efforts need to be carefully analyzed . | [
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"rna",... | 2019 | Effectiveness of Ultra-Low Volume insecticide spraying to prevent dengue in a non-endemic metropolitan area of Brazil |
Histone modifications affect DNA–templated processes ranging from transcription to genomic replication . In this study , we examine the cell cycle dynamics of the trimethylated form of histone H3 lysine 4 ( H3K4me3 ) , a mark of active chromatin that is viewed as “long-lived” and that is involved in memory during cell state inheritance in metazoans . We synchronized yeast using two different protocols , then followed H3K4me3 patterns as yeast passed through subsequent cell cycles . While most H3K4me3 patterns were conserved from one generation to the next , we found that methylation patterns induced by alpha factor or high temperature were erased within one cell cycle , during S phase . Early-replicating regions were erased before late-replicating regions , implicating replication in H3K4me3 loss . However , nearly complete H3K4me3 erasure occurred at the majority of loci even when replication was prevented , suggesting that most erasure results from an active process . Indeed , deletion of the demethylase Jhd2 slowed erasure at most loci . Together , these results indicate overlapping roles for passive dilution and active enzymatic demethylation in erasing ancestral histone methylation states in yeast .
The histone proteins that package eukaryotic genomes into chromatin are subject to a huge number of covalent modifications [1]–[4] . Histone modifications have wide-ranging effects on cellular physiology , and play roles in gene expression , RNA transcript structure , meiotic recombination , and many other processes . Histone H3 lysine 4 trimethylation ( H3K4me3 ) is an active chromatin mark found at the 5′ ends of coding regions at levels that scale with transcription rate [5]–[9] . Unlike histone acetylation , which has a typical half-life on the order of minutes [10] , [11] , histone methylation is considered more stable: in yeast , H3K4me3 is rapidly deposited by RNA polymerase-associated Set1 when genes are activated , but after gene repression the mark persists well after Set1 has dissociated from the gene ( [1] , see below ) leading to the suggestion that it forms a “memory mark” of recent transcription . Indeed , H3K4 methylation is associated with the Trithorax complex of genes involved in cell state memory in Drosophila [2] , indicating that in this context it plays a role in epigenetic inheritance . Several single-locus studies in yeast indicate that H3K4 methylation is lost upon gene repression . In one study , H3K4 methylation over the GAL1-10 locus decreased ∼2-fold by one hour after glucose repression , but did not return to baseline levels until 5 hours of repression [1] . More recently , the partial loss of methylation observed 75 minutes after dextrose addition was shown to be dramatically slowed , but not eliminated , in the absence of the histone demethylase JHD2/KDM5 [12] . At the epigenetically-regulated heterochromatic loci in budding yeast [13] , [14] , high levels of H3K4me3 are observed in mutants lacking the Sir silencing complex . Interestingly , even at these loci , H3K4me3 is erased over several generations ( ∼7 . 5 hours ) upon reintroduction of a functional Sir complex [15] . This loss of H3K4me3 was suggested to rely predominantly on enzymatic demethylation , although a modest effect of replication in H3K4me3 erasure was not ruled out [15] . Together , these results indicate that H3K4 methylation is not maintained in budding yeast after removal of an activating stimulus , even at loci known to be subject to epigenetic regulation . However , these conclusions come from a small number of unusual genomic loci ( largely the GAL genes and heterochromatic loci ) , and their generality is unknown . Furthermore , in different studies H3K4me3 erasure has been suggested to occur predominantly via passive replication-mediated dilution [1] , enzymatic demethylation [12] , or both [15] , raising the mechanistic question of how H3K4me3 erasure occurs . In other words , how long does elevated H3K4me3 at an active gene last after gene repression , does it vary between genomic loci , and how is memory lost or erased ? Here , we investigate temporal aspects of H3K4me3 memory of recent transcription during the cell cycle in budding yeast . We find that H3K4 methylation induced by three different stimuli ( alpha factor , high temperature , and galactose ) is rapidly erased after removal of the activating stimulus . We find that this erasure typically occurs during S phase in a manner consistent with replication , but we find that replication only contributes modestly to the bulk of H3K4me3 erasure . Instead , the demethylase Jhd2/Kdm5 is responsible for most H3K4 erasure .
How long might H3K4 methylation persist after gene repression , and how does it differ at different genes ? We carried out genome-scale profiling of H3K4me3 patterns during cell cycle progression using a 20 bp resolution tiling microarray covering 4% of the yeast genome [6] , [16] . We synchronized yeast using one of two methods ( Table S1 and Table S2 ) . In one , yeast carrying the temperature-sensitive cdc28-13 allele were arrested at 37 degrees , then rapidly returned to 25 degrees to allow re-entry into the cell cycle . Cells were fixed every 10 minutes for ∼1 . 5 cell cycles , and H3K4me3 levels and nucleosome occupancy were measured . The alternative synchrony protocol involved alpha factor arrest of bar1Δ yeast , followed by release into fresh media lacking alpha factor , with fixation every 5 ( early ) to 10 ( late ) minutes for ∼3 cell cycles . Gene expression analysis ( Figure S1 ) showed good synchrony in both cell cycles , with a notable distinction being the more rapid cycling of cells released from alpha factor block ( CCA ) . Furthermore , we recently reported mapping of H3K56ac during the cdc28-13 “temperature shift” cell cycle ( CCTS ) , with S phase peaks of H3K56ac demonstrating good synchrony [17] . We initially analyzed H3K4me3 patterns to search for correlations with cell cycle-regulated gene expression . As predicted ( due to the long life of H3K4me3 ) , we found no correlation between a gene's peak phase of expression and variation in H3K4me3 levels of nearby nucleosomes . Most genes exhibiting maximal expression during G1 , for example , did not exhibit periodic 5′ H3K4me3 over the time courses ( not shown ) . However , we did observe periodic variation in nucleosome occupancy and H3K4me3 levels at a number of nucleosomes , and found that this variation was correlated with replication timing [18] of the region in question ( Figure 1A shows data for CCA ) . Specifically , nucleosome occupancy varied markedly through the cell cycle in early and late-replicating regions of the genome , with increased occupancy of early-replicating regions during S phase ( Figure 1B ) . H3K4me3 levels at the same sets of nucleosomes showed cell cycle variation that was precisely out of phase with nucleosome occupancy ( Figure 1C ) . Similar results , of lesser magnitude , were observed in the CCTS dataset ( Figure S2 ) . The variation in nucleosome occupancy can be ascribed to partial genomic replication at intermediate time points – early in S phase , early-replicating regions of the genome will be present in two copies per cell , and since nucleosome assembly after replication is rapid [19] , this will result in increased nucleosome occupancy of these regions relative to late-replicating domains . The variation in H3K4me3 is consistent with previous reports that newly-incorporated nucleosomes lack H3K4me3 [20] , resulting in relative enrichment of late-replicating nucleosomes with H3K4me3 when early-replicating regions have been half-assembled into H3K4me3-lacking nucleosomes . In other words , our results are consistent with an intermediate chromatin assembly state at early-replicating regions where the genome has been duplicated and assembled into undermethylated chromatin , before methylation is restored during the next round of transcription . Further analysis of the combined cell cycle methylation data for both CCA and CCTS by k-means clustering revealed an interesting , nonperiodic cluster of hundreds of nucleosomes exhibiting high levels of H3K4me3 from arrest until the first S phase , when H3K4me3 levels dropped precipitously ( Figure 2 , Cluster 6 ) . Genes associated with these “Cluster 6” nucleosomes in CCTS were not enriched for periodic expression during the cell cycle , but we noticed that many of the genes were previously shown to be induced during heat stress [21] ( Figure 3A ) . We reasoned that since these genes would be expressed at relatively high levels during the arrest at 37 degrees but would return to lower levels at 25 degrees , the pattern observed might indicate the process of disassembly of the active chromatin state induced by the cell cycle arrest protocol ( Figure S3 ) . Since different methods of cell cycle arrest induce different gene expression patterns , this hypothesis could be tested by examining cell cycle synchrony by a different arrest/release method such as alpha factor synchronization . While many nucleosomes showed similar behavior in both time courses ( Figure 2 , Cluster 6 ) , a notable difference can be seen at the pheromone response gene FUS1 ( Figure 3A ) . FUS1 is strongly induced by alpha factor treatment [22] and exhibits high levels of H3K4me3 during alpha factor arrest , but its K4 methylation levels drop dramatically during early S phase ( arrow ) . This does not occur in the CCTS time course , consistent with the idea that the S phase drop-off represents disassembly of the activated chromatin state . This active state erasure is not specific to H3K4me3 – we have also mapped “active” acetylation marks H3K14ac and H2AK7ac in a separate CCTS , and they are erased at Cluster 6 nucleosomes within 10 minutes , as expected ( not shown ) [10] . We explored H3K4me3 erasure more broadly by identifying nucleosomes that exhibited S phase H3K4me3 loss in at least one of the two time courses ( Materials and Methods ) , then divided this set of nucleosomes into three groups: TS>A for nucleosomes with greater S phase H3K4me3 loss in CCTS than in CCA , A>TS for the converse , and TS∼A for nucleosomes with similar S phase loss in both time courses ( Figure 3B ) . As predicted , TS>A nucleosomes were associated with more heat-inducible genes than A>TS nucleosomes , which were associated with more alpha factor-responsive genes ( Figure 3C ) . It is important to note that only a subset of genes exhibiting this S phase drop are induced by the relevant stimulus – in some cases , the nucleosome in question is H3K4 methylated despite being associated with a gene whose RNA level is not increased by the arrest . We do not fully understand the role of K4 methylation at these nucleosomes , although regulatory noncoding transcription [23] , or similar phenomena , could be responsible . Interestingly , we find nucleosomes at telomere 3L , but not at HMR or HML , are hypermethylated in alpha factor arrest ( Figure S4 ) , potentially secondary to the reported phosphorylation of Sir3 in response to MAPK signaling [24] , [25] . Subtelomeric nucleosomes also lose their excess methylation during S phase , suggesting that alpha factor arrest may provide a physiological system to study the cell cycle dependence of heterochromatin maintenance and induction [26]–[28] . Whatever the physiological role of H3K4 methylation at genes whose mRNAs are not upregulated , it is important to note that this methylation is erased just as is the methylation associated with gene activity ( see below ) . Average H3K4me3 profiles of the nucleosomes matching Cluster 6 for each time course show a marked drop in S phase ( Figure 3D ) , but the average quantitative level of this drop is less than two-fold . Of course , this represents the mean of many different nucleosomal profiles . We investigated H3K4me3 loss in more detail by comparing H3K4me3 levels per nucleosome in the CCA time course to H3K4me3 levels per nucleosome previously measured for midlog growth [6] . Figure 4A shows that Cluster 6 nucleosomes start out on average ∼40–50% more K4-trimethylated during CCA arrest than they are in midlog growth . Some notable outliers can be observed , such as 5′ nucleosomes over canonical alpha factor-inducible genes such as FUS1 and ERG24 ( arrows ) , both of which are more than two-fold more methylated in alpha arrest than in midlog growth . After two generations of growth , Cluster 6 nucleosomes reverted to baseline H3K4me3 levels ( Figure 4B ) . How quickly are K4 methylation levels returned to baseline ? The histogram of differences between H3K4me3 levels in midlog growth and levels during arrest for Cluster 6 nucleosomes reveals a distribution centered ( in log2 space ) on 0 . 5 ( Figure 4C ) , as expected from Figure 4A . Notably , within one generation this distribution has shifted to an average of nearly zero , demonstrating that the majority of nucleosomes have reverted to midlog methylation levels within a single generation . The distribution changes little in the second or third generations after release , demonstrating that the majority of chromatin reprogramming occurred in the first generation . Importantly , the remainder of nucleosomes do not change appreciably during this time period ( Figure 4D ) . Taken together , these data show that , by and large , H3K4 methylation patterns in one generation do not play an instructive role in establishment of chromatin structure in the next generation , as a single generation is largely sufficient to revert chromatin marks to new baseline levels . Is the S phase loss of H3K4me3 active or passive ? A distinction can be made based on the extent of H3K4me3 loss . In principle , if an “old” maternal nucleosome re-associates with precisely the same location in one of the two daughter genomes that it occupied in the maternal genome , but the corresponding “new” nucleosome in the other genome is not methylated , then the level of H3K4me3 at that position will drop two-fold during S phase ( seeMaterials and Methods for further discussion ) . A number of different mechanisms can give rise to a drop greater than two-fold , including but not limited to enzymatic demethylation [12] , [15] , [29] , histone replacement [30] , [31] , tail cleavage [32] , [33] , or simple failure of some nucleosomes to re-associate with a daughter genome at the same locus they came from . We found that methylation levels at some of the most “over-methylated” nucleosomes did fall slightly more than two-fold across consecutive time points ( for example , most nucleosomes over FUS1 drop ∼2 . 5-fold in H3K4me3 at the beginning of S phase ) ( Figure S5 ) , although they did not completely return to baseline after this drop . Methylation levels for these nucleosomes typically dropped most dramatically during S phase , then slowly decreased over the next cell cycle ( see FUS1 in Figure 3A and Figure S5 for an example ) . To further explore the potential role of replication in H3K4me3 loss , we aligned Cluster 6 nucleosomes by replication time [18] , reasoning that simple failure to methylate newly-incorporated nucleosomes during S phase would manifest as S phase drops that correspond to replication timing . Indeed , for the CCA time course this is precisely what we see in Figure 5—K4 methylation is maintained at early-replicating nucleosomes until early S phase , while late-replicating nucleosomes lose H3K4me3 later in S phase . The correlation between replication timing and time of H3K4me3 loss was much more subtle during the poorer CCTS synchrony , but the same trend could be observed ( Figure S6 ) . These data are consistent with the simplest model for the majority of H3K4me3 loss – that newly-synthesized nucleosomes simply fail to be re-methylated in the absence of inducing stimuli . We therefore tested the hypothesis that erasure of H3K4me3 was a result of passive dilution during replication . The Cdc7 kinase activates eukaryotic replication origins by phosphorylating Mcm proteins , and is required throughout S phase for origin firing [34] , [35] . We synchronized bar1Δ cdc7ts yeast with alpha factor at the cdc7ts permissive temperature 24 C , then released them from alpha factor to either 24 C or the restrictive temperature 37 C ( Figure 6A ) . FACS analysis confirmed the failure of these cells to replicate their genome at the restrictive temperature ( Figure S7 ) . Surprisingly , we found that FUS1 H3K4 trimethylation was erased with identical kinetics in the presence and absence of genomic replication ( Figure 6B ) . Interestingly , during release at the restrictive temperature an increase in H3K4 methylation is seen at the 3′ end of FUS1 . Hybridization of H3K4me3 material from the 90 minute release time points to whole-genome tiling microarrays revealed that a subtle enhancement of 3′ methylation did occur broadly at the restrictive temperature , though it was not specific to loci undergoing erasure ( see below ) . Whole-genome data did confirm that loci erased at the permissive temperature were generally erased even without genomic replication , although this erasure was of lower magnitude ( ∼75% ) in the absence of replication ( Figure 6C , Figure S8 ) . Interestingly , a recent study reported genome-wide mapping of H3K4me3 during meiosis in yeast [36] , and argued that replication was not required for H3K4me3 erasure . Our reanalysis of this data revealed that H3K4me3 erasure during meiosis in that study was ∼70% efficient in the absence of replication ( not shown ) , remarkably concordant with our value of ∼75% . We therefore identify a quantitative role for replication-dependent erasure/dilution in erasure of H3K4me3 . To further test the role of cell cycle progression in H3K4me3 erasure , we measured methylation levels genome-wide in galactose-grown yeast arrested in alpha factor ( Figure 6D ) . Yeast were then shifted to dextrose-containing media to repress GAL and other carbon-related genes , and were either maintained in alpha factor to prevent cell cycle transit , or were released into the cell cycle . Consistent with the results obtained using the cdc7ts mutant , we found that erasure of H3K4 trimethylation over newly-repressed genes did not require cell cycle re-entry ( Figure 6E and 6F ) . Again , the extent of H3K4me3 erasure was diminished in the absence of cell cycle transit ( Figure 6F ) , indicating that dilution by replication likely does contribute quantitatively to H3K4me3 erasure . It is important to note that there is substantial locus-to-locus variability in the extent to which replication contributes to H3K4me3 erasure ( Figure 6C and 6F ) . Of course , much of this variation can be attributed to the relevant experimental manipulations – many of the loci that apparently fail to demethylate when cdc7ts yeast are shifted to 37 C ( Figure 6C , left side of red curve ) are associated with genes induced at high temperatures ( not shown ) . In Figure 6F we attempted to control for this by eliminating probes associated with alpha factor-dependent methylation , so differences between continued alpha arrest and cell cycle release should not contribute to the variability in this case . To further explore locus-to-locus variability in the extent to which K4me3 can be erased via active mechanisms in the absence of genomic replication , we clustered patterns of K4 erasure in the genome-wide galactose-dextrose shift experiments ( Figure S9 ) . This analysis revealed that release-independent loss of methylation tended to occur at 5′ coding regions , whereas loss of methylation at 3′ ends of coding regions required release from alpha factor . These results suggest that active and passive erasure mechanisms may operate at distinct genomic loci – active mechanisms such as demethylation or histone replacement appear to preferentially operate at 5′ ends of coding regions , whereas excess H3K4me3 at 3′ ends of genes may be primarily cleared by replication . What is the mechanism for active erasure of K4 methylation ? Yeast encode one major H3K4me3 demethylase , JHD2/KDM5 [12] , [37] , [38] . We deleted JHD2 in a bar1Δ background , arrested these yeast in alpha factor , then released parent and jhd2Δ yeast into the cell cycle . H3K4me3 loss was delayed in jhd2Δ yeast after release from alpha factor arrest ( Figure 7A and 7B ) . This was not an artifact of a change in the kinetics of genomic replication , as FACS profiles of synchronized wild-type and jhd2Δ yeast were indistinguishable ( Figure S10 ) . Profiling of all three methylation states of H3K4 during a CCTS time course also provided circumstantial evidence for demethylation in H3K4me3 loss: the decrease in H3K4me3 at cluster 6 nucleosomes was followed by progressive peaks of di- and mono-methylation of H3K4 ( Figure S11 ) , as previously observed during a time course of heterochromatin establishment in yeast [15] . To further explore the role of Jhd2 in H3K4 demethylation , we compared genome-wide H3K4me3 profiles from wild-type and jhd2Δ yeast in alpha factor arrest and after 60 minutes of release . As shown in Figure 7C and 7D ( and Figure S12 ) , probes exhibiting excess H3K4 trimethylation in alpha factor arrest were demethylated in wild-type yeast much more efficiently than in jhd2Δ . These results together implicate enzymatic demethylation in H3K4me3 loss upon gene repression .
Previous studies on the GAL genes and on heterochromatic loci indicated that H3K4me3 is erased upon repression of the genes in question [1] , [12] , [15] . Here , we extended these studies to the whole genome in three different signaling contexts – alpha factor treatment , heat shock , and galactose . We found that upon release from these signaling regimes H3K4me3 levels fall completely to midlog baseline levels within 2 generations , with most demethylation occurring within the first generation . These results clearly demonstrate that typical active chromatin states , as characterized by levels of the long-lived activation mark H3K4me3 , are not epigenetically maintained at genes after removal of an inducing stimulus . In other studies we have mapped other transcription-related histone modifications such H3K14ac and H2AK7ac ( not shown ) , finding that they are erased within minutes of the condition shift , consistent with prior reports [10] . These results demonstrate that typical environmentally-responsive chromatin states are not epigenetically heritable per se , but are erased upon removal of activating stimulus , and therefore their maintenance requires active reestablishment . An unanticipated aspect of H3K4 methylation loss at repressed genes is its occurrence during S phase . The greatest drop in K4me3 levels at newly-repressed genes in both alpha factor-synchronized and cdc28-13-synchronized yeast occurred during S phase . Furthermore , we found that H3K4me3 loss occurred early during S phase at early-replicating genomic loci , and occurred later at late-replicating regions ( Figure 5 ) . We also found that loci with the greatest levels of H3K4me3 relative to baseline ( such as FUS1 during alpha factor arrest—Figure S5 ) did not completely return to baseline during the first S phase , instead falling little over 2-fold at this stage . These results together strongly suggested a role for genomic replication , and resulting incorporation of unmethylated histone H3 at one of two daughter loci , in erasure of old active chromatin marks . Evidence for a general role for replication in methylation dynamics comes from Figure 1—even loci that are not “overmethylated” during arrest transiently lose methylation immediately after replication , suggesting that the role for replication in H3K4me3 loss is not specific to genes that are being repressed . Surprisingly , direct tests of the role for replication in H3K4me3 loss revealed that replication is not absolutely required for H3K4me3 erasure at canonical targets of alpha factor signaling ( FUS1 ) or galactose ( GAL genes ) —see Figure 6 . We did find a quantitative role for replication in K4me3 erasure , with a global ∼25–40% decrease in H3K4 “demethylation” under two independent non-replicating conditions . Interestingly , re-analysis of H3K4me3 erasure during meiosis [36] identifies a similar 25% decrease in methylation loss when genomic replication is blocked in this system . These results are also in quantitative agreement with a single-locus study in S . cerevisiae in which mating loci were excised onto nonreplicating circles before induction of heterochromatin [15] – here too H3K4me3 is erased in the absence of replication , but H3K4me3 levels were ∼30% higher in the absence of replication than in the native ( replicating ) context . Together , these results have implications for the establishment of epigenetic silencing states , which appears to require S phase passage [26] , [39] , but may not require replication [27] , [28] , [40] . We interpret these results together to indicate that genomic replication does result in a general two-fold methylation erasure ( even at loci that are not being actively demethylated—see Figure 1 ) , but this dilution is one of at least two mechanisms contributing to H3K4me3 loss along with enzymatic demethylation . One simple prediction of this model is that jhd2Δ yeast should eventually erase ancestral H3K4me3 states via replication alone , a prediction borne out by the fact that H3K4me3 mapping in these mutants does not show signatures of ancestral exposures to cold shock ( the refrigerator ) or diauxic shift ( overnight culture ) . The coincidence of the major loss of methylation with S-phase in this context therefore suggests that enzymatic demethylation is typically gradual ( Figure S5 ) , and thus for a given time point in a synchronized population the two-fold loss secondary to replication dominates the overall methylation decrease at that time point . Interestingly , active demethylation occurred independently of replication at 5′ ends of genes , but clearance of excess methylation at 3′ ends of genes appeared to require replication ( Figure 6A , Figure S9 ) , suggesting that active demethylation is specifically targeted to the 5′ end of the gene where most H3K4me3 normally occurs [6] , [7] . In this framework , one pair of results still remains to be reconciled . Specifically , a dramatic H3K4 demethylation event occurs after a significant ( ∼45 minute ) delay even in the absence of genomic replication in cdc7ts yeast ( Figure 6B ) , which might suggest an S phase event ( independent of replication ) resulting in enhanced demethylase activity . However , demethylation at GAL genes upon carbon source shift occurred even when yeast were maintained in alpha factor , indicating that at least some H3K4me3 erasure occurs during G1 arrest , albeit after a long time ( Figure 6E ) . Thus , we do not currently understand the reason for the delay that typically occurs between removal of an activating stimulus and the major demethylation event . The idea that chromatin states are heritable , while widely cited , is still a matter of debate [41] . A major piece of evidence for the heritability of chromatin states is the genetic requirement for histone modifying enzymes in a number of epigenetic inheritance systems – in S . cerevisiae and P . falciparum , variegated repression of subtelomeric genes requires histone deacetylases [13] , [42]–[44] , and subtelomeric loci in the epigenetic OFF state are packaged into a distinctive chromatin structure that differs significantly from the packaging state of the epigenetically ON state [45] , [46] . During metazoan development , memory of repressed states requires Polycomb proteins , which are involved in methylation of H3K27 , while memory of active states is associated with methylation of histone H3 lysine 4 by Trithorax group proteins [2] . However , in yeast a handful of studies on GAL and SUC genes indicated that H3K4me3 was lost upon dextrose repression [1] , [12] , and similar results were found even at the epigenetically-regulated heterochromatic loci upon induction of heterochromatin [15] . The kinetics of H3K4me3 loss in these studies suggested that erasure was a consequence of active demethylation , and deletion of JHD2/KDM5 implicated this demethylase in the process . Our results with jhd2Δ mutants confirm a global role for Jhd2 in accelerating loss of H3K4 methylation , although it is important to note that in the absence of Jhd2 , H3K4 methylation still could be erased , but only after an extended delay ( Figure 7A ) . Thus , these results demonstrate that most chromatin states are unlikely to be heritable per se , and if chromatin states ever are maintained through cell division this is likely to require specific maintenance machinery that operates at specialized genomic loci ( for example , the Hox loci in metazoans ) . We hypothesize that most other environmentally-responsive chromatin states will also be erased and require active reestablishment for their maintenance . Intriguingly , recent results in C . elegans show that enzymatic erasure of H3K4me3 patterns is required for correct functioning of the germline [47] , consistent with a general role for demethylases in erasing inappropriate ancestral chromatin states . In this view , the majority of chromatin modifications do not generate epigenetic signals [41] .
jhd2Δ ( MATa ura3Δ leu2Δ his3Δ met15Δ bar1Δ::HIS5 jhd2Δ::KanR ) : A PCR fragment containing the Kanamycin resistance gene and ends complementary to the JHD2 locus ( primer sequences: 5′ATGGAGGAAATTCCTGCCCTGTATCCAACGGAACAAGACCAGCTGAAGCTTCGTACGC and 5′CTATCTATCTAACTTAACACCAACTTGCTTTATTAAAGAGGGCGCGAGGATCGTAATAAG; pCM224 plasmid as template ) was transformed into the yeast JOY1 strain ( MATa ura3Δ leu2Δ his3Δ met15Δ bar1Δ::HIS5 ) . Stable transformants were selected on G418 plates and correct integration into the JHD2 locus was confirmed by PCR ( JHD2 locus primers: 5′TCATGGAGGAAATTCCTGCCCTGTATCCAA and CTATCTATCTAACTTAACACCAACTTGCTTTATTAAAGAG; KanR-specific primers: 5′AGGAATCGAATGCAACCGGC and 5′TATGGGTATAAATGGGCTCGCG ) . RM14-3a ( MAT a cdc7-1 bar1 ura3-52 trp1-289 leu2-3 , 112 his6 ) [34] was obtained from Manolis Papamichos-Chronakis . For the CCA time course , 18 2L flasks each of 450 mL BY4741 bar1Δ cells were grown in YPD as three groups of 6 flasks , to an A600 OD of 0 . 25 ( Group 1 , time points 100–150 min . at 10 min . intervals ) , 0 . 4 ( Group 2 , time points 40–90 min . at 10 min . intervals ) , and 0 . 7 ( Group 3 , 0–30 min . at 5–10 min . intervals ) . Cells were arrested for 3 hours with the addition of 450 µL of 1 mg/ml alpha factor , then filtered with 0 . 22 µM Whatman filter , washed with water , and transferred to a prewarmed flask containing 450 ml YPD and 22 . 5 mg pronase . Each 450 mL culture was stopped with 1% formaldehyde at the appropriate time . For the CCTS time course , cells were cultured as described in [17] . For each time point , 50 ml of cell culture were spun and flash frozen on liquid nitrogen for mRNA isolation . With the remainder , 37% formaldehyde was added to a 1% final concentration , and the cells were incubated for 15 minutes at room temperature , shaking , at 200 rpm . 2 . 5 M glycine was added to a final concentration of 125 mM to quench the formaldehyde . The cells were transferred to a 500 mL centrifuge jar on ice and then let stand until groups of 6 time points had accumulated . The cells were spun down at 3000×g for 5 minutes at 4°C and washed once with 50 mL of room temperature MilliQ water . Subsequent procedures were performed in batches of 6 time points . For CCA repeat with wt and cdc7ts yeast ( Figure 6A–6C ) , two flasks of cells were grown to OD 0 . 2 at 24 C and arrested for 4hrs with 1 µg/ml alpha factor . Each culture was released from arrest by filtering , and cells were added to media prewarmed to either 24 C or 37 C to which 20 µg/ml pronase ( SIGMA ) was added . 100ml were taken from the common culture at indicated times and fixed in 2% formaldehyde . Figure 6B shows results from two interleaved time courses at each temperature . For CCA repeat with galactose to dextrose media switch ( Figure 6D–6F ) , one flask of bar1Δ cells ( JOY1 strain ) was grown in YPgalactose to OD 0 . 3 at 30C and arrested for 3hrs with 1 µg/ml alpha factor . The culture was then filtered in two batches and each filter was put into YPdextrose media either with 1 µg/ml alpha factor or with 20 µg/ml pronase ( SIGMA ) . 100ml were taken from the two cultures at indicated times and fixed in 2% formaldehyde . For CCA repeat with wt and jhd2Δ yeast ( Figure 7 ) , cells were grown to OD 0 . 2 and arrested for 3hrs with 1 µg/ml alpha factor . The whole culture ( 750ml ) was released from arrest by filtering and addition of 20 µg/ml pronase and 100ml were taken from the common culture at indicated times and fixed in 2% formaldehyde . H3K4me3 levels were determined essentially as described previously [6] , [17] . For both CCA and CCTS time courses , cells were first digested with micrococcal nuclease and immunoprecipitated , except with the following antibodies: ( CCA Ab: 4 µL anti-H3K4Me3 ( polyclonal , Abcam #Ab8580 ) ; CCTS and CCA repeat Ab: 5 µL anti-H3K4Me3 mAb clone MC315 ( Upstate #05-745; affinity purified; this is no longer being offered by Upstate ) ) . Antibody specificity information is available at the suppliers' websites . Following the ChIP , proteins were degraded and DNA purified as described in [17] , and linear amplification was carried out as in Liu et al [6] , [48] . For nucleosome occupancy profiling , amplified nucleosomal material from a given time point was competitively hybridized against pooled nucleosomal DNA from the entire time course , resulting in relative occupancy measures over the two cell cycle time courses . Protocol is available online at: http://www . broad . harvard . edu/chembio/lab_schreiber/pubs/protocols/IVT_Supplement/supplement . html . 3 µg of aRNA produced from the linear amplification were used to label probe via the amino-allyl method , and microarrays were hybridized , scanned , and processed as described previously [6] , [17] . CCA experiments were hybridized as ChIP against input [6] , whereas CCTS experiments were hybridized as ChIP or nucleosomal input at time = t against a reference pool composed of all time points in the time course [17] . Data for both time courses are zero-centered per nucleosome ( except for analysis in Figure 4 ) to make them comparable . For Figure 6 , Figure 7 , Figure S7 , Figure S8 , and Figure S12 , material from the indicated strains/conditions was hybridized to Agilent 4X44K whole genome microarrays . Data have been submitted to GEO , accession #GSE14565 . For Figure 1 and Figure 5 , replication timing data was taken from Yabuki et al [18] , mapped to our array as in Kaplan et al [17] . For Figure 2 , H3K4me3 data for CCA and CCTS were concatenated and subjected to k-means clustering with k = 6 . Average time course profiles for CCA and CCTS were generated from original Cluster 6 nucleosomes , and all CCA ( or CCTS , respectively ) nucleosomes with a correlation> = 0 . 5 to this average were added to the “Cluster 6” set . In Figure 3B nucleosomes from CCA and CCTS Cluster 6 sets were combined , are sorted by the difference in correlation to the original Cluster 6 CCA or CCTS average profile . For Figure 3C , each Cluster 6 nucleosome was associated with a nearby gene [6] , and the median expression previously reported during a heat shock [21] or alpha factor [22] time course was calculated . Expression data is presented and average +/− standard error of the mean for each group of nucleosomes . When discussing the mechanism of loss of H3K4me3 ( active vs . passive ) in the text , we ignore the presence of two H3 molecules in a nucleosome . There are two reasons for this . First , it is currently unknown how ChIP efficiency will vary for nucleosomes carrying one vs . two H3K4me3 marks , and we suspect these efficiencies might be quite similar . The possibility that ChIP is efficient for both mono- and di-modified nucleosomes simply implies that our measurements of H3K4me3 decreases could be underestimates . Second , the majority of evidence suggests that H3/H4 tetramers do not split during replication . The lack of splitting would lead to the expected ceiling of two-fold loss of methylation via dilution . These considerations do not affect any conclusions of the paper , but are worth keeping in mind when evaluating mechanistic models to be tested against histone modification mapping data . Of course since we directly test replication and enzymatic demethylation here , we only include this issue as a supplemental note for interested readers . During the CCA cdc7ts and jhd2Δ experiments in Figure 6 and Figure 7 , 1 ml cell aliquots were taken at indicated times , cells were pelleted and fixed in 5ml 70% ethanol and refrigerated overnight . Cells were then pelleted and washed twice with PBS , resuspended in 300ul PBS/0 . 08mg/ml RNAseA ( Qiagen ) . A 2hrs 37C incubation was followed by a 30sec sonication in a Bronson cup sonicator ( setting 3 , constant duty cycle ) . Pelleted cells were than resuspended in 400 µl PBS/1 µM SYTOX green ( Invitrogen ) and analyzed with a Beckton Dickson FACS machine . | Organisms can inherit information beyond DNA sequence , a phenomenon known as epigenetic inheritance . It is widely believed that chromatin marks provide a carrier for epigenetic information , a hypothesis that is less-supported than generally believed . In this study , we measure the erasure of a “memory” mark of active transcription , H3K4me3 . We find that this signal-responsive chromatin mark largely returns to baseline levels within one generation . Furthermore , we find that this erasure occurs during S phase in a manner consistent with its loss during replication , yet we find that replication only contributes modestly to the erasure process . Instead , active enzymatic demethylation is required for erasure . Together , these results show that even chromatin states widely associated with epigenetic memory are only maintained in the ongoing presence of activating signals , and are not generally heritable . | [
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] | 2010 | Replication and Active Demethylation Represent Partially Overlapping Mechanisms for Erasure of H3K4me3 in Budding Yeast |
Recent structural and biochemical studies have identified a novel control mechanism of gene expression mediated through the secondary channel of RNA Polymerase ( RNAP ) during transcription initiation . Specifically , the small nucleotide ppGpp , along with DksA , a RNAP secondary channel interacting factor , modifies the kinetics of transcription initiation , resulting in , among other events , down-regulation of ribosomal RNA synthesis and up-regulation of several amino acid biosynthetic and transport genes during nutritional stress . Until now , this mode of regulation of RNAP was primarily associated with ppGpp . Here , we identify TraR , a DksA homolog that mimics ppGpp/DksA effects on RNAP . First , expression of TraR compensates for dksA transcriptional repression and activation activities in vivo . Second , mutagenesis of a conserved amino acid of TraR known to be critical for DksA function abolishes its activity , implying both structural and functional similarity to DksA . Third , unlike DksA , TraR does not require ppGpp for repression of the rrnB P1 promoter in vivo and in vitro or activation of amino acid biosynthesis/transport genes in vivo . Implications for DksA/ppGpp mechanism and roles of TraR in horizontal gene transfer and virulence are discussed .
The ability to respond to changes in nutritional environment is a universal need inherent in all cells and is characterized by rapid global changes in gene expression . Regulation of transcription initiation is a central way to control gene expression and is largely achieved through the use of DNA-binding proteins ( activators and repressors ) restricted to distinct promoters through recognition of specific DNA elements . Study of the nutritional response in Escherichia coli has detailed a novel mechanism of modulating transcription initiation , both positively and negatively , through the use of a single small nucleotide effector , guanosine tetraphosphate ( ppGpp ) , that interacts with RNA polymerase [1] . In E . coli , the accumulation of ppGpp causes rapid effects on transcription; ppGpp binds to RNA polymerase , provoking an alteration in transcription kinetics that is proposed to result from a reduction in open complex stability [2] . Such effects include , but are not limited to , upregulation of amino acid biosynthesis and transport genes , as well as genes involved in stasis/stress survival , and downregulation of translational components such as rRNA and tRNA genes [3] . Recently , an additional factor , DksA , has been shown to potentiate the action of ppGpp on RNAP both in vitro and in vivo [4]–[6] . The loss of either ppGpp or DksA results in similar , though not identical , phenotypes including the downregulation of several amino acid biosynthetic pathways , and the inability to negatively regulate ribosomal RNA transcription [7] , [8] . Separate from mediating the stringent response , DksA has roles in other processes including chromosome segregation , DNA repair , protein folding , bacterial motility , virulence , and the expression of type 1 fimbriae [7]–[14] . The crystal structure of DksA has been determined and shows that the 151 amino acid-long protein folds into three distinct structural domains: an N-terminal region containing two α-helices ( coiled coil ) , a globular domain with a C4 Zn+2 finger motif , and a short C-terminal helix [15] . DksA is structurally analogous to GreA and GreB , transcriptional anti-pausing/fidelity factors that are homologs of the eukaryotic TFIIS [16]–[19] . The Gre factors bind RNAP and protrude their coiled coils deep into the secondary channel toward the active site [20] , [21] . DksA also binds RNAP , and it has been suggested that , similar to the Gre factors , DksA could also interact with the secondary channel . This is supported by a growing body of evidence indicating that the DksA and Gre proteins compete in vivo for the same substrate , the secondary channel of RNAP [22] , [23] . A proposed mechanism of action for DksA positions the coiled coil region deep within the RNAP secondary channel near the active site . At the tip of the coiled coil region , two invariant aspartic acid residues , Asp71 and Asp74 , are thought to coordinate the ppGpp bound Mg+2 ion to effectively position ppGpp near the active site , and allow it to exert its transcriptional modulation effects [15] . Mutation of these two conserved aspartic acid residues abolishes DksA’s ability to modulate transcription with ppGpp [15] . The proposed DksA mechanism remains highly speculative , and it is unknown exactly how ppGpp and DksA influence each other or how their binding alters RNAP activity . Furthermore , detailed mutational analysis of the defined RNAP binding site for ppGpp has cast doubt on the biological relevance of the placement of ppGpp in the original ppGpp/RNAP co-crystal structure [24] . Comparisons of DksA with sequence databases have previously found similarities of DksA to several bacteriophage ORFs and TraR , a protein found on conjugative plasmids that promote horizontal gene transfer ( for alignments , refer to [15] , [25] ) . Horizontal transfer of DNA allows the acquisition of new traits in the recipient bacterium , such as virulence or resistance to antibacterial agents [26] . The well-studied F plasmid of E . coli is considered a model for bacterial conjugation and is facilitated by a large protein complex , the F pilus , which bridges the donor and recipient cell membranes , enabling F plasmid DNA transfer [27] . The components of the F pilus , along with regulatory , accessory , and unknown factors , are encoded on the single 33-kb transfer ( tra ) operon [27] . traR encodes a gene in the downstream region of the tra operon that is dispensable for F plasmid transfer , at least under normal laboratory conditions [25] , [28] , [29] . The sequence homology of TraR to DksA , while weak ( 30% identity ) , raises the possibility that episomal TraR possesses some functional similarities to DksA . In this study , we show that TraR modulates gene expression similarly to ppGpp/DksA , but in the absence of any nucleotide effector , like ppGpp . Expression of TraR compensates for dksA transcriptional repression and activation activities in vivo . Mutagenesis of a TraR amino acid corresponding to a critical residue for DksA function abolishes activity , implying structural similarity to DksA . Compensation by TraR is inhibited by overexpression of GreB , a factor known to interact with the RNAP secondary channel [21] , suggesting , like ppGpp/DksA , that TraR also interacts similarly with RNAP . Surprisingly , unlike DksA , TraR does not require ppGpp for repression of the rrnB P1 promoter either in vivo or in vitro , or for activation of amino acid biosynthesis/transport promoters in vivo at physiological levels . The activity of TraR in the absence of ppGpp could provide clues on the mechanistic role of DksA in modulating RNA polymerase for at least several cellular processes . The implications of our findings on current models of DksA/ppGpp action will be discussed , as well as the implications for roles of episomal traR in conjugation , pathogenicity , and the evolution of gene expression .
Since TraR shares limited sequence homology to the transcriptional modulator DksA , we hypothesized that the two proteins could possess some functional similarities . Loss of DksA function causes permanent downregulation of several amino acid biosynthetic and transport pathways and results in an inability of E . coli cells to grow on minimal media without supplementation of required amino acids [4] , [6] , [11] . If TraR shares functions with DksA , expression of TraR in a ΔdksA strain should compensate for the multiple auxotrophies . Indeed , when a mini-F plasmid ( pOX38 , see Supplemental Materials ) , which naturally contains traR in the transfer operon [28] , was conjugated into a dksA null strain , prototrophic growth on minimal media was observed ( Table 1 ) . The restoration to prototrophy by the F plasmid is TraR-dependent since complete deletion of traR abolishes the prototrophy observed in the ΔdksA F factor strain . The ability of TraR to compensate for the multiple auxotrophies of ΔdksA suggests that TraR possesses functional similarities to DksA . Furthermore , overexpression of GreB ( from an inducible pTrc plasmid , see Supplemental Materials ) , a factor known to interact with the RNAP secondary channel [21] , reduced the prototrophic compensation by TraR expressed from the F episome ( Table 1 ) . GreB was previously shown to compete with DksA and does not rescue ΔdksA auxotrophies [22] , [23] ( unpublished data ) . The competition of GreB with TraR suggests that TraR interacts with the secondary channel . Given the sequence homology between TraR and DksA ( for alignments see Figure 1A , or refer to [15] , [25] ) and our demonstration that TraR rescues defects associated with loss of DksA , it is likely that TraR and DksA share structural similarities . Sequence analysis predicts that TraR possesses a globular domain with the C4 Zn+2 finger motif , characteristic of the DksA family [15] . The sequence of TraR also begins with the two conserved aspartic acid residues that are important for DksA function [15] . Unlike DksA however , TraR is 73 amino acids long , making TraR approximately half the size of DksA . Secondary structure prediction suggests that TraR starts with a long helical structure , which could correspond to half of the coiled coil domain of DksA and the length of this predicted helix would be shorter than the corresponding region of DksA ( Figure 1A , B ) . To address whether TraR is sufficient to compensate for dksA defects , traR and dksA were separately cloned onto a multi-copy plasmid under an inducible pTrc promoter ( pBA169 , see Supplemental Materials ) . Ectopic expression of TraR rescued the inability of ΔdksA cells to grow in the absence of required amino acids . The plating efficiencies of ΔdksA cells containing TraR or DksA plasmids grown on M9-glucose plates compared to those grown on M9-glucose-casamino acid plates approached 100% , approximately 5 orders of magnitude higher than with the control plasmid ( Table 1 ) . Interestingly , both uninduced and induced pTraR and pDksA plasmids provided a complete rescue of the ΔdksA auxotrophy , suggesting that only a few copies of TraR or DksA in the cell are needed to restore appropriate regulation of amino acid biosynthesis and transport . To address the functional similarities between TraR and DksA , the second aspartic acid residue of TraR , D6 , which corresponds to the invariant Asp74 of DksA ( see above ) , was mutated to asparagine . As shown in Table 1 , TraR ( D6N ) was no longer able to fully compensate for ΔdksA auxotrophy . This observation emphasizes the functional importance of this aspartic acid residue conserved in TraR and DksA . To examine in more detail the restoration of prototrophy by TraR in a ΔdksA strain , activation of the livJ promoter was explored . livJ encodes a transporter for branched-chain amino acids and is activated by ppGpp/DksA [6] . β-galactosidase assays with a wild-type PlivJ-lacZ fusion strain yielded strong activation of the livJ promoter by induction of TraR in exponential growth ( Figure 2A ) . In contrast , we observed little effect with DksA overexpression . The lack of seeing effects with DksA , which is ppGpp-dependent with respect to the livJ promoter [6] , is probably due to the low levels of ppGpp present in rich media during exponential growth . Identical results were obtained when the experiments were repeated in a ΔdksA background , supporting that TraR can work independently of DksA in the activation of livJ ( Figure 2B ) . Thus , TraR , unlike DksA , is able to activate the livJ promoter in exponential phase . DksA is a pleiotropic regulator of transcription with positive and negative effects on a wide array of genes [4] , [6] , [7] , [13] , [15] . One of the more extensively characterized effects of DksA ( and ppGpp ) is the negative regulation of ribosomal RNA ( rRNA ) accumulation [22] , [23] . Since TraR was shown to compensate for DksA in the positive regulation of amino acid genes , we explored whether TraR can also negatively affect the transcription of rRNA . Since expression of both TraR and DksA from the uninduced plasmids fully compensated for ΔdksA auxotrophies ( above ) , uninduced levels of TraR and DksA on the rrnB P1 promoter were examined first . Uninduced levels of TraR expressed from the plasmid did not cause a significant negative effect on rrnB P1-lacZ activity compared to the control plasmid in a wild-type background ( Figure 3A ) . We next examined rrnB P1-lacZ activity during IPTG-induced TraR and DksA overexpression . TraR reduced rrnB P1 transcription to negligible levels immediately after induction in early exponential growth ( Figure 3B ) . This inhibition of transcription by overexpression of TraR was specific to the rrnB P1 promoter since no significant effect was observed on the wild-type lac promoter ( Figure 3E ) . DksA , when overexpressed , also had inhibitory effects on the rrnB P1-lacZ construct compared to the control plasmid , though the effect was much less pronounced than that of TraR ( Figure 3B ) . The DksA-mediated repression became greater as cells exited exponential growth , corresponding to the accumulation of ppGpp as cells approach stationary phase [1] , [30] . The repression of rRNA transcription exerted by TraR on rrnB P1 activity in a wild-type strain strongly reinforces the suggestion that TraR can act like DksA . Since TraR expressed from the mini-F compensated for the ΔdksA amino acid auxotrophies in minimal media , we asked whether endogenous expression of traR from the mini-F plasmid could also affect the rrnB P1 promoter under the same conditions . Upon mating traR+ and ΔtraR mini-F plasmids into a wild-type rrnB P1-lacZ fusion strain , we observed that the presence of TraR negatively affected activity from of rrnB P1 ( Supplemental Materials , Figure S1 ) . Although this effect was modest , a large effect of endogenously expressed traR on the rrnB P1 promoter was not expected since the presence of the F episome does not significantly diminish growth rate and the tra operon is only partially derepressed on the F plasmid in E . coli [31] , [32] . That endogenous traR expressed from the F plasmid rescues the ΔdksA amino acid auxotrophies near 100% , but the F plasmid’s effects on the rrnB P1 promoter are modest , suggests that activation of amino acid synthesis is more sensitive to a lower concentration of TraR than the inhibition of rRNA synthesis . While activation of several amino acid genes could conceivably take only a few copies of TraR , inhibition of even small portion of rRNA transcription , which constitutes the majority of all transcription in E . coli [33] , [34] would take many more copies of TraR . Given that derepression of rRNA synthesis is observed in dksA mutants [5] , [22] and that TraR compensates for the amino acid requirements of a ΔdksA strain , we next asked whether the inhibitory effect of TraR on rrnB P1 is independent of DksA and whether TraR can fully compensate for the derepression seen in a ΔdksA strain . As expected , rrnB P1-lacZ activity was derepressed about 50% in ΔdksA compared to its dksA+ counterpart during exponential growth ( compare pControl curves from Figure 3A and Figure 3C ) . Uninduced pDksA complemented the ΔdksA rrnB P1 derepression to wild-type levels . Furthermore , uninduced pTraR not only fully compensated for ΔdksA , but also again exerted stronger inhibition of rrnB P1 transcription compared to DksA ( Figure 3C ) . We then studied the effects of overexpression of TraR and DksA on rrnB P1-lacZ activity in a ΔdksA background . As in the uninduced experiments , rrnB P1 was derepressed in ΔdksA cells , and overexpression of DksA fully complemented the derepression to wild-type levels during exponential growth ( Figure 3D ) . Overexpression of TraR in the ΔdksA rrnB P1-lacZ background again resulted in a strong repression of rrnB P1-lacZ activity immediately after induction during early exponential growth ( Figure 3D ) . As seen for the uninduced plasmids , the magnitude of TraR-mediated repression in early exponential growth was greater in the ΔdksA background ( 40-fold ) than wild-type ( 27-fold ) ( Figure 3D and Figure 3B , respectively ) . This difference had a variation of about 10% and matches the 50% rrnB P1 derepression observed in ΔdksA . The enhanced effects of TraR in the ΔdksA strain further reveal shared functions between TraR and DksA . The combined rrnB P1 results not only extend the functional similarity of TraR and DksA to negative regulatory effects , but also further demonstrate that TraR may function as a more effective modulator of transcription than DksA in exponential phase . The strong downregulation of transcription from the rrnB P1 promoter by TraR is expected to decrease the growth rate due to an inhibition of ribosome synthesis [1] , [35] . Hence , we asked whether TraR could inhibit bacterial growth . Figure 4A shows growth curves for WT rrnB P1-lacZ strains overexpressing TraR , DksA , or control plasmids . When overexpressed , TraR is shown to slow growth compared to DksA and control plasmids . Specifically , the doubling time increased after the second generation from 31 minutes ( control plasmid ) to 49 minutes when TraR is expressed . Little growth difference resulted from DksA overexpression in logarithmic growing cells ( doubling time 35 minutes ) , highlighting an important difference between the two homologs . With near complete inhibition of ribosome synthesis and cell reliance on pre-existing ribosomes , it was further predicted that doubling time would be progressively reduced due to dilution of ribosome pools during subsequent growth . To test this , we diluted an overnight culture ( pTraR ) one hundred-fold in LB media containing IPTG and measured growth over time . When the logarithmic culture reached an OD600 of 0 . 6 , a six-fold dilution was performed in the same media and growth continued to be monitored . A second six-fold dilution was performed when the culture reached OD600 0 . 6 a third time . The doubling time increased from 49 to 89 and 125 minutes , respectively , and showed an overall 4-fold increase after being carried out for seven doublings ( Figure 4B ) . In agreement with the growth results above ( Figure 4A ) , neither the pDksA ( doubling times of 36 , 36 , and 37 minutes ) or control plasmid ( doubling times of 35 , 33 , and 35 minutes ) resulted in any significant growth defects when serially diluted ( see Supplemental Materials , Figure S2 ) . Despite lowered growth rates , cells never stopped growing and appeared to undergo an adaptation to the strong rrnB P1 repression by TraR . Assaying for rrnB P1 activity in WT rrnB P1-lacZ cells overexpressing TraR after overnight growth , we observed that rrnB P1 activity was still repressed , although not as strongly as before ( data not shown ) . This new low level of rrnB P1 transcription may result from a feedback inhibition mechanism . Complete inhibition of the protein synthesis machinery by TraR would ultimately inhibit its own production , resulting in upregulation of rrnB P1 transcription . Protein synthesis would resume until TraR reaccumulates . This negative feedback loop would produce slower balanced growth . Such a scenario is also likely for the colonies forming on plates containing IPTG . The data described above indicate that TraR , unlike DksA , has pronounced negative effects on cells during logarithmic growth . These negative effects on bacterial growth became more pronounced as the cells continued to divide and were likely caused by dilution of the pre-existing ribosomes after each cell division . Western blots were performed to measure induced levels of TraR and DksA in order to ascertain whether differences in activation and repression activity were due to differences in protein expression . We first constructed C-terminal epitope-tagged TraR-His6 and DksA-His6 fusions and confirmed their wild-type behavior in vivo ( data not shown ) . Western blot analysis , as well as Coomassie blue staining , showed the presence of a DksA band upon induction ( ∼8-fold higher than wild-type DksA levels , data not shown ) , but no band corresponding to TraR was detected ( Figure 4C ) . To rule out complications inherent to the Western blot , we performed pull-down experiments to concentrate and measure the relative amount of TraR-His6 and DksA-His6 present in extracts made after two hours of induction . As shown in Figure 4C , the amount of TraR pulled-down is significantly less than DksA . As a positive control for Western blotting and pull-downs , TraR induced from our purification plasmid ( pET24a-TraR-His6 , see below ) was successfully detected in both assays . Overall , these data suggest that low levels of TraR , compared to DksA , can achieve both strong activation of amino acid biosynthesis and potent inhibition of rRNA accumulation in exponential growth . It is well established that DksA influences the regulatory activities of ppGpp . Cells lacking DksA share a wide variety of phenotypes with those deficient for ppGpp [7] . Because of the intimacy between DksA and ppGpp , we sought to examine whether this characteristic extends to TraR . Our first hints that TraR and DksA might differ with respect to ppGpp were the observed potent livJ activation and rrnB P1 repression caused by TraR in early exponential growth , when little ppGpp is present [1] , [30] . To directly examine the effects of ppGpp on TraR function , we examined livJ promoter activity in a ΔrelA ΔspoT ( ppGpp0 ) strain , which lacks both synthetases for ppGpp production [36] . As observed previously [7] , ppGpp deficiency caused decreased expression of the livJ promoter compared to wild-type ( compare plasmid controls in Figure 5A and 2A ) , and DksA overexpression compensated for this defect ( Figure 5A ) . It is interesting and unknown why the livJ promoter has lower activity in a ppGpp0 background . It is also unknown how overexpression of DksA restores the livJ activity to wild-type levels in this background . Induced TraR expression in ppGpp0 cells resulted in strong activation of the livJ promoter identical to a ppGpp+ background ( Figure 5A and 2A , B , respectively ) . Previous studies have shown the functional importance of the conserved , invariant aspartic acid residues ( D71 and D74 , tip of coiled coil domain ) of DksA . To address the functional importance of these residues with respect to the TraR , the second aspartic acid residue of TraR was changed to asparagine ( D6N ) and assayed for PlivJ-lacZ activity in the absence of ppGpp0 ( Figure 5A ) . Altering this second aspartic acid residue abolished the strong activation seen with TraR expression , supporting the shared functional importance of the invariant aspartic acid residues in both TraR and DksA . We next examined the effects of ppGpp on the negative regulatory aspects of TraR by measuring TraR-mediated inhibition of rrnB P1 activity in the ΔrelA ΔspoT ( ppGpp0 ) background . As expected , loss of ppGpp disrupted rrnB P1 repression mediated by DksA , resulting in activity identical to the control plasmid ( Figure 5B ) . However , induction of TraR in the ppGpp0 strain caused an immediate and striking downregulation of the rrnB P1 promoter as observed in a ppGpp+ strain ( Figure 5B and Figure 3B , respectively ) . Therefore , with respect to the rrnB P1 promoter , TraR and DksA are not entirely functionally interchangeable because TraR appears to be ppGpp-independent while DksA is ppGpp-dependent . Interestingly , the D6N mutation of TraR , which alters a conserved aspartic acid residue , completely abolished the rrnB P1 repression in the ppGpp0 strain ( Figure 5B ) , again emphasizing the importance of the invariant aspartic acid residues for TraR function . We next asked whether TraR could also satisfy the multiple amino acid requirements of a ppGpp0 strain [36] . Table 2 shows the TraR-mediated rescue of ΔrelA ΔspoT ( ppGpp0 ) auxotrophies . The control plasmid in the ppGpp0 strain exhibited the expected very low plating efficiency ( 10−3% ) on M9-glucose vs . M9-glucose-casamino acid plates . Also , no compensation of the ppGpp0 auxotrophies was observed when TraR was not overexpressed . In contrast , IPTG-induced levels of TraR resulted in an 85% plating efficiency on M9-glucose plates in the ppGpp0 strain . DksA overexpression in the ppGpp0 strain exhibited only a weak rescue for growth on M9-glucose . The resulting plating efficiency of 0 . 5% was similar to results reported by Magnusson et al . [7] in a MG1655 ppGpp0 background . However , their observation was noted to be strain specific ( see Discussion ) . The predicted structural homology and functional similarities between TraR and DksA suggest that TraR interacts with the RNAP secondary channel , further supported by the in vivo GreB competition data above ( Table 1 ) . It is likely that TraR , because of low expression levels , has to compete for the secondary channel with endogenous DksA to exert its positive biosynthetic effects . If this is correct , then deleting dksA might not only enhance the ability of TraR to rescue the multiple ppGpp0 amino acid auxotrophies , but also provide evidence of similar binding to RNAP . As predicted , the uninduced TraR plating efficiency on M9-glucose increased from 10−3% in the ppGpp0 dksA+ background to 47% in the ppGpp0 ΔdksA background ( Table 2 ) . For the DksA and control plasmids , the loss of dksA to the ppGpp0 caused no noticeable effects compared to the ppGpp0 dksA+ strain . This striking difference in compensation activity suggests competitive binding between TraR and DksA to RNA polymerase , supporting a modulatory role of TraR within the RNA polymerase secondary channel . In addition , these data further support the ppGpp-independent nature of gene regulation by TraR . No suppression of ppGpp0 ΔdksA amino acid auxotrophies was observed with the F plasmid ( Table 2 ) , contrasting the full suppression of ΔdksA amino acid auxotrophies by endogenous traR . The reason for this discrepancy remains unknown . Considering that low uninduced levels of cloned traR can rescue ppGpp0 ΔdksA amino acid auxotrophies , one possibility is TraR expression from the F plasmid is reduced in a ppGpp0 background . It has been previously shown that bacterial motility is impaired in ppGpp0 cells , and that expression of DksA could suppress this motility defect [7] . DksA may activate motility by enhancing the competitiveness of the sigma factor , σF , required for the production of flagella and chemotaxis [7] , [37] . Uninduced pTraR , like DksA , activated motility in both ΔdksA and ppGpp0 ΔdksA backgrounds ( Figure 5C ) . The above results demonstrate that TraR in the absence of ppGpp activates livJ and rescues the multiple ppGpp0 amino acid auxotrophies . TraR also suppresses motility defects of ppGpp0 and ΔdksA cells . Furthermore , TraR alone causes rapid , near complete repression of rRNA transcription . These observations clearly identify important ( and unexpected ) differences between TraR and DksA . The above in vivo results clearly demonstrate that TraR can both repress rRNA transcription and activate amino acid biosynthesis independently of ppGpp . To determine whether the ppGpp-independent action of TraR on the rrnB P1 promoter is direct , we performed a single round in vitro transcription assay with purified TraR ( His6-tagged ) or DksA ( His6-tagged ) as described in [22] . Addition of increasing amounts of TraR to the transcription mixture showed a linear inhibition of rrnB P1 transcription with or without ppGpp ( Figure 6A , B ) . Using identical conditions for this range of concentrations , DksA only showed inhibition of rrnB P1 promoter in the presence of ppGpp ( Figure 6A , B and [22] [5] ) . The in vitro data confirm that TraR alone can directly inhibit the rrnB P1 promoter in a ppGpp-independent manner .
TraR highlights a growing class of transcriptional regulators that may interact directly with the secondary channel . This class spans both prokaryotes and eukaryotes and includes DksA , GreA , GreB , TFIIS , Microcin J25 , Gfh1 , and Rnk [11] , [15] , [19]–[21] , [39]–[43] . Other putative members are bacteriophage ORFs [15] and the uncharacterized E . coli ORF ybiI , which clusters in an iron metabolism operon . YbiI was identified through a BLAST search for sequences sharing homology to TraR and is capable of rescuing the amino acid auxotrophies of ΔdksA , but not of ΔrelA ΔspoT , when expressed at high levels ( data not shown ) . However , YbiI is more likely involved in iron metabolism . Secondary channel interactors can be sequence diverse , but structurally similar , as is the case between the Gre proteins and DksA [15] , [20] , [21] . Despite likely structural similarities throughout this class of regulators and their interactions inside the secondary channel , their transcriptional effects can vary with respect to initiation and their relationships to ppGpp . TraR , like DksA , is unique compared to the rest of the known secondary channel interacting proteins in that it functions in both positive and negative regulation of transcription initiation . GreB , for instance , has been shown to function similarly to DksA with respect to rRNA inhibition , but is unable to activate amino acid promoters [23] . GreA , on the other hand , activates rRNA transcription in vitro at the level of open complex formation , but not by altering the half-life of formed complexes [22] . The molecular basis for the differences in transcriptional initiation by these factors remains to be determined . Identification of factors like TraR will provide a basis for a better understanding of the molecular mechanism ( s ) of the secondary channel regulators . DksA , together with ppGpp , activates amino acid biosynthetic pathways and represses rRNA transcription [5] , [6] . Prevailing thought presents ppGpp as the mechanistic effector of these aspects of the stringent response because ppGpp levels correlate with the transcriptional effects observed , whereas the levels of DksA remain constant during the growth phases of E . coli [5] , [6] , [44] . DksA is therefore thought to act primarily as a cofactor to stabilize binding of ppGpp to RNAP , enhancing ppGpp effects on transcription initiation [5] , [15] . These ideas arose from the ppGpp/RNAP co-crystal structure localizing ppGpp binding near the active center of RNAP in the secondary channel [45] . In addition , in vitro experiments have shown that ppGpp directly affects the rrnB P1 rRNA promoter by decreasing the stability and half-lives of RNAP open complexes [46] . We show that TraR can substitute for DksA function in the absence of ppGpp , indicating that ppGpp may not be required , either for positive or negative transcriptional regulation . In addition , recent studies have also suggested that DksA can work independently of ppGpp , fueling the question of exactly how DksA modulates the activity of ppGpp ( or vice versa ) [7] , [8] . Magnusson et al . [7] have shown that high levels of DksA can partially rescue the multiple ppGpp0 amino acid auxotrophies observed in a MC4100 background , although these effects were not seen in wild-type MG1655 , the strain used in this study . Both in vivo and in vitro , a large excess of DksA over RNAP can repress the rrnB P1 promoter in the absence of ppGpp , and in vitro , ppGpp alone has no effect on amino acid biosynthetic promoters [5] , [6] , [46] . Furthermore , the biological significance of the placement of ppGpp in the original ppGpp/RNAP co-crystal structure is questionable [24] . Based on these findings and our results with TraR , we postulate that DksA may be more than a passive coregulator for at least several promoters during the stringent response . Since TraR can mimic DksA function in vivo , the ppGpp-independent nature of TraR may reveal several important mechanistic implications for ppGpp and DksA . DksA has two N-terminal α-helices ( coiled coil ) and is ppGpp-dependent for many processes while TraR may possess only one α-helix and can function independently of ppGpp . Two coils interacting inside the secondary channel would be more spatially restrictive than one . TraR , with only one putative protruding α-helix , would be more dynamic within the secondary channel and more able to adopt a conformation required to modulate transcription . The structural/functional conservation of the invariant aspartic acid residues of DksA in TraR is of particular interest . When the second conserved aspartic acid residue of TraR was mutated ( D6N ) , the ability of TraR to function both in positively activating amino acid biosynthesis and repressing the rrnB P1 promoter was abolished , similar to results seen with DksA on the phage T7 A1 promoter [15] . Surprisingly , the D6N mutation abolished TraR-mediated activation of livJ and repression of rRNA transcription in a ppGpp0 strain . Since TraR can work without ppGpp , the invariant aspartic acid residues are unlikely to function solely in the coordination of ppGpp and are likely exerting the transcriptional effects in some other manner . Although such speculation remains to be verified , the results presented in this study suggest that only one coil tipped by aspartic residues is sufficient to substitute for DksA function in the absence of ppGpp . Homologs for traR are found in the transfer operons of many naturally occurring transmissible plasmids , including those involved in pathogenicity and multidrug resistance , highlighting selective pressure for an important function ( s ) . To date , neither we or others [29] have identified a role for TraR in F plasmid transfer in E . coli ( data not shown ) . This lack of traR function might be due to the laboratory domestication of the F factor to constitutively promote conjugation in E . coli . TraR may be a broad range host factor important for conjugation in other species or required for transfer in a “wild-type” F ( the F plasmid carries a mutation in finO , a gene involved in the repression of the transfer operon [47] ) . In addition , little is known about conjugation in the wild , and it remains to be determined if TraR plays a role in conjugation under more natural conditions . The presence of TraR could also provide indirect fitness advantages for the host during conjugation . Both DksA and ppGpp modulate the cellular responses of autoaggregation and bacterial motility [7] , [8] . Both functions can easily be imagined to be important for conjugation ( e . g . the quest for a recipient bacteria and maintaining physical contact during DNA transfer ) . TraR , independently of nutritional stress , may also control these processes . One of the consequences of the stringent response is the reallocation or partitioning of RNAP among promoters in the cell [48] , [49] . Here , we show that induction of TraR decreases rRNA synthesis , which encompasses the majority of transcription [33] , [34] . This inhibition of rRNA synthesis would free a major portion of RNAP which could be re-assigned , in this case , to the transcription of episomal genes or stress genes induced by the act of conjugation ( e . g . periplasmic stress due to pilus formation ) . Indeed , we have observed a role of TraR in the upregulation of several stress response pathways similar to DksA and ppGpp together ( manuscript in preparation ) [50] . Finally , genes found in pathogenicity islands are preferentially activated by DksA/ppGpp [12]–[14] , [42] , [51] , [52] . The presence of TraR on congugative plasmids may allow the bacteria to control expression of these genes independently of ppGpp accumulation . Thus , TraR may play an important role in activation of virulence in presence of conjugative plasmids . TraR may represent a novel and unique member of the growing family of RNAP secondary channel regulators . Its small size compared to DksA and its regulatory differences with respect to ppGpp provide the ability to dissect the functional similarities and differences between the two homologs , providing not only a better mechanistic understanding of TraR and DksA/ppGpp , but that of the other secondary channel regulators as well . Based on the functions of TraR and its presence on conjugative plasmids , we propose that TraR and DksA may have a fitness role during bacteria mating , promoting horizontal gene transfer , and consequently , bacterial evolution . These observations may provide the basis for new studies designed to combat antibiotic resistance and virulence in emerging pathogens .
Standard methods of E . coli genetics were performed [53] . Unless otherwise stated , all work was done at 32°C with either LB medium or M9 medium , supplemented , when required , with sodium citrate ( 5–20 mM ) , ampicillin ( 50 µg/mL ) , kanamycin ( 30 µg/mL ) , chloramphenicol ( 12 . 5 µg/mL ) , tetracycline ( 3 . 33 µg/mL with sodium citrate and 10 µg/mL without ) , glucose ( 0 . 1% ) , casamino-acids ( 0 . 3% ) , and IPTG ( 0 . 1 mM ) . M9 media was always supplemented with FeCl2 ( 10 µM ) and thiamine ( vitamin B1 ) ( 2 µg/mL ) . The backgrounds , genotypes , and sources of the strains of E . coli and plasmids used in this study are listed in Table S1 ( Supplemental Material ) . Primers for construction of deletion alleles and plasmids are listed in Table S2 ( Supplemental Material ) . Unless otherwise stated , all strains used are derivatives of MG1655 . Mutant alleles were moved into this background via standard P1 transduction [53] or linear transformation techniques with subsequent elimination of the drug-resistance marker by FLP recombinase if necessary [54] . Plasmids were constructed and transformed into strains by standard cloning , mutagenesis , and transformation techniques [55] . For traR and dksA , the Shine-Dalgarno and ORF were amplified from pOX38 and pJK537 , respectively . Antibiotic resistance , PCR , DNA sequencing , and/or phenotypic assays were performed for verification of alleles and plasmids . Serial dilutions of overnight cultures grown in LB were performed in 10 mM MgSO4 . Appropriate volumes of the dilutions of interest were then plated on M9-glucose and M9-glucose-casamino-acid plates , both supplemented with IPTG ( 0 . 1 mM ) and antibiotics when appropriate . The plates were incubated for 4 days at 32°C and colonies were counted . Percentages were obtained from the ratio of colonies growing on M9-glucose vs . the M9-glucose ( glu ) -casamino-acid ( CAA ) plates . Errors bars depict 1 standard deviation calculated from 3 independent experiments . Overnight cultures were diluted 1/100 and grown aerobically at 32°C in LB supplemented with ampicillin , and , when appropriate , IPTG for plasmid induction . For β-galactosidase assays involving the F plasmid , M9 media supplemented with glucose and tetracycline was used . Samples were taken at appropriate OD600 intervals and assayed as previously described [53] . For β-galactosidase activity ( per ml ) , OD420×1000 / reaction time vs . OD600 was plotted . All graphs are representative of multiple independent experiments that had a maximum variability of 12% . Polynomial ( 3rd order ) regression lines were plotted using Microsoft Excel . Bacterial cells were grown overnight in LB media containing ampicillin , after which each strain was inoculated via toothpick onto low agar ( 0 . 375% ) LB plates . The plates were incubated at room temperature for ∼24 hours at which the growth halos formed were measured directly [7] . Assays were performed as previously described [55] . Briefly , 26 mL cultures of MG1655 [pTraR-His6] , MG1655 [pDksA-His6] , and BL21 [pET24-TraR-His6] were grown to an OD600 of 0 . 4 at which a 1 mL aliquot was spun down and resuspended in 100 µL of SDS loading buffer . The remaining 25 mL cultures were induced with IPTG ( 1 mM ) for 2 hours after which another 1 mL aliquot was taken , spun down , and resuspended in 100 µL of SDS loading buffer for every OD600 0 . 4 of culture . Extracts were made of the remaining 24 mL cultures by spinning down and resuspending the pellets in 1 mL of 6 M guanidine , Tris-HCl , ( pH 7 . 4 ) for every 24 mL of OD600 1 . 0 . TALON 50% slurry resin ( 100 µL ) ( Qiagen ) was added to 1 mL of the extracts and incubated for 2 hours at 4°C after which the resin was washed twice with the above buffer and once with 6 M Urea , 25 mM Tris-HCl , 500 mM NaCl ( pH 7 . 9 ) and resuspended in 100 µL of TBS buffer . Samples ( 15 µL ) were then run on 12% polyacrylamide gels and analyzed by Western blotting and Coomassie blue staining . Western blots were performed with antibodies against His6 ( primary ) ( 1:2000 dilution of His-probe ( H-15 ) rabbit polyclonal IgG , Santa Cruz Biotechnology ) and goat anti-rabbit IgG ( secondary ) ( 1:2000 dilution of Alex Fluor 647 , Invitrogen ) . The PVDF membrane was scanned with a Typhoon Trio according to the manufacturer ( GE ) . TraR-His6 ( encoded by pET24-TraR-His6 plasmid ) was purified with nickel-nitrilotriacetic acid-agarose columns basically as described by Qiagen , except that the binding buffer ( BB ) was 50 mM NaPO4 , ( pH 8 . 0 ) , 0 . 5 M NaCl , 20 mM imidazole , and 10% glycerol . The resin with bound proteins was washed extensively with BB containing 40 mM imidazole , followed by TraR-His6 elution with 300 mM imidazole in BB . Pure protein fractions were then dialyzed against storage buffer ( 10 mM Tris-Cl , ( pH 8 . 0 ) , 0 . 1 mM EDTA , 0 . 1 mM DTT , 250 mM NaCl , 50% glycerol ) . DksA-His6 was purified as previously described [22] . In vitro transcription reactions were performed as previously described [22] . Briefly , 30 nM RNAP was pre-incubated ( 25°C ) with or without 250 µM ppGpp for 7 min prior to the addition of potassium glutamate ( 90 mM ) , and this was followed by a 20 min incubation at 30°C with rrnB P1 DNA ( 10 nM final ) and the indicated TraR or DksA concentrations ( 0–600 nM ) . The reactions were initiated by adding NTP substrates ( 100 µM ATP , GTP , and CTP , and 10 µM UTP ( 10 µCi/reaction [α32P]UTP , Amersham Biosciences ) ) with heparin ( 100 mg/mL final ) and terminated after 8 min by the addition of an equal volume of stop solution ( 95% formamide , 20 mM EDTA , 0 . 05% bromophenol blue , and 0 . 05% xylene cyanol ) . Samples were analyzed on 7 M urea , 6% polyacrylamide sequencing gels and quantified by phosphorimaging on a GE Healthcare imaging system . Supplemental material includes: three data figures and legends , a table listing bacterial strains and plasmids used , and a table listing the primers used in this study to construct new deletion alleles and plasmids . | Control of gene expression is central for cell operation . Transcription regulation is a first step to control gene expression and is largely mediated by DNA-binding factors . These recruit or prevent RNA polymerase binding to promoters of specific genes . Recently , a novel way to control transcription has emerged from studying nutritional stress in bacteria . In this case , a small nucleotide effector , ppGpp , with the help of a protein DksA , interacts with the secondary channel of RNAP , affecting RNA polymerase kinetics at promoters without binding to specific DNA sequences . This interaction results in up-regulation and down-regulation of genes involved in responding to nutritional stress . This work describes TraR , a factor found on conjugative plasmids that can regulate gene expression similarly to DksA , but in the absence of any nucleotide effector , like ppGpp . Thus , regulation of transcription similarly to DksA/ppGpp may be a more general mechanism . The presence of TraR on conjugative plasmids suggests a role for TraR in pathogenicity , virulence , and antibiotic resistance . These observations should provide a basis for new studies designed to combat antibiotic resistance and virulence in emerging pathogens . | [
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"evol... | 2009 | TraR, a Homolog of a RNAP Secondary Channel Interactor, Modulates Transcription |
Mycetoma is a neglected , chronic , and deforming infectious disease caused by fungi and actinomycetes . In Mexico , N . brasiliensis is the predominant etiologic agent . Therapeutic alternatives are necessary because the current drug regimens have several disadvantages . Benzothiazinones ( BTZ ) are a new class of candidate drugs that inhibit decaprenyl-phosphoribose-epimerase ( DprE1 ) , an essential enzyme involved in the cell wall biosynthesis of Corynebacterineae . In this study , the in vitro activity of the next generation BTZ , PBTZ169 , was tested against thirty Nocardia brasiliensis isolates . The MIC50 and MIC90 values for PBTZ169 were 0 . 0075 and 0 . 03 μg/mL , respectively . Because Nocardia is a potential intracellular bacterium , a THP-1 macrophage monolayer was infected with N . brasiliensis HUJEG-1 and then treated with PBTZ169 , resulting in a decrease in the number of colony-forming units ( CFUs ) at a concentration of 0 . 25X the in vitro value . The in vivo activity was evaluated after infecting female BALB/c mice in the right hind food-pad . After 6 weeks , treatment was initiated with PBTZ169 and its activity was compared with the first generation compound , BTZ043 . Both BTZ compounds were administered at 100 mg/kg twice daily by gavage , and sulfamethoxazole/trimethoprim ( SXT ) , at 100 mg/kg sulfamethoxazole , was used as a positive control . After 22 weeks of therapy , only PBTZ169 and SXT displayed statistically significant activity . These results indicate that DprE1 inhibitors may be useful for treating infections of Nocardia and may therefore be active against other actinomycetoma agents . We must test combinations of these compounds with other antimicrobial agents , such as linezolid , tedizolid or SXT , that have good to excellent in vivo activity , as well as new DprE1 inhibitors that can achieve higher plasma levels .
Nocardia brasiliensis mycetoma is a subcutaneous infection characterized by tumefaction and the production of abscesses and fistulae . There are also microcolonies of the etiologic agent in pus . Mycetoma is also produced by fungi and a wide variety of actinomycetes , including Nocardia , Actinomadura and Streptomyces [1] . Because these etiologic agents originate from the soil , the dominant species in specific areas depend on the geographic location . In México , the majority of cases are caused by actinomycetales . Inflammation and scar tissue make it difficult for antimicrobials to penetrate and kill the bacteria . Several antimicrobials , including sulfonamides , aminoglycosides , and beta-lactams , have been used for the therapy of actinomycetoma [2] . However , in some cases , cure is not achieved , and because the disease is stigmatizing and disabling , it is important to evaluate new antimicrobials for use as therapeutic alternatives . Benzothiazinones ( 1 , 3-benzothiazin-4-ones , BTZs ) are a novel class of anti-mycobacterial agents that block the synthesis of decaprenyl-phospho-arabinose , the precursor of cell-wall arabinans [3] . Benzothiazinones have shown excellent activity against several Corynebacterineae genera , including Corynebacterium , Mycobacterium , Rhodococcus and Nocardia . BTZs are particularly active against Mycobacterium tuberculosis , displaying nanomolar minimal inhibitory concentrations ( MIC ) , and are therefore more active than the existing tuberculosis drugs , including rifampin and isoniazid . The biochemical target of BTZs , the essential enzyme decaprenyl phosphoribose-2´-epimerase ( DprE1 ) , is commonly distributed among actinobacteria [4] . A new enhanced series of benzothiazinones , the PBTZs , have been produced by introducing a piperazine group into the scaffold [5] . Similar to BTZ043 , the preclinical candidate PBTZ169 binds covalently to DprE1 . The crystal structure of the M . tuberculosis DprE1-PBTZ169 complex revealed the formation of a semimercaptal adduct with Cys387 in the active site that may explain the irreversible inactivation of the enzyme [5] . Because of the close phylogenetic relationship among Corynebacterineae , it is possible that these anti-tubercular agents are also active against Nocardiae . In a previous study , we reported the in vitro activity of the early lead compound BTZ043 against N . brasiliensis [6] . Here , we analyze the susceptibility of 30 N . brasiliensis isolates from human mycetoma against the clinical candidate PBTZ169 using a microbroth dilution assay . We also tested the ex vivo activity of PBTZ169 in macrophage monolayers infected with N . brasiliensis and its in vivo activity in a BALB/c murine model of mycetoma .
In this study , we tested 30 isolates from the collection of the Laboratorio Interdisciplinario de Investigación Dermatológica ( LIID ) of the Servicio de Dermatología , Hospital Universitario , UANL , including N . brasiliensis HUJEG-1 ( ATCC700358 ) previously used in other in vitro and in vivo assays [7 , 8] . These isolates are from human mycetoma lesions and were identified by conventional biochemical methods as well as by sequence analysis of a portion of the 16S rRNA gene using the NOC-3 and NOC-4 primers [9] . The BTZ043 and PBTZ169 were provided by two authors of the current study , VM and STC . Trimethoprim-sulfamethoxazole ( suspension ) , at a concentration of 40 mg/200 mg , was obtained from Roche , New Jersey . D-7218 ( tedizolid ) was kindly donated by the Research Laboratory of the Dong-A Pharmaceutical Company ( Yongin , South Korea ) . For animal studies , the SXT suspension was diluted in distilled water . BTZ043 and PBTZ169 were suspended in 0 . 25% hydroxy-propylmethyl-cellulose . We used the broth microdilution method based on the CLSI M24-A document that we previously described [10 , 11] . As external controls , we used Escherichia coli ATCC 25922 and Staphylococcus aureus ATCC 29213 . Because of the high susceptibility of Nocardiae , the concentrations ranged from 0 . 125 μg/mL to 0 . 0002 μg/mL . Because N . brasiliensis grows as filaments , a unicellular suspension was prepared as published previously [12] . N . brasiliensis HUJEG-1 was cultured on Sabouraud agar ( Difco Laboratories , Detroit , MI , USA ) for 1 week and then sub-cultured in brain heart infusion ( BHI; Difco , Labaratories ) at 37°C in a shaker ( New Brunswick Scientific C24 , Edison , NJ , USA ) at 110 rpm for 72 hrs . The bacterial mass was then separated by centrifugation ( Eppendorf 5810R Hamburg Germany ) and washed four times with saline . After grinding in an Evelham-Potter device ( Fisher Scientific , Pittsburg , PA , USA ) , the suspension was centrifuged twice at 100 ×g; the supernatant was the unicellular suspension . The bacterial concentration was determined by plating on BHI agar with 5% sheep blood , and the suspension was stored in 20% glycerol at -70°C until use . The human monocyte cell line THP-1 ( ATCC TIB-202 ) ( American Type Culture Collection , Manassas , VA ) was maintained in RPMI 1640 medium ( Gibco-BRL , Gran Island , NY , USA ) supplemented with 10% fetal calf serum ( FCS; Gibco-BRL ) and 1 mM sodium pyruvate ( Sigma , St . Louis , MO , USA ) . To transform the cells into macrophages , the cells were sub-cultured four times without sodium pyruvate . The cell density was then determined in a hemocytometer , and the cell suspension was diluted as required in complete RPMI 1640 supplemented with 10% FCS and 6 . 25 ng/mL phorbol-12-myristate 13-acetate ( Calbiochem Biosciences , Darmstadt , Germany ) to obtain a density of 4 x 105 cells/mL . A 1 mL aliquot of the cell suspension was seeded into each well of a 24-well microplate ( Costar Corning , Daly City , USA ) , and the cell cultures were washed twice with RPMI 1640 every 48 h for no longer than 4 days . The technique used has been published previously [12] . Briefly , a 3:1 multiplicity of infection ( MOI ) was used to determine the effect of antimicrobials on Nocardia intracellular growth . Two hours after infecting the monolayer , the medium was discarded and the monolayer was washed twice with warm PBS , pH 7 . 4 . PBTZ was added at 0 . 25X , 1X , 4X and 16X the MIC in RPMI 1640 with 10% FCS and incubated for 6 h at 37°C in 5% CO2 . We cannot use rifampin as an intracellular active control because N . brasiliensis is a naturally resistant bacteria . Instead , we used DA-7218 , an oxazolidinone drug that previously demonstrated good intracellular and in vivo activity against N . brasiliensis [13] . The culture medium was discarded , and 1 mL of cold distilled water was added and incubated for 15 min . To release the intracellular bacteria , the monolayer was disrupted by pipetting up and down several times , and the suspension was collected in 1 . 5 mL Eppendorf tubes . Nocardia growth was quantified on BHI agar . To quantitate the plasma levels in mice , we administered the compounds to 8–12-week-old female BALB/c mice by gavage using BTZ043 , PBTZ169 or SXT , all at 100 mg/kg . Blood samples from the periorbitary plexus were collected at 0 , 20 , 40 , 60 , 120 , 240 , 360 , 480 , and 600 min . The concentrations of BTZ 043 , BTZ 169 and SXT were analyzed using a high-pressure liquid chromatography method developed in our laboratory . To 50 μL of thawed plasma , 150 μL acetonitrile was added to precipitate the proteins . The mixture was vortexed for 1 minute and centrifuged for 5 min at 5304 × g , and the supernatant was filtered through 0 . 45 μm nylon filters ( Waters , Milford , Mass . ) . Filtrates were collected into 150 μL inserts and analyzed by HPLC . Chromatographic separation was achieved using a HP1100 liquid chromatograph with a UV detector . A Synergi 4μPolar-RP 80A column 150 mm × 2 mm I . D . , with a 4-μm particle size ( Phenomenex , Torrance , CA ) was used . Samples were eluted with a mobile phase consisting of 0 . 1% formic acid in water ( solvent A ) and 0 . 1% formic acid in acetonitrile ( solvent B ) in a 60:40 proportion . The flow rate of the mobile phase was 0 . 2 mL/min . The injection volume was 5 μL . Detection and quantification of benzothiazinones was performed by ultraviolet at a wavelength of 240 nm . The total run time was 10 min . Eight- to twelve-week-old female BALB/c mice ( Harlan Mexico S . A . de C . V . , Mexico City ) were infected with N . brasiliensis HUJEG-1 . Experimental mycetoma was produced by injecting 20 mg ( wet weight ) of a N . brasiliensis suspension into the left hind footpad , as previously described [14] . Four weeks later , therapy was initiated . Groups of 15 animals were tested . One group of animals received saline solution by gavage as a negative control . The remainder were treated with PBTZ169 , BTZ043 , or SXT at 100 mg/kg administered twice daily by gavage for 10 weeks . The latter was used a positive control of experimental therapy . After 2 weeks of rest , the compounds were administered for a final period of 6 weeks . The effect of the drugs on the development of mycetomatous lesions was assessed by a blind reader using a previously published scale [14] . Potential differences among the groups against a control inoculated with saline solution were established using a variance test analysis . The study was approved by the Comité Local de Investigación en Salud 1906 , Centro de Investigación Biomédica del Noreste , IMSS , and the Comite de Investigacion , Facultad de Medicina , U . A . N . L . code DE11-002 . The animal handling was performed according to the NORMA Oficial Mexicana NOM-062-ZOO-1999 , ( Especificaciones técnicas para la producción , cuidado y uso de los animales de laboratorio; Technical specifications for the production , care and handling of laboratory animals ) . We performed an ANOVA ( analysis of variance ) for the intracellular killing assay with THP-1 macrophages . For the animal assays , a test of variance was used . Likewise multiple comparisons tests were also performed using the statistical LSD ( least significant difference ) . Because we lost some animals , the variance test was adjusted . To verify the presence of orthologs of DprE1 in Nocardia species , we utilized the internet BLAST program to scan microbial genomes ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ? PAGE_TYPE=BlastSearch&BLAST_SPEC=MicrobialGenomes ) , selecting Nocardia as organism database and Mycobacterium tuberculosis H37Rv DprE1 sequence ( locus Rv3790 ) as the query . Because other complete genomes have been reported for actinomycetoma etiologic agents , we also used the MTB protein sequence to analyze the data from Streptomyces somaliensis ( NCBI reference sequence WP_010471675 . 1 ) and Actinomadura madurae LIDD AJ290 ( NCBI reference sequence WP_021594179 . 1 ) .
The MIC values ranged from 0 . 03 μg/mL to 0 . 0037 μg/mL PBTZ169 . The MIC50 and MIC90 values were 0 . 0075 and 0 . 030 μg/mL , respectively . The MIC for PBTZ169 for N . brasiliensis HUJEG-1 was 0 . 0037 μg/mL . The MICs of SXT , DA-7218 , and BTZ043 for this strain were previously published [6 , 13] and are 9 . 5/0 . 5 , 8 , and 0 . 125 μg/mL , respectively . As shown in Fig 1 , DA-7218 shows dose-dependent activity with statistical significance for the 4X and 16X concentrations . By contrast , PBTZ169 presents significant killing activity , even at 0 . 25X MIC . Statistically significant differences were observed for all PBTZ169 concentrations ( P = 0 . 021 ) compared to both the negative control and DA-7218 ( P = 0 . 0311 ) . BTZ043 plasma levels in mouse were previously published [3] . At a dose of 100 mg/kg , it reaches a concentration of 4 . 06 μg/mL ( Tmax of 40 min ) ; which is quite similar to the levels in plasma observed in our case ( Fig 2 ) . In the case of PBTZ169 , it presented a Cmax of 1 . 74 micrograms/mL , with a Tmax of 40 min ( Fig 3 ) . In Fig 3 , we also present the plasma concentrations of SXT at 100 mg/kg , presenting a maximum concentration of 553 . 88 μg /mL 40 min after drug administration . The t ½ was 1 . 66 h , and the AUC was 1507 . 69 mg/L*h . When PBTZ169 and BTZ043 were administered at 100 mg/kg twice daily by gavage ( Fig 4 ) , only the former showed a statistically significant effect compared to the saline control ( P = 0 . 017 ) . No significant difference was detected in the BTZ043-treated group ( P = 0 . 667 ) . The mouse group treated with SXT showed a statistically significant difference compared to the control group ( P = 0 . 007 ) . The susceptibility of M . tuberculosis DprE1 depends on the covalent binding of BTZ drugs to Cys387 , and drug resistance results from the replacement of Cys387 by serine or alanine [5] . Our comparison of the sequence of DprE1 from M . tuberculosis with the genomes of Nocardia revealed that the majority of Nocardia spp . associated with human disease possesses dprE1 orthologs with a cysteine at this position ( Fig 5 ) . Comparison of the M . tuberculosis DprE1 protein sequence with N . brasiliensis HUJEG-1 revealed the presence of two proteins , YP_006805098 , with 99% query cover and 74% identity , and YP_006807368 , with 97% query cover and 62% identity . However , the latter protein possesses a serine instead of a cysteine at position 368 ( Fig 6 ) . In N . cyriacigeorgica GUH-2 and N . farcinica IFM 10152 , we found only one protein similar to MTB DprE1 . Actinomycetoma etiologic agents include other actinomycetales of the genera Actinomadura , ( A . pelletieri and A . madurae ) , and Streptomyces ( S . somaliensis ) . Because their genome sequences are available , except for A . pelletieri , we also searched for orthologous genes , and found only proteins with low identity , less than 30% of both the query cover and identity .
Benzothiazinones are highly potent drug candidates for the treatment of tuberculosis and other actinobacterial infections . Because of the nanomolar activity of benzothiazinones , we expected excellent in vivo activity . At 100 mg/kg twice daily , we observed a therapeutic effect , but only with PBTZ169 . In M . tuberculosis , a microorganism with a thicker and more hydrophobic cell-wall than N . brasiliensis , BTZ043 at 50 mg/kg once daily resulted in a significant decrease in the lung and spleen bacterial burden [5] . PBTZ169 is a more effective drug , and in the mouse model of infection , it significantly decreases the amount bacilli at 25 mg/kg once daily compared with BTZ043 [3] . For N . brasiliensis , we used higher concentrations for experimental mycetoma ( 100 mg/kg , twice daily ) to obtain a significant result . These results may be explained by the pharmacokinetic properties because the plasma levels reached by these drugs were relatively low , with a Cmax of 4 μg/mL and a Tmax of 20 min . However , previous studies showed that the PBTZ169 concentrations remain above the MIC for M . tuberculosis for nearly 24 hours following administration of a single dose [5] , and this is also likely the case for N . brasiliensis . The lower than expected activity of BTZ043 and PBTZ169 may also be because of the genetic nature of N . brasiliensis , a soil organism with a large chromosome of approximately 10 MB [15] that possesses a large amount of metabolic genes , including a second dprE1 gene . Although BTZs and PBTZs are “suicide” substrates that react with the active site cysteine of DprE1 , the second DprE1 enzyme in N . brasiliensis has a resistant genotype , with a Ser instead of a Cys in the position corresponding to 387 . For rifampin and most quinolones , many Nocardia spp . are susceptible to these drugs , with the exception of N . brasiliensis , which is naturally resistant due to the presence of second rpoB and gyrB genes [16] . For N . cyriacigeorgica and N . farcinica , other important nocardial pathogens , only one gene was identified , which will likely make the use of these compounds in nocardiosis successful . Other actinomycetales that produce mycetoma , such as Actinomadura and Streptomyces , do not possess DprE1 orthologs , and may be resistant to this type of compounds , although the vitro susceptibility of these microorganisms must be tested . Although PBTZ169 alone is highly active in vivo in both the acute and chronic models of murine tuberculosis , its activity is even better in combination with other drugs , particularly with bedaquiline and pyrazinamide , with better results than the standard of care treatment , including rifampin , isoniazid and pyrazinamide [5] . Multidrug therapy is essential for long-term , chronic infections , such as leprosy , tuberculosis and mycetoma , primarily to avoid the development of resistant strains . Future studies , both in vitro and in vivo , must be performed to determine the concentration for combinations of PBTZ169 , possibly with SXT , linezolid or DA-7218 ( tedizolid ) . This may result in new therapeutic schemes for the treatment of mycetoma caused by N . brasiliensis . | Mycetoma is a neglected tropical disease caused by many etiological agents , including actinobacteria and true fungi . In Mexico , Nocardia brasiliensis and Actinomadura madurae account for more than 90% of the total cases . This subcutaneous infectious disease can affect skin and subcutaneous tissue; actinomycetomas are particularly osteolytic . The presence of abundant scar tissue , pus , and the intracellular growth of Nocardia make treatment very difficult . Current N . brasiliensis actinomycetoma therapy includes the use of trimethoprim-sulamethozaxole , diamino-diphenyl-sulphone ( DDS ) , amikacin , and amoxicillin-clavulanate . N . brasiliensis is resistant to many other antimicrobials due in part to its richness in copies of genes related to pharmacoresistance , for instance rpoB , gyrase , beta-lactams , P450 cytochromes , etc . DprE1 inhibitors are new types of compounds that target a completely different gene , dprE1 , encoding the decaprenylphosphoryl-d-ribose oxidase . Assays evaluating these experimental or other new drugs are necessary to develop a better therapeutic scheme for actinomycetoma , with more potent , less toxic antimicrobials . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | In Vivo Activity of the Benzothiazinones PBTZ169 and BTZ043 against Nocardia brasiliensis |
Dengue fever epidemic dynamics are driven by complex interactions between hosts , vectors and viruses . Associations between climate and dengue have been studied around the world , but the results have shown that the impact of the climate can vary widely from one study site to another . In French Guiana , climate-based models are not available to assist in developing an early warning system . This study aims to evaluate the potential of using oceanic and atmospheric conditions to help predict dengue fever outbreaks in French Guiana . Lagged correlations and composite analyses were performed to identify the climatic conditions that characterized a typical epidemic year and to define the best indices for predicting dengue fever outbreaks during the period 1991–2013 . A logistic regression was then performed to build a forecast model . We demonstrate that a model based on summer Equatorial Pacific Ocean sea surface temperatures and Azores High sea-level pressure had predictive value and was able to predict 80% of the outbreaks while incorrectly predicting only 15% of the non-epidemic years . Predictions for 2014–2015 were consistent with the observed non-epidemic conditions , and an outbreak in early 2016 was predicted . These findings indicate that outbreak resurgence can be modeled using a simple combination of climate indicators . This might be useful for anticipating public health actions to mitigate the effects of major outbreaks , particularly in areas where resources are limited and medical infrastructures are generally insufficient .
Dengue fever ( DF ) is one of the most important mosquito-borne diseases in the world [1 , 2] . Recent estimates indicate that there are 390 million dengue infections per year , of which 96 million manifest as disease [3] . Infection is caused by the dengue virus ( DENV ) , which has four closely related serotypes ( DENV1 to DENV4 ) [4] that are transmitted to humans by infected Aedes sp . mosquitos . Infection produces a spectrum of illnesses that range from indiscernible or mildly nonspecific febrile syndrome to severe disease forms , including dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) . Currently , there are no specific dengue therapeutics , and prevention strategies are limited to vector control measures [5] . The recent development of the first dengue vaccine represents a major advance in our ability to control the disease [6–8] . In Latin American and Caribbean countries , the reintroduction and dissemination of Aedes aegypti occurred in the 1970s [9] . Since then , regular outbreaks have occurred on a 3- to 5-year cycle , and an increase has been observed in the frequency of severe forms of dengue [10] . In French Guiana , a French overseas territory of 250 , 000 inhabitants that is located in South America along the Atlantic Ocean , the epidemiology of dengue evolved from endemo-epidemic to hyper-endemic conditions over the two last decades [11] . Since the first DHF cases were reported in 1992 [12] , transmission in French Guiana has followed a seasonal pattern that is punctuated every few years by major outbreaks that have been linked to the circulation of one or two predominant serotypes [11 , 13] . With the increasing frequency of such epidemics and the associated public health and socioeconomic issues [14] , the surveillance , prevention and control of dengue have become social , political and public health challenges that require specific preparedness activities , particularly in areas where resources are limited . Although dengue ecology is known to be influenced by a complex multi-scale interplay of intrinsic factors that include human host demographics , vectors , and viruses and extrinsic factors that include environmental , meteorological and climate conditions , the factors that drive DF epidemics are not yet clearly understood [15–22] . Interactions between climate and DF outbreaks have been studied worldwide [23–34] . The findings of these studies suggest that the effects of climate parameters on the incidence of DF can vary widely from one study site to another [35 , 36] and that they depend largely on local context and epidemiological patterns . In South America , studies designed to determine the impact of climate on DF epidemics have suggested a role for El Niño events as triggers for epidemics [35] . El Niño conditions are likely to influence DF dynamics indirectly by modulating temperature , humidity and rainfall . In French Guiana , the sole study that focused on the DF-climate relationship identified a synchronous positive association between the occurrence of El Niño events , warmer temperatures , less abundant rainfall and dengue epidemics [37] . These results were obtained using basic analytical methods , and the study investigated El Niño conditions on a coarse annual scale . These results must be explored further , particularly if they are to be useful for prediction purposes . Moreover , the quality of epidemiological data ( i . e . , estimated suspected cases ) that were available for the period covered by the study ( 1963–1993 ) was highly questionable . Thus , even if associations between El Niño conditions , meteorology and DF epidemics are suspected , there is currently no climate-based model to assist in developing an early warning system in French Guiana . Based on the a priori hypothesis put forward in Gagnon et al . [37] , the current study explores the potential of integrating sea surface temperature ( SST ) conditions to serve as a proxy for epidemic risk several months before the onset of a DF outbreak . In addition , we push our analysis further by also investigating the use of large-scale atmospheric circulation and regional climate patterns as more optimal indicators for predicting outbreaks . Using a long-term epidemiological surveillance dataset , this study explores the possibility of using a predictive model to assist public health authorities in implementing timely , appropriate and efficient prevention and mitigation strategies .
French Guiana is an overseas region of France that is located in northern South America between Brazil and Surinam . The climate is equatorial , hot and wet . The monthly mean temperatures ( near 27°C ) and relative humidity , which rarely falls below 80% , are nearly constant year-round . Spatial variations across the territory , particularly in the coastal area ( regrouping 90% of the population ) , are low . Among meteorological parameters , only rainfall presents significant seasonal variations influenced by the migration of the intertropical convergence zone ( ITCZ ) . The mean annual cumulative rainfall is approximately three meters , and there are four alternating seasons: a long rainy season from the beginning of April to mid-July , a long dry season from mid-July to mid-November , a short rainy season from mid-November to mid-February , and a shorter dry season from mid-February to the beginning of April . Large inter-annual variations in the total cumulated rainfall have been observed , and they are partly governed by large-scale atmospheric and oceanic patterns . A well-documented issue is the impact of El Niño conditions . During El Niño ( La Niña ) years , a rainfall deficit ( surplus ) occurs in French Guiana [38–42] . Epidemiologic data on DF were obtained from two different sources , depending on the collection period . For the data from 1991–2006 , a surveillance system was used , and data were based on a weekly census of biologically confirmed cases ( BCCs ) that were recorded by the Arbovirus National Reference Centre , which is based at the Pasteur Institute of French Guiana . In 2006 , a multi-source surveillance system was implemented by the Regional Epidemiology Unit of the Institut de Veille Sanitaire that included all seven biological laboratories ( public hospital and private laboratories ) that are located in the coastal area . Concurrently , in 2006 , a new dengue diagnostic test based on NS1 antigen detection was made available to all laboratories , and it contributed substantially to improving surveillance . Cases were biologically confirmed by isolating the virus and detecting viral RNA using reverse-transcription PCR ( RT-PCR ) , NS1 antigen detection methods or serological tests that are based on an immunoglobulin M ( IgM ) -capture enzyme-linked immunosorbent assay ( MAC-ELISA ) [13] . This surveillance system was authorized by the French Data Protection Agency ( CNIL , N°1213498 ) . The DF incidence rates in French Guiana , which are defined as the yearly number of cases/100 000 inhabitants , were calculated for the 1991–2013 period . A standardization procedure was performed separately for the 1991 –April 2006 and May 2006–2013 periods . For this procedure , we used a z-score scaling method to take into account the improvement in the epidemiological surveillance system that was observed in April 2006 . This approach led to a trend toward increasing numbers of cases and enabled us to work with a single dataset . The standardization was calculated using the following equation: z=x−x´σ where x , x´ and σ were the observed value , mean and standard deviation of the incidence , respectively . The epidemic years were identified by applying the tercile method to the normalized and standardized sum of the monthly cases that occurred during the high incidence period . The first tercile was defined as the “low” incidence group , the second was defined as the “intermediate” incidence group , and the third was defined as the “high” incidence group . We used sets of meteorological parameters and large-scale atmospheric and global SST data for 1990–2013 for this study . Meteorological records included rainfall , temperature and relative humidity and were obtained from Météo-France . We calculated monthly country means from these daily station data throughout the study period . A set of atmospheric and SST predictors was constructed from the ERA-Interim reanalysis data that were obtained from the European Centre for Medium-Range Weather Forecasts [43] . The ERA-Interim system assimilates observations and outputs using a forecast model . The climate fields were available at a 0 . 75°x0 . 75° spatial resolution and 60 vertical levels . First , time-lagged Spearman’s correlations were used to explore associations among the occurrence of El Niño events , warmer temperatures , less abundant rainfall and dengue epidemics as previously suggested by Gagnon et al . [37] . Different El Niño-related SST and sea level pressure ( SLP ) indices were tested , including Niño areas 1 to 4 , the Southern Oscillation Index ( SOI ) and the multivariate ENSO index ( MEI ) . Yearly DF incidences were correlated with the monthly climate data for each month in the preceding year . Second , the relationships between DF outbreaks and large-scale atmospheric and oceanic parameters were assessed using a composite analysis [44] and following an exploratory approach . The composite method was used to identify the conditions that characterized a typical epidemic year and to assess the optimal indices to use to analyze DF outbreak predictions . Two samples ( the composites ) were built that contained the climate data for both epidemic and non-epidemic years . The differences between the means of the two samples were calculated at each grid point between 50°N-50°S and 150°W-0°E . The significance of the differences between the epidemic and non-epidemic years was assessed using Student’s t-tests . Considering that major outbreaks affect a very large part of French Guiana , we built a climate-based forecast model using the climate factors identified as having an influence on DF at a country level . A logistic binomial ( epidemic or non-epidemic year ) regression model was used . If p is the probability of an outbreak , then ( p1−p ) is the odds of observing an outbreak . Thus , the following logistic regression model was used: log ( p1−p ) =β0+∑ i=1kβixi where log represents the natural logarithm , k represents the number of selected climate predictors , βi represents the coefficient of the ith predictor and xi represents the ith predictor . This model can be restated as follows: p=exp ( β0+∑ i=1kβixi ) 1+exp ( β0+∑ i=1kβixi ) Logistic binomial regressions were fitted using univariate and multivariate methods by applying all of the possible predictor combinations . The model that maximized the AUC ( area under the curve ) from the receiver operating characteristic ( ROC ) analysis [45] and minimized the AIC ( Akaike information criterion ) [46] was selected . The final model performances were evaluated by calculating ROC scores and cross-validating the data [47] . The ROC is a method of testing the skill of categorical forecasts using the hit rate ( HR ) and false alarm rate ( FAR ) . The HR indicates the proportion of epidemic years that were categorically forecast ( sensitivity ) . It ranges from 0 to 1 ( 1 being desirable ) and is calculated as follows: HR=HitsHits+Misses The FAR is the proportion of non-epidemic years that were forecast as epidemic years ( 1-specificity ) . It ranges from 0 to 1 ( 0 being desirable ) and is defined as follows: FAR=False alarmsHits+False alarms Second , a cross-validation on chunks of multiple years was performed to measure the model stability . Leave-one-out cross-validation ( LOOCV ) ( i . e . 23-fold ) was used . The model was refitted according to the number of observations , and the observations were then temporarily removed one by one . The resulting LOOCV δ was the cross-validation estimate of prediction error .
The year-to-year variability in DF incidence rates in French Guiana was described over a 23-year period from 1991–2013 ( Fig 1A ) . The monthly mean cycle of DF standardized anomalies showed that there was strong seasonality ( Fig 1B ) . The mean onset of the high incidence period was in January ( positive anomalies ) during the short rainy season . DF case peaks generally occurred in March , and the anomalies then decreased until May ( negative in June ) . The high incidence period was therefore defined as January–May . Eight major outbreaks ( third tercile ) were identified: 1992 , 1997 , 1998 , 2005 , 2006 , 2009 , 2010 and 2013 ( Fig 1C ) . Spearman’s lagged correlations indicated the presence of associations between DF and monthly pre-epidemic climate factors ( Fig 2 ) . Among El Niño indicators , the Niño 3 area index showed the highest correspondence with DF . A significant negative correlation was observed between DF and rainfall in October ( r = -0 . 49 , p-value = 0 . 02 ) and November ( r = -0 . 52 , p-value = 0 . 01 ) , which are one and three months before the mean onset of the epidemics , respectively . El Niño event-related indices and temperatures were not significantly associated with DF ( p-value > 0 . 05 ) . However , an interesting , persistent , positive and nearly significant correlation was observed between the Niño 3 area index and DF during the summer months , and this relationship deserves further investigation . SST composite maps were calculated for the 12 months from January to December . The results indicated that epidemic years were characterized by increased Pacific Ocean SSTs during the pre-epidemic months of July and August ( Fig 3 , only July–December is shown here ) . This warming was particularly strong ( approximately 1 . 5°C ) at the equator at approximately 120°W , and the maximal spatial extent was observed in July . The analysis of differences in atmospheric circulation between epidemic and non-epidemic years at the end of the dry season in October–November ( when there were significant negative correlations between DF and rainfall; Fig 2 ) showed that epidemic years were characterized by northward positioning and a strengthening of the Azores High in November ( Fig 4 ) . The mean difference between epidemic and non-epidemic years was approximately 5 hPa and was maximal at 40°N , 30°W . Based on previous results , the following climate indices were included in logistic binomial univariate and multivariate models ( Table 1 ) : ( 1 ) October–November , mean rainfall in French Guiana ( FG-ON-RAIN ) ; ( 2 ) July–August , mean Equatorial Pacific Ocean ( 2° N-20°S , 135°W-90°W ) SST ( EPO-JA-SST ) ; and ( 3 ) November , Azores High ( 45°N-35°N , 40°W-20°W ) SLP ( AH-N-SLP ) . Because previous SST and SLP indices were found to be associated with rainfall in French Guiana , their common association in the same model was discarded . The multivariate model that included the two predictors EPO-JA-SST and AH-N-SLP yielded the best results for the AIC ( 27 ) and AUC ( 0 . 88 ) , which suggested that it had good predictive value . Warming in the mean Equatorial Pacific Ocean SST in July–August and the strengthening of the Azores High in November greatly increased the probability that an outbreak would occur in the following year in French Guiana ( Fig 5 ) . Accordingly , 80% of the epidemic conditions were correctly predicted ( HR = 0 . 80 ) . Outbreaks in the years 2001 and 2005 were incorrectly predicted to be non-epidemic , and two years were predicted as false alarms ( 1994 and 1999 ) . Finally , the LOOCV δ of 0 . 18 indicates that the model was robust and that only 18% of the years were misclassified when the LOOCV procedure was used . Yearly cross-validated probabilities are shown in S1 Fig . The scatter plot between the observed DF incidence rate standardized anomalies and the predicted outbreak probabilities ( Fig 6 ) revealed a nearly linear relationship ( Pearson’s correlation: r = 0 . 76; P-value < 0 . 01 ) . Forecasts for 2014 and 2015 ( not included in the training dataset ) indicated that the model predictions were consistent with the non-epidemic conditions that were observed in French Guiana ( the DF IR/100 000 inhabitants was 350 in 2014 and 106 for January to September , 2015 ) ( Table 2 ) . In 2016 , as a result of the warm SST conditions over the Equatorial Pacific Ocean that occurred in August and July ( 25 . 26°C ) and the high pressures over the Azores High in November ( 1021 . 36 hPa ) , the model predicted that French Guiana would likely experience an outbreak ( probability of 0 . 92 ) .
Among the wide panel of factors that can influence DF outbreaks , these results suggest that large-scale climate factors play an important role . We found that the climatic indices that were assessed in this study were important for DF monitoring and for predicting outbreaks in French Guiana over a period of 2–3 months . This delay may give public health authorities the ability to anticipate outbreaks and implement social communication and vector control measures , and to adapt healthcare capacity and increase preparedness in a timely manner . Importantly , this model could be easily and regularly updated using newly collected data that was retrieved from the ongoing dengue surveillance system . Because the identified climate indicators are simple and easy to access , they could be used to estimate the probability of future epidemics occurring according to climate change simulations and help to evaluate the effectiveness of potential intervention strategies . | Climatic determinants are amongst the most frequently cited in studies aimed at understanding and explaining the dynamics of vector-borne infections , and dengue in particular . French Guiana , a French overseas territory in which the vector Aedes aegypti is well established , experiences an epidemic cycle of dengue with large and prolonged epidemics occurring approximately every 3 years . Dengue is one of the most prioritized infectious diseases , and it requires an intense mobilization of local public health authorities , health services , and health professional and vector control services . A specific surveillance , preparedness and response plan has been developed based upon these needs . Gaining an accurate understanding of the drivers of dengue transmission is required to develop a model to predict the risk of an epidemic and to plan activities aimed at controlling it . Here , we assessed the effects of climatic factors on dengue spread to develop a predictive model of the epidemics in French Guiana on a country-wide scale . The goal of the model is to anticipate and plan both preventive and control activities . Given climate conditions , the model predicts that a dengue epidemic is likely to occur in early 2016 . These conditions , which are favorable for Aedes mosquito proliferation , could also enhance the diffusion of other arboviruses , such as the Zika virus , in northeastern South America . | [
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"diseas... | 2016 | Predicting Dengue Fever Outbreaks in French Guiana Using Climate Indicators |
Kaposi's sarcoma-associated herpesvirus ( KSHV ) establishes a latent infection in the host following an acute infection . Reactivation from latency contributes to the development of KSHV-induced malignancies , which include Kaposi's sarcoma ( KS ) , the most common cancer in untreated AIDS patients , primary effusion lymphoma and multicentric Castleman's disease . However , the physiological cues that trigger KSHV reactivation remain unclear . Here , we show that the reactive oxygen species ( ROS ) hydrogen peroxide ( H2O2 ) induces KSHV reactivation from latency through both autocrine and paracrine signaling . Furthermore , KSHV spontaneous lytic replication , and KSHV reactivation from latency induced by oxidative stress , hypoxia , and proinflammatory and proangiogenic cytokines are mediated by H2O2 . Mechanistically , H2O2 induction of KSHV reactivation depends on the activation of mitogen-activated protein kinase ERK1/2 , JNK , and p38 pathways . Significantly , H2O2 scavengers N-acetyl-L-cysteine ( NAC ) , catalase and glutathione inhibit KSHV lytic replication in culture . In a mouse model of KSHV-induced lymphoma , NAC effectively inhibits KSHV lytic replication and significantly prolongs the lifespan of the mice . These results directly relate KSHV reactivation to oxidative stress and inflammation , which are physiological hallmarks of KS patients . The discovery of this novel mechanism of KSHV reactivation indicates that antioxidants and anti-inflammation drugs could be promising preventive and therapeutic agents for effectively targeting KSHV replication and KSHV-related malignancies .
A hallmark of herpesviral infections is the establishment of latency in the hosts following acute infections [1] . Reactivation of herpesviruses from latency results in production of infectious virions and often development of their associated diseases . KSHV is a gammaherpesvirus associated with KS , a vascular malignancy of endothelial cells commonly seen in AIDS patients [2] . KSHV is also linked to other lymphoproliferative diseases including primary effusion lymphoma ( PEL ) and multicentric Castleman's disease ( MCD ) [2]–[4] . Similar to other herpesviruses , KSHV establishes a lifelong persistent infection in the host [1] . In KS tumors , most tumor cells are latently infected by KSHV , indicating an essential role of viral latency in tumor development [5] . However , KSHV lytic replication also contributes to KS pathogenesis [6] . Both viral lytic products and de novo infection promote cell proliferation , invasion , angiogenesis , inflammation and vascular permeability [6] . In fact , higher KSHV lytic antibody titers and peripheral blood viral loads are correlated with high incidence and advanced stage of KS [7]–[13] , and KS regressed following anti-herpesviral treatments that inhibit lytic replication [14] , [15] . While several cellular pathways such as mitogen-activated protein kinase ( MAPK ) pathways and protein kinase C delta regulate KSHV lytic replication [16]–[20] , the common physiological trigger that reactivates KSHV from latency in patients remains unclear . A number of factors including proinflammatory and proangiogenic cytokines [21] , [22] , hypoxia [23] , HIV and its product Tat [24]–[26] , coinfection with human cytomegalovirus and human herpesvirus 6 [27] , [28] , and the activation of toll-like receptors [29] can cause KSHV reactivation in cultures . However , none of them is likely the trigger in all the clinical scenarios , which include different forms of KS , PEL and MCD . The mechanisms by which these factors reactivate KSHV from latency also remain unclear . There are four clinical forms of KS . Patients with all forms of KS are characterized by high levels of inflammation and oxidative stress [30] , [31] . Classical KS , mostly seen in elderly men in the Mediterranean and Eastern European regions , is ubiquitously associated with high level of inflammation and oxidative stress because of its close link with aging [32] . In African endemic KS , excessive iron exposure due to high content of iron in the local soils coupled with bare foot walking is a possible cofactor that can induce inflammation and oxidative stress [33] , [34] . In transplantation KS , inflammation and oxidative stress are common because of immunosuppression and organ rejection [35] . Patients with AIDS-related KS ( AIDS-KS ) have high levels of inflammation and oxidative stress as a result of host responses to HIV infection and chronic inflammation [36] . In all clinical forms of KS , inflammation and oxidative stress are also the hallmarks in the tumors [37] . Both PEL and MCD often coexist with KS , and are commonly seen in HIV-infected patients [6] . PEL is often found in elderly men , particularly in HIV-negative cases . Thus , similar to KS patients , these patients often have high levels of inflammation and oxidative stress . Since inflammation and oxidative stress induce ROS , and high level of ROS activates the MAPK pathways [38] , we postulated that ROS , as a result of inflammation and oxidative stress , might mediate KSHV reactivation from latency . The most common ROS molecule in non-immune cells is H2O2 , which is mainly produced by mitochondria as a byproduct of oxidative metabolism [38] . Because high level of H2O2 is cytotoxic , cells express multiple antioxidant enzymes such as catalase and glutathione peroxidase to remove H2O2 so that it is below the detrimental threshold in normal condition . During oxidative stress , cells produce and release a large amount of H2O2 as a consequence of lost balance between its production and its scavenging [38] . During infections and inflammatory responses , host phagocytes such as macrophages and neutrophils produce and release excessive amounts of H2O2 [39]–[41] . Thus , KS patients are deemed to have high levels of H2O2 . In this study , we investigated the physiological role of H2O2 on KSHV reactivation .
To examine the relationship of H2O2 with KSHV lytic replication , we stably expressed an H2O2-specific yellow fluorescent protein ( cpYFP ) sensor from the HyPer-cyto cassette in KSHV-infected BCBL1 cells [42] . As previously reported [20] , the majority of BCBL1 cells were latently infected by KSHV but a small percentage of them underwent spontaneous lytic replication , which was detected by staining for viral late lytic protein ORF65 ( Figure 1A ) . Notably , these ORF65-positive cells were strongly positive for cpYFP ( Figure 1A ) . In contrast , ORF65-negative cells were either weakly positive or negative for cpYFP . Treatment with 12-O-tetradecanoylphorbol-13-acetate ( TPA ) , a common chemical inducer for KSHV lytic replication , increased the number of lytic cells , all of which also expressed high level of cpYFP while ORF65-negative cells remained weakly positive for cpYFP ( Figure 1A ) . Extended exposure of the images or adjustment of the contrast showed that almost all the TPA-treated cells were positive for cpYFP albeit with diverse intensities ( data not shown ) . These diverse levels of H2O2 among the individual cells could be due to their different sizes of intracellular antioxidant enzyme pools . Together , these results showed a close correlation between high level of intracellular H2O2 and KSHV lytic replication . To investigate the role of H2O2 in KSHV lytic replication , we examined whether exogenous H2O2 , which uses water channels ( aquaporins ) to cross the cell membrane [43] , is sufficient to induce KSHV reactivation . We observed a dose-dependent induction , at both mRNA and protein levels , of KSHV replication and transcription activator ( RTA ) encoded by ORF50 , a key transactivator of viral lytic replication ( Figure 1B–C ) , by H2O2 . Consistent with these results , H2O2 increased the expression of several other KSHV lytic transcripts including ORF57 , ORF59 , kbZIP ( ORF-K8 ) and ORF65 ( Figure 1D ) . The expression of KSHV major latent gene LANA ( ORF73 ) was also increased by 2 . 2-fold while that of another latent gene vFLIP ( ORF71 ) remained almost unchanged . Furthermore , H2O2 increased the expression of viral lytic proteins ORF65 and ORF59 , and production of infectious virions in a dose-dependent manner ( Figure 1E–G ) . We further extended the observation to primary human umbilical vein endothelial cells ( HUVEC ) latently infected by KSHV [44] . Similar to BCBL1 cells , H2O2 increased the expression of viral lytic transcripts including RTA , ORF57 and ORF-K2 but not latent transcript vCyclin ( Figure 1H ) . H2O2 also increased the expression of ORF65 protein ( Figure 1I–J ) . These results indicate that H2O2 induction of KSHV reactivation is not cell type specific . Next , we determined whether an increase in intracellular H2O2 level is sufficient to induce KSHV reactivation . Treatment of BCBL1 cells with 3-amino-1 , 2 , 4-triazole ( ATZ ) , an inhibitor of H2O2 scavenging enzyme catalase , reduced cellular catalase activity by 57 . 2% and increased the intracellular H2O2 level by 2 . 2-fold ( Figure 2A–B ) . ATZ increased the expression of KSHV lytic transcripts of RTA , ORF57 , ORF59 , kbZIP and ORF65 genes , the expression of lytic protein ORF65 , and production of infectious virions ( Figure 2C–E ) . Interestingly , we observed an additive effect when either H2O2 or ATZ was used together with TPA to induce KSHV lytic replication ( Figure 2C–E ) . To confirm that the effect of ATZ on KSHV reactivation was due to an increase in intracellular H2O2 level , we stably expressed a siRNA specific to catalase in BCBL1 cells harboring a recombinant KSHV BAC36 [45] . Compared to cells stably expressing a scrambled siRNA , those expressing the catalase-specific siRNA had significantly lower expression levels of catalase transcript and protein ( Figure 2F–G ) . Similar to treatment with ATZ , knockdown of catalase increased the expression of KSHV lytic transcripts of RTA , ORF57 , ORF59 , kbZIP and ORF65 , lytic protein ORF65 , and production of infectious virions ( Figure 2F–H ) . Taken together , our results so far have shown that an increase in intracellular or exogenous H2O2 level induces KSHV reactivation , indicating that H2O2 produced during inflammation and oxidative stress in KS patients can be the physiological trigger that reactivates KSHV from latency through both autocrine and paracrine mechanisms . To determine whether H2O2 is required for KSHV lytic replication , we used H2O2 scavengers to reduce the intracellular H2O2 level . As shown in Figure 1A , TPA not only induced KSHV reactivation but also increased the intracellular H2O2 level . At 12 h , TPA increased the intracellular H2O2 level by 3 . 9-fold ( Figure 3A ) , which could be the result of reduced expression of catalase ( Figure 3B ) . Treatment with H2O2 scavengers including catalase , reduced glutathione and NAC inhibited TPA induction of intracellular H2O2 as shown by the reduced median fluorescent levels in the cpYFP-expressing BCBL1 cells ( Figure 3C ) . None of these treatments affected the viability and growth rate of the cells ( data not shown ) . As expected , H2O2 scavengers inhibited TPA induction of RTA transcript and protein ( Figure 3D–E ) . Consistent with these results , RTA promoter activities were induced 2 . 2- and 4 . 3-fold by H2O2 and TPA , respectively , and these induction effects were inhibited by NAC ( Figure 3F ) . In contrast , a latent LANA promoter was not induced by H2O2 and only marginally induced by TPA for 1 . 4-fold ( Figure 3G ) . NAC also abolished TPA induction of the LANA promoter activity . Furthermore , TPA induction of ORF65 protein and production of infectious virions were inhibited by H2O2 scavengers in a dose-dependent fashion ( Figure 3H–I ) . To examine whether H2O2 scavengers also inhibit KSHV spontaneous lytic replication , we measured the expression of ORF65 protein in BCBL1 cells treated with the scavengers . As shown in Figure 3J , both catalase and NAC inhibited the expression of ORF65 protein after 6 days but not 1 day of treatment , which is consistent with the late expression kinetics of this viral capsid protein . These results indicate that H2O2 is required for KSHV spontaneous lytic replication and TPA-induced KSHV reactivation , and antioxidants such as reduced glutathione and NAC can suppress KSHV lytic replication . Because high levels of hypoxia , and proinflammatory and proangiogenic cytokines are features of KS tumors , and previous studies have shown that these conditions can induce KSHV reactivation [21]–[23] , we determined whether KSHV reactivation induced by these conditions is mediated by H2O2 . Short-time treatment with sodium azide ( NaN3 ) , which induces hypoxia [46] , increased intracellular H2O2 level as shown by cpYFP fluorescent level in BCBL1 cells ( Figure 4A–B ) . As a result , the expression of RTA transcript was increased 12 . 2-fold by NaN3 , which was inhibited by both NAC and catalase ( Figure 4C ) . Similarly , the expression of RTA and ORF65 proteins were induced by NaN3 , which was also inhibited by NAC and catalase ( Figure 4D ) . As expected , HIF-1α was induced by NaN3 , which was also inhibited by NAC and catalase , suggesting that H2O2 mediates hypoxia induction of HIF-1α . These results are consistent with previous observations that H2O2 can directly induce HIF-1α [47] . Next , we determined the role of H2O2 in KSHV reactivation induced by proinflammatory and proangiogenic cytokines . Treatment of BCBL1 cells with vascular endothelial growth factor ( VEGF ) , fibroblast growth factor-B ( bFGF ) , interleukin-6 ( IL-6 ) or tumor necrosis factor-alpha ( TNF-α ) alone minimally induced the expression of ORF65 protein by 1 . 5- , 1 . 2- , 1 . 4- and 1 . 2-fold , respectively , and these induction effects were reversed by NAC and catalase ( Figure 4E ) . In contrast , insulin-like growth factor-1 ( IGF-1 ) , epithelial growth factor ( EGF ) and interferon gamma ( IFN-γ ) were more potent inducers , which increased the expression of ORF65 protein by 2 . 6- , 2 . 3- and 3 . 2-fold , respectively . Similarly , NAC and catalase inhibited the induction of ORF65 proteins by these cytokines . Since KS tumors contain abundant infiltration of proinflammatory immune cells such as monocytes , we further examined the effects of proinflammatory and proangiogenic cytokines on KSHV reactivation in the presence of monocytic cells U937 ( Figure 4E ) . Co-culture of BCBL1 cells with U937 cells alone increased the expression of ORF65 protein by 2 . 4-fold . In the presence of U937 cells , weak inducers VEGF , bFGF , IL-6 and TNF-α more effectively increased the expression of ORF65 protein by 3 . 5- , 2 . 8- , 4 . 2- and 7-fold , respectively , suggesting a synergistic effect of these cytokines with U937 cells . These synergistic effects were also observed with strong inducers EGF and IFN-γ , which increased the expression of ORF65 protein by 5 . 2- and 10-fold in the presence of U937 cells . In contrast , IGF-1 did not further increase the expression of ORF65 protein in the presence of U937 cells . Both NAC and catalase inhibited the induction of ORF65 protein by proinflammatory and proangiogenic cytokines in the presence of U937 cells ( Figure 4E ) . Together , these results indicate that H2O2 mediates KSHV reactivation induced by proinflammatory and proangiogenic cytokines with and without co-culture with the monocytic cells . We next sought to inhibit KSHV lytic replication in vivo with H2O2 scavengers . To monitor KSHV lytic activity in vivo , we generated a recombinant KSHV Δ65Luc by replacing ORF65 with a firefly luciferase gene ( Figure S1A–C ) . Because ORF65 is a late viral lytic gene , detection of its expression would imply nearly complete KSHV lytic replication cycle . We reconstituted Δ65Luc in BCBL1 cells and generated a cell line harboring both wild type KSHV and Δ65Luc . As expected , BAC36-Δ65Luc cells expressed low level of ORF65 protein and luciferase , reflecting the spontaneous viral lytic replication in a small number of cells ( Figure S1D ) . Treatment with TPA increased the expression of ORF65 and luciferase proteins . The corresponding luciferase activity was also increased by 6 . 2-fold ( Figure S1E ) . These results indicate that the luciferase activity closely mimicked the expression of ORF65 protein , and thus can be used to monitor KSHV lytic activity . As expected , addition of NAC inhibited the luciferase activity in a dose-dependent manner ( Figure 5A ) . To examine KSHV lytic replication and determine the inhibitory effect of antioxidant NAC in vivo , we intraperitoneally inoculated NOD/SCID mice with BCBL1 cells harboring Δ65Luc . The mice were then fed daily with drinking water containing 5 mM NAC . All mice developed PEL at about five weeks post-inoculation as previously reported [48] . However , mice fed with NAC had an average 15 . 6-fold lower luciferase activities than those fed with drinking water alone ( Figure 5B–C ) . We also detected lower expression levels of ORF65 protein in lymphoma cells isolated from the NAC-treated mice than those from the control mice by Western-blotting ( Figure 5D ) . Immunohistochemical staining showed that the majority of the lymphoma cells from both groups were positive for LANA; however , cells from NAC-treated mice had significantly lower number of ORF65-positive cells than those from the control group ( Figure S2A–B ) . To determine the production of infectious virions by the lymphoma cells , we used cell-free supernatants from the pleural fluids of the mice to infect HUVEC and examined the presence of infectious virions by staining for ORF65 protein [49] . We observed abundant virus particles in many of the cells infected with supernatants from the control mice while those infected with supernatants from NAC-treated mice had almost no detectable virus particles ( Figure S2C ) . Consistent with these results , mice fed with NAC had an average 2 . 7-fold lower virus loads in the blood than the control group ( Figure 5E ) . As many as 50% of the mice from both groups also developed solid tumors . ORF65 protein was detected in over 10% of the tumor cells from the control solid tumors but was almost not detectable in the solid tumors from the NAC-treated mice ( Figure S2D ) . By examining the survival curves , we found that NAC-treated mice had an extended lifespan compared to the control group ( Figure 5F ) . At 12-week post-inoculation , 72 . 2% of the NAC-treated mice survived compared to only 45 . 4% in the untreated group ( P = 0 . 016 ) . Collectively , results from these in vivo experiments indicated that PEL induced in mice had active KSHV lytic replication , and antioxidant NAC effectively inhibited KSHV lytic replication , and extended the lifespan of the mice . H2O2 is known to activate multiple MAPK pathways [50] , which are required for KSHV lytic replication [17] , [20] . Similar to TPA , both exogenous and ATZ-induced endogenous H2O2 activated ERK1/2 , JNK , and p38 MAPK pathways , and increased the total and phosphorylated forms of their downstream target c-Jun in a dose-dependent manner in BCBL1 cells in addition to induction of RTA protein expression ( Figure 6A ) . Treatment with specific inhibitors of all three MAPK pathways effectively inhibited TPA activation of their respective MAPKs and c-Jun , as well as the induction of RTA protein ( Figure 6A ) . Importantly , these inhibitors also strongly inhibited H2O2 and ATZ induction of RTA expression and production of infectious virions ( Figure 6B–C ) . To further confirm the essential roles of MAPK pathways in H2O2-induced KSHV reactivation , we used dominant negative ( DN ) constructs to block these pathways . In 293T cells harboring BAC36 , treatment with TPA induced KSHV reactivation [51] . Treatment with H2O2 and ATZ induced the expression of RTA transcript ( Figure 6D ) . As expected , DN constructs of all three MAPK pathways and c-Jun effectively inhibited the induction of RTA by TPA and H2O2 ( Figure 6E ) . Together , these results indicate that H2O2 induction of KSHV reactivation is mediated by all three MAPK pathways .
We have shown that ROS H2O2 induces KSHV lytic replication through both paracrine and autocrine mechanisms , and in both PEL and endothelial cells . Because oxidative stress and chronic inflammation are characteristic features in patients of all clinical forms of KS , as well as PEL and MCD [6] , [30] , [31] , H2O2 could be an important physiological factor that triggers KSHV reactivation in these patients . Several other factors that induce KSHV lytic replication [21]–[29] also induce oxidative stress and inflammation [36] , [52]–[56] . Thus , it is likely that H2O2 mediates KSHV lytic replication induced by these factors . Indeed , our data show that KSHV reactivation induced by oxidative stress , hypoxia , and proinflammatory and proangiogenic cytokines depends on the induction of H2O2 . Importantly , co-culture of BCBL1 cells with monocytic U973 cells enhances KSHV reactivation induced by proinflammatory and proangiogenic cytokines , particularly IL-6 , TNF-α and IFN-γ , which are highly expressed in KS tumors [6] . Because of the abundance of proinflammatory cells , and proinflammatory and proangiogenic cytokines in KS tumors , these synergistic effects could further boost KSHV lytic replication . Thus , tumor microenvironments consisting of proinflammatory cells , proinflammatory and proangiogenic cytokines , and possibly extracellular matrix and other stromal cells are likely to have essential roles in inducing and mediating KSHV lytic replication in KS tumors , which should be further examined in more details . Mechanistically , we have shown that H2O2 induction of KSHV reactivation is mediated by ERK , JNK , and p38 MAPK pathways . Previous studies have shown that these pathways are required for KSHV infection and lytic replication [17]–[20] . Consistent with these observations , oxidative stress , hypoxia , and a number of proinflammatory and proangiogenic cytokines are known to induce MAPK pathways [57]–[65] . Based on our results , we propose a model in which KS tumors are initiated by either the homing of KSHV-infected cells , most likely progenitor endothelial cells or B-cells , from virus reservoirs to the affected sites or de novo infection by the newly produced virions , both of which are promoted by inflammation and oxidative stress [6] . Since KSHV de novo infection and lytic replication further promote inflammation and oxidative stress [6] , one can expect the establishment of a positive feedback loop once the cycle is initiated . If these inflammatory conditions are not appropriately contained , they can lead to the rapid progression of KS as in the case of untreated AIDS-KS . Therefore , both active inflammation and host control of viral replication are likely to determine the course of KS . It is interesting that only a subset of KSHV-infected cells undergo lytic replication in KS tumors or in cell culture induced for KSHV lytic replication ( Figure 1A and F ) . KSHV has evolved a complex mechanism consisting of multiple blocks to regulate its replication [66] . Whether a cell undergoes lytic replication is likely to depend on the extent of release of these blocks . While KSHV lytic replication induces inflammation and promotes the overall tumor growth through a paracrine mechanism , it is also detrimental to the lytic cells [6] . Thus , a fine balance of latent and lytic programs in KS tumors combined with active inflammation and oxidative stress in the tumor microenvironment is likely required for the development of advanced stage of KS . We have shown that H2O2 scavengers such as NAC can inhibit KSHV lytic replication in vitro and in a KSHV lymphoma animal model . Significantly , NAC extends the lifespan of the lymphoma-bearing mice . These results indicate that antioxidants and anti-inflammation drugs might be effective for inhibiting KSHV lytic replication , and thus could be promising preventive and therapeutic agents for KSHV-induced malignancies . Because many of these agents are affordable , their use is attractive , particularly in the African settings .
BCBL1 cells and BCBL1 cells carrying pHyer-cyto or BAC36 , a recombinant KSHV [45] , were cultured in RPMI 1640 supplemented with 10% fetal bovine serum ( FBS ) . Human embryonic kidney 293T cells , 293 cells and 293T cells carrying BAC36 were cultured in DMEM plus 10% FBS . H2O2 , ATZ , NaN3 , and antioxidants NAC , reduced L-glutathione , and bovine liver catalase were purchased from Sigma Life Science ( St . Louis , MO ) . The ERK inhibitor U0126 , p38 inhibitor SB203580 , and JNK inhibitor JNK inhibitor II were all purchased from Calbiochem ( Gibbstown , NJ ) . TPA was from Sigma . The p38 DN plasmid pcDNA3-p38/AF was provided by Jiahua Han at The Scripps Institute [67] . The JNK DN ( HA-JNK1 [APF] ) plasmid was from Lin Mantell at New York University School of Medicine [68] . The ERK DN ( pCEP4L-HA-ERK1K71R ) plasmid was from Melanie Cobb at the University of Texas Southwestern Medical Center [69] . The c-Jun DN plasmid pCMV-TAM67 was from Bradford W . Ozanne at Beatson Institute [70] . The pHyPer-cyto plasmid was purchased from Biocompare ( South San Francisco , CA ) . Transfection of 293T and BCBL1 cells was carried out using the Lipofectamine LTX Reagent from Invitrogen ( Carlsbad , CA ) . The lentiviruses expressing specific siRNA to human catalase and scrambled control were purchased from Santa Cruz ( Santa Cruz , CA ) . BCBL1-BAC36 cells stably expressing catalase-specific or scrambled siRNAs were obtained following lentiviral infection and puromycin selection according to the instructions of the manufacturer . A total of 5×106 BCBL1 cells cultured with or without ATZ or TPA for 12 h were harvested by brief centrifugation . The cell pellets were homogenized in 0 . 5 ml ice cold phosphate saline buffer at pH 7 . 4 containing 1 mM EDTA . After centrifugation at 10 , 000 g for 15 min at 4°C , the supernatants were collected and used for measuring catalase activity or intracellular H2O2 . Cellular catalase activity was determined with the OxiSelect Catalase Activity Assay Kit ( Cell Biolabs , San Diego , CA ) , and intracellular H2O2 determined with the Fluorescent Hydrogen Peroxide/Peroxidase Detection Kit from Cell Technology ( Columbia , MD ) . Alternatively , we tracked the intracellular H2O2 level with a H2O2 sensor by stable transfection of BCBL1 cells with a HyPer-cyto cassette consisting of a circularly permuted yellow fluorescent protein ( cpYFP ) under the control of the regulatory domain of the prokaryotic H2O2-sensing protein , OxyR [42] . To induce viral lytic replication , 2×106 BCBL1 cells were treated with H2O2 , ATZ or TPA alone , or in combination , in 10 ml RPMI 1640 medium containing 10% FBS for 48 h . The cells were then harvested , washed 1 time by centrifugation to eliminate the chemicals , and cultured in 5 ml fresh RPMI 1640 media with 10% FBS for three additional days . The supernatants were collected following centrifugation to eliminate cells and cell debris at 5 , 000 g for 15 min , and used for titration as previously described [44] . Relative virus titers were calculated based on the numbers of GFP-positive cells . Detection of virus particles in lymphoma supernatants from mice was carried out by staining for ORF65 with a monoclonal antibody following infection of HUVEC for 4 h as previously described [49] . To examine the effects of antioxidants on KSHV spontaneous lytic replication , BCBL1 cells were cultured in the presence NAC ( 400 µM ) or catalase ( 400 U/ml ) for 1 or 6 day , and cells were collected for Western-blotting analysis of ORF65 protein . For induction of lytic replication by hypoxia , BCBL1 cells at 1 . 5×107 cells/ml were treated with 10 mM NaN3 with or without antioxidants NAC ( 400 µM ) and catalase ( 400 U/ml ) for 90 min . Following washing by centrifugation , the cells were cultured for the specified lengths of time with or without NAC and catalase . For induction of lytic replication by cytokines , BCBL1 cells at 1 . 5×107 cells/ml were cultured in fresh medium with cytokines with or without antioxidants NAC ( 400 µM ) and catalase ( 400 U/ml ) for 72 h , and collected for Western-blotting . In parallel induction experiments , BCBL1 cells at 1 . 5×107 cells/ml in fresh medium were induced with cytokines with or without antioxidants by co-culture with U937 cells at 5×104 cells/ml pretreated with cytokines with or without antioxidants for 4 h . The following cytokines and concentrations were used: recombinant human VEGF at 200 ng/ml ( Lonza , Walkersville , MD ) , recombinant human long R IGF-1 at 200 ng/ml ( Lonza ) , recombinant human bFGF at 200 ng/ml ( Lonza ) , recombinant human EGF at 200 ng/ml ( Lonza ) , human IL-6 at 1 µg/ml ( R&D Systems , Minneapolis , MN ) , human TNF-α at 1 µg/ml ( R&D Systems ) and human IFN-γ at 4000 U/ml ( Sigma ) . To induce KSHV lytic replication in endothelial cells , latent KSHV-infected HUVEC obtained as previously described [44] were treated with H2O2 ( 150 µM ) for 24 h or 72 h , and cells were collected for RNA analysis or immunostaining for ORF65 protein , respectively . RNA was purified using a Total RNA Purification Kit ( Promega , Madison , WI ) . Total RNA ( 10 µg ) was reversely transcribed into first-strand cDNAs by using a Superscript III First-Strand cDNA Synthesis Kit ( Invitrogen ) . RT-qPCR was carried out in a DNA Engine Opticon 2 Continuous Fluorescence Detector ( Bio-Rad , Hercules , CA ) . Each sample was measured in triplicate . The expression level of each transcript ( mRNA ) was first normalized to β-actin mRNA as previously described [71] . The relative expression level of a transcript in the treated cells was compared to the untreated cells , and calculated as fold changes . Specific primers for all KSHV genes were previously described [71] . Primers for human catalase were: 5′aggactaccctctcatcccagttg3′ ( forward ) and 5′gggtcccaggcgatggcggtgag3′ ( reverse ) . KSHV lytic proteins ORF59 or ORF65 in BCBL1 cells were detected by IFA as previously described [51] . Expression of KSHV lytic proteins in lymphomas and solid tumors in mice were detected by immunohistochemistry . Briefly , cells from PEL induced in NOD/SCID mice were collected by centrifugation at 1 , 000 g for 5 min , fixed with formalin , and embedded in paraffin . Sections at 5 nm cut from the paraffin blocks were deparaffinized at 60°C , cleared , and rehydrated in xylene and graded alcohols . Antigen retrieval was done with citrate buffer at pH 6 for 20 min at 121°C in a pressure chamber . Sections were blocked successively with 3% H2O2 and bovine serum albumin buffer . Sections were then incubated with a monoclonal antibody to ORF65 or a mouse immunoglobulin fraction ( DAKO , Carpinteria , CA ) as a negative control for 1 h at 25°C . After three washes with PBS , the slides were further incubated with a secondary antibody conjugated to horseradish peroxidase ( DAKO ) for 15 min . The slides were then incubated with the diaminobenzidine substrate ( DAKO ) , counterstained with hematoxylin , and mounted for observation . Western-blotting was carried out as previously described [51] . The rabbit polyclonal antibodies to ERK1 , p-ERK ( Tyr 204 ) , JNK1 , p-JNK ( Thr 183/Tyr 185 ) , p38 , p-p38 ( Tyr 182 ) , c-Jun , and catalase were from Santa Cruz; a polyclonal antibody to p-c-Jun ( Ser73 ) was from Calbiochem; and a monoclonal antibody to β-tubulin was from Sigma . A rabbit polyclonal antibody to KSHV lytic protein RTA was a generous gift from Dr . Charles Wood at the University of Nebraska , Lincoln . 293 cells transfected with either the RTA promoter luciferase reporter plasmid or the latent LANA promoter ( LTd ) luciferase reporter plasmid using Lipofectamine-2000 Transfection Reagent ( Invitrogen ) for 24 h were treated with H2O2 ( 300 µM ) or TPA ( 20 ng/ml ) with or without antioxidants NAC ( 400 µM ) or catalase ( 400 U/ml ) for 12 h . Cells were collected and their luciferase activities determined as previously described [51] . Transfection efficiency was calibrated by co-transfection with the pSV-β-galactosidase construct ( Promega ) . Cell-free DNA was isolated from two drops of blood from each mouse collected by tail bleeding using the QiAamp DNA Blood Mini Kit ( Qiagen ) . KSHV DNA was detected by real-time PCR using vCyclin ( ORF72 ) primers and purified BAC36 as copy number control as previously described [71] . KSHV viral loads expressed as genome copies per ml of blood were calculated . PCR assay for human β-actin gene was also carried out for these DNA samples to monitor the absence of any contamination of human cells [71] . None of the samples had any detectable signal for human β-actin gene . A recombinant KSHV genome with the entire ORF65-coding frame deleted and replaced with the firefly luciferase gene was constructed using a “two-step” homologous recombination strategy as previously described ( Figure S1A ) [51] . Firstly , the firefly luciferase gene was amplified from the luciferase reporter plasmid pGL3-Basic Luciferase Reporter Vector ( Promega ) using primers 5′ttctcgagatggaagacgccaaaaacataaagaaaggcccg3′ ( luciferase forward ) and 5′ctcgagttaattaattacacggcgatctttccgcccttc3′ ( luciferase reverse ) . The Kanamycin resistance cassette ( KanR ) flanked by two LoxP sites was amplified from the transposon EZ-Tn5™ <Kan-2> ( Epicenter , Madison , WI ) using primers 5′tttttaattaagtgtaggctggagctgcttc3′ ( KanR forward ) and 5′ttttttaattaacatatgaatatcctccttag3′ ( KanR reverse ) . The two PCR products were ligated using a T4 DNA ligase ( New England Biolabs , Ipswich , MA ) . The resulting fragment was then used as a template to generate the KanR-Luc cassette by PCR amplification using primers 5′cttgtgactccacggttgtccaatcgttgcctatttctttttgccagagg tttttaattaagtgtaggctggagctgcttc3′ ( forward ) and 5′aggtgagagaccccgtgatccaggagcgactggatcatgactacgctcac ttctcgagatggaagacgccaaaaacataaagaaaggcccg3′ ( reverse ) . This PCR product , flanked by a 50 bp sequence from the immediate downstream region of ORF65 at its 5′end and a 50 bp sequence from the start codon ( ATG ) region of ORF65 at its 3′end , was electroporated into Escherichia coli strain DH10B containing recombinant KSHV BAC36 [45] . Upon homologous recombination , the KanR-Luc cassette was integrated into KSHV genome . The Kanamycin-resistant colonies , containing the mutant KSHV genome , named Δ65Kan-Luc , with ORF65 replaced with KanR-Luc cassette , were selected . To eliminate the KanR cassette , a Cre-expression plasmid pCre carrying a tetracycline resistant marker and a temperature ( 37°C ) -sensitive replication origin was electroporated into the selected bacteria . The expression of Cre protein led to the removal of KanR cassette by LoxP-mediated recombination . The resulting colonies containing the mutant KSHV genome , named Δ65Luc , with ORF65 replaced with firefly luciferase gene , which was Tetracycline resistant but Kanamycin sensitive , were selected . The pCre plasmid was removed by culturing the bacteria at 37°C . The mutant KSHV genomes were purified using the Large Construct DNA Purification Kit ( Qiagen , Valencia , CA ) , verified for integrity by restriction digestion and PCR amplification of specific genes ( Figure S1B–C ) , and electroporated into BCBL1 cells as previously described [45] . Following Hygromycin selection , a cell line harboring both the wild type KSHV genome and Δ65Luc was established . Expression of luciferase by this cell line was confirmed by Western-blotting using a luciferase-specific antibody ( Figure S1D ) , and by measuring luciferase activity using the firefly luciferase substrate ( Promega ) and a Veritas Microplate Luminometer ( Turner BioSystems , Sunnyvale , CA ) ( Figure S1E ) . This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved by the Institutional Animal Care and Use Committee ( IACUC ) at the University of Texas Health Science Center at San Antonio ( Animal Welfare Assurance Number: A3345-01 ) . All surgery was performed under sodium pentobarbital anesthesia , and all efforts were made to minimize suffering . Male NOD/SCID mice at 6 weeks from Jackson Laboratories ( Bar Harbor , ME ) were intraperitoneally inoculated with BCBL1 cells carrying Δ65Luc at 5×106 per mouse . One week after inoculation , mice ( n = 36 ) were given drinking water supplemented with 5 mM of NAC while the control mice ( n = 44 ) were given drinking water without the antioxidant . Mice were monitored daily for “PEL-like” symptoms . Luciferase activity and GFP intensity were measured five weeks after injection using a Xenogen IVIS 200 Imaging System ( Xenogen , Alameda , CA ) . Mice were monitored daily , and terminated when they became immobile . Lymphoma cells , supernatants and solid tumors were collected and analyzed as indicated . | Kaposi's sarcoma-associated herpesvirus ( KSHV ) is the etiologic agent of all clinical forms of Kaposi's sarcoma ( KS ) and several other malignancies . The life cycle of KSHV consists of latent and lytic phases . While establishment of viral latency is essential for KSHV to evade host immune surveillances , viral lytic replication promotes KSHV-induced malignancies . In this study , we show that the reactive oxygen species ( ROS ) hydrogen peroxide ( H2O2 ) induces KSHV reactivation from latency . Furthermore , induction of KSHV reactivation by oxidative stress , hypoxia , and proinflammatory and proangiogenic cytokines , which are physiological hallmarks in all clinical forms of KS patients , is mediated by H2O2 . Significantly , antioxidants inhibit H2O2-induced KSHV lytic replication in culture and in a mouse model of KSHV-induced lymphoma . These results show that ROS is likely an important physiological cue that triggers KSHV replication . The discovery of this novel mechanism of KSHV reactivation indicates that antioxidants and anti-inflammation drugs might be promising preventive and therapeutic agents for effectively targeting KSHV replication and KSHV-related malignancies . | [
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] | 2011 | Reactive Oxygen Species Hydrogen Peroxide Mediates Kaposi's
Sarcoma-Associated Herpesvirus Reactivation from Latency |
Genome-scale metabolic reconstructions are typically validated by comparing in silico growth predictions across different mutants utilizing different carbon sources with in vivo growth data . This comparison results in two types of model-prediction inconsistencies; either the model predicts growth when no growth is observed in the experiment ( GNG inconsistencies ) or the model predicts no growth when the experiment reveals growth ( NGG inconsistencies ) . Here we propose an optimization-based framework , GrowMatch , to automatically reconcile GNG predictions ( by suppressing functionalities in the model ) and NGG predictions ( by adding functionalities to the model ) . We use GrowMatch to resolve inconsistencies between the predictions of the latest in silico Escherichia coli ( iAF1260 ) model and the in vivo data available in the Keio collection and improved the consistency of in silico with in vivo predictions from 90 . 6% to 96 . 7% . Specifically , we were able to suggest consistency-restoring hypotheses for 56/72 GNG mutants and 13/38 NGG mutants . GrowMatch resolved 18 GNG inconsistencies by suggesting suppressions in the mutant metabolic networks . Fifteen inconsistencies were resolved by suppressing isozymes in the metabolic network , and the remaining 23 GNG mutants corresponding to blocked genes were resolved by suitably modifying the biomass equation of iAF1260 . GrowMatch suggested consistency-restoring hypotheses for five NGG mutants by adding functionalities to the model whereas the remaining eight inconsistencies were resolved by pinpointing possible alternate genes that carry out the function of the deleted gene . For many cases , GrowMatch identified fairly nonintuitive model modification hypotheses that would have been difficult to pinpoint through inspection alone . In addition , GrowMatch can be used during the construction phase of new , as opposed to existing , genome-scale metabolic models , leading to more expedient and accurate reconstructions .
There are currently 700 completely sequenced genomes along with extensive compilations of data [1] assembled after decades of experimental studies on the metabolic behavior of organisms . This has enabled the reconstruction of stoichiometric models of metabolism for about twenty [2] organisms . This process began with the metabolic characterization of prokaryotic organisms such as Escherichia coli [1] , moved to the reconstruction of eukaryotic organisms such as Saccharomyces cerevisiae [3] and , more recently , to the first reconstruction of the more complex Homo Sapiens metabolic map [4] . The completeness and accuracy of microbial metabolic reconstructions are typically assessed by comparing the model growth predictions ( i . e . , presence or absence ) of single and/or multiple knockout mutants for a variety of substrates against experimental data [5]–[7] . As shown in Figure 1 , these comparisons lead to four possible outcomes: GG when both model and experimental point at growth , GNG when the model predicts growth but the experiment does not , NGG when the model fails to predict the experimentally observed growth , and finally NGNG when both model and experiment show no growth . Cases GG and NGNG are indicative of agreement between model predictions and experimental data whereas cases GNG and NGG signify disagreement . Specifically , in GNG cases the model over-predicts the metabolic capabilities of the organism due to the use of reactions that are absent in vivo , down-regulation or inhibition of genes/enzymes under the experimental conditions , or absence of biomass constituents from the in silico biomass description . Conversely in NGG cases , the model under-predicts the metabolic capabilities of the organism due to the absence of relevant functionalities/reactions in the model . In this study , we introduce optimization-based techniques to systematically suggest modifications ( conditionally add/delete reactions , restrict/expand directionalities or add/suppress uptake/secretion mechanisms for NGG/GNG inconsistencies ) in genome-scale metabolic reconstructions in order to reconcile experimental and computational growth predictions across different mutants . The proposed method makes use of gene essentiality data sets currently available for many microorganisms [8]–[17] . For example , the Keio collection [17] catalogues the optical density ( OD ) , under different substrate conditions , of the single gene deletion mutants of all 3 , 985 non essential genes in the E . coli K-12 BW25113 . Several studies are already available that use gene essentiality data available at the Keio database and other sources to suggest targeted improvements in existing metabolic reconstructions [3] , [5] , [7] , [18]–[20] . As seen in Figure 2 , in these studies , in silico models of increasing complexity were successively contrasted against in vivo datasets of differing size to correct the predictive capabilities of the models . Recently , Joyce et al . [7] used the Keio mutant collection [17] to pinpoint conditionally essential genes in vivo in a glycerol supplemented minimal medium and then compared them with the corresponding in silico predictions to suggest improvements in the model [7] . In another study , Harrison and co-workers identified computationally predicted synthetic lethal gene deletion pairs in yeast and then proceeded to test the growth characteristics of these double deletion mutants in vivo [21] . While these studies have successfully used gene deletion datasets in many different contexts to pinpoint gaps in in silico models , the key step of resolving these gaps was performed manually . The need to develop automated procedures to improve the accuracy of existing metabolic reconstructions has been recognized and has led to the development of a number of computational procedures . To this end , Reed et al . [22] recently described a systems based approach to modify an existing genome-scale metabolic reconstruction of E . coli [1] by adding new reactions that ensured growth in NGG cases by enabling in silico growth consistent with in vivo data across various carbon/nitrogen substrates . Alternatively , methods to identify and fill gaps in metabolic models based on connectivity information have also been described and applied to the genome scale models of E . coli and S . cerevisiae [23] . These studies represent only the beginning of efforts geared towards methods that automatically resolve network inconsistencies using a variety of metrics [22]–[28] ranging from unreachable metabolites , DNA microarray data and gene essentiality data . It is becoming increasingly clear that it is necessary to bring to bear all types of experimental data to achieve the aim of a high quality metabolic model . In this paper , we supplement previous efforts [23] on identifying ( i . e . , GapFind ) and filling ( i . e . , GapFill ) gaps in metabolic reconstructions with an automated procedure for resolving growth prediction inconsistencies while minimally perturbing the original model . Briefly , we resolve GNG inconsistencies by converting them into NGNG one-by-one by identifying the minimal set of restrictions that need to be imposed ( i . e . , through reaction or transport mechanism suppression or reaction reversibility prohibition ) on the model describing the GNG mutant so that biomass formation is negated ( or reduced below a pre-specified cutoff ) . If a particular identified restriction does not invalidate any correct GG predictions then we refer to it as global suppression meaning that it can be imposed universally for all experimental perturbations ( e . g . , single gene deletion mutants and wild type ) . Alternatively , if an identified restriction clashes with one or more GG predictions then it is referred to as a conditional suppression meaning that it is imposed only in the mutant strain associated with the GNG prediction for which it is correcting . Similarly , NGG inconsistencies are corrected one-by-one to GG by identifying the minimal set of model modifications ( i . e . , through reaction or transport mechanism addition or reaction reversibility allowance ) that enable biomass formation ( above a pre-specified cutoff ) . If none of these modifications affect any of the consistent NGNG cases , we refer to them as global additions; otherwise , we refer to them as conditional additions . In the next section we discuss the results obtained by applying GrowMatch to the most recent genome-scale model of E . coli , iAF1260 [20] . We note here that we can also use GrowMatch to reconcile growth prediction inconsistencies across different substrates . The E . coli reconstruction was chosen as the focus of this study to benchmark the ability of GrowMatch to identify model corrections even for a very well curated model . Using GrowMatch , we improved the growth prediction consistency of the iAF1260 model with the data available at the Keio database from 90 . 6% to 94 . 6% when considering only globally valid corrections and to 96 . 7% when additionally considering conditional corrections .
Figure 4A shows the distribution across pathways of the deleted genes in GNG single-gene deletion mutants . As shown , the majority of these genes are in tRNA charging and cofactor biosynthesis pathways . The presence of genes associated with GNG mutants in these pathways indicates that alternative biomass production mechanisms are implied in silico that are unavailable in vivo . Figure 5 groups these deleted genes into three categories depending on the effect of their deletion on the metabolic network . The first group ( i . e . , 22 GNG mutants ) accounts for deleted genes whose gene-products are isozymes for reactions in the metabolic network . The presence of isozymes implies that the gene deletions do not affect the model predicted flux distributions even though in vivo these deletions are fatal . In these cases , we hypothesize that the in silico growth can be negated by simply deactivating the reaction that is catalyzed by the corresponding isozymes . In fifteen out of the twenty-two cases , the suppression of the isozymes ( and the corresponding catalyzed reactions ) negates growth thus converting the GNG mutants into NGNG mutants . It appears that in vivo , under the specific experimental conditions ( aerobic glucose ) , the alternative isozyme does not exhibit sufficient activity to restore the activity of the deleted isozyme . Note that all these reaction suppressions are conditional suppressions as the reactions are essential for growth in all GG mutants . Table 2 summarizes the identified conditional suppressions . It should be noted here that these generated hypotheses may not be the only way to resolve GNG mutants associated with isozymes . We define complementary ( non-complementary ) isozymes as pairs of isozymes that satisfy the following two conditions: ( a ) at least one of the isozymes is encoded by a gene associated with a GG ( GNG ) mutant and ( b ) the isozymes catalyze an essential reaction ( under aerobic glucose conditions ) . We checked the sequence similarity of complementary and non-complementary isozymes using the BlastP algorithm . The results are available in Table S3 Interestingly , we found that complementary isozymes have , on average , greater sequence similarity ( average BLAST score ∼148 bits ) than non-complementary isozymes ( average BLAST score ∼69 bits ) . To see if the genes that code for non-complementary isozymes are inactive under aerobic minimal glucose , we checked their expression levels . Specifically , we examined the relative expression levels for these pairs of genes ( deleted gene and gene associated with non-complementing isozyme ) available at Covert et al . , [19] . For cases with more than one non-complementing isozyme , we checked expression data of all genes encoding non-complementing isozymes . We excluded from consideration two pairs of genes ( [thrA , metL] and [mrdA , ftsI] ) as all these genes are associated with GNG mutants . The 95% confidence intervals ( assuming a normal distribution ) for this expression data are tabulated in Table S3 . In eight of the eleven cases , the deleted gene is expressed at least twice as much ( using average expression as a metric ) as the gene ( s ) associated with the non-complementing isozyme ( s ) ( Table S3 ) . This suggests that , in these eight cases , the genes as are expressed in very low amounts ( relative to the deleted gene ) in aerobic glucose conditions which indicates that the corresponding isozymes may not be at sufficient levels to insure compensation . Figure 6 shows an example of GNG mutants associated with isozymes . Biomass formation for both single gene-deletion mutants , ΔmetL and ΔthrA , can be eliminated by suppressing any of the two associated essential reactions , aspartate kinase ( ASPK ) or homoserine dehydrogenase ( HSDy ) ( see Table 2 ) . Therefore , whenever one of the genes is deleted the other gene appears to be unable to complement the mutation and activate the two essential reactions . This implies that , as identified by GrowMatch , HSDy is inactive in both ΔmetL and ΔthrA mutants thus preventing biomass formation . Notably , HSDy is a conditional suppression as it is essential for growth in the wild-type metabolic network . The deleted genes in the second group ( i . e . , 26 GNG mutants ) encode for enzymes that catalyze blocked reactions in the metabolic network . Blocked reactions are defined as reactions that cannot carry any flux under given substrate conditions [29] . Twenty-four of these mutants correspond to reactions that are unconditionally blocked ( i . e . , for all possible substrate choices ) . One such example ( reaction A ) is shown in Figure 5 . The remaining two mutants ( ΔubiG , ΔuxaB ) correspond to reactions that are conditionally blocked for a glucose minimal medium ( e . g . , reaction B in Figure 5 ) . GrowMatch resolved 23 of these 26 inconsistencies by suitably adding biomass components to the biomass equation . Specifically , consistency to six GNG mutants ( ΔbioB , ΔbioD , ΔbioF , ΔcaiT , ΔalsB , Δint ) can be restored by adding components produced by the corresponding reactions to the biomass equation ( see Table S4 ) . Modifications that restore consistency to ΔbioB , ΔbioD , ΔbioF are by definition conditional modifications since they affect the prediction for GG mutant ΔbioA . However , we note here that the in vivo OD for ΔbioA is very close to the cutoff ( i . e . , of 0 . 116 ) and it is likely that these hypotheses can be implemented as global modifications . The remaining mutants ( ΔcaiT , ΔalsB , Δint ) are resolved by making global modifications . Also , seventeen of these 26 GNG mutants correspond to reactions involved in tRNA charging reactions . GrowMatch converted these seventeen GNG mutants into NGNG mutants by modifying the biomass equation by explicitly including the charged and the uncharged tRNA molecules in place of the amino acids . For example , in the GNG mutant ΔleuS , the deleted reaction LEUTRS ( Equation: atp+leu-L+trnaleu→amp+leutrna+ppi ) is blocked . This reaction is unblocked by including leutrna ( charged tRNA ) and trnaleu ( uncharged tRNA ) as a reactant and product in the biomass equation , respectively . This restores flux through the reaction LEUTRS and converts ΔleuS into an NGNG mutant . We note that the consistency of these seventeen GNG mutants is restored by making global modifications , as adding these components to biomass does not affect any correct model predictions . For the remaining three GNG mutants , we first attempted to restore flow connectivity using ( GapFill ) before using GrowMatch . However , GapFill was unable to restore flow through any of these reactions by filling functionalities using reactions from the multi-organism databases of MetaCyc [30] and KEGG [31] ( see Materials and Methods ) thus preventing the use of GrowMatch . The third group of GNG mutants involves deleted genes that do not encode isozymes and are not associated exclusively with blocked reactions . We used GrowMatch to identify reaction suppressions that drop the biomass production below the predefined growth cutoff . We allowed for up to three simultaneous suppressions per GNG mutant to ensure parsimony of correction and maintain computational tractability . As summarized in Table 3 , we were able to restore consistency for eighteen of the 24 mutants . Here , ten of the identified sets of suppressions ( CBMKr and OXAMTC , PPM , R15BPK , R1PK , GTHOr , GRXR . HXAND , XPPT , NACODA , R15BK ) are global suppressions , as they did not prohibit growth in any GG mutants or wild-type strain while the remaining suppressions are conditional . As shown in Table 3 , thirteen of the inconsistencies are resolved by suppressing one additional reaction whereas five ( i . e . , ΔcarA , ΔcarB , ΔcydC , ΔptsI , ΔpyrH ) are resolved by suppressing two additional reactions in the network . Also , for ten of these GNG mutants , GrowMatch identified alternative suppression candidates ( see Table 3 ) . We tested the sensitivity of the identified suppressions to the growth medium by changing the medium from minimal glucose to minimal glycerol . Based on the data available in [7] , all the mutants in Table 3 maintain their GNG characterization when the cell grows on minimal glycerol . As shown in Table 3 , many of the identified conditional suppressions ( shown in bold ) needed to correct GNG predictions remain the same upon the medium change alluding to conserved regulation even under different substrates . Figure 7A shows how GrowMatch restores consistency to three GNG mutants , ΔglyA , ΔserA and ΔserB . As shown , the gene products are involved in serine and 5 , 10-methylenetetrahydrofolate ( mlthf ) biosynthesis , both of which are essential metabolites for biomass formation . GrowMatch restores consistency in ΔglyA either by suppressing serine production ( by deleting reactions associated with serA , serB or serC ) or alternatively by disabling mlthf production ( by suppressing the Glycine Cleavage System ) . In ΔserA and ΔserB , GrowMatch suggests blocking serine production by disallowing the reversibility of glycine hydroxymethyltransferase ( glyA ) ( Table 3 ) . Alternatively , as in ΔglyA , suppressing the Glycine Cleavage System prevents mlthf formation and thereby prohibits biomass formation . All three GNG mutants are resolved by suppressing reactions that are in the same linear pathway as the deleted reaction which is in line with evidence that genes catalyzing linear pathways of reactions tend to be co-expressed [32] . Figure 7B shows the restoration of GNG mutants , ΔcarA and ΔcarB . These genes encode for a multi-domain protein that catalyzes the reaction carbamoyl phosphate synthase ( CBPS ) ( glutamine-hydrolysing ) , which is involved in the production of carbamoyl-phosphate . As shown in Figure 7B , carbamoyl phosphate ( CBP ) production is required for the downstream production of the biomass precursors such as L-arginine and pyrimidine ribonucleotides . GrowMatch restores consistency to these two mutants by prohibiting formation of CBP by suppressing the reactions OXAMTC and CBMKr in these mutants . In another example , GrowMatch restores consistency to the GNG mutant ΔcydC by suppressing GLYAT and GLYCL ( Glycine Cleavage System ) to prohibit biomass formation ( Table 3 ) . Note that these are conditional suppressions valid only in ΔcydC . Suppressing these reactions ensures that the biomass precursor metabolites , siroheme ( shem ) and S-Adenosyl-L-methionine ( amet ) , are not produced in this mutant network . Closer investigation reveals that the reaction uroporphyrinogen methyltransferase , which is a reaction that consumes amet and is involved in the siroheme biosynthesis pathway , cannot carry any flux when these suppressions are carried out in ΔcydC . This results in no production of these biomass precursors resulting in zero biomass formation in silico . All the examples highlighted above lead to model modification that would have been difficult to come up with by inspection without the aid the alternatives provided by GrowMatch . Restoring growth for the NGG predictions requires that production routes be established in the metabolic model for all 63 precursor metabolites to biomass . Figure 4B shows the location of the deleted genes across all NGG mutants . A majority of these genes are located in cofactor , cell envelope and amino acid biosynthesis pathways . As a pre-processing step , we first check if there are alternative genes that carry out the deleted function by conducting a self-BLAST search of the deleted gene against the E . coli K12 genome . These results are summarized in Table S5 available in the supplementary material . As seen , eight of these genes have a high sequence similarity ( i . e . , a protein-protein BLAST expectation value of less than 10−13 ) with other open reading frames in E . coli . For example , the gene argD whose deletion results in a NGG mutant , shares high sequence similarity with astC ( protein-protein BLAST E-value = 5·10−146 ) . Also , the gene aspC whose deletion results in a NGG mutant , shares a high sequence similarity ( protein-protein BLAST E-value = 4·10−94 ) with tyrB , which transcribes to form a subunit of tyrosine aminotransferase . Hence , it is possible that it encodes for the activities of these genes in the respective NGG mutants in vivo thereby preserving growth . We next use GrowMatch to resolve the NGG inconsistencies by adding pathways using one or more of the three mechanisms discussed previously . GrowMatch identified consistency-restoring hypotheses for 5/38 mutants . Interestingly , one NGG mutant ΔluxS , had alternative means of consistency restoration , one by adding reactions and the other by allowing the secretion of a metabolite . Three ( including ΔluxS ) were resolved by adding reactions from KEGG and MetaCyc [30] , [31] and three ( including ΔluxS ) by allowing the secretion of metabolites from the cell into the extracellular space . None of the inconsistencies could be resolved by modifying the directionality of existing reactions in the model . The first three NGG resolutions were corrected by adding single reactions from the multi-organism databases of KEGG and MetaCyc . Specifically , ΔluxS is corrected by adding the reaction putative adenosylhomocysteinase ( from the organism Rhizobium leguminosarum ) and Δasd is corrected by adding the reaction catalyzed by Protein APA1 ( from the organism Saccaromyces cerevisiae ) . We note , however , that proteins catalyzing these reactions have low sequence similarity with the E . coli K12 genome ( BLAST score = 28 . 1 bits with gene product of ybcK and 29 . 6 bits with gene product of yshA respectively ) and that the validity of these hypotheses , like all those generated by GrowMatch , must be explored experimentally . Consistency in one NGG mutant ( ΔcysN ) is achieved by adding the reaction catalyzed by sulfate adenylyltransferase , the activity of which is documented in EcoCyc but was not included in the iAF1260 reconstruction [20] , [33] . Note that adding these reactions does not disrupt any of the consistent NGNG mutants , thus these additions are referred to as global additions . The other three resolutions ( see Table 4 ) are all achieved by allowing the secretion of metabolites from the cytosol into the periplasm and out into the extracellular space . As shown , the NGG mutant ΔfolD is resolved by allowing the secretion of 3 , 4-dihydroxy-2-butanone 4-phosphate that serves as the biosynthetic precursor for the xylene ring of riboflavin . Glycolaldehyde and S-ribosyl-L-homocysteine are reactants in the reactions catalyzed by aldA and luxS respectively . To resolve the NGG mutants ΔaldA and ΔluxS , GrowMatch hypothesizes the presence of secretion mechanisms ( currently absent from the model ) for glycolaldehyde and S-ribosyl-L-homocysteine , respectively ( Table 4 ) . Interestingly , there is evidence that suggests that homocysteines are toxic for E . coli [34] . Also , as the flux value in the added secretion reaction for glycolaldehyde is very low ( i . e . , 2 . 6×10−4 mmol/gDW hr ) , it is possible that its toxic accumulation is prevented either by the ( possibly non-specific ) activity of a transporter that is already present or by its diffusion out of the cell .
Here we have developed an automated procedure , GrowMatch , to resolve in silico/in vivo growth prediction inconsistencies in single gene-deletion mutants . In GNG mutants , GrowMatch restores consistency by suppressing reactions to prohibit growth . In NGG mutants , GrowMatch restores consistency by adding growth-enabling pathways . We demonstrated this procedure by reconciling the growth prediction inconsistencies between the most recent in silico model of E . coli , iAF1260 [20] , with the in vivo growth data available at the Keio mutant collection [17] . Using GrowMatch , we suggested consistency-restoring hypotheses for 56/72 GNG mutants and 13/38 NGG mutants . The inconsistencies in 26 GNG mutants were resolved by carrying out conditional suppressions . In the case of NGG mutants , all the suggested modifications were global modifications . By carrying out only global modifications in wild-type E . coli , we were able to improve the consistency from 90 . 6% to 94 . 6% . In addition , by carrying out conditional modifications in the specific mutants , we further improve the overall consistency in growth predictions to 96 . 7% . Moreover , specificity has been recently proposed to be an important measure to determine the effectiveness of in silico simulations as a screen in computational gene essentiality predictions [35] . Notably , we improved the specificity from 67 . 6% to 79 . 3% ( considering only global corrections ) using GrowMatch . This value further improves to 92 . 8% when we also consider conditional corrections . GrowMatch resolved eighteen GNG inconsistencies by suggesting suppressions in the mutant metabolic networks whereas fifteen inconsistencies were resolved by suppressing isozymes in the metabolic network . The remaining 23 inconsistencies corresponding to blocked genes were repaired by simply adding component ( s ) of the associated blocked reactions to the biomass equation ( Table S4 ) . GrowMatch suggested consistency-restoring hypotheses for five of the NGG mutants by adding functionalities to the model whereas eight inconsistencies were resolved by pinpointing alternate genes that have a high likelihood of carrying out the deleted function . Note that one NGG mutant ( ΔluxS ) had alternative means of consistency restoration . In this study , we were able to pinpoint missing functionalities that may have been overlooked during model reconstruction . In one such example , were able to resolve a NGG mutant by adding a reaction ( i . e . , sulfate adenylyltransferase ) with documented evidence of its being present in E . coli but absent in the in silico model iAF1260 [20] . Furthermore , when checking for alternative genes that restore consistency to NGG mutants , we identified possible alternative activities for aldA and epd that were not associated with them in the iAF1260 model ( succinate semialdehyde dehydrogenase and glyceraldehyde-3-phosphate dehydrogenase , respectively ) . GrowMatch also resolved two NGG mutants by indirectly preventing the toxic accumulation of metabolites . Surprisingly , in the case of NGG mutants , none of the resolutions were achieved by allowing the reversibility of irreversible reactions in the model . This result is in contrast to previous results in which a large proportion of connectivity problems in the previous version of the E . coli genome-scale model were resolved by expanding reversibility of reactions in the model [23] . This finding may be due to the increased accuracy in the characterization of reversible reactions in the latest E . coli model [20] brought about by making use of ΔG values during the reconstruction process . In line with recent explanations for GNG inconsistencies in in silico models [35] , we find that about 33% of the GNG mutants correspond to genes associated with blocked reactions in the metabolic network . Using GapFill , we were unable to identify any flow restoring hypotheses for blocked reactions corresponding to three NGG mutants using reactions from the multi-organism databases of MetaCyc and KEGG . Also , these databases of reactions were also unable to contribute growth-enabling functionalities in 25 NGG mutants , which is likely due to the recent systematic reconciliation of the latest reconstruction of E . coli with data available in the MetaCyc and EcoCyc databases [30] , [33] . This motivates the need to further expand the size of catalogued functionalities ( e . g . , the increase of experimentally determined enzyme functionalities ) , and also to supplement these reaction compilations with hypothetical reactions that will serve as missing links to bridge pathway gaps . There is already a large body of research focusing on deriving hypothetical reactions by iteratively changing the substrate specificity or cofactor dependence of well-characterized enzymes [36]–[40] . It is important to note that GrowMatch makes use of parsimony criteria to prioritize alternative model correcting hypotheses . Therefore , biologically relevant hypotheses that involve more than the selected maximum allowed limit of model modifications will be missed . Also , using alternate cellular objectives such as MOMA [41] or ROOM [42] instead of maximizing biomass as the objective function may help correct some GNG mutants into NGNG mutants . A recent study by Motter et al . , [43] addresses this concern and defines the corresponding genes as suboptimally essential genes . It would be worthwhile to explore whether , in addition to model modifications , if more elaborate ( re ) definitions of objective functions [44] may be needed to improve consistency with experimental data . Furthermore , GrowMatch can also be used to reconcile growth prediction inconsistencies across various substrates . To this end , Biolog data [20] for substrate utilization ( e . g . , carbon , nitrogen , phosphorous and sulphur sources ) can be used to propose model modifications that will ensure in silico growth prediction consistency with the available data . In summary , we believe that GrowMatch , in conjunction with GapFill , are useful model-refinement tools during the reconstruction of new metabolic models or testing/curation of existing ones . In addition to the use of GrowMatch to restore growth inconsistencies for the latest E . coli model presented here , our group has recently used it ( Suthers 2008 , accepted ) during the construction phase of the genome-scale metabolic model of Mycoplasma genitalium iPS189 .
First , we define the sets , parameters and variables that are common to the mathematical procedures formulated to resolve NGG and GNG inconsistencies . To this end , we define the index sets , {i|i = 1 , 2… M} , {j|j = 1 , 2… N} and {k|k = 1 , 2… K} that span the M metabolites , N reactions and K genes , respectively present in the metabolic network . Furthermore , we define the index set {l|l = 1 , 2… L} to represent the L in vivo experiments under consideration . Set KOl is defined to include genes that are knocked out in experiment l . We define a set Model to include all reactions in the existing genome-scale metabolic reconstruction . We maximize the formation of biomass subject to the available substrate feed and mass balance constraints implied by the stoichiometric model . . The in silico predictions are then compared with in vivo data . Sij is the stoichiometric coefficient of metabolite i in reaction j and parameters , link reactions j to genes k as follows: These definitions imply that if there exists two isozymes k1 and k2 for reaction j then whereas . Alternatively , if the enzyme catalyzing reaction j is multimeric requiring both genes k1 and k2 then whereas . Upper and lower bounds , UBj and LBj , were chosen not to exclude any physiologically relevant metabolic flux values . The upper bound for all reactions was set to 1 , 000 . Unless specified otherwise , the lower bound was set equal to zero for irreversible reactions and to −1 , 000 for reversible reactions . The flux in reaction j is denoted by variable vj and is restricted to vary between lower and upper bounds LBj and UBj , respectively . Using these definitions , we will now discuss the mathematical procedures developed to resolve GNG and NGG inconsistencies . A GNG single gene deletion mutant occurs when the model predicts growth whereas no growth is observed in vivo . This could be due to the erroneous presence in the model of pathways that produce biomass precursor metabolites . The aim here is to identify the minimum number of suppressions that need to be imposed for a given experiment l* corresponding to a GNG mutant to ensure that the maximum biomass formation is zero . These suppressions are carried out by either ( a ) restricting flux in transport/ intracellular reactions or ( b ) restricting the reversibility of reactions defined as reversible in the model . The description of these suppressions requires the definition of the binary variable yj to pinpoint them in the network . The suppressions required to ensure that the maximum biomass formation is below the imposed cut-off for a GNG mutant corresponding to in vivo experiment l* are identified by solving the following bilevel optimization problem GrowMatch: The aim of GrowMatch is to identify the minimal number of reaction suppressions needed to zero the maximum biomass formation . We do this by ensuring that there is no biomass formation even when fluxes in the network are systematically re-apportioned so as biomass formation is maximized . This leads to a min-max formulation . Specifically , the inner optimization problem identifies the maximum possible amount of biomass formation by redirecting metabolic fluxes subject to stoichiometry , uptake and ATP maintenance . The outer optimization problem minimizes biomass formation by choosing a pre-specified number n* of reactions in the network to suppress . A zero objective function value implies that the n* selected reaction suppressions ( i . e . , yj = 0 ) successfully prevent the network from forming biomass . This converts the GNG occurrence for in vivo experiment l* into NGNG restoring consistency of prediction . Alternative ways of restoring prediction consistency can be obtained by imposing successive integer cuts [45] to exclude previously identified solutions until all possible feasible solutions are exhausted . Reaction suppressions that do not inadvertently affect biomass formation in any of consistent GG prediction are referred to as global suppressions . On the other hand , if any of these suppressions restrict biomass production in any of the GG mutants , they are referred to as conditional suppressions . The identified set of suppressions ( including alternative ones ) is finally tested by contrasting them against literature evidence regarding the presence or absence of activity of the suppressed reaction under the experimental conditions . For GNG mutants associated with genes encoding isozymes , we check if simply deleting the associated reaction prohibits in silico growth thereby restoring consistency to the mutant . For GNG mutants associated with blocked genes , we check if adding a component from the corresponding reaction to the biomass equation converts it into an NGNG mutant . NGG mutants are characterized by the lack of growth in silico despite growth in vivo . This means that at least one precursor metabolite in the biomass equation cannot be produced . The aim is to modify the existing genome-scale model by adding pathways so as to restore biomass production that may achieve this . To this end , we first construct a database of reactions consisting of ( a ) reactions from an external database of reactions , ( b ) irreversible reactions from the original genome-scale model with their directionalities reversed , and ( c ) transport reactions that enable secretion pathways for metabolites . We define the set Database to represent the reactions that populate this database . For the external databases of reactions , we use the multi-organism databases , MetaCyc [46] and KEGG [47] , as sources of non-native functionalities . We attempt to resolve inconsistencies by adding reactions from these databases sequentially since we were unable to integrate them into a single database due to their different naming conventions . The following binary variables are defined to describe the addition of to the model . Based on these definitions , we next identify the minimal number of modifications required to correct a single NGG mutant corresponding to the in vivo experiment l* using the following optimization formulation GrowMatch: In GrowMatch , the objective function minimizes the number of modifications ( addition of reactions or activation of secretion of metabolites ) in the metabolic model . The first constraint enforces zero flux through reactions that are rendered absent through the elimination of the genes that are knocked out in experiment l* . The next constraint imposes stoichiometric balance on all metabolites in the model . The requirement of meeting a minimum amount of biomass , quantified by parameter , to ensure growth is imposed in the next constraint while energy requirements and uptake restrictions are imposed in the next two constraints . The final constraint ensures that if yj = 1 for a reaction j from the database , then there is a non-zero flux through it . The optimal solution to GrowMatch identifies the reactions that need to be added from the database and/or the metabolites that need to be secreted from the metabolic network to ensure a minimum necessary biomass production in the NGG mutant . As in the case of GNG mutants , GrowMatch can be used to identify exhaustively all sets of reactions that need to be added to resolve a particular NGG mutant using integer cuts . We test the hypotheses generated to resolve the NGG mutant using the following two criteria . For reactions added from the database , we check the two-way protein-protein BLAST expectation value between the enzyme that catalyzes that reaction and the genome of interest ( in this case E . coli ) . For irreversible reactions selected to be made reversible , we query for such evidence in the literature and also estimate the ΔG values [48] whenever available for the biotransformation in question . Finally , for secretion pathways , we query the TransportDB database [49] . A similar set of criteria were followed before in GapFill [23] . In our simulations , we set the glucose uptake rate to 10 mmol/gDW hr , ATP maintenance to 8 . 39 mmol/gDW and oxygen uptake rate to 15 mmol/gDW hr . We also turn off the reactions given in [20] that are down regulated in aerobic glucose conditions . We use the core biomass composition available in iAF1260 [20] as the in silico biomass description . In summary , by using the GNG and NGG GrowMatch optimization formulations , the following procedure is put forth for correcting model growth predictions: | Over the past decade , mathematical models of cellular metabolism have been constructed for describing existing metabolic processes . The gold standard for testing the accuracy and completeness of these models is to compare their cellular growth predictions ( i . e . , cell life/death ) across different scenarios with available experimental data . Although these comparisons have been used to suggest model modifications , the key step of identifying these modifications has often been performed manually . Here , we describe an automated procedure GrowMatch that addresses this challenge . When the model overpredicts the metabolic capabilities of the organism by predicting growth in contrast with experimental data , we use GrowMatch to restore consistency by suppressing growth enabling biotransformations in the model . Alternatively , when the model underpredicts the metabolic capabilities of the organism by predicting no growth ( i . e . , cell death ) in contrast with available data , we use GrowMatch to restore consistency by adding growth-enabling biotransformations to the model . We demonstrate the use of GrowMatch by reconciling growth prediction inconsistencies of the latest Escherichia coli model with data available at the Keio database . Despite the highly curated nature of the Escherichia coli model , GrowMatch identified and resolved a large number of model prediction inconsistencies by taking advantage of available compilations of experimental data . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"molecular",
"biology/bioinformatics",
"computational",
"biology/metabolic",
"networks",
"computational",
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"biology"
] | 2009 | GrowMatch: An Automated Method for Reconciling In Silico/In Vivo Growth Predictions |
Salmonella enterica serovar Typhimurium ( Salmonella ) is one of the most significant food-borne pathogens affecting both humans and agriculture . We have determined that Salmonella encodes an uptake and utilization pathway specific for a novel nutrient , fructose-asparagine ( F-Asn ) , which is essential for Salmonella fitness in the inflamed intestine ( modeled using germ-free , streptomycin-treated , ex-germ-free with human microbiota , and IL10−/− mice ) . The locus encoding F-Asn utilization , fra , provides an advantage only if Salmonella can initiate inflammation and use tetrathionate as a terminal electron acceptor for anaerobic respiration ( the fra phenotype is lost in Salmonella SPI1− SPI2− or ttrA mutants , respectively ) . The severe fitness defect of a Salmonella fra mutant suggests that F-Asn is the primary nutrient utilized by Salmonella in the inflamed intestine and that this system provides a valuable target for novel therapies .
Salmonella is a foodborne pathogen that causes significant morbidity and mortality in both developing and developed countries [1] , [2] . It is widely believed that there are no undiscovered drug targets in Salmonella enterica , largely due to the high number of nutrients available during infection and redundancy in metabolic pathways [3] , [4] . To acquire nutrients in the intestine , Salmonella initiates inflammation , which disrupts the microbiota and causes an oxidative burst that leads to the formation of tetrathionate [1]–[3] , [5]–[7] . Tetrathionate is used as a terminal electron acceptor for the anaerobic respiration of carbon compounds that otherwise would not be metabolized [8] . One of these carbon sources is ethanolamine , which is derived from host phospholipids . Ethanolamine can be respired by Salmonella , but not fermented [8] . Salmonella actively initiates inflammation using two Type 3 Secretion Systems ( T3SS ) , each encoded within a distinct , horizontally acquired pathogenicity island . SPI1 ( Salmonella Pathogenicity Island 1 ) contributes to invasion of host cells and elicitation of inflammation in the host . SPI2 is required for survival within macrophages and contributes to intestinal inflammation . Salmonella strains lacking SPI1 and SPI2 cause very little intestinal inflammation [5] , [6] , [8] , [9] . Here , we have identified fructose-asparagine ( F-Asn ) as another carbon source that is consumed by Salmonella using tetrathionate respiration during the host inflammatory response . The phenotypes of mutants lacking this utilization system are quite severe , suggesting that this is the primary nutrient utilized during Salmonella-mediated gastroenteritis . No other organism is known to synthesize or utilize F-Asn .
Competition experiments between wild-type and the fraB1::kan mutant were performed as described above using conventional mice ( with normal microbiota ) and mice treated orally with streptomycin ( strep-treated ) one day earlier to disrupt the microbiota ( Figure 4C , D , E ) . Conventional mice do not become inflamed from Salmonella , while strep-treated mice ( or germ-free ) do become inflamed [5] , [6] , [8] , [21]–[24] . Disruption of the fra locus caused no fitness defect in conventional mice , but caused a severe defect in the strep-treated mice at one and four days post-infection ( Figure 4C , D , E ) . The phenotype in strep-treated mice was confirmed by complementation ( Figure 4F ) . It is expected that the fraB1::kan mutation is polar on the remainder of the fraBDAE operon . Therefore , the fraB1::kan mutation was complemented with a low copy number plasmid encoding the entire fra island ( Figure 4F ) . The phenotype was confirmed again using a separately constructed mutation , fraB4::kan , and complementation ( Figure 4G , H , I ) . In both instances , greater than 99% of the phenotype was restored ( Figure 4F , I ) . The observation of a phenotype in germ-free and strep-treated mice , but not conventional mice , suggested that Salmonella might require inflammation in order to acquire or utilize the fra-dependent nutrient source . It is known that inflammation causes the accumulation of tetrathionate in the lumen , a terminal electron acceptor that allows Salmonella to respire anaerobically [6] . Histopathology results confirmed that infection with Salmonella caused inflammation in the germ-free and strep-treated mice , but not in the conventional mice ( Figure 5A , D , E ) . To test the hypothesis that Salmonella must induce inflammation for fra to affect the phenotype , we repeated the competition experiments in a Salmonella genetic background lacking SPI1 and SPI2 , so that both the wild-type and the fra mutant would be defective for induction of inflammation . The severe fitness phenotype of the fra mutant was not observed in these strains ( Figure 4J–L ) and histopathology results confirmed that inflammation was indeed low during these experiments ( Figure 5B , F ) . To test the hypothesis that tetrathionate respiration was required for use of the fra-dependent nutrient source , the competition experiments were repeated in a ttrA mutant background . TtrA is part of a tetrathionate reductase , which is required for the utilization of tetrathionate as a terminal electron acceptor during anaerobic respiration [6] , [25] . As in the SPI1 SPI2 background , there was no phenotype of a fra mutant in a ttrA mutant background indicating that Salmonella must be able to respire using tetrathionate to gain advantage from the fra locus ( Figure 4M–O ) . Histopathology results confirmed the presence of moderate inflammation during these experiments ( Figure 5C , G ) . To determine if the fra locus is required during the systemic phase of disease , we performed competition experiments between the wild-type and fra mutant after intraperitoneal inoculation of conventional or strep-treated mice , with bacterial recovery from the spleen . The fra mutant had no fitness defect during systemic infection ( Figure 4P , Q ) . So far , we have seen the fra phenotype in C57BL/6 mice , which are mutated at the Nramp1 locus , and this required that the mice be either germ-free or strep-treated so that Salmonella could induce inflammation . Ideally , we would like to determine the significance of the fra locus in a model that is not mutated and does not require strep-treatment or a germ-free status . It is known that humans with a complete microbiota are quickly inflamed by Salmonella infection while conventional mice are not , and more recently it was discovered that germ-free mice colonized with human fecal microbiota ( “humanized” mice ) become inflamed from Salmonella infection without disturbance of the gut microbiota by streptomycin [26] . Therefore , we “humanized” germ-free Swiss Webster mice , which are Nramp1+/+ , with human feces obtained from a healthy adult donor from the Ohio State University fecal transplant center . Competition experiments were then performed between wild-type and fra mutant Salmonella in these mice . Histopathology results confirmed the presence of mild inflammation during these experiments and the fra locus had a greater than 10 , 000-fold fitness phenotype ( Figure 6 ) . IL10 knockout mice were used as another method to facilitate Salmonella-induced inflammation without using streptomycin [5] . Histopathology results indicated that , unexpectedly , there was not very much inflammation in these mice by day 3 post-infection although the fra locus still had a modest fitness phenotype ( greater than 100-fold ) ( Figure 6 ) . The phenotypes of the fra locus in IL10 knockout mice and in the humanized Swiss Webster mice demonstrate that the fra phenotype is not limited to germ-free or streptomycin-treated mice . Finally , to test for the possibility that these severe fra mutant phenotypes were the result of interaction between the wild-type and fra mutant during infection , we performed experiments in which strep-treated C57BL/6 Nramp1+/− heterozygous mice were infected separately with the wild-type , the fra mutant , or the complemented fra mutant . The strains were quantitated in the feces each day post-infection for four days at which point the mice were sacrificed and the strains were quantitated in the cecum . The fra mutant was recovered in 30-fold lower numbers than wild-type on the fourth day in the feces and 98-fold lower in the cecum ( Figure 7 ) . This defect was restored by complementation with the fra locus on a plasmid in the cecum , while in the feces the restoration did not reach statistical significance ( Figure 7 ) . FraA is homologous to the Dcu family of dicarboxylate transporters . However , authentic dicarboxylate acquisition loci do not encode a sugar kinase or phosphosugar isomerase . Furthermore , none of the dicarboxylates that we tested ( malate , fumarate or succinate ) provided a growth advantage to the wild-type strain vs . a fraB1::kan mutant , suggesting that they are not substrates of the Fra pathway . BLAST searches using the entire operon revealed that the closest homolog is the frl operon of E . coli , although the frl operon is at a different location within the genome and does not encode an asparaginase ( and the Salmonella fra locus does not encode a frlC homolog ) . The products of the E . coli frl operon transport and degrade the Amadori product fructose-lysine ( F-Lys ) [27] , [28] . Amadori products most often result from a spontaneous reaction between a carbonyl group ( often of glucose , although numerous other compounds can also react ) and an amino group of an amino acid in vivo , and are then referred to as non-enzymatic glycation products [29] , [30] . With F-Lys and fructose-arginine ( F-Arg ) this can happen with the free amino acid , or the side groups of the lysine and arginine residues of a protein . In contrast , fructose-asparagine ( F-Asn ) can only result from reaction of glucose with the alpha amino group of free asparagine or the N-terminal asparagine of a protein . We synthesized three different Amadori products , F-Lys , F-Arg , and F-Asn and used them as sole carbon sources during growth experiments . The preparations were free of glucose but contained some free amino acid . However , control experiments demonstrated that Salmonella was unable to grow on any of the three amino acids alone , so these contaminants are inconsequential ( Figure 8D ) . Salmonella was unable to grow on F-Arg , and grew slowly and with low yield on F-Lys ( Figure 8B , C ) . The growth on F-Lys was independent of the fra locus . In contrast , Salmonella grew as well on F-Asn as on glucose , and growth on F-Asn was dependent upon the fra locus ( hence the name fra , for fructose-asparagine utilization ) ( Figure 8A ) . A commercial source of F-Asn was obtained and it also allowed Salmonella to grow in a fra-dependent manner ( structure shown in Figure 8F ) . Complementation of the fraB1::kan mutant with a plasmid encoding the fra island restored the ability of the mutant to grow on F-Asn ( Figure 8E ) . In addition to serving as a sole carbon source , F-Asn , also served as sole nitrogen source ( Figure 9 ) . Growth with F-Asn was tested under aerobic and anaerobic conditions in the presence or absence of the terminal electron acceptor tetrathionate ( Figure 10 ) . The F-Asn was utilized under all conditions , but respiratory conditions were superior with a doubling time of 1 . 6+/−0 . 1 hours aerobically with tetrathionate , 2 . 0+/−0 . 3 hours aerobically without tetrathionate , 1 . 9+/−0 . 1 hours anaerobically with tetrathionate , and 2 . 9+/−0 . 4 hours anaerobically without tetrathionate . Competition experiments in which the wild-type and fraB1::kan mutant were grown in the same culture were performed in minimal medium containing F-Asn . As expected , the mutant was severely attenuated during aerobic and anaerobic growth , and in the presence or absence of tetrathionate ( Figure 11 ) . The attenuation was most severe during anaerobic growth in the presence of tetrathionate .
The mechanisms by which microbes interact with each other in the gastrointestinal tract are largely unknown . Screening large libraries of bacterial mutants for fitness defects in animals with defined microbiota can be used to identify those genes that are only required in the presence of specific members of the microbiota [15] . In this report , we took a highly reductionist approach and screened for genes that were differentially required in germ-free mice versus ex-germ-free mice colonized with a single commensal Enterobacter cloacae isolate . Only five genes were differentially required , a two component response regulatory pair , barA/sirA , and three genes within the fra locus ( Table 1 ) . Individual sirA and fraB mutants were used to confirm the findings . The sirA gene was required for fitness in the presence of E . cloacae but not in its absence ( Figure 2 ) . The fra locus was required for fitness in both situations , but the phenotype was more severe in the presence of E . cloacae ( Figure 4A , B ) . Thus , the differential screening strategy was successful in identifying genes that are more important in the presence of other bacteria within the gastrointestinal tract . The reason ( s ) that sirA is required in the presence , but not the absence , of E . cloacae is not known . It is thought that BarA detects short chain fatty acids produced by the normal microbiota and then phosphorylates SirA [31]–[34] . SirA then activates the transcription of two small RNAs , csrB and csrC , which antagonize the activity of the CsrA protein [20] , [35]–[39] . The CsrA protein is an RNA-binding protein that regulates the stability and translation of hundreds of mRNAs involved with metabolism and virulence [17] , [19] , [40] . One possible reason that sirA differentially affects fitness in the two mouse models may be that the Enterobacter-colonized mouse offers an environment richer in carboxylic acids that act as stimuli for BarA-SirA signaling with resulting effects on metabolism and growth [31]–[34] . The fitness effects could also be due to the regulation of genes involved in the induction of inflammation and/or anareobic metabolism including SPI1 , SPI2 , ethanolamine utilization , and vitamin B12 biosynthesis by CsrA [19] , [20] , [41]–[44] . Finally , SirA or CsrA may regulate the fra locus itself . The fra locus was annotated as a C4 dicarboxylate uptake system . However , we found that the fra locus played no role in the utilization of C4 dicarboxylates . BLAST searches revealed that the operon is similar to the frl locus of E . coli which is required for the utilization of fructose-lysine ( F-Lys ) . The frl locus of E . coli has a different genomic context than the fra locus of Salmonella , and is only distantly related . We determined that the fra locus of Salmonella plays no role in the utilization of F-Lys ( Figure 8C ) . However , the presence of an asparaginase in the fra locus ( fraE ) , but not the frl locus , led us to hypothesize that F-Asn may be the correct nutrient , and indeed , this was the case . Wild-type Salmonella is able to grow as well on F-Asn as on glucose , and this ability is dependent upon the fra locus ( Figures 8 , 10 ) . While the individual members of the fra operon have not been characterized , we hypothesize as to their functions in Figure 12 . F-Asn differs from ethanolamine in that it can be fermented ( Figure 10B ) , which would be consistent with the proposed release of glucose-6-P by FraB ( Figure 12 ) . Although F-Asn can be fermented , it only provides a fitness advantage in vivo when it can be respired , i . e . , when tetrathionate reductase is functional ( Figure 4M–O ) , possibly because of the much greater energy yield from respiration versus fermentation . E . cloacae grows very poorly on F-Asn and does not encode the fra locus . Therefore , E . cloacae likely exacerbated the fra phenotype of Salmonella by competing for other nutrients . F-Asn is an Amadori compound ( also known as a glycation product ) formed by reaction between glucose in its open chain form with the alpha amino group of asparagine followed by a rearrangement that gives the fructose derivative . Until this report , no organism had been shown to synthesize or utilize F-Asn . However , in the early 2000s it was discovered that acrylamide is present in many foods , especially French fries and potato chips . F-Asn is a precursor to acrylamide . After the acrylamide discovery , numerous papers measured acrylamide concentration , and the precursor molecules , glucose and asparagine , in foods [45]–[52] . However , to the best of our knowledge , only two reports have measured the concentration of F-Asn in a few fruits and vegetables [53] , [54] . The concentrations are surprisingly high , ranging between 0 . 1% ( carrot ) and 1 . 4% dry weight ( asparagus ) [54] . Factors that influence these concentrations are time , temperature , pressure , and perhaps less obviously , moisture content [55] . Any reducing sugar and any amino acid ( or other amines ) can form compounds analogous to F-Asn . It is important to note that these Amadori compounds are not the ultimate products since with further time and heating they decompose to a large variety of other products , some of which are responsible for a variety of flavors , and the brown color , in cooked foods [55]–[57] . In fact , glycation products form spontaneously in the human body and provide an indication of glucose concentration over time [30] , [58]–[60] . A common diabetes test measures the glycation of the N-terminal valine of hemoglobin [30] . The severity of the fra fitness phenotype suggests that F-Asn is the primary nutrient used by Salmonella during growth in the inflamed intestine . For perspective , in strep-treated mice the fitness defect of a fra mutant is 1000-fold , while mutants unable to utilize ethanolamine or sialic acid are attenuated 10-fold and 2-fold , respectively [8] , [61] . The fra operon was previously identified by transcription profiling as up-regulated by Fur under anaerobic conditions [62] . Other genes activated under the same conditions included ethanolamine utilization ( eut ) , and hilA , a regulator of SPI1 expression . Both of these loci are associated with induction of inflammation or growth during inflammation [8] , [62] . The fra locus is present among most Salmonella serovars , but is disrupted in serovars Typhi and Paratyphi A , consistent with the marked degradation of numerous loci involved with anaerobic respiration among these extra-intestinal serovars [63] . Interestingly , a putative fra locus is present in Citrobacter rodentium and Citrobacter freundii , but not in numerous other non-pathogenic Citrobacter species . The frl locus , encoding the ability to utilize F-Lys is present in E . coli , Shigella , and Cronobacter . It will be interesting to determine which , if any , members of the normal microbiota can compete with E . coli and Salmonella for Amadori products . The apparent species-specificity of the F-Asn utilization system , and the severity of the fitness defect associated with mutants that cannot metabolize F-Asn , indicate that the Fra system represents a specific and valuable therapeutic target . Further studies are needed to determine the role of each gene in the fra locus with regard to F-Asn metabolism . Similarly , further studies are needed to determine the mechanism by which the proposed transcription factor , FraR , regulates F-Asn metabolism . These structure-function studies will facilitate small molecule drug screens targeting F-Asn utilization . It will also be interesting to determine the concentration of F-Asn and other Amadori products in a wide variety of foods , to determine if these products can affect disease susceptibility , and to explore the possibility of preventing salmonellosis or other infections by removing Amadori products from specific food products or from the diet in general . The utilization systems for many more Amadori products are likely awaiting discovery within bacterial genomes and these may play interesting roles in microbial ecology and human health .
Bacteria were grown in Luria-Bertani ( LB ) broth or on LB agar plates ( EM Science ) unless otherwise noted . The minimal medium used was NCE ( no carbon E ) [64] containing trace metals [25] . Chloramphenicol ( cam ) , streptomycin ( strep ) , or kanamycin ( kan ) were added at 30 , 200 , or 60 µg/ml , respectively , when appropriate . Fructose-asparagine was either synthesized or purchased from Toronto Research Chemicals , catalog #F792525 . Anaerobic growth was performed in a Bactron 1 anaerobic chamber containing 90% N2 , 5% CO2 , and 5% H2 ( Shel Lab ) . Strains used are described in Table 2 . Enterobacter cloacae strain JLD400 was isolated in our laboratory by plating fecal samples from a conventional BALB/c mouse onto LB agar plates . This particular isolate was chosen because it is easy to culture and genetically manipulable ( the strain can be electroporated , maintains ColE1-based plasmids , and can act as a recipient in RP4-mediated mobilization of a suicide vector used to deliver mTn5-luxCDABE , not shown ) . The species identification was performed using a Dade Microscan Walkaway 96si at the Ohio State University medical center . Additionally , genomic DNA sequences have been obtained that flank mTn5-luxCDABE insertions in JLD400 and these DNA sequences match the draft genome sequence of E . cloacae NCTC 9394 . A transposon mutant library was constructed in S . enterica serovar Typhimurium strain 14028 . EZ-Tn5 <T7/kan> transposomes from Epicentre Technologies were delivered to Salmonella by electroporation . This transposon encodes kanamycin resistance and has a T7 RNA Polymerase promoter at the edge of the transposon pointed outward . The resulting library contains between 190 , 000 and 200 , 000 independent transposon insertions and is referred to as the JLD200k library . The insertion points of this library have been determined previously by next-generation sequencing [65] . It is estimated that approximately 4400 of the 4800 genes in the Salmonella genome are non-essential with regard to growth on LB agar plates [65] . Therefore , the JLD200k library is saturated with each gene having an average of 43 independent transposon insertions . A FRT-kan-FRT or FRT-cam-FRT cassette , generated using PCR with the primers listed in Table 3 and pKD3 or pKD4 as template , was inserted into each gene of interest ( replacing all but the first ten and last ten codons ) using lambda Red mutagenesis of strain 14028+pKD46 followed by growth at 37°C to remove the plasmid [66] . A temperature sensitive plasmid encoding FLP recombinase , pCP20 , was then added to each strain to remove the antibiotic resistance marker [66] . The pCP20 plasmid was cured by growth at 37°C . A fraB4::kan mutation was constructed using primers BA2552 and BA2553 ( Table 3 ) . A FRT-cam-FRT was placed in an intergenic region downstream of pagC using primers BA1561 and BA1562 ( deleting and inserting between nucleotides 1342878 and 1343056 of the 14028 genome sequence ( accession number NC_016856 . 1 ) ( Table 3 ) . Germ-free C57BL/6 mice were obtained from Balfour Sartor of the NIH gnotobiotic resource facility at the University of North Carolina and from Kate Eaton at the University of Michigan . Germ-free Swiss Webster mice were obtained from Taconic Farms . The mice were bred and maintained under germ-free conditions in sterile isolators ( Park Bioservices ) . Periodic Gram-staining , 16 s PCR , and pathology tests performed by the Ohio State University lab animal resources department and our own laboratory were used to confirm that the mice contained no detectable microorganisms . Conventional C57BL/6 mice were obtained from Taconic Farms . C57BL/6 mice that were heterozygous for the Nramp1 gene were generated by breeding the standard Nramp1−/− mice from Taconic Farms with C57BL/6 Nramp1+/+ mice from Greg Barton [67] . IL10 knockout mice ( B6 . 129P2-IL10tm1Cgn/J ) were obtained from Jackson Laboratory . Germ-free Swiss Webster mice were “humanized” by intragastric inoculation of 200 µl of human feces obtained from an anonymous healthy donor from the OSU fecal transplant center . The JLD200k transposon mutant library was grown in germ-free C57BL/6 mice in the presence or absence of E . cloacae strain JLD400 . Four mice were inoculated intragastrically ( i . g . ) with 107 cfu of Enterobacter cloacae strain JLD400 that had been grown overnight in LB shaking at 37°C . After 24 hours these mice , and an additional four germ-free mice , were inoculated with 107 cfu of the JLD200k library that had been grown overnight in shaking LB kan at 37°C . Prior to inoculation of the mice , the library was spiked with an additional mutant , JLD1214 , at a 1∶10∶000 ratio . This mutant contains a chloramphenicol resistance ( camr ) gene at a neutral location in the chromosome in the intergenic region downstream of pagC [68] . After inoculation of mice with the spiked library , the inoculum was dilution plated to quantitate the kanamycin resistant ( kanr ) Salmonella library members and the camr spike strain . The remainder of the inoculum was pelleted and saved as the “input” for hybridization to microarrays . After 24 hours of infection with the JLD200k library , the mice were euthanized and organs were harvested ( small intestine , cecum , large intestine , and spleen ) . One germ-free mouse died prior to organ harvest and was not used . All samples were homogenized and dilution plated to determine Salmonella counts . The remainder of the homogenate was added to 25 ml LB kan and grown overnight with shaking at 37°C to recover the library members . Each culture was then pelleted and frozen as a potential “output” sample for microarray analysis . The kanr and camr colony counts recovered from each organ indicated that the spike ratio of 1∶10 , 000 was maintained in the intestinal samples but not in the spleen samples . This indicates that the library underwent a population bottleneck on the way to the spleen so microarray analysis of spleen samples would not be informative . The cecum samples were chosen for microarray analysis . There was one “input” sample for all arrays . There were seven separate “output” samples for the arrays; four from the cecums of Enterobacter-associated mice and three from germ-free mice . The output from each mouse was compared to the input on a single array . We also did a single “in vitro” array experiment in which the JLD200k library was grown in the presence of Enterobacter in liquid LB broth shaking at 37°C . Genomic DNA was isolated from the input and output bacterial pellets . The purity and concentration of the DNA samples was assessed using a Nanodrop spectrophotometer and the quality of the DNA was assessed via agarose gel electrophoresis . All seven samples had high quality intact genomic DNA . The DNA was digested using a restriction endonuclease ( RsaI ) . Labeled RNA transcripts were obtained from the T7 promoter by in vitro transcription . A two-color hybridization strategy was employed . RNA transcripts from the output samples were fluorescently labeled with Cyanine-5 ( Cy5 , red ) , while the input sample was labeled with Cyanine-3 ( Cy3 , green ) . Equal molar concentrations of the output and input sample were combined and hybridized to genome-wide tiling microarrays printed commercially by Agilent Technologies . Agilent's SurePrint technology employs phosphoramadite chemistry in combination with high performance Hewlett Packard inkjet technology for in situ synthesis of 60-mer oligos . Using Agilent eArray , an easy-to-use web-based application , we were able to synthesize the arrays used by Chaudhuri et al . that completely tiled both the sense and anti-sense strands of the Salmonella SL1344 genome ( AMADID 015511 ) [10] . Each slide contained 2 arrays , each array with 105 , 000 features , densely tiling the entire genome . The strain of Salmonella used in our experiments was 14028 and its genome sequence was only recently published ( GenBank Nucleotide Accession CP001363 ( complete genome ) and CP001362 ( plasmid ) ) . As such , each of the 60-mer probes used by Chaudhuri et al . [10] were mapped to the 14028 genome using blast , and then annotated with any open reading frames ( ORFs ) that the probe spanned . A total of 96 , 749 probes mapped to the 14028 genome , with a median gap between each probe of 35 nucleotides on both strands . After purification , the labeled samples were denatured and hybridized to the array overnight . Microarray slides were then washed and scanned with an Agilent G2505C Microarray Scanner , at 2 µm resolution . Images were analyzed with Feature Extraction 10 . 5 ( Agilent Technologies , CA ) . Median foreground intensities were obtained for each spot and imported into the mathematical software package “R” , which was used for all data input , diagnostic plots , normalization and quality checking steps of the analysis process using scripts developed specifically for this analysis . In outline , the intensities were not background corrected as this has been shown to only introduce noise . The dataset was filtered to remove positive control elements and any elements that had been flagged as bad , or not present in the 14028 genome . Using the negative controls on the arrays , the background threshold was determined and all values less than this value were flagged . Finally , the Log2 ratio of output Cy5/input Cy3 ( red/green ) was determined for each replicate , and the data was normalized by the loess method using the LIMMA ( Linear models for microarray data ) package in “R” as described [69] , [70] . Complete statistical analysis was then performed in “R” . Insertion mutants where the ORF is essential for survival will be selected against , and thus a negative ratio of Cy5/Cy3 ( red/green ) will be observed in the probes adjacent to the insertion point , resulting from higher Cy3 ( green ) signal from the input . Conversely , insertion mutants that were advantageous to growth in the output samples would have a positive ratio , resulting from the higher Cy5 ( red ) signal in the output . Mutants having no effect on growth would have equal ratios in both the output and input samples ( yellow ) . A spreadsheet of these data is available in Dataset S1 . We carried out the syntheses of three fructosyl amino acids with asparagine , lysine , and arginine . Hodge and Fisher's review of Amadori products was consulted as an essential starting point for synthesis [71] and the recent review by Mossine and Mawhinney of all aspects of fructose-amines was a treasure house of information [55] . We found the method of Wang et al . [72] to be the most satisfactory , however reaction times cannot be standardized and excess glucose must be removed . The reaction with asparagine is slow because asparagine is sparingly soluble in methanol . By contrast , the reaction with α-Boc-lysine is fast . Arginine is an intermediate case . Previous syntheses of F-Asn include those of Stadler et al . [46] , Wang et al . [72] , and Miura et al . [73] . The procedure of Stadler et al . [46] uses alkaline conditions which we thought could bring about isomerization of the sugar and racemization of the amino acid . We chose to develop the synthesis of Wang et al . [72] after trying a number of different protocols described for other amino acids [74]–[77] . Wang et al . [72] , however , describe only a general method and asparagine presents some particular problems , the most important of which is the poor solubility of asparagine in methanol . We added bisulfite to the reaction mixture to reduce the formation of colored by-products [57] and finally removed excess glucose by use of a cation-exchange column according to the method of Mossine et al . [78] . Using methanol alone as solvent gives the product after refluxing for 24 hr . in approximately 10–15% yield together with recovery of about 90% of the asparagine . Although the yield is low , the starting materials are inexpensive , and the insolubility of asparagine has the advantage that F-Asn , which is quite soluble in methanol , emerges from the ion exchange column almost free of asparagine . This gave a free-flowing off-white non-hygroscopic solid . The 1H-NMR spectrum is complex due to the equilibrating mixture of alpha- and beta- pyranose and furanose forms [55] , but integration of the upfield resonances due to asparagine and the downfield resonances due to the sugar are in the proper ratio . The material was also characterized by its specific rotation and infrared ( IR ) spectrum: [α]23D −48° ( c = 0 . 1 , water ) ( reference [73] −40° , c = 1 , water ) ; IR ( Nujol ) : 3350 , 3155 , 1668 , 1633 , 1455 , 1408 , 1080 cm−1 . Compare our preparations to results in [71] , [73] . Competition assays were performed in which a mutant strain was mixed in a 1∶1 ratio with an isogenic wild-type and inoculated by the intragastric ( i . g . ) or intraperitoneal ( i . p . ) route to mice . Fecal samples , intestinal sections , spleen and liver were recovered at specific times post-infection , homogenized and plated on selective plates . The wild-type and mutant strains were differentiated by antibiotic resistance . The competitive index was calculated as CI = ( cfu of mutant recovered/cfu w . t . recovered ) / ( cfu mutant input/cfu w . t . input ) . If the mutant is defective compared to the wild-type it will have a CI of less than 1 . The fra island was PCR amplified from purified 14028 genomic DNA with primers BA2228 and BA2229 using Phusion polymerase ( New England Biolabs ) . The PCR product was cloned into pPCR-Blunt II-TOPO ( Invitrogen ) . The resulting clones were digested with EcoRI ( New England Biolabs ) , run on an agarose gel and the 8 . 6 kbp fra fragment was gel purified ( Qiagen ) . This purified DNA fragment was ligated into pWSK29 digested with EcoRI ( NEB ) using T4 DNA ligase ( New England Biolabs ) overnight at 4°C . The ligation reaction was transformed into DH5α and plated on LB containing ampicillin at 37°C . The resulting plasmid , pASD5006 , or the vector control pWSK29 , were electroporated into the appropriate strains . All animal work was performed in accordance with the protocols approved by our Institutional Animal Care and Use Committee ( OSU 2009A0035 ) . The IACUC ensures compliance of this protocol with the U . S Animal Welfare Act , Guide for Care and Use of Laboratory Animals and Public Health Service Policy on Humane Care and Use of Laboratory Animals . Human fecal material was obtained from an anonymous healthy donor at the Ohio State University fecal transplant center in accordance with the protocol approved by our Institutional Review Board ( OSU 2012H0367 ) . | It has long been thought that the nutrient utilization systems of Salmonella would not make effective drug targets because there are simply too many nutrients available to Salmonella in the intestine . Surprisingly , we have discovered that Salmonella relies heavily on a single nutrient during growth in the inflamed intestine , fructose-asparagine ( F-Asn ) . A mutant of Salmonella that cannot obtain F-Asn is severely attenuated , suggesting that F-Asn is the primary nutrient utilized by Salmonella during inflammation . No other organism has been reported to synthesize or utilize this novel biological compound . The novelty of this nutrient and the apparent lack of utilization systems in mammals and most other bacteria suggest that the F-Asn utilization system represents a specific and potent therapeutic target for Salmonella . | [
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] | 2014 | Fructose-Asparagine Is a Primary Nutrient during Growth of Salmonella in the Inflamed Intestine |
Much of the HSV-1 life cycle is carried out in the cell nucleus , including the expression , replication , repair , and packaging of viral genomes . Viral proteins , as well as cellular factors , play essential roles in these processes . Isolation of proteins on nascent DNA ( iPOND ) was developed to label and purify cellular replication forks . We adapted aspects of this method to label viral genomes to both image , and purify replicating HSV-1 genomes for the identification of associated proteins . Many viral and cellular factors were enriched on viral genomes , including factors that mediate DNA replication , repair , chromatin remodeling , transcription , and RNA processing . As infection proceeded , packaging and structural components were enriched to a greater extent . Among the more abundant proteins that copurified with genomes were the viral transcription factor ICP4 and the replication protein ICP8 . Furthermore , all seven viral replication proteins were enriched on viral genomes , along with cellular PCNA and topoisomerases , while other cellular replication proteins were not detected . The chromatin-remodeling complexes present on viral genomes included the INO80 , SWI/SNF , NURD , and FACT complexes , which may prevent chromatinization of the genome . Consistent with this conclusion , histones were not readily recovered with purified viral genomes , and imaging studies revealed an underrepresentation of histones on viral genomes . RNA polymerase II , the mediator complex , TFIID , TFIIH , and several other transcriptional activators and repressors were also affinity purified with viral DNA . The presence of INO80 , NURD , SWI/SNF , mediator , TFIID , and TFIIH components is consistent with previous studies in which these complexes copurified with ICP4 . Therefore , ICP4 is likely involved in the recruitment of these key cellular chromatin remodeling and transcription factors to viral genomes . Taken together , iPOND is a valuable method for the study of viral genome dynamics during infection and provides a comprehensive view of how HSV-1 selectively utilizes cellular resources .
The genomes of eukaryotic DNA viruses vary in complexity with respect to the number of genes they encode , and hence their dependence on host-cell functions . With the exception of poxviruses , all replicate in the cell nucleus and therefore utilize the nuclear machinery for the maintenance , replication , and expression of their genomes . The dynamic interactions between viral and cellular proteins and the viral genome , function to mediate the different steps in the life cycle of the virus , and hence determine the outcome of infection . These include interactions that mediate the entry of the genome into the nucleus , its expression and replication , and ultimately the packaging of nascent genomes in capsids . Herpes simplex virus 1 ( HSV-1 ) has a linear genome comprised of 152 kilobasepairs [1 , 2] . It enters the nucleus from the capsid through pores in the nuclear envelope [3–5] . The genome then participates in a series of interactions that results in a nucleo-protein complex near ND10 structures [6] . Here , the genome is susceptible to activities of the intrinsic cellular antiviral response . The genome also contains nicks and gaps , and these along with the genomic termini elicit a DNA damage response , the nature of which may be consequential to viral infection [7] . Viral genomes initially associate with ND10 structures , where through the action of ICP0 , ND10 proteins are degraded or dispersed resulting in the prerequisite structure for efficient transcription and replication [6 , 8] . Viral DNA replication then results in the formation large replication compartments , which fill the host nucleus and concentrate viral and cellular factors to replicating viral genomes [9] . HSV-1 encodes two transcription factors , VP16 [10 , 11] and ICP4 [12] , which function along with the cellular RNA polymerase II transcription machinery [13] to transcribe the viral genome . These factors initially colocalize with prereplicative genomes [14–16] and these interactions as well as those involving viral and cellular RNA-processing factors result in an ordered cascade of viral gene expression [17 , 18] . Seven HSV gene products are sufficient in cells to replicate DNA in an HSV-origin dependent manner [19] . While this set of viral proteins includes a DNA-dependent DNA polymerase and other functional analogs of cellular DNA replication proteins , it is not sufficient to drive origin-dependent replication in vitro , suggesting the requirement for as yet unknown cellular proteins [20] . Finally , an additional set of proteins interacts with the genome in the processes of cleaving unit length genomes and their packaging in capsids [21] . These processes have been , and continue to be the focus of studies in many laboratories since significant gaps exist in our understanding of all these processes , and how they ultimately contribute to viral multiplication and pathogenesis . A shortcoming contributing to these gaps is our relative lack of knowledge of the proteins , particularly cell-derived , which interact with viral genomes in different phases of infection . Recently , ethynyl-modified nucleosides along with click chemistry and immunofluorescence were used to trace the fate of input adenovirus genomes in infected cells [22] . Nucleoside analogs were also incorporated into replicating herpes simplex and vaccinia viral DNA to demonstrate that this technique can be used to label other viral genomes and could potentially be used to track these genomes throughout infection . In addition , ethynyl-modified nucleosides have been used in a procedure known as isolation of proteins on nascent DNA ( iPOND ) to identify the proteins at cellular replication forks [23–28] . This procedure involves the metabolic incorporation of 5-ethynyl-2´-deoxyuridine ( EdU ) into the DNA , biotinylating the EdU-labeled DNA by click chemistry , followed by the affinity purification of the biotinylated DNA , and the subsequent analysis of the proteins associated with it . We have adopted and modified these procedures to enable the visualization of the HSV genome at different stages of infection , as well as the interrogation of the viral and cellular proteins on replicated/replicating viral genomes . The results elucidate the viral and cellular proteins associating with viral DNA during infection and provide a comprehensive view of the cellular machinery functioning on HSV genomes .
Ethynyl-modified nucleosides have been used to prelabel and then track single incoming adenovirus genomes within infected cells [22] . While this approach was also used to examine HSV genomes in replication compartments , input genomes were not imaged . We sought to determine if ethynyl-modified nucleosides could be used to label and track HSV genomes during early ( before DNA replication ) , as well as late ( after DNA replication ) stages of infection . We also intended to use viral DNA imaging to optimize HSV genome labeling for purification of viral genomes by iPOND . Preliminary experiments demonstrated that EdU and EdC were poorly incorporated in HSV DNA . We hypothesized that deletion of the HSV deoxyuridine triphosphatase ( dUTPase ) and uracil glycosylase enzymes would increase incorporation into the viral genome . HSV-1 uracil glycosylase and dUTPase mutant strains were generated by introducing premature termination codons early in the reading frames of the UL2 and UL50 genes ( Fig A in S1 Text ) . As found for labeling of adenovirus genomes [22] , ethynyl nucleoside incorporation into HSV genomes resulted in slightly reduced virus titers ( Fig B in S1 Text ) . The same concentrations of EdU or EdC had a greater effect on the titer of the UL2/UL50 double mutant virus than on wild type KOS , suggesting that the double mutant is more efficiently labeled by both EdU and EdC . EdU was used in all subsequent experiments . To compare the relative amount of EdU incorporated into wild type and mutant genomes , we carried out viral infection and DNA imaging as outlined in Fig 1A and 1B . EdU labeled input genomes and replication compartments were tagged with Alexa Fluor 488 by click chemistry and visualized by fluorescence microscopy ( Fig 2 ) . Prelabeled UL2/UL50 mutant genomes that colocalized with the viral transcription factor , ICP4 , were visualized in the nucleus of infected cells ( Fig 2A ) . The distribution of ICP4 foci two hours after infection largely resembles that observed previously after infection with HSV-1 at a MOI of 10 PFU/cell [29] . Using these same conditions , we were unable to detect KOS genomes prelabeled with 2 . 5 μM EdU ( Fig 2A ) . We also visualized viral replication compartments that colocalize with ICP4 8 hpi in both wild type KOS and UL2/UL50 mutant virus infected cells ( Fig 2B ) . While it was possible to detect EdU labeling coinciding with ICP4 staining in KOS infected cells , significantly more was observed with the UL2/UL50 mutant . Taken together , the UL2/UL50 mutant virus incorporates more EdU into its genome during DNA replication and allows for more sensitive imaging of HSV-1 viral DNA during infection . To identify the viral and cellular proteins that function on viral DNA at different stages of infection , we adapted the iPOND method [25] for analysis of viral genomes and associated proteins . To optimize viral iPOND , we initially considered several factors . Proliferating cells grown in the presence of EdU incorporate EdU into their genomes during DNA replication ( Fig 2B , uninfected , 2 . 5 μM ) . Therefore , conditions in which viral DNA , but not cellular DNA is labeled in the presence of EdU were established . Addition of EdU to the growth medium of proliferating Vero cells that were mock- or HSV-1-infected resulted in labeling of 65% or 29% of cellular genomes ( Fig C in S1 Text , panels Vero cells ) , respectively . HSV infection inhibits G1/S and G2/M phases of the cell cycle [30 , 31] consistent with less labeling of cellular DNA in infected cells . In contrast to proliferating cells , less than 1% of cellular genomes were labeled with EdU when human MRC-5 fibroblast cells that were grown to confluency were infected with HSV in the presence of EdU ( Fig C in S1 Text , panels MRC-5 cells ) . Therefore , resting MRC-5 cells were used in iPOND experiments to avoid contamination with cellular DNA . These cells also have the added advantage that they are a natural host to lytic HSV infection and they do not express high levels of cellular glycosylases to limit cleavage of labeled viral genomes [32] . One of the limitations of iPOND is that a large amount of EdU-labeled DNA is required to pull down enough protein for proteomic analysis [25] . Because the UL2/UL50 mutant virus is more efficiently labeled with EdU than wild type virus , we hypothesized that more viral DNA and associated proteins could be purified by iPOND of the mutant virus . We tested iPOND for the purification of proteins associated with genomes of wild type KOS , UL2 and UL50 single mutant , and UL2/UL50 double mutant viruses ( outlined in Fig 1C ) . The relative protein yield for each virus was compared by western blot for the viral transcription factor ICP4 ( Fig D in S1 Text ) . ICP4 associates with viral genomes throughout infection and is a good indicator of protein yield . The negative control was iPOND carried out on virus-infected cells incubated in the absence of EdU . For all viruses tested , ICP4 was not detected in the negative control , but was detected when iPOND was carried out on viral genomes that were labeled with EdU . The greatest relative amount of ICP4 was detected with the UL2/UL50 mutant virus , consistent with fluorescence imaging of labeled viral genomes ( Fig 2B ) . Therefore , the UL2/UL50 mutant virus was used for iPOND experiments . To identify the proteins associated with viral genomes by iPOND , we labeled viral DNA at three time points during DNA replication . EdU was added to the medium of infected cells at 4–6 , 6–8 , or 8–12 hpi and cells were fixed for iPOND at 6 , 8 , or 12 hpi , respectively . Proteins recovered by iPOND were probed for ICP4 by western blotting ( Fig 3A ) . ICP4 was detected at all time points , but not in the unlabeled negative control . To ensure that DNA isolated by iPOND was viral , input DNA from cell lysates and DNA bound to streptavidin-coated beads was extracted , the amount of viral DNA was measured , and the percentage of viral/total DNA was calculated ( Fig 3B ) . DNA eluted from beads during iPOND experiments was nearly 100% viral in nature . This is a significant enrichment compared to input DNA ( 0 . 2–1 . 5% viral ) . To determine if the entire viral genome was labeled and purified in our assays , high throughput DNA sequencing was carried out on DNA eluted from streptavidin-coated beads ( Fig E in S1 Text ) . At all time points , the distribution of bead-bound DNA was relatively homogeneous across the viral genome . Taken together , iPOND should enable the specific purification of proteins associated with the entire replicated HSV-1 genome . To determine the identity of proteins bound to viral genomes at 6 , 8 , and 12 hpi , mass spectrometry was carried out on proteins that were crosslinked to viral DNA and purified by iPOND . Two independent iPOND experiments were carried out for each time point , each with an unlabeled virus negative control that was prepared on the same day with the same cells , virus , and reagents . Proteins were considered significantly enriched on viral genomes if they were identified with high confidence in duplicate experiments to be enriched by at least four fold over the negative control . The types of proteins identified at all three time points are summarized in Fig 3C and individual complexes and proteins are listed in Tables 1–6 and Table A in S1 Text . The most abundant types of proteins enriched on isolated viral DNA include those involved in RNA processing , transcription , chromatin remodeling , DNA repair , and DNA replication . Furthermore , proteins that mediate nuclear transport , components of the nuclear cytoskeleton , and HSV structural proteins were bound to viral genomes . Several trends are present in these data . First , the total number of proteins that were recovered increased with time of infection . This is consistent with increasing amounts of labeled DNA as replication proceeds , allowing for more sensitive detection of bound proteins . Second , there was a relative increase in proteins that function in post-transcriptional RNA processing , as well as viral structural proteins with time . The increase in viral structural proteins including tegument proteins , capsid assembly factors , portal protein ( UL6 ) , and capsid proteins reflects the packaging of nascent genomes at later times during infection ( Table 1 ) . Comparison of proteins identified at each time point suggests that the individual proteins found on replicated/replicating viral genomes at 6 , 8 , and 12 hpi were relatively similar ( Fig 3D ) . There are significant overlaps between the three different time points with most proteins identified at two or more of the times sampled . The biggest difference was seen at 12 hpi and this reflects the increase in structural proteins , as well as the larger number of proteins recovered by iPOND at this time point . Comparative analysis of replication proteins found on replicated cellular and viral DNA reveals the specificity of isolation of proteins on viral DNA ( Table 2 ) . Cellular replication forks are enriched for cellular replication factors including components of cellular DNA polymerase , clamp loader complex , MCM complex , as well as other replisome-associated proteins such as topoisomerases and PCNA [23 , 26 , 28] . In contrast , in our studies viral DNA was enriched for all seven components of the viral replication machinery including: ICP8 , UL30 ( polymerase ) , UL5/UL8/UL52 ( helicase/primase complex ) , UL9 ( origin binding protein ) , and UL42 ( processivity factor ) . The cellular counterparts to these viral proteins were not enriched on viral genomes . One exception to this is the cellular processivity factor , PCNA . This protein was enriched on viral genomes at all times tested with the highest levels at 6 hpi , decreasing with time . Furthermore , cellular topoisomerases TOP1 , TOP2a , and TOP2b were abundant on viral genomes and likely play a role in virus replication or other process . Accelerated native iPOND ( aniPOND ) is a modified version of iPOND that is quicker and does not utilize crosslinking [33] . It involves native conditions during purification , while iPOND involves crosslinking and stringent wash conditions ( Fig 1C and 1D ) . We therefore predicted that aniPOND would reveal a unique set of proteins involved in viral genome mechanics compared to iPOND because less direct interactors could be detected . To obtain a more comprehensive view of proteins bound to viral genomes , we carried out aniPOND on KOS and UL2/UL50 mutant viruses that were incubated in the presence of EdU from 4–8 hpi and harvested at 8 hpi . Proteins eluted from viral DNA during aniPOND were assayed for ICP4 by western blotting ( Fig 4A ) . ICP4 was detected when the infection was carried out in the presence of EdU ( lanes 3 and 4 ) , but not in the absence of EdU ( lane 1 ) . Importantly , using aniPOND it was also possible to recover ICP4 associated with wild type genomes , however , greater amounts where recovered in the sample with the mutant virus extract . In this experiment , significantly less ( ~2% ) sample was required to isolate a similar amount of ICP4 to that recovered with iPOND . Therefore aniPOND is more efficient for the recovery of labeled viral DNA and associated proteins than iPOND . This is in agreement with comparison of the purification of replisome-associated proteins by each method [33] . To verify the specificity of aniPOND for the purification of replicated viral genomes , we carried out aniPOND on cells infected with the UL2/UL50 mutant virus that was maintained in the presence of acycloguanosine ( ACG , acyclovir ) , a potent inhibitor of viral DNA replication . In the absence of viral DNA replication , DNA was not recovered by aniPOND and ICP4 was not detected by western blotting ( Fig 4A , lane 2 ) . To further validate aniPOND for purification of viral genomes , we determined the relative amount of viral DNA/total DNA purified by this method . DNA eluted from beads during aniPOND experiments was almost 100% viral in nature ( Fig 4B , bound DNA , +EdU-ACG ) . This is a significant enrichment compared to the percent viral DNA present in lysates for this condition ( input DNA , <2% ) . Very little DNA was detected when aniPOND was carried out on virus grown in the absence of EdU ( bound DNA , -EdU ) or in the presence of ACG ( +ACG ) , consistent with specific purification of replicated viral DNA by aniPOND . To determine the identity of proteins that copurified with viral genomes , mass spectrometry was carried out on samples prepared by aniPOND of labeled UL2/UL50 mutant and wild type KOS genomes at 8 hpi . Two independent experiments were carried out for each virus , with an unlabeled virus negative control that was done on the same day with the same cells and reagents . Almost twice as many proteins were identified with high confidence by aniPOND compared to iPOND at 8 hpi ( 184 compared to 96 ) . The types of proteins identified by aniPOND are summarized in Fig 4C and individual proteins are listed in Tables 1–6 and Table A in S1 Text . Proteins that copurified with viral genomes by aniPOND at 8 hpi share the same functional categories as proteins that were purified by iPOND . In fact , pie charts that summarize the findings from these two experiments show very similar trends ( compare Figs 4C to 3C 8hpi ) . Proteins that copurified with UL2/UL50 mutant genomes by iPOND and aniPOND at 8 hpi were compared ( Fig 4D ) . Fifty-three proteins were identified by both methods , 131 by only aniPOND , and 43 by only iPOND . Differences in proteins identified by each method likely reflect differences in the nature of DNA-protein interactions . For example , the viral helicase/primase complex was identified by iPOND but not aniPOND ( Table 2 ) . Crosslinking during iPOND could capture transient DNA-protein interactions or interactions that are lost during purification , which may be the case for ATPases such as the helicase/primase complex . On the other hand , the mediator of RNA polymerase II complex , as well as components of general transcription factor TFIID and TFIIH were identified by aniPOND but not iPOND ( Table 3 ) . Members of these complexes may not be in direct contact with the viral genome or may bind in an orientation that is not conducive to crosslinking . We have shown previously that the mediator complex , TFIID , and TFIIH copurify with ICP4 from virus-infected cells [34 , 35] . Here we also confirmed that ICP4 coprecipitates with mediator and TFIID from virus infected resting MRC-5 cells , along with a subset of transcription and chromatin remodeling factors that copurify with viral DNA ( Table B in S1 Text ) . Therefore , ICP4 may provide a means to target these complexes to viral DNA . Comparison of proteins identified by aniPOND of mutant genomes and wild type genomes ( Tables 1–6 , Mutant vs . KOS ) reveal similar trends and in almost all cases the same proteins were found to be associated with both genomes . In fact , the most obvious difference is that viral peptides for UL2 and UL50 gene products were not enriched on UL2/UL50 mutant genomes but were enriched on wild type genomes ( Table 1 ) . This provides validation for these mutants not expressing UL2 and UL50 gene products and supports the use of mutant genomes for the purification and identification of virus-associated proteins . To provide support for the specificity of iPOND and aniPOND methods for the purification of bona fide viral genome associated proteins , we searched the Contaminant Repository for Affinity Purification ( CRAPome ) [36] for cellular proteins identified by these methods ( Fig F in S1 Text ) . This web-based database includes 411 datasets of common contaminants present in negative controls for protein purification . Most proteins that were identified in this study were found in less than 20% of the negative control datasets , consistent with specific enrichment of viral genome associated proteins by these methods . Taken together , aniPOND is an alternative method for the purification of virus-associated proteins and may be more useful in situations were few genomes are present ( for example before DNA replication ) or when genomes are not efficiently labeled with nucleoside analogs ( for example wild type KOS ) . Furthermore , the combination of both methods reveals a comprehensive look at proteins associated with viral genomes . To better visualize the reorganization of host nuclear factors to viral replication compartments during lytic infection with HSV , we used immunofluorescence to compare the distribution of cellular factors in the nucleus of mock-infected cells to cells infected with KOS for 8 hours ( Fig 5 ) . Ten cellular proteins that were identified by iPOND and/or aniPOND , including replication proteins PCNA and TOP2 , transcription factors TFII-I , Spt5 , Spt6 , and XPD , chromatin remodeling factors SSRP1 , HMGB1 , and HDAC2 , as well as the repair protein Ku70 were tested for colocalization with viral DNA . In all cases , the cellular proteins relocalized to viral replication compartments . Interestingly , these cellular factors were relocated from multiple locations within the nucleus . Taken together , it is clear that HSV infection induces gross reorganization of the host nucleus and compartmentalization of cellular factors that likely participate in multiple aspects of the virus life cycle . Micrococcal nuclease digestion assays indicate that packaged genomes are not associated with nucleosomes , only a small portion of incoming unreplicated genomes are associated with nucleosomes , and newly replicated genomes are not associated with nucleosomes [37–39] . However , ChIP mapping data indicate that histones are bound to many HSV promoters and genes , and often have marks of active chromatin [40 , 41] . The working model is that histones are present on viral genomes during early lytic infection , the distribution and density of histones on lytic genomes is significantly less than the host genome , and histones likely play a role in the regulation of viral gene expression . In contrast , latent genomes are associated with ordered chromatin similar to host cell DNA [42] . Many components of chromatin remodeling complexes were identified on replicated viral genomes by iPOND and aniPOND ( Table 4 ) . These include members of the FACT , INO80 , NURD , and SWI/SNF chromatin remodeling complexes , as well as DNA and chromatin modifying enzymes . However , histones were not enriched on purified replicated genomes , with the exceptions of a few histone H1 variants , which were also abundant in negative controls ( Table 4 and Fig F in S1 Text ) . Perhaps chromatin remodeling factors associate with viral DNA to facilitate the removal of histones or to keep histones from binding to newly replicated genomes . To provide support for the absence of histones on replicating genomes , colocalization of viral genomes with histones was assayed by fluorescence microscopy . EdU-labeled viral replication compartments were tagged with Alexa Fluor 488 and either histone H1 ( all subtypes; Fig 6A ) or H3 ( 6B ) was labeled with specific antibodies for immunofluorescence . Less dense localization of both histones was observed with viral DNA relative to cellular DNA . This localization pattern greatly contrasts the pattern observed for proteins that were identified to associate with viral genomes ( Fig 5 ) . These data support iPOND and aniPOND results and confirm that histones are not enriched on viral genomes during DNA replication . To assay for the colocalizaiton of viral genomes with histones during early lytic infection , fluorescence imaging of prelabeled incoming viral genomes was carried out at 2 hpi ( Fig 7 ) . Histones H1 and H3 did not colocalize with incoming viral genomes , at least within limits of detection by immunofluorescence . This is in stark contrast to the pattern of ICP4 colocalization with incoming genomes . In conclusion , iPOND , aniPOND , and imaging data provide support for a deficiency of histones on viral genomes throughout lytic infection
In addition to UL29 , UL30 , and UL42 , four other viral replication factors , UL9 ( origin binding protein ) and UL5/UL8/UL52 ( helicase primase complex ) were enriched on viral genomes . In contrast to iPOND studies of cellular replication forks [23 , 26 , 28] , most cellular DNA replication proteins did not copurify with viral genomes . However , the cellular processivity factor PCNA and topoisomerases TOP1 , TOP2a , and TOP2b were reproducibly enriched on replicating viral genomes . iPOND data indicate that the levels of PCNA on viral genomes is higher at 6 hpi than at 8 and 12 hpi , suggesting that PCNA may play a role in early phases of viral DNA replication . Topoisomerases are important for relaxing supercoiled DNA as a consequence of helicase unwinding during replication and transcription [43] , and likely carryout this same function on viral DNA . PCNA ( Fig 5 ) [44] and Top2 ( Fig 5 ) redistribute to viral replication compartments during viral DNA replication , however a direct role in HSV replication has yet to be demonstrated . Currently , there is not a good system to study origin-primed viral DNA replication in vitro [20] . It is possible that cellular PCNA or topoisomerases are the missing players in these reconstitution assays . We also identified several components involved in double strand break ( DSB ) recognition and repair associated with replicating viral genomes in our assays . These include Ku70 and Ku80 , the Mre11/Rad50/Nbs1 ( MRN ) complex , ataxia telangiectasia mutated ( ATM ) , and the catalytic subunit of DNA dependent protein kinase ( DNA-PKcs ) ( Table 5 ) . Ku70 ( Fig 5 ) and Ku80 [45] colocalize with viral replication compartments . However , Ku70 expression is inhibitory for viral DNA replication [45] . Perhaps , these proteins participate in a cellular antiviral response in attempt to control virus multiplication . The MRN complex , ATM , and activation of the DNA damage response are beneficial for HSV genome replication [46–48] . The MRN complex and ATM are recruited to viral replication compartments and ATM is activated through autophosphorylation to trigger the DNA damage response and cell cycle arrest through multiple pathways . In this way , the cell recognizes the viral genome as DNA damage . However , downstream binding of cellular proteins that mediate repair through nonhomologous end joining ( NHEJ ) and homologous recombination ( HR ) pathways are inhibited by the actions of the viral E3 ubiquitin ligase , ICP0 [49–51] . ICP0 targets downstream factors in these double strand break repair pathways for degradation , including DNA-PKcs , RNF8 , and RNF168 . Consistent with these data , we did not identify RNF8 or RNF168 to be recruited to viral DNA in our assays . Purification of DNA-PKcs is not inconsistent with these observations because only 50% of DNA-PKcs is degraded by ICP0 and this is likely cell type specific [45] . These data support a scenario whereby viral genomes trigger the DNA damage response and cell cycle arrest to create an environment that is conducive to viral DNA replication . ICP0 may inhibit the actions of cellular HR and NHEJ pathways for the repair of virus ends , as well as nicks and gaps that occur during viral DNA replication . It is possible that HSV-1 instead uses its own machinery for recombination and repair during DNA replication , mediated by the actions of ICP8 and UL12 ( alkaline nuclease ) [52 , 53] . In fact , UL12 has been shown to interact with components of the MRN complex and may therefore act with the MRN complex to carryout virus specific recombination [54] . The structural maintenance of chromosomes ( SMC ) family of ATPases function to stabilize and organize chromosomes during mitosis [55] . Of these complex members , SMC1 and SMC3 , which make up the core of the cohesion complex , reproducibly copurify with replicating viral genomes . The cohesion complex is essential for sister chromatid cohesion during mitosis , but also plays a role in transcription and DNA repair by recombination [56] . Cohesin complex proteins SMC3 and Rad21 have previously been shown to associate with Epstein-Barr virus genomes [57 , 58] . Perhaps these proteins are involved in HSV gene expression or recombination during DNA replication . Mismatch repair [59] and base excision repair [60] pathways also function in maintaining HSV genomes , and specific factors involved in both of these types of repair were found to be associated with viral genomes in this study ( Table 5 ) . RNA processing factors involved in all steps in pre-mRNA processing including capping , splicing , polyadenylation , and export were abundant on viral genomes ( Table 6 ) . Interestingly , ICP27 , an essential viral immediate early gene product that regulates all steps in viral RNA processing [61] was not readily detectable on genomes . However , the TREX complex was found in our studies , which has been shown to interact with ICP27 [62 , 63] and to be involved in the export of KSHV intronless mRNAs [64] . RNA helicases , which are involved in all aspects of RNA metabolism , as well as components of the nuclear transport machinery were also found associated with viral genomes . The abundant isolation of all of these RNA processing factors is most likely consistent with the high level accumulation of viral mRNA late after infection and the fact that transcription and RNA processing are coupled [65–67] . Multiple components of several chromatin remodeling complexes were enriched on viral genomes including the FACT , INO80 , NURD , and SWI/SNF complexes ( Table 4 ) . This is consistent with proteomic analysis of proteins bound to ICP4 extracted from virus infected cells , in which components of INO80 , NURD , and SWI/SNF complexes were identified [34] . One of the FACT complex members , SPT16 , was shown to copurify with ICP8 in the absence of DNAse treatment [45] and here we demonstrated the redistribution of the other FACT complex member SSRP1 to viral replication compartments ( Fig 5 ) . As discussed above , histones were not enriched on viral genomes , raising the possibility that these complexes maintain a nucleosome or histone free state , greatly facilitating processes such as replication and transcription on the genome . The FACT complex has been shown to disrupt nucleosome structure and allow DNA and RNA polymerases to access the DNA [68] , the INO80 complex mediates nucleosome sliding [69] , the NURD complex has both histone deacetylase and nucleosome remodeling functions [70] , and high mobility group ( HMG ) proteins , which are also found on viral genomes , have been shown to increase accessibility of chromatin-bound DNA [71] . Furthermore , the INO80 and FACT complexes have also been implicated in cellular DNA damage repair by homologous recombination [72 , 73] , and may therefore also play roles in mechanisms of viral DNA recombination . HMGB1 was previously shown to function as a coactivator for ICP4 mediated transcription in vitro [74] , and may therefore function to mediate promoter specific activation of viral genes . RNA polymerase II ( polII ) was abundant on isolated viral genomes ( Table 3 ) , with RPB1 and RPB2 being the most enriched subunits , most likely because they make direct contact with DNA during transcription [75] . The transcription elongation factors Spt5 , Spt6 [76] , and Trim28 [77] were also found associated with viral genomes and Spt5 and Spt6 were shown to relocalize to viral replication compartments ( Fig 5 ) . These are therefore likely candidates to regulate elongation during HSV transcription . TFII-I binds to initiator ( inr ) elements in cellular promoters [78] and therefore may play a role in the expression of late viral genes . The viral transcriptional regulators VP16 , ICP4 , and ICP22 were found on viral genomes by both iPOND and aniPOND . ICP22 was previously found to associate with ICP4 and RNA polII in transcription complexes [79] and to mediate phosphorylation of polII [80] . VP16 is a tegument protein that activates transcription of immediate early viral genes [11] . ICP4 regulates expression from early and late HSV promoters and repression of immediate early promoters . It interacts with TFIID , TFIIH , and a specific form of the mediator complex that lacks Med26 and contains the kinase domain [34 , 35] . Here we show that viral genomes copurified with subunits of TFIID , TFIIH , and the same form of the mediator complex that copurified with ICP4 from Vero [34] and resting MRC-5 cells ( Table B in S1 Text ) . This form of mediator possesses the kinase domain , but lacks med26 , and thus may be involved in repression , possibly of immediate early promoters late after infection . The viral genes transcribed late after infection all possess relatively simple TATA box-containing promoters , yet are abundantly transcribed . The accumulated data support a model where ICP4 plays an integral role in recruiting most of the key polII transcription factors , such as TFIID , required for abundant late transcription . This study has provided a comprehensive view of the viral and cellular proteins associated with replicating HSV genomes and provides new insight into cellular mechanisms that regulate HSV infection . The presence of cellular proteins involved in a variety of nuclear processes is consistent with the rapid and high level of accumulation of viral transcripts , replicated genomes , and progeny virions shortly after infection . This must be accompanied by the recombination and repair of replicating genomes . It is probable that the association and function of these factors is facilitated by the relative dearth of cellular chromatin , which may be a function of the recruitment of multiple chromatin remodeling complexes . In this model , ICP4 binding to the genome , may have multiple roles in recruiting chromatin remodeling complexes and key polII transcription complexes , although a direct role of ICP4 in chromatin organization has yet to be demonstrated . What remains to be studied is to what extent this state is determined prior to the onset of viral DNA replication .
Experiments were performed using MRC-5 ( human embryonic lung ) or Vero ( African green monkey kidney ) cells obtained from and propagated as recommended by ATCC . The viruses used in this study include the wild type HSV-1 strain , KOS , as well as UL2 , UL50 , and UL2/UL50 mutant viruses . Mutants were generated in bacterial artificial chromosomes ( BACs ) containing full-length , infectious KOS DNA [81] using two-step red-mediated recombination [82 , 83] . To generate the UL2 null virus , the cassette GGCTAGTTAACTAGCC , which contains a premature termination codon in all three reading frames , as well as an HpaI restriction site for validation , was inserted after the codon for cysteine 75 of the UL2 open reading frame . For the UL50 null virus , the codon for alanine 110 was replaced with this cassette . Mutant KOS-BAC constructs were transfected using Lipofectamine 2000 Transfection Reagent ( Life Technologies ) and propagated in Vero cells . Viral DNA was isolated from individual plaques [84] and screened for mutations by Southern blotting [85] . To generate prelabeled virus stocks , 1x108 Vero cells were infected with unlabeled KOS or UL2/UL50 at an MOI of 10 PFU/cell at 37°C for 1 hour . After rinsing with tris-buffered saline ( TBS ) to remove unadsorbed virus , media was replaced with Dulbecco’s Modified Eagle Medium ( DMEM ) containing 5% fetal bovine serum ( FBS ) . Four hpi , EdU ( Sigma-Aldrich ) was added to the growth medium at the indicated concentration and incubated for an additional 34–36 hours . Monolayers were harvested , freeze-thawed three times at -80°C , sonicated , and clarified by low-speed centrifugation . Viral titers were determined by plaque assay on Vero cells . A total of 2x105 Vero cells were grown on glass coverslips in 12-well dishes . Infections were carried out at an MOI of 10 in 100 μl TBS for 1 hour at room temperature . After infection , inoculum was removed and cells were rinsed with 1 ml TBS prior to addition of 1 ml DMEM plus 5% FBS . Infections were carried out at 37°C for the indicated period of time , with or without the addition of EdU to the growth medium . Cells were fixed with 3 . 7% formaldehyde for 15 min , washed two times with phosphate-buffered saline ( PBS ) , permeabilized with 0 . 5% Triton-X 100 for 20 min , and blocked with 3% bovine serum albumin ( BSA ) for 30 min . EdU-labeled DNA was conjugated to Alexa Fluor 488 azide using the Click-iT EdU imaging kit according to manufacturer’s protocol ( Life Technologies ) . Cells were rinsed with PBS plus 3% BSA , then PBS , labeled with Hoechst 33342 ( 1:2000 dilution ) for 30 min , washed two times with PBS , then incubated with primary antibody ( mouse anti-ICP4: 58S , 1:500; mouse anti-histone H1: ab4269 ( Abcam ) , 1:1000; rabbit anti-histone H3: ab1791 ( Abcam ) , 1:1000; mouse anti-PCNA: sc-056 ( Santa Cruz ) , 1:200; mouse anti-topoisomerase 2: NA14 ( Calbiochem ) , 1:20; goat anti-TFII-I: sc-9943x ( Santa Cruz ) , 1:200; rabbit anti-Spt5: A300-869A ( Bethyl Laboratories ) , 1:200; rabbit anti-Spt6: ab32820 ( Abcam ) , 1:200; rabbit anti-TFIIH p89 ( XPD ) : sc-293 ( Santa Cruz ) , 1:200; mouse anti-SSRP1: 10D1 ( Biolegend ) , 1:200; rabbit anti-HMGB1: ab18256 ( Abcam ) , 1:1000; rabbit anti-HDAC2: sc-7899 ( Santa Cruz ) , 1:200; rabbit anti-Ku70: sc-9033 ( Santa Cruz ) , 1:200 ) and Alexa Fluor 594-conjugated secondary antibodies ( Santa Cruz , 1:500 ) as described previously [34] . Images were obtained using an Olympus Fluoview FV1000 confocal microscope . iPOND was carried out as described previously [25] with the following modifications . For each condition , three 500 cm2 tissue culture dishes containing confluent monolayers of MRC-5 cells ( ~7x107 cells/dish ) were infected with UL2/UL50 double mutant virus at an MOI of 10 PFU/cell for one hour at room temperature . After adsorption , the inoculum was removed and cells were rinsed with TBS before addition of fresh DMEM plus 5% FBS . Cells were incubated at 37°C for the indicated period of time before addition of EdU at a final concentration of 2 . 5 μM . After incubation for an additional 2–4 hours , cells were fixed with 1% ( wt/vol ) formaldehyde in PBS for 15 min at room temperature , quenched with 125 mM glycine , and harvested by scraping . Cell permeabilization , click chemistry , cell lysis , sonication , and streptavidin capture were carried out as described except the samples were sonicated 6 times for 30 sec each at 7 watts using a Cole-Palmer ultrasonic processor with microtip . For each condition , samples from three plates were combined , and proteins were eluted from streptavidin-coated beads by boiling in 200 μl 2x SDS Laemmli sample buffer to reverse formaldehyde crosslinks . aniPOND was carried out as described previously [33] with the following modifications . For each condition , one 500 cm2 tissue culture dish containing a confluent monolayer of MRC-5 cells ( ~7x107 cells ) was infected with wild type KOS or UL2/UL50 double mutant virus at an MOI of 10 PFU/cell for one hour at room temperature . After adsorption , the inoculum was removed and cells were rinsed with TBS before addition of fresh DMEM plus 5% FBS . Cells were incubated at 37°C for four hours before the addition of EdU ( Sigma-Aldrich ) at a final concentration of 2 . 5 μM , followed by an additional four-hour incubation . To detach the monolayer and extract nuclei , 20 ml nuclear extraction buffer was added directly to each plate , incubated at 4°C for 15 min , and harvested by scraping . Cell washes , click chemistry , cell lysis , sonication , and streptavidin capture were carried out as described except for cell lysis cells were incubated for 30 min total in lysis buffer and sonicated 8 times for 30 sec each at 7 watts . Proteins were eluted from streptavidin-coated beads by boiling in 66 μl 2x SDS Laemmli sample buffer . For aniPOND experiment 2 , two plates of MRC-5 cells were used for analysis and for protein elution , streptavidin-coated beads from both samples were combined and proteins were eluted in 66 μl 2x sample buffer to generate a 2x concentrated sample . SDS polyacrylamide gel electrophoresis and western blotting were carried out as described previously [86] . Proteins were transferred to polyvinylidine fluoride membranes ( Amersham ) for chemi-luminescent detection with ECL reagent ( Amersham ) . For detection of ICP4 , membranes were probed with the 58S polyclonal mouse antibody ( 1:5000 dilution ) . Mass spectrometry was carried out by MSBioworks . The entire sample was separated ~1 . 5cm on a 10% Bis-Tris Novex mini-gel ( Invitrogen ) using the MES buffer system . The gel was stained with coomassie and excised into ten equally sized segments . Gel segments were processed as described and analyzed by nano liquid chromatography with tandem mass spectrometry ( LC/MS/MS ) [87] . Data were searched using Mascot and Mascot DAT files were parsed into the Scaffold software for validation , filtering , and to create a nonredundant list per sample . Data were filtered at 1% protein and peptide level false discovery rates and requiring at least two unique peptides per protein . Proteins were considered most significantly enriched by iPOND or aniPOND based on the following criteria: 1 ) protein had at least 5 spectral counts ( SpC ) in the experimental sample , 2 ) protein was not detected in the control or was enriched over the control by at least four-fold based on dividing SpC values , and 3 ) was detected in duplicate experiments . Raw SpC data without normalization are presented in Tables 1–6 and Table A in S1 Text . DNA was isolated from 1/20th volume cell lysates or 1/10th volume streptavidin-coated beads during iPOND and aniPOND experiments . For isolation of DNA from cell lysates , an equal volume of 2x SDS-bicarb solution ( 2% SDS , 0 . 2 M NaHCO3 ) was added to the sample and for isolation of bead-bound DNA , beads were resuspended in 1x SDS-bicarb solution . Samples were incubated at 65°C overnight , followed by extraction with phenol:chloroform:isoamyl alcohol ( 25:24:1 ) and chloroform:isoamyl alcohol ( 24:1 ) . DNA was recovered using the MinElute PCR Purification kit ( Qiagen ) . DNA concentrations were measure using a Quibit Fluorometer and the Qubit dsDNA HS Assay Kit ( Life Technologies ) . qRT-PCR was carried out as described previously [88] . The HSV-1 TK gene was amplified to estimate the amount of viral DNA in each sample . Primers used for amplification of the HSV TK gene were TkdsF1 ( 5´-ACCCGCTTAACAGCGTCAACA-3´ ) and TkdsR1 ( 5´-CCAAAGAGGTGCGGGAGTTT-3´ ) . Standard curves were generated using purified KOS DNA . | HSV-1 is a human pathogen that infects over 50% of the population . The virus persists as a latent infection in the ganglia of an infected host and upon stressful conditions is reactivated to a lytic state in which it causes recurrent sores at the initial site of infection . During lytic infection , HSV highjacks the host cell to propagate its genome and produce new virus particles . However , there is limited knowledge of what cellular proteins interact with and function on the viral genome . We therefore developed methods to purify viral genomes from productively infected cells to identify associated viral and cellular proteins . We found proteins and protein complexes that have previously been implicated in HSV infection to be enriched on viral genomes , as well as several novel proteins that are likely involved in productive infection . These data provide valuable insight into HSV biology . Furthermore , these methods can be adapted to study other viruses , as well as other aspects of the HSV life cycle . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Selective Recruitment of Nuclear Factors to Productively Replicating Herpes Simplex Virus Genomes |
Chikungunya virus ( CHIKV ) is a globally re-emerging arbovirus for which previous studies have indicated the majority of infections result in symptomatic febrile illness . We sought to characterize the proportion of subclinical and symptomatic CHIKV infections in a prospective cohort study in a country with known CHIKV circulation . A prospective longitudinal cohort of subjects ≥6 months old underwent community-based active surveillance for acute febrile illness in Cebu City , Philippines from 2012-13 . Subjects with fever history were clinically evaluated at acute , 2 , 5 , and 8 day visits , and at a 3-week convalescent visit . Blood was collected at the acute and 3-week convalescent visits . Symptomatic CHIKV infections were identified by positive CHIKV PCR in acute blood samples and/or CHIKV IgM/IgG ELISA seroconversion in paired acute/convalescent samples . Enrollment and 12-month blood samples underwent plaque reduction neutralization test ( PRNT ) using CHIKV attenuated strain 181/clone25 . Subclinical CHIKV infections were identified by ≥8-fold rise from a baseline enrollment PRNT titer <10 without symptomatic infection detected during the intervening surveillance period . Selected CHIKV PCR-positive samples underwent viral isolation and envelope protein-1 gene sequencing . Of 853 subjects who completed all study procedures at 12 months , 19 symptomatic infections ( 2 . 19 per 100 person-years ) and 87 subclinical infections ( 10 . 03 per 100 person-years ) occurred . The ratio of subclinical-to-symptomatic infections was 4 . 6:1 varying with age from 2:1 in 6 month-5 year olds to 12:1 in those >50 years old . Baseline CHIKV PRNT titer ≥10 was associated with 100% ( 95%CI: 46 . 1 , 100 . 0 ) protection from symptomatic CHIKV infection . Phylogenetic analysis demonstrated Asian genotype closely related to strains from Asia and the Caribbean . Subclinical infections accounted for a majority of total CHIKV infections . A positive baseline CHIKV PRNT titer was associated with protection from symptomatic CHIKV infection . These findings have implications for assessing disease burden , understanding virus transmission , and supporting vaccine development .
Chikungunya virus ( CHIKV ) is a re-emerging mosquito-borne pathogen that has rapidly expanded its geographic reach over the past decade in Africa , Asia , the Indian and Pacific Ocean regions , and Europe . In December 2013 , the first case of autochthonous CHIKV infection was confirmed in the Americas on the Caribbean island of Saint Martin and quickly spread to other Caribbean islands and parts of Central/South America [1 , 2] . The continental United States confirmed its first locally acquired case in Florida in July 2014 [3] . Given the large number of travelers to currently affected areas and the widespread distribution of appropriate Aedes mosquito vectors , spread to other parts of the Americas and Europe is likely [2 , 4] . CHIKV is a 12kb single-stranded , positive-sense RNA virus in the genus Alphavirus and family Togaviridae . CHIKV was first isolated from a patient in Tanzania during a febrile illness outbreak in 1952–53 [5 , 6] . The primary vectors in large human outbreaks are Aedes aegypti and Aedes albopictus with humans serving as the amplifying host in urban settings . Three CHIKV genotypes are known to circulate: West African , East/Central/South African ( ECSA ) , and Asian . In 2005 , CHIKV re-emerged in widespread epidemics in the Indian Ocean region and subsequently in Asia , perhaps aided by an A226V mutation in the envelope protein-1 ( E1 ) gene of the ECSA genotype allowing the virus to spread more efficiently in Ae . albopictus [7] . In the current Caribbean epidemic , the circulating CHIKV has been identified as Asian genotype closely related to strains from China ( 2012 ) , the Philippines ( 2012 ) , and Micronesia/Yap ( 2013 ) [8] . Although Ae . aegypti is thought to be the main vector in the Caribbean outbreak , both Ae . aegypti and Ae . albopictus populations from different regions of the Americas may be able to transmit all three CHIKV genotypes [9] . The classic clinical presentation of chikungunya consists of febrile illness with symmetric polyarthralgia in the extremities [10] . In a minority of patients , arthralgias/arthritis can persist for months or even years . Severe or atypical disease is more likely in neonates , older adults and those with chronic medical conditions . Previous studies have demonstrated that a majority of infections present with acute fever accompanied by other overt symptoms . However , assumptions about the clinical presentation of CHIKV infection have largely been based on sentinel surveillance or cross-sectional serosurveys [11 , 12 , 13 , 14] . At present , no licensed chikungunya vaccine exists although several are in development [15 , 16] . Efficacy trials of vaccine candidates have been hampered by the unpredictability of chikungunya incidence in the field [15 , 17] . Development efforts would be enhanced by a better understanding of infection risk in endemic areas and the establishment of immune correlates of risk and protection . Here , we present results from a prospective longitudinal seroepidemiological cohort study of acute febrile illness conducted in Cebu City , Philippines from 2012–13 . Chikungunya was first reported in the Philippines in the 1950’s with sporadic but infrequent outbreaks occurring over the next several decades [18 , 19 , 20] . Since 2011 , an increasing number of chikungunya outbreaks have been reported to the Philippine Department of Health ( DOH ) [20] . However , no prospective longitudinal community-based cohort study has detected a meaningful number of confirmed CHIKV infections . In this first year of a two-year study , we sought to characterize the proportion of subclinical and symptomatic CHIKV infections . We were able to identify >12 total CHIKV infections per 100 person-years of surveillance and classify these as subclinical or symptomatic .
The study was approved by the Institutional Review Boards of Vicente Sotto Memorial Medical Center ( Philippine DOH ) in Cebu City , Philippines , and the Walter Reed Army Institute of Research . Written informed consent was obtained from subjects ≥18 years old and from parents of subjects <18 years old . Written assent was obtained from children ≥12 years old . The study was conducted in Cebu City , Philippines , an urban center of >800 , 000 residents located within the central Visayas region , one of three major island groups in the Philippines . All subjects were enrolled from the community of Punta Princesa , a dense urban area of 0 . 96 sq km with approximately 27 , 000 residents ( Philippine National Statistics Office , 2010 ) . A prospective longitudinal community-based seroepidemiological cohort study of acute febrile illness was initiated in 2012 in Cebu City primarily to evaluate the incidence of influenza and dengue virus infections and secondarily to evaluate the incidence of other causes of acute febrile illness . From March-May 2012 , subjects ≥6 months of age were recruited by door-to-door canvassing within the community . Approximately 200 subjects were targeted for enrollment in each of five age groups: 6 months-5 years , 6–15 years , 16–30 years , 31–50 years , and >50 years old . Since this was an exploratory study , the target size of the cohort was largely based on logistical considerations . Only one subject per household was enrolled . At enrollment , demographic and health questionnaires were administered and baseline blood samples collected . Subjects were instructed to report any fevers during the study period , and were monitored for acute febrile illnesses by weekly telephone calls or home visits . Any fever history within the prior seven days triggered an acute illness visit by study nurses who performed a clinical assessment that included a standardized symptoms questionnaire and acute blood collection . All acute blood samples were collected within seven days of illness onset . Follow-up visits to assess clinical status were conducted at 2 , 5 and 8 days after the acute visit . Nasal and throat swabs were also collected at the acute , 2 , 5 and 8 day visits but are not part of the current analysis . At 3 weeks , a convalescent visit was performed that included a clinical assessment and convalescent blood collection . Twelve months after enrollment ( approximately March-June 2013 ) , routine follow-up visits were conducted that included blood collections . All blood samples were processed into serum aliquots on the day of collection and stored at -70°C . These banked sera were available for further chikungunya testing . Acute serum samples were tested by reverse transcriptase polymerase chain reaction ( RT-PCR ) to detect CHIKV RNA [21 , 22] . Paired acute and 3-week convalescent serum samples were tested by an in-house CHIKV IgM/IgG enzyme-linked immunosorbent assay ( ELISA ) [23] . Paired enrollment and 12-month serum samples were tested by CHIKV plaque reduction neutralization test ( PRNT ) [23] . Acute symptomatic CHIKV infection was defined as a febrile illness with positive CHIKV PCR in the acute sample , or positive CHIKV IgM ELISA in the acute and/or convalescent samples with rising IgM levels , or ≥4-fold rise in CHIKV IgG ELISA in the paired acute/convalescent samples . Subclinical CHIKV infection was defined as ≥8-fold rise in the 12-month PRNT titer from a baseline enrollment titer of <10 with no acute symptomatic CHIKV infection detected during the intervening surveillance period . An enrollment PRNT titer ≥10 was considered to indicate past CHIKV infection . Descriptive statistics for infection rates , symptoms and other characteristics were performed . Associations among categorical variables were estimated and tested for significance using chi-square or Fisher’s exact test as appropriate . P-value <0 . 05 was considered significant . Exact odds ratio ( OR ) and confidence interval ( CI ) for baseline PRNT status were calculated using conditional maximum likelihood estimates . Percent protection attributable to PRNT status was calculated as 100* ( 1-OR ) . All analyses were performed using R version 3 . 0 . 2 ( R Foundation for Statistical Computing , Vienna , Austria ) .
A total of 1007 subjects with approximately equal gender distribution were enrolled in the cohort with about 200 subjects contained in each of the five age groups ( Table 1 ) . Two hundred seventy ( 270 ) acute febrile illnesses were detected with 267 ( 98 . 9% ) acute and 261 ( 96 . 7% ) convalescent blood samples collected among 223 subjects . Twenty ( 7 . 5% ) of 267 acute samples were CHIKV PCR-positive from 20 different individuals , of which all were also positive by ELISA . All subjects with ELISA seroconversion between acute and convalescent samples were also PCR-positive in the acute sample . Of the initial 1007 enrolled subjects , 853 completed all study activities per-protocol including enrollment and 12-month blood collections . Among these 853 per-protocol subjects , 19 symptomatic CHIKV infections occurred during 867 person-years of surveillance . An additional 87 subclinical CHIKV infections occurred in these per-protocol subjects based on ≥8-fold rise in 12-month PRNT titer from a baseline enrollment titer of <10 ( Fig 1 ) [Note: PRNT values using 50% plaque reduction in addition to 80% were also calculated for all PRNT results . Two subjects meeting the definition of “subclinical infection” by PRNT80 values but with baseline enrollment PRNT50 titer of ≥10 were not included among the 87 subclinical infections due to the possibility of prior CHIKV infection . ] . The 12-month PRNT titers ranged from 229 to 2 , 030 in symptomatic infections and from 64 to 3 , 347 in subclinical infections ( Fig 2 ) . The incidence of symptomatic CHIKV infection per-protocol , therefore , was 2 . 19 per 100 person-years ( 95%CI: 1 . 36 , 3 . 35 ) , and of subclinical infection per-protocol was 10 . 03 per 100 person-years ( 95%CI: 8 . 09 , 12 . 31 ) . Sequencing of the E1 gene from five PCR-positive samples/isolates revealed all five to be Asian genotype ( GenBank accession numbers KM014692 to KM014696 ) , closely related to strains from New Caledonia ( 2011 ) , China ( 2012 ) , Micronesia/Yap ( 2013 ) , Saint Martin ( 2013 ) and British Virgin Islands ( 2014 ) ( Fig 3 ) . The incidence of symptomatic CHIKV infection in different age groups ranged from 0 . 55 ( >50 years old ) to 4 . 23 ( 6–15 years old ) per 100 person-years ( see Table 2 for rates in each age group with confidence intervals ) . The incidence of subclinical CHIKV infection ranged from 6 . 46 ( 6 months-5 years old ) to 12 . 68 ( 6–15 years old ) per 100 person-years ( Table 2 ) . The ratio of subclinical-to-symptomatic infections ranged from 2:1 ( 6 months-5 years old ) to 12:1 ( >50 years old ) . Seroprevalence at enrollment of positive CHIKV PRNT titer ( i . e . , ≥10 ) increased with each older age group although the degree of increase with each age group varied ( Table 2 ) . Among just those subjects with negative CHIKV PRNT at enrollment , the incidence of symptomatic CHIKV infection ranged from 1 . 41 ( >50 years old ) to 4 . 27 ( 6–15 years old ) per 100 person-years; subclinical CHIKV infection ranged from 6 . 51 ( 6 months-5 years old ) to 26 . 7 ( 31–50 years old ) per 100 person-years; total CHIKV infection ranged from 9 . 77 ( 6 months-5 years old ) to 30 . 51 ( 31–50 years old ) per 100 person-years ( see Table 3 for rates in each age group with confidence intervals ) . The ratio of subclinical-to-symptomatic infections was the same as in all per-protocol subjects since all CHIKV infections occurred in subjects with negative baseline PRNT titer . Clinical features of all 20 symptomatic CHIKV infections ( of which 19 occurred in per-protocol subjects ) are shown in Table 4 . Only two ( 10% ) subjects sought medical care; one was hospitalized . Arthralgia was present in nine ( 45% ) cases: 3/14 ( 21% ) subjects ≤15 years old and 6/6 ( 100% ) subjects >15 years old . At the 3-week convalescent visit , symptoms had completely resolved in 18 ( 90% ) subjects . The remaining two subjects had rhinorrhea/nasal congestion and/or cough at 3 weeks . The relationship between occurrence of symptomatic CHIKV infection and baseline enrollment CHIKV PRNT titer is shown in Table 5 . PRNT data was available only in per-protocol subjects . Among the 853 per-protocol subjects , all 19 symptomatic CHIKV infections occurred in subjects with baseline PRNT titer <10 ( Table 5 ) . A positive baseline CHIKV PRNT titer ( i . e . , ≥10 ) was associated with 100% ( 95%CI: 46 . 1 , 100 . 0 ) protection from symptomatic CHIKV infection . A positive baseline PRNT titer was present in 239 per-protocol subjects with titers ranging from 10 to 1 , 785 ( Fig 2 ) .
The incidence of CHIKV infection in our study conducted in a non-naïve population , where CHIKV has been endemic for decades , was relatively high with the ratio of subclinical-to-symptomatic infections notably greater than previous estimates [6 , 11 , 12 , 13 , 14 , 17] . The incidence and relative proportion of subclinical infection appeared to be age-dependent . This is one of the first prospective longitudinal seroepidemiological cohorts undergoing active surveillance with sufficient chikungunya incidence to characterize the proportion of subclinical and symptomatic CHIKV infections in an endemic region . In so doing , our results support CHIKV PRNT titer as a potential immune correlate of protection from infection . The proportion of subclinical CHIKV infections in our study was 82 . 0% compared to 3 . 8–27 . 7% in prior studies [6] . This difference could be due to our study design in which baseline blood samples were collected in a defined community-based cohort prior to infection followed by the implementation of a sensitive active surveillance system to detect infection . Thus , we were able to characterize subclinical versus symptomatic infections more accurately than past seroprevalence studies which relied on obtaining retrospective histories of symptoms consistent with chikungunya [11 , 12 , 13 , 14] . Of note , since the active surveillance in our study focused on detecting febrile illness , it is possible that some subjects with “subclinical” infection may have had no fevers but still had non-febrile symptoms . For example , arthralgia without fever has been documented in a small percentage of acute CHIKV infections in prior studies [29 , 30] . Additionally , the genotype or strain of the infecting virus may have been a factor in the high proportion of subclinical infections . Most studies that have estimated the rate of subclinical infections have been conducted during ECSA genotype outbreaks whereas our study involved Asian genotype [11 , 12 , 13 , 14] . Interestingly , a large study of chikungunya caused by Asian genotype in 1962 in Bangkok , Thailand , estimated that 25% of Bangkok children had experienced CHIKV infection based on hemagglutination inhibition ( HAI ) seroconversion between the start and end of rainy season while up to 7% of all Bangkok children were estimated to have sought outpatient medical care for chikungunya , implying the majority of infected children had subclinical or mild infection [31] . A recent small study of blood donors from the Caribbean island of Saint Martin in 2014 estimated a substantial asymptomatic rate of 40% [32] . Clearly , the relevance of genotype or strain to clinical presentation requires further investigation . Finally , even the symptomatic CHIKV infections in our study were clinically mild with only two subjects seeking medical care , and all with complete resolution or minimal symptoms by 3 weeks . The overall high rate of both subclinical and mild infections suggests that the more clinically-apparent chikungunya cases typically reported to public health authorities may represent a small fraction of all infections . This has important implications for estimating disease burden , understanding virus transmission , and assessing blood transfusion risk . The role of subclinical and mild CHIKV infections in transmitting virus is incompletely understood . However , two blood donors with asymptomatic infections from the ongoing Caribbean outbreak have been shown to harbor viable CHIKV [33] . This finding suggests that at least some of the subclinical infections in our study could potentially participate in virus transmission . Although CHIKV infections occurred in all age groups , the incidence of total and subclinical infections seemed to vary with age . Considering only subjects with negative CHIKV PRNT titer at enrollment ( among whom all infections occurred ) , the incidence of total infections was lowest in subjects 6 months-5 years old . Moreover , the ratio of subclinical-to-symptomatic infection was higher in individuals >15 years old than in those between 6 months and 15 years old , although the ratio in all ages was quite high . Whether these differences represent typical age-related patterns or were specific to our study setting is unclear . Many factors could contribute to these age differences including age-related behavior patterns possibly leading to higher vector exposure in adolescents and adults , higher mosquito feeding intensity in adults compared to infants and toddlers [34] , and more extensive immune histories in adults than children not necessarily reflected by PRNT titers that may have contributed to disease prevention or mitigation . For example , some older adults with negative baseline PRNT titers may , in fact , have had past infection such that subsequent CHIKV exposure caused a boost in PRNT titer . This situation would be less likely in children and in naïve populations . Thus , in countries where CHIKV has not previously circulated , the proportion of mild and subclinical infections may be lower than in our study . It is also possible that some of the differences among age groups may have been an artifact of relatively low case numbers in some groups . The relatively high incidence of CHIKV infection in our study occurred in the Philippines where sporadic outbreaks have been reported for several decades but with increasing frequency over the past three years . Cebu province has never reported a chikungunya outbreak and has documented very few individual cases including during the current study period ( communication , Dr . Vito G . Roque , Philippine DOH ) . The nonspecific nature of symptoms and the high rate of subclinical and mild infections may partly account for this lack of reported cases . Additional prospective cohort studies with active fever surveillance may be highly informative even in regions with few reported cases . Our study allowed for the evaluation of potential immune correlates of protection from CHIKV infection in humans . Previous animal studies have demonstrated that CHIKV neutralizing antibodies are associated with protection in mice [35 , 36] and non-human primates [37 , 38] . Human studies of CHIKV antibodies following infection have suggested neutralizing antibodies correlate with viral clearance and long-term clinical protection [39 , 40] . Our results demonstrated that a positive baseline CHIKV PRNT titer was associated with protection from symptomatic CHIKV infection in humans . This is one of the first human field studies to find such an association and will contribute to the increasing body of evidence supporting CHIKV neutralizing antibodies as an immune correlate of protection from infection . Although not included among those with symptomatic and subclinical CHIKV infections , four subjects with an enrollment PRNT titer ≥10 had a 4 to 8-fold rise in PRNT titer at 12 months ( increasing from 10 to 42 , 59 to 286 , 83 to 555 , and 162 to 799 , respectively ) , and one subject had a ≥8-fold rise ( from 95 to 959 ) . It is unclear whether these results were due to assay variability or to immunological boosting after virus exposure . The ≥8-fold rise was probably due to the latter . In any event , the small number of such cases and the relatively small increase in titer suggest that most subjects with a positive PRNT titer probably had a certain degree of sterile immunity . Several limitations of the study should be noted . First , the results are from a single study year in a limited study population and geographic area potentially limiting the generalizability of the results . Second , the CHIKV strain identified in this study was Asian genotype which could behave differently from other CHIKV genotypes such as ECSA . Nevertheless , the findings are directly applicable to closely-related Asian strains such as those detected recently from the Caribbean outbreak . Third , although serological cross-reactivity with other alphaviruses is theoretically possible , the relatively large number of CHIKV PCR-positive samples in our study combined with the extremely low CHIKV seroprevalence in subjects ≤15 years old suggesting minimal CHIKV activity over the past 15 years , and the lack of any confirmed non-CHIKV human alphavirus infections in the Philippines make it unlikely that other alphaviruses were responsible for the seropositivity seen in our study . Fourth , although active surveillance was used to identify febrile episodes , some illnesses may have gone undetected leading to an overestimation of subclinical infections . Finally , the relatively low number of symptomatic infections led to relatively large confidence intervals . Therefore , a larger number of CHIKV infections in future studies may be necessary to confirm our findings . In summary , subclinical and mild CHIKV infections may account for a much larger proportion of total infections than previously reported , with substantial underreporting to public health systems . This has implications for accurate assessments of chikungunya disease burden , virus transmission , and blood transfusion risk . The finding that a positive CHIKV PRNT titer was associated with protection from infection will contribute to ongoing vaccine development efforts . Additional prospective longitudinal cohort studies should be done to validate our findings in other regions with known chikungunya activity . | Chikungunya virus ( CHIKV ) is a re-emerging mosquito-borne pathogen for which the majority of infections have been considered to result in febrile illness . We sought to characterize the proportion of subclinical and symptomatic CHIKV infections in a prospective cohort of subjects ≥6 months old who underwent active surveillance for acute febrile illness from 2012–13 in Cebu City , Philippines . Symptomatic CHIKV infections were detected by PCR and/or ELISA in acute/convalescent blood samples . Subclinical infections were identified by neutralizing antibody seroconversion between enrollment and 12-month visits without symptomatic infection . Among 853 subjects who completed all study activities at 12 months , 19 symptomatic and 87 subclinical infections occurred ( 2 . 19 and 10 . 03 per 100 person-years , respectively ) . A positive baseline CHIKV PRNT titer was associated with 100% ( 95%CI: 46 . 1 , 100 . 0 ) protection from symptomatic infection . Phylogenetic analysis showed Asian genotype closely related to strains from the recent Caribbean epidemic . These findings can help to assess disease burden , understand virus transmission , and support vaccine development . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | High Rate of Subclinical Chikungunya Virus Infection and Association of Neutralizing Antibody with Protection in a Prospective Cohort in The Philippines |
Mutator strains are expected to evolve when the availability and effect of beneficial mutations are high enough to counteract the disadvantage from deleterious mutations that will inevitably accumulate . As the population becomes more adapted to its environment , both availability and effect of beneficial mutations necessarily decrease and mutation rates are predicted to decrease . It has been shown that certain molecular mechanisms can lead to increased mutation rates when the organism finds itself in a stressful environment . While this may be a correlated response to other functions , it could also be an adaptive mechanism , raising mutation rates only when it is most advantageous . Here , we use a mathematical model to investigate the plausibility of the adaptive hypothesis . We show that such a mechanism can be mantained if the population is subjected to diverse stresses . By simulating various antibiotic treatment schemes , we find that combination treatments can reduce the effectiveness of second-order selection on stress-induced mutagenesis . We discuss the implications of our results to strategies of antibiotic therapy .
We set up a model of a hypothetical stress-induced mutator ( SIM ) allele; its properties are based on the features of existing SIM mechanisms , yet focusing on the essence of a SIM mechanism independent of the molecular implementation . We are interested in exploring specifically the effectiveness of second-order selection in the evolution of a SIM allele . To do so , we need to isolate second-order selection from any direct benefit of the SIM system . Direct effects , for example faster DNA repair , are likely co-determinants of the persistence of SIM mechanisms in the wild , but such dynamics have also been extensively studied with existing evolutionary models , e . g . [26] . We therefore assume that the SIM allele does not confer any direct fitness cost or benefit , and consider a population of haploid individuals with two non-recombining loci . At the first locus , the SIM allele can be present or absent ( alleles M or m , respectively ) , and the second locus carries alleles that may or may not grant resistance to a given stress ( alleles R or r ) . The resulting four possible genotypes are displayed in Fig 1A and 1B . In the absence of stress , we assume that transitions between the genotypes are only due to mutations , as indicated by the arrows in Fig 1; thus in particular , we assume that there is no cost to being resistant . Individuals may lose or gain resistance at rates μR and νR , respectively . The SIM allele M may lose its function at rate μM; since we are interested in conditions for the ultimate loss of the SIM allele , we neglect back-mutation from m to M . In the stress environment , genotypes containing the resistance allele R have increased fitness w = 1 + s relative to susceptible genotypes . Furthermore , the Mr genotype increases all outgoing mutation rates by a factor σ > 1 due to stress-induced mutagenesis , see Fig 1B . Key assumptions behind this modelling approach are: First , stress does not activate the SIM allele in resistant individuals . This is reasonable if , for example , the stressor is effective inside the cell but the resistant mutation makes the cell membrane impermeable to it . Second , the only cost of an active SIM allele is that it increases the rate of its own loss . This at best partially represents the detrimental effects of elevated mutation rates not considered in this model . However , artificially creating an idealised situation for the SIM allele allows us to keep the model tractable ( but see S3 Appendix for a treatment of lethal mutations ) . We cast the schematic dynamics of Fig 1 into two sets of differential equations for the variables p = {pmr , pMr , pmR , pMR} . Using the classical mutation-selection dynamics of population genetics , they take the form [27] p ˙ = p ( w - w ¯ ) + M . p , ( 1 ) where w is the vector containing the marginal fitnesses of the genotypes , w ¯ is the mean fitness of the population , and M is a matrix encoding the mutation scheme ( see Methods for the explicit set of equations ) . In order to make analytical progress , we make a number of simplifying assumptions . First , we assume that selection under stress is strong compared to mutation . This is justified in treatment-like scenarios we consider here . Second , we assume that the mutation rate leading to a loss of a resistance mechanism is larger than the mutation rate leading to its gain . This is plausible , since by random genetic modifications it is more likely to disable a functional mechanism than to create one . Hence , it seems reasonable to assume the following hierarchy among the parameters: s ≫ ( μ M , μ R ) ≫ ν R . ( 2 ) Given this hierarchy , it can be shown that the SIM allele is not maintained in any of the two environments separately . Switching between the stress and no-stress environments , however , gives rise to non-trivial dynamics . During a stress phase , the SIM allele may increase in frequency along with the resistance mutations it produces . As resistance levels in the population rise , this effect weakens and the SIM allele frequency falls off because of mutations degrading the SIM mechanism . Periods of no stress allow resistance levels to decline , such that the SIM allele becomes effective again in the next stress phase .
In order to obtain analytical insight into the dynamics of SIM alleles we first consider two extreme scenarios: the recurrent ( R ) and non-recurrent ( NR ) stress scenarios . In both scenarios , an infinitely large population is subjected to an environment that alternates between periods of stress ( for τS time units ) and of no stress ( for τNS time units ) . In the recurrent scenario , the stress periods are assumed to be all the same ( i . e . resistance acquired in the previous stress period carries over to the next stress period ) . In the non-recurrent stress scenario we assume that each new stress period is different such that resistance acquired in previous stress periods is not beneficial in any subsequent stress period . In both regimes , the genotype frequencies evolve as described by the dynamical system Eq ( 1 ) and according to the schematics in Fig 1 . Iterating this procedure leads to oscillations in the SIM allele frequency pM = pMr + pMR as depicted in Fig 1C . We measure genotype frequencies at discrete time points directly before the onset of each stress period ( bold points in Fig 1C ) . The long-term equilibria of this time series thus describe the long-term prevalence of the SIM allele , which we denote by p ^ M . Since our model assumes an effectively infinite population , the SIM allele cannot be lost within one cycle . Nevertheless , it is possible that the SIM allele frequency asymptotically declines to zero as the cycles are iterated ( i . e . , that p ^ M = 0 ) . We assume that during stress selection is strong relative to mutation , and that the effect of the SIM allele is large . As a consequence , the stress dynamics has two phases; during the rapid first phase , genotype frequencies are almost exclusively due to selection ( s ) and those mutation rates that are amplified by the SIM allele ( σμM and σνR ) . At the end of the first phase , almost all individuals have acquired resistance to the stress . In the second , slower , phase , resistance levels remain high and the SIM allele slowly degrades due to mutation ( μM ) . We further assume that stresses are of short duration , so that we may ignore this second phase . Mathematically , we replace s ↦ αs and σ ↦ ασ , rescale time by dt ↦ dt/α , divide by α , and let α → ∞ ( see the Methods section and S1 Appendix for details ) . We aim to calculate the SIM allele frequencies pM = pMr + pMR before the onset of each stress period , i . e . , at the end of each cycle of stress followed by no stress . Under stress , the relative proportions of the mR and MR genotypes are maintained except for an excess of MR genotypes being generated by amplified mutation from the Mr genotypes . This excess is νR/ ( s/σ + μM+νR ) . In the absence of stress , resistance levels relax to pR ( τNS ) , which approaches mutation balance ( νR/ ( μR + νR ) ) for long periods without stress ( τNS → ∞ ) . At the same time , the frequency of the SIM allele decays exponentially due to mutations from its initial value pM ( 0 ) to pM ( 0 ) exp ( −μM τS ) . Heuristically , the SIM allele frequency p M ′ before the next stress is thus obtained from the SIM allele frequency pM before the current stress as p M ′ = p M e - μ M τ N S 1 + λ 1 + p M λ , ( 3 ) where λ = ( 1 − pR ( τNS ) ) /pR ( τNS ) νR/ ( ( s/σ + μM + νR ) . This intuitive derivation of the dynamics is made precise in S1 Appendix , where we also calculate pR ( τNS ) for the recurrent stress ( R ) scenario . In the non-recurrent ( NR ) , we have pR ( τNS ) = νR/ ( μR + νR ) , since resistance levels to yet unknown stresses can be assumed to be at mutation balance . Solving Eq ( 3 ) for equilibria yields the long-term prevalences of the SIM allele in the ( R ) and ( NR ) scenarios as p^M ( R ) =max{0 , e−μMτ−Γ ( 1−e−μMτ ) ·· ( 1+μR+νRνR ( e ( μR+νR ) τ−1 ) −1 ) } , ( 4a ) p ^ M ( N R ) = max { 0 , e - μ M τ - Γ ( 1 - e - μ M τ ) } ( 4b ) ( see S1 Appendix ) , where τ = τS + τNS is the length of one cycle of stress and no-stress , and Γ = s / σ + μ M + ν R μ R . ( 5 ) In particular , we thus see that the stress intensity s and the strength of the SIM allele σ enter the long-term SIM allele prevalences only via their ratio s/σ . To test our analytical predictions , we explicitly simulate the dynamics ( Eq ( 1 ) ) of a population experiencing stress and no-stress phases according to the schematics in Fig 1 without the simplifications that lead to the above formulae . Fig 2A shows the long-term SIM prevalences as functions of the cycle length τ for a representative choice of the remaining parameters . For both the ( R ) and ( NR ) regimes , the simulated values ( points ) align well with the above formulae ( solid lines ) . In the non-recurrent regime , the SIM allele is maintained in the population as long as stresses occur frequently enough; more precisely , there is a critical cycle length τc such that the SIM allele is not maintained for cycle lengths exceeding τc , p ^ M ( N R ) = 0 if τ > τ c = 1 μ M log ( 1 + 1 Γ ) . ( 6 ) Furthermore , in this regime there is a strictly monotone dependence between the steady state SIM allele frequency and the frequency of stress occurrence; in particular , the SIM allele becomes fixed in the population in the limit of infinitely rapid stress occurrence ( i . e . , p ^ M ( N R ) → 1 for τ → 0 ) . In the recurrent regime , the dependence of the equilibrium SIM levels p ^ M ( R ) on the cycle length τ is less simple . If the rate of gaining resistance without the SIM allele is sufficiently low ( i . e . , νR ≪ 1 , in particular νR ≪ μR ) , the SIM allele is not maintained in the population for any choice of τ ( Fig 2A , see also S1 Appendix ) . Note that in general there are conditions that do lead to the maintenance of SIM alleles in the recurrent regime . Such cases , however , are not in concordance with our basic ranking of parameters , inequality Eq ( 2 ) ( see S1 Appendix ) . Furthermore , we show in S1 Appendix that the non-recurrent regime generally maintains a higher SIM prevalence than the recurrent regime , i . e . , p ^ M ( N R ) ≥ p ^ M ( R ) for any choice of parameters . We can extend our basic model to include additional biologically relevant factors , such as cost of resistance or the presence of lethal mutations ( see S2 Appendix ) . These factors change the long-term SIM prevalences in intuitive ways , yet leave our qualitative statements unchanged . For example , maintaining a resistance mechanism in the absence of stress may incur a fitness cost . Consequently , resistance levels decrease faster in the no-stress environment if resistance is costly , which increases the benefit of increased mutation rates to acquire resistance under stress . Accordingly , including a cost of resistance to our model increases the long-term SIM prevalences ( see Fig B in S2 Appendix ) . We observe the opposite effect if we consider a mutational load due to lethal mutations . The greater risk for mutator phenotypes to acquire deleterious mutations can be expected to cause indirect selection pressure against the SIM allele . Describing a gradual accumulation of deleterious mutations requires the consideration of multiple fitness classes , which is infeasible in our approach . Instead , we show in S3 Appendix ( see Fig D in S3 Appendix ) that lethal mutations translate into selection against the SIM allele and thus reduce long-term SIM prevalences . We explore the prevalence of the SIM allele when subjected to a finite number of stresses . To this end , we simulate the full system as explained earlier for the ( R ) and ( NR ) regimes , but for a finite number χ of challenges . This is done by taking into account a separate resistance locus for every challenge . Each of these extra resistance loci is neutral during non-cognate environmental challenges . During this time period , their frequency changes only by mutational degradation or if they are associated to the resistance allele that is under selection . As in the ( NR ) regime , we assume no cross-resistance and there is complete linkage between all loci . Hence , there are 2χ+1 different genotypes to consider . The stresses are applied in a deterministically cycling manner . Each stress period is of constant length τS , and is followed by a no-stress period of length τNS . The results interpolate between the ( R ) and ( NR ) regimes , where every increase in the number of stresses , χ , also increases the SIM allele equilibrium frequency and the parameter regime where it is maintained ( Fig 2 ) . In particular , the simulations suggest a simple classification of the possible dynamical regimes , based on the length of the stress periods ( τ = τS + τNS ) . First , for small values of τ , we observe the loss of the SIM allele . The upper bound of this region is inversely proportional to the time it takes for the same stress to recur . Keeping the cycle length τ constant and increasing the number of challenges χ also increases this time and therefore allows for the maintenance of the SIM allele for smaller values of τ . The scaling with the time between two stresses of the same type can be seen clearly in Fig 2B . We may deduce that a too frequent occurrence of the same stress is not beneficial for the SIM allele . This is not surprising; the SIM allele has no fitness advantage on its own and therefore can only rise in frequency if the relevant resistance levels in the population are low . When stresses re-occur frequently , resistance levels are kept high , preventing the SIM allele from hitchhiking . Second , if the number of different stresses is high enough , a SIM allele can be kept for intermediate frequencies of stress occurrence . The size of this region expands with increasing stress diversity up to the level of the ( NR ) regime of infinite stress diversity . The maximum allele frequency that can be kept also increases with increasing stress diversity , geometrically approaching the analytically determined value of the ( NR ) case . Third , if stresses occur too infrequently , the SIM allele is lost . The critical time between two consecutive stresses , above which the SIM allele is lost for any number of stresses χ , was calculated analytically as τc , see Eq ( 6 ) . With χ different stresses , each particular stress occurs every χτ time units . Assuming that resistance alleles to different stresses do not interact , we thus may replace pR ( τNS ) by pR ( χτNS ) in the heuristic derivation of the recursion Eq ( 3 ) to obtain an approximation to the dynamics of SIM allele frequencies with χ different stresses . In our actual model , however , resistance mutations to different stresses do not evolve independently since they are linked to the genetic backgrounds they appear on and cross resistance against multiple stresses is possible . The approximation thus captures the qualitative behaviour of the long-term SIM allele frequencies for multiple stresses , yet overestimates the numerical results for the parameters used in our simulations , ( see S3 Appendix , in particular Fig D ) . To relax our assumption of stresses occurring in a strict cycle , we randomize our model by choosing one out of the χ stresses at each iteration of the simulation . Qualitatively , this leaves the picture unchanged , see Fig 3A: The SIM prevalence levels p ^ M and the interval of stress occurrence times τ that maintain the SIM allele both increase with increasing stress diversity , though not as readily as in the deterministic case . However , a shift can be seen in which values of τ make maintenance of SIM possible , leading to a small interval of cycle lengths τ when randomization enables SIM allele maintanence . This happens because the effective time interval between two identical stresses is now a random variable , and there is some probability that the same stress is seen sooner than in the deterministic regime . This means that a cycle length , that in the deterministic regime is not conducive to the maintenance of the SIM allele , can now sustain it because there is some probability that the same stress is seen at an interval that does support it . One important point is that the minimum time interval between two identical stresses is the time of cycle . This means that the distribution of time intervals is right-skewed , which explains why the “shift” seen on the simulation curves is to the left ( the simulations “sample” times to the right ) . For practical questions in antibiotic therapy , it is of interest to investigate treatment scenarios in which a set of pharmaceuticals is administered simultaneously or separately over a given period of time ( combined versus sequential treatment [17 , 28 , 29] ) . To this end , we simulate and compare four stresses either occurring simultaneously , being grouped in two pairs , or being applied separately . We assume that the stresses do not allow for cross-resistance mutations ( i . e . , single mutations that provide resistance to multiple stresses ) , that their effects on fitness in genotypes with multiple resistance mutations are additive , and that one cycle through all stresses or stress combinations takes τ time units in each case . The results of our simulations are depicted in Fig 3B: while applying all stresses at once does not maintain the SIM allele for our choice of parameters ( Fig 3B , black ) , the SIM allele prevalence increases if stresses occur more frequently , yet in a less clustered fashion ( Fig 3B , red and blue ) . We have also measured the levels of multi-resistance in these scenarios ( S4 Appendix ) . Interestingly , we find that for short treatment cycles ( in which the SIM allele is not expected to be maintained ) a mixed strategy in which different sets of multiple stresses are applied sequentially seems to perform best at avoiding multi-resistance , even if at the cost of a higher prevalence of single resistant strains ( see Table A in S4 Appendix ) .
Our study investigates the fate of a hypothetical stress-induced mutagenesis ( SIM ) mechanism under various schemes of environmental fluctuation . We assume that stress-induced mutagenesis is brought about by an active mechanism that increases mutation rates as a response to stress , modeled by a modifier allele for stress-induced mutagenesis that is much easier lost than gained . As a consequence , it decays over time unless maintained by recurrent second-order selection due to changes in the environment . This is what would be expected under the adaptive hypothesis which we are testing . Our results indicate that there are plausible regimes under which the SIM allele could be kept purely by second-order selection under the adaptive hypothesis . What is needed is that the basic hierarchy of parameters outlined in Eq ( 2 ) is met . This is reasonable if one considers relatively strong stress episodes and a resistance mechanism such as an antibiotic degrading beta-lactamase enzyme , which is difficult to acquire , but easier to degrade by mutation [30] . Furthermore , a regime of environmental fluctuations is needed such that resistance levels are not kept very high between repeated strikes of the same stress , which can be aided in natural populations by fitness costs associated with resistance mutations [31] ( see also S2 Appendix ) . Also , stresses in which SIM helps bring about a beneficial mutation need to occur frequently enough to prevent the degradation of SIM . Finally , stress diversity greatly facilitates the maintenance of SIM by requiring resistance mutations that are new or less prevalent in the population . Considering that bacteria in a human body can often experience starvation , acid stress , inflammation , or treatment induced antibiotic stress , these conditions are also plausible [32 , 33] . Although the maintanance of SIM due to second order selection is plausible , our model tends to underestimate SIM frequencies observed in natural populations which are close to 100% [4] . Direct benefits of SIM mechanisms [34] or a high cost of resistance mutations are common phenomena which are expected to increase the frequency of SIM alleles and could explain the higher frequency found in nature . Under our assumptions , environmental fluctuations are essential for the SIM allele to be maintained in the population: in the absence of environmental challenges ( stresses ) , the SIM allele is lost due to the neutral accumulation of loss-of-function mutations . Repeatedly occurring stresses , however , give rise to second-order selection on the SIM allele . Under reasonable assumptions on the model parameters , c . f . Eq ( 2 ) , we show that simple fluctuations caused by a repetitive stress generally fail to maintain the SIM allele . As the stress diversity—i . e . , the number of different stresses available—increases , the SIM allele may be maintained at increasingly high levels ( see Fig 2 ) . In the limit of infinite stress diversity , the SIM allele is maintained for any frequency of stress occurrence above a given threshold , which we characterized analytically by τc . Interestingly , when a fixed number of stresses are applied in a random order , the prevalence levels of the SIM allele generally decrease , and the parameter region conducive to maintenance shifts: maintanence can happen at shorter time intervals , and τc is apparently reduced ( Fig 3A ) . This is because in this scenario , the time between two stresses of the same kind is now stochastic: there is a probability distribution for the time a particular stress is re-applied . This effectively “smoothes” the deterministic expectation for the steady state frequency . This leads to the “shift” of the simulation curves seen on Fig 3 . It should be noted that we model the dynamics of an infinite population which prevents the examination of the stochastic effects introduced by genetic drift . In our model the SIM allele can never truly fix or be lost from the population . The first point is not very consequential , since it is natural to assume that deleterious mutation will always act to degrade the SIM mechanism and lower its frequency from fixation . However , the second point may be more important since mutations that reintroduce the SIM mechanism after it has been lost may be rare . However , our results can still provide some insights: if the frequency of the SIM allele drops below 1/N , where N is the population size , one can say that it is effectively lost . Furthermore , it is not clear if the rate of back-mutations in nature is effectively zero . If indeed there is some probability of reintroducing the SIM mechanism then our deterministic results provide an expectation for its long-term frequency . Our results focus on how the maintenance of a SIM allele depends on the frequency and diversity of stresses . We find that in the case of cycling a finite number of stresses , the SIM allele can only be maintained at intermediate stress frequencies . Irrespective of the number of available stresses , a lower bound for the stress frequency can be determined analytically as 1/τc . For the upper bound , we find that the time between two stresses of the same kind is crucial ( Fig 2B ) . This could inform the choice of therapeutic strategies by identifying treatment schedules that exert extensive selection pressure to keep a SIM allele and possibly strengthen its effect . To date , various temporal treatment strategies have been investigated to counter the current antibiotic resistance crisis [35–37] . To prevent the emergence of resistant strains , one approach is to inhibit known resistance mechanisms directly [38] . Another is to use combinations of existing drugs in treatment regimes that are rationally designed to suppress resistance levels [39 , 40] . However , to keep drugs effective in the long term , it is desirable to develop strategies that not only decrease resistance levels , but also restrict evolvability . To this end , there have been efforts to directly inhibit SIM mechanisms [5 , 41] . Our study complements this approach by assessing how temporal treatment schemes prevent second-order selection on a SIM mechanism . We find that an increasing diversity of stresses encountered increases long-term SIM frequencies ( see Figs 2 and 3B ) . This suggests a trade-off between controlling resistance and controlling evolvability when designing multi-drug therapies: in most proposed schemes , one tries to prevent the evolution of resistance by diversifying the stresses ( antibiotics ) [35–37] . However , our findings suggest that this is precisely the scenario in which SIM alleles are more likely to persist and hence promote the evolvability of the population . Experimental work is needed to further characterize this trade-off and assess its relevance in a clinical setting . Currently , it is known that SIM mechanisms are common in bacteria , vary greatly in their potency [4] and can be lost due to a variety of mutations [25] . Selection pressures we describe here could therefore favor those strains that have a significantly higher mutation rate in stress also in the clinic . To confirm this relevance , studies measuring temporal dynamics of SIM alleles in a clinical setting are needed . Also , long-term microbial evolution experiments in a more controlled setting that would follow the prevalence of a synthetic or natural SIM allele over time under different treatment schemes are plausible . Our results may inform such experiments to confirm the suggested trade-off between the evolution and evolvability of resistance . It has been proposed that the simultaneous application of drugs that exhibit no cross-resistance may be more effective against resistant strains than their sequential application [17 , 28 , 29] , but also the opposite [42] . In our model , the same applies to reducing positive second-order selection on SIM alleles . Exploring this finding further may provide a resolution of the trade-off between fighting resistance and evolvability , at least for those drug combinations that allow for simultaneous application despite common toxicity or dosage problems .
Casting the schematic dynamics of Fig 1 into differential equations of the form Eq ( 1 ) yields p ˙ m r= μ M p M r + μ R p m R - ν R p m r , ( 7a ) p ˙ M r= μ R p M R - ( μ M + ν R ) p M r , ( 7b ) p ˙ m R= ν R p m r + μ M p M R - μ R p m R , ( 7c ) p ˙ M R= ν R p M r - ( μ M + μ R ) p M R , ( 7d ) for the no-stress environment . This system of ordinary differential equations can be solved explicitly . Given an initial SIM allele frequency p M * , we find that G ( p M * ) = p M * exp [ - μ P τ N S ] is the SIM allele frequency after τNS time units of no stress , see S1 Appendix . For the stress environment , we have p ˙ m r= - s p m r ( p m R + p M R ) + σ μ M p M r - ν R p m r + μ R p m R , ( 8a ) p ˙ M r= - s p M r ( p m R + p M R ) - σ ( μ M + ν R ) p M r + μ R p M R , ( 8b ) p ˙ m R= s p m R ( 1 - p m R - p M R ) + ν R p m r - μ R p m R + μ M p M R , ( 8c ) p ˙ M R= s p M R ( 1 - p m R - p M R ) + σ ν R p M r - ( μ R + μ M ) p M R . ( 8d ) Assuming that stress is strong and of short duration , and that the SIM allele has a large effect , we may replace s ↦ αs , σ ↦ ασ , and rescale time dt ↦ dt/α . Dividing by α and letting α → ∞ , Eq ( 8 ) simplifies ( see S1 Appendix ) and permits an approximation for the SIM allele frequency after a short period of stress . We write p M * = F ( p M ) for the SIM allele frequency after stress; the mapping F is derived in S1 Appendix and depends on whether stress is recurrent or non-recurrent ( the ( R ) and ( NR ) regimes ) . Measuring genotype frequencies directly before each stress , we thus obtain a recursion for the SIM allele frequency pM as p M ′ = ( G ∘ F ) ( p M ) , which can be written as Eq ( 3 ) . Solving this recursion for p M ′ = p M leads to the long-term SIM allele prevalences in Eq ( 4 ) . Our numerical simulations were implemented using the software Mathematica . For a single recurrent stress ( the ( R ) regime ) , we alternate periods of stress ( dynamics Eq ( 8 ) ) for τS time units with periods of no stress ( dynamics Eq ( 7 ) ) for τNS time units . Genotype frequencies are recorded before each stress period , and the procedure is stopped after 104 iterations or once the genotype values reach an equilibrium . To simulate the ( NR ) regime , we proceed likewise but replace the genotype frequencies {pmr , pMr , pmR , pMR} before every stress by { ( 1 - ε ) ( p m r + p m R ) , ( 1 - ε ) ( p M r + p M R ) , ε ( p m r + p m R ) , ε ( p M r + p M R ) } before every new stress , where ε = νR/ ( μR + νR ) . Since the particular kind of stress has never occurred before , the probability of being resistant to it is given by the balance ε between the rates of gaining and losing resistance due to mutation . With χ > 1 different stresses , there are 2χ+1 different genotypes . We consider only single point mutations; the SIM allele is lost at rate μM , and each resistance allele is gained ( lost ) at rate νR ( μR ) independently . The fitness of genotypes is w = 1 under no stress . In the presence of a stress , the corresponding resistance mutation provides a selective advantage s > 0 . If multiple stresses occur simultaneously ( as is the case in Fig 3B ) , the fitness advantages due to resistance to the individual stresses are assumed to be additive . There are no cross-resistances , i . e . , each resistance allele confers resistance against exactly one stress . | Many organisms display increased mutation or recombination rates when exposed to a stressful environment , which can increase the probability that the population acquires adaptations that allow it to avoid extinction . Because of this , it has been suggested that the increase in production rate of genetic variation is itself an adaptation . Here , we use a mathematical model to test this hypothesis . We find that this hypothesis is plausible when the environment is variable enough such that populations do not experience particular stresses too often . We provide an explicit expression for the critical time interval between exposures and discuss its implication for the evolution of resistance . Our results highlight how and when this form of evolvability can evolve by natural selection . | [
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"g... | 2017 | Stress-induced mutagenesis: Stress diversity facilitates the persistence of mutator genes |
The World Health Organization has called for an effort to eliminate Lymphatic Filariasis ( LF ) around the world . In regions where the disease is endemic , local production and distribution of medicated salt dosed with diethylcarbamazine ( DEC ) has been an effective method for eradicating LF . A partner of the Notre Dame Haiti program , Group SPES in Port-au-Prince , Haiti , produces a medicated salt called Bon Sel . Coarse salt is pre-washed and sprayed with a solution of DEC citrate and potassium iodate . Iodine levels are routinely monitored on site by a titrimetric method . However , the factory had no method for monitoring DEC . Critical analytical issues include 1 ) determining whether the amount of DEC in each lot of Bon Sel is within safe and therapeutically useful limits , 2 ) monitoring variability within and between production runs , and 3 ) determining the effect of a common local practice ( washing salt before use ) on the availability of DEC . This paper describes a novel titrimetric method for analysis of DEC citrate in medicated salt . The analysis needs no electrical power and requires only a balance , volumetric glassware , and burets that most salt production programs have on hand for monitoring iodine levels . The staff of the factory used this analysis method on site to detect underloading of DEC on the salt by their sprayer and to test a process change that fixed the problem .
The World Health Organization has called for an effort to eliminate Lymphatic Filariasis ( LF ) around the world . [1] A nematode worm ( Wuchereria bancrofti ) is the cause of 90% of lymphatic filariasis cases globally . Mosquito bites transmit larval nematodes ( microfilariae ) present in the blood stream of infected persons , and although the adult nematodes are resistant to medical treatment , human transmission in endemic regions can be stopped by administering drugs , such as diethylcarbamazine ( DEC ) , that kill the microfilariae . DEC has had a long history of safe use in mass drug administration ( MDA ) LF eradication programs , [2]–[4] and so far , W . bancrofti do not appear to have developed resistance to DEC . [5]–[6] A course of treatment of 6 mg/kg per day of DEC citrate for 12 days ( daily dose around 300 mg ) can significantly reduce the microfilariae count in an infected person . However , in regions where the disease is endemic , yearly drug administration to infected individuals must be continued over the adult worm lifetime of 4–6 years to eradicate the disease . As an alternative to pill-based MDA , DEC can be administered to local populations in the form of medicated cooking salt , with DEC citrate present at 0 . 2–0 . 4% w/w , which corresponds to a daily dose of 20–40 mg DEC citrate . Local production and distribution of medicated salt fortified with DEC has proved to be a particularly effective method [7]–[8] for eradicating LF from endemic regions [9]–[10] . A partner of the Notre Dame Haiti program , Group SPES in Port-au-Prince , Haiti , produces a double-supplemented salt called “Bon Sel” . [11] Coarse salt is pre-washed and sprayed with a solution of DEC citrate and potassium iodate . Iodine levels are routinely monitored on site by a titrimetric method . However , as of 2010 , the factory had no analytical process for monitoring DEC levels . Critical analytical issues include 1 ) determining whether the amount of DEC citrate in each lot of Bon Sel is within safe and therapeutically useful limits , 2 ) monitoring variability within and between production runs , and 3 ) determining the effect of a common local practice ( washing salt before use ) on the availability of DEC . The “gold standard” assay for DEC citrate uses high-performance liquid chromatography ( HPLC ) . [12] Sending samples out for analysis would impose unwanted costs and prevent real time analysis of production runs , yet it was impossible to implement this process at the factory in Haiti , which has no access to an HPLC or to the supplies and expertise necessary to maintain one . Color tests and spectrophotometry have been used for monitoring DEC-medicated salt production , [13]–[15] although usually for qualitative monitoring . [16] The facility in Haiti wanted quantitative information but did not have a spectrometer . The goal of our group was to develop a back titration assay for DEC citrate in medicated salt requiring only a balance , volumetric glassware , and burets , equipment that most iodized salt production programs have on hand for monitoring iodine levels , and compare this method against the benchmark HPLC method .
Samples of untreated NaCl and pharmaceutical grade DEC citrate ( EPICO ) were obtained from the Bon Sel plant in Haiti; pure DEC citrate for HPLC standardization was obtained from Sigma-Aldrich . The untreated NaCl was a coarse grade produced by evaporation of seawater and had visible contaminants ( dirt , sand , plant matter ) . 0 . 0040 M HCl was prepared by sequential volumetric dilution of concentrated HCl , and stored in a plastic bottle . Dilute NaOH solutions are unstable due to reaction with atmospheric CO2 . A 0 . 200 M NaOH stock solution should be prepared ( it is stable for at least 4 weeks ) and diluted each day to give the working 0 . 0100 M NaOH solution . Phenolphthalein indicator solution was prepared by dissolving 0 . 5 g of phenolphthalein ( Aldrich ) in 500 mL of a 50% ethanol:water solution . Standards: DEC citrate standards ( 0 . 05% , 0 . 125% , 0 . 25% , and 0 . 50% w/w of salt ) are prepared in the same matrix as the medicated salt samples . The final solutions are 10% w/v in salt , thus , to prepare the 0 . 50% standard , 10 g NaCl and 0 . 0500 g DEC citrate are mixed with DI water to give a final volume of 100 ml . Samples: 5 . 00 g of medicated salt is dissolved in deionized or distilled water to a final volume of 50 . 00 ml with vigorous shaking ( or 10 g/100 ml final volume ) . A small amount of insoluble residue is usually present in these samples .
Standard DEC citrate used in this study ( from Sigma-Aldrich ) was identical by NMR ( spectra acquired in D2O and d6-DMSO at 400 MHz ) to a sample of the DEC citrate ( manufactured by EPICO ) that is used at the Bon Sel factory in Haiti . The 1∶1 DEC∶citrate stoichiometry was confirmed by integration of the 1H-NMR peaks from the diastereotopic methylene groups on the citrate and the triplet from the ethyl groups on the DEC ( predicted for a one-to-one stoichiometry of DEC∶citrate: 4∶6 , found 4 . 2∶6 . 0 . ) From the DEC∶citrate stoichiometry , each equivalent of DEC citrate ( see structure in Figure 1 ) contains three acidic protons ( two carboxylic acids and one protonated tertiary amine ) . These three acidic protons are visible as a very broad peak at 10 . 5 ppm when the spectrum is acquired in dry DMSO-d6 . Direct titration of DEC citrate with base did not prove analytically useful . Due to the range of pKa values in the polyprotic citrate , the end point of the titration was not clear enough . However , back titration gave a clear endpoint . In the back titration , a sample of DEC citrate is added to a known excess of the strong base sodium hydroxide , which reacts completely with the acidic protons . The remaining hydroxide is titrated with standard HCl , giving a clear endpoint with the common indicator phenolphthalein . Bon Sel also contains small amounts of potassium iodate to supply 40 ppm iodine as a nutritional supplement . Calibration with DEC citrate standards compensates for any matrix effects from the salt or interference from the iodate . It should be noted that this analytical method is not as specific or generally useful as the HPLC analysis , because any acidic or basic compound will interfere with the back-titration . Thus , this test cannot be applied to complex matrices ( e . g . , determination of DEC concentration in cooked food or in body fluids ) . Titration of standard samples gave a linear calibration curve ( Figure 2 ) ; the linear least-squares parameters were determined in Excel using the LINEST function and used to fit unknown samples . The linear range extends from 0 . 050% to 0 . 88% ( w/w DEC citrate in salt ) , which covers the normal therapeutic range of DEC in salt ( 0 . 1–0 . 6% , recommended 0 . 2–0 . 4% ) . [17] The average relative standard deviation ( RSD ) for the concentration of known DEC samples at Notre Dame was 16±9% by the titration method , based on triplicate analysis of samples ranging from 0 . 10% to 0 . 90% DEC citrate . Samples analyzed in Haiti gave an average RSD of 33±7% . The limit of detection ( LOD = 3*s/m ) and limit of quantification ( LOQ = 10*s/m ) were calculated; [18] m is given by the least square fit to the slope of the calibration curve , and s is the standard deviation of 7 determinations of DEC concentration for the 0 . 050% standard sample . The LOD is 0 . 029% and the LOQ is 0 . 096% for the titration method . To compare the titration method and the HPLC method , multiple standards and unknowns were analyzed with both methods . Figure 3 shows the results plotted against each other; the observed slope of the line is 1 . 014 ( for perfect agreement it would be 1 . 00 ) . The accuracy of the titration method was indistinguishable from that of the HPLC method . Applying the paired t-test [19] for the 10 samples listed in Table 1 , the mean difference between the titration and HPLC results was −0 . 0018 , the std deviation was 0 . 016 , and tcalc is 0 . 35 . This indicates that the difference between the titration and HPLC results was not statistically significant for samples at concentrations of 0 . 1%–0 . 8% , although the precision of the HPLC method was superior ( RSD <5% for HPLC ) and its LOQ was much lower . Analysis of Bon Sel samples from seven production runs in mid-2009 showed that all seven production lots ranged from 0 . 09–0 . 13% DEC citrate , with an average of 0 . 10%±0 . 01% . ( Table 2 ) This shows that spray coating is an effective technique for achieving uniform DEC loading on salt at the kg-to-kg and lot-to-lot level . The loading achieved , while in the therapeutic range ( 0 . 1–0 . 6% w/w ) , was lower than the desired loading of 0 . 2–0 . 4% w/w . The loading is a function of the solubility of the DEC citrate in the spraying solution , the drying rate of the salt , and the salt feed rate , and could not be improved with the equipment on hand . However , the group in Haiti tried an experimental run where a finished batch of salt was dried and fed back into the sprayer; this double-sprayed salt analyzed at 50±7 ppm iodine and 0 . 28±0 . 7% w/w DEC citrate ( Table 2 , entries X1 ( single sprayed ) and X2 ( double sprayed ) ) . To monitor heterogeneity within the bags of Bon Sel , three 10 g grab samples from each of several 1 kg bags of Bon Sel ( taken from different lots ) were tested; the levels of DEC citrate varied from 0 . 08 to 0 . 15% for samples taken within the same bag of Bon Sel . This heterogeneity was not due to errors in the titration analysis , as the results were confirmed by HPLC analysis , which has a much higher precision . Because the DEC is sprayed onto the salt , which contains both coarse ( low surface area ) and fine ( high surface area ) crystals , DEC loading is expected to be a function of salt crystal size . Two lots of Bon Sel from the mid-2009 production runs were screened to separate particles >4 mm in size from particles <4 mm in size; in each case , the large crystals had significantly lower loading of DEC than the small crystals . For example , in one lot , the large crystals gave a DEC loading of 0 . 034±0 . 001% while the small crystals came in at 0 . 085±0 . 002% ( these low loadings were measured using HPLC to obtain more precise results ) . The variation in loading with crystal size appears to be large enough to account for most of the heterogeneity in the within-lot analyses , and suggests that more uniform spray coating and higher loadings would be achieved by crushing the salt before spraying it . The salt available in Haitian markets is often of low purity , and many people rinse the salt before using it in cooking . Although Bon Sel is pre-washed and the packaging advises consumers not to wash the salt , habits can be hard to break , and some people probably still wash the Bon Sel . Tests on the effect of hand rinsing ( ∼5 seconds swirling in a bowl of water , or a similar time under a stream of water ) showed retention of 40–50% of the DEC citrate and 60–70% of the iodate after the medicated salt was washed . This result suggests that a fortification level of 0 . 3–0 . 4% DEC citrate , at the high end of the recommended scale , would be likely to deliver therapeutically useful doses to consumers of the medicated salt regardless of whether or not they rinse it . A simple titration-based assay allows determination of diethylcarbamazine ( DEC ) citrate concentrations in medicated salt produced in Haiti for an anti-lymphatic filariasis program . The assay can be carried out with widely available equipment and materials and thus offers a useful tool for quality control and field analysis of DEC . The development of this method , which allows quantification of the medication , DEC citrate , has already proven useful for quality control in the Haiti plant where salt fortification takes place . Historically , identification and communication of flaws in the salt fortification levels have taken several months as samples were sent back to the US for analysis . Using the back titration analysis of DEC , chemists in Haiti can now identify variation in DEC loading as batches of Bon Sel are produced . This analysis will allow the Bon Sel plant to act more rapidly and independently in their effort to supply the area with properly medicated salt . An increased efficiency in Bon Sel production should bolster the endeavor to reduce and eventually eliminate lymphatic filariasis in Haiti . | As researchers develop more sophisticated technologies , parts of the world are left behind . The front lines of fighting many diseases lie in regions where expensive technology is not feasible . As part of the effort to eradicate lymphatic filariasis in Haiti , our group's goal was to design an assay that would allow a chemist , with basic equipment , to quantify the levels of diethylcarbamazine citrate on medicated salt . With access to university research facilities , we were able to devise and test a back-titration procedure that can measure the medication levels with sufficient accuracy and precision . Our method capitalized on the fact that the medication is acidic . This characteristic allows us to combine an unknown , medicated salt sample with a known quantity of base and then back-titrate with acid to determine diethylcarbamazine citrate concentration based on the neutralization point . Developing this protocol has put the power of quality control into the hands of the Haitian factory producing the medicated salt . With the ability to better monitor dosing levels , we have increased the effectiveness of this program in Haiti . Using modern research facilities to produce effective , low-tech methods could be a useful approach for tackling many worldwide medical and environmental issues . | [
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"diseases"
] | 2011 | A Low-Tech Analytical Method for Diethylcarbamazine Citrate in Medicated Salt |
Bacteria of many species rely on a simple molecule , the intracellular secondary messenger c-di-GMP ( Bis- ( 3'-5' ) -cyclic dimeric guanosine monophosphate ) , to make a vital choice: whether to stay in one place and form a biofilm , or to leave it in search of better conditions . The c-di-GMP network has a bow-tie shaped architecture that integrates many signals from the outside world—the input stimuli—into intracellular c-di-GMP levels that then regulate genes for biofilm formation or for swarming motility—the output phenotypes . How does the ‘uninformed’ process of evolution produce a network with the right input/output association and enable bacteria to make the right choice ? Inspired by new data from 28 clinical isolates of Pseudomonas aeruginosa and strains evolved in laboratory experiments we propose a mathematical model where the c-di-GMP network is analogous to a machine learning classifier . The analogy immediately suggests a mechanism for learning through evolution: adaptation though incremental changes in c-di-GMP network proteins acquires knowledge from past experiences and enables bacteria to use it to direct future behaviors . Our model clarifies the elusive function of the ubiquitous c-di-GMP network , a key regulator of bacterial social traits associated with virulence . More broadly , the link between evolution and machine learning can help explain how natural selection across fluctuating environments produces networks that enable living organisms to make sophisticated decisions .
Cells use networks of biochemical reactions to collect cues from the world around them , process that information internally and respond appropriately [1] . Understanding how evolution by natural selection has turned biochemical reactions into information-processing circuits remains a major challenge [2] . The intracellular secondary messenger c-di-GMP ( Bis- ( 3'-5' ) -cyclic dimeric guanosine monophosphate ) , ubiquitous in bacteria , is a network hub lying at the core of signaling pathways with dozens of inputs and outputs . This type of network is called a bow-tie because of its shape ( Fig 1A ) [3] . The key feature of a bow-tie is its ability to compress multiple inputs and command multiple outputs [4] . We find bow-ties in cells that do sophisticated information processing . For instance , macrophages and dendritic cells that integrate toll-like receptor signals to decide on immune responses [5] , and a neuron must integrate multiple stimuli into sequences of action potentials which it then delivers to several other neurons [6] . What is the function of the c-di-GMP bow-tie architecture in the bacterial cell ? We investigated this question in Pseudomonas aeruginosa . Like other bacteria [7] , P . aeruginosa uses c-di-GMP to decide whether to stay in a place and form a biofilm , or to swarm away in search of better conditions . Biofilm formation is a social behavior in which bacteria attach to surfaces , secrete polymeric substances and form protective communities that make infections hard to treat with antibiotics [8 , 9] . Swarming is also a social behavior , but swarms are motile and biofilms are sessile; the two behaviors are mutually exclusive and require expressing different sets of genes [10] . A better understanding of how the P . aeruginosa cell commands biofilm and swarming behaviors could lead to anti-biofilm therapies against this major pathogen [11] . P . aeruginosa has dozens of proteins that make and break c-di-GMP . Diguanylate cyclase ( DGC ) proteins with GGDEF domains synthesize c-di-GMP , and phosphodiesterase ( PDE ) proteins with EAL or HD-GYP domains degrade c-di-GMP . DGCs and PDEs can respond to diverse stimuli such as contact with a surface or the presence of a chemical attractant . They modulate intracellular levels of c-di-GMP that then regulate expression of downstream genes [7] . According to a well-established model , when c-di-GMP levels are low the enhancer-binding protein FleQ activates flagella genes needed for swarming motility and represses extracellular matrix genes needed for biofilm formation [12 , 13] ( Fig 1B ) . When c-di-GMP levels are high FleQ forms a complex with another protein , FleN , and the FleN-FleQ complex converts its function to repress flagella genes and de-repress biofilm matrix genes [14] ( Fig 1C ) . The FleN-FleQ is therefore a c-di-GMP-responsive switch that creates an opposed co-regulation of biofilm and motility genes . Co-regulation is efficient because P . aeruginosa cannot move and stay encased in a matrix at the same time [15] , but it comes with a risk: Experimental evolution in swarming conditions selects for FleN mutants with many flagella called hyperswarmers , which are locked in a perpetual motile mode and cannot make proper biofilms [16] . This tradeoff between biofilms and swarming—a dichotomy due to their co-regulation by c-di-GMP—could be exploited in therapies against P . aeruginosa infections . However , two key obstacles remain: First , we lack systems-level understanding of the c-di-GMP network . We know reasonably well how some network components work—for example , physical contact with a solid surface stimulates the Wsp transmembrane complex to synthesize c-di-GMP [12 , 17]—but we know little about how they work together as a network [18] . Second , we know little about the network’s diversity across the P . aeruginosa species . The link between c-di-GMP , biofilm and swarming was repeatedly validated in isogenic mutants [19] but seems to be absent when compared across different strains [16 , 20] . Is the tradeoff really absent outside the laboratory , or is it buried by many genetic differences accumulated between strains since their common ancestor ? Understanding how selective pressures shape the c-di-GMP network is crucial to new therapies , especially to prevent the emergence of resistance . Here , we combined genomics , experimental evolution and mathematical modeling to elucidate the function of the c-di-GMP network . We investigated P . aeruginosa isolates from acutely infected cancer patients; this population is distinct from isolates from chronic infections , such as those formed in cystic fibrosis lungs where microbial strains already experienced long-term evolution within the host [21–25] . Against our expectations , we saw no correlation between c-di-GMP , biofilm and swarming levels . To explain these observations , we developed a mathematical model from biochemical reaction principles; we derived a mechanism of how selection across fluctuating environments can tune the c-di-GMP network analogous to machine learning . The model explains why fluctuating environments , such as natural systems and short-term infections , could select for generalist strains but stable environments , such as laboratory evolution or long-term infections , could select for specialists locked in a phenotypic mode . We then applied our knowledge to directed-evolution experiments that revealed new mutations causing loss of biofilm specialism .
We selected a cohort of 28 clinical isolates of P . aeruginosa to investigate associations between c-di-GMP and two social phenotypes—biofilm formation and swarming—that it regulates . The 28 strains originated from a diversity of sample types ( blood , urine , etc . ) obtained from acutely infected patients at Memorial Sloan Kettering Cancer Center ( MSKCC ) , and belonged to a larger set of P . aeruginosa strains that—we had described before [16]—vary in their capacity for biofilm formation and swarming . To understand how the diverse levels of biofilm formation and swarming relate to c-di-GMP , we measured each strain’s bulk c-di-GMP levels from extracts obtained from dense colonies grown on Petri dishes [26] . The c-di-GMP levels varied significantly between the isolates and from those measured for the laboratory strain PA14 ( Fig 2A , p<0 . 05 ) . We found no association between the c-di-GMP level and the sample type ( blood , urine , etc . , p>0 . 05 ) , and also no correlation between c-di-GMP and biofilm formation ( quantified by the microtiter crystal violet assay [27] ) or swarming motility ( quantified by the colony area at 16 h [16] , Figs 2B , 2C , S1A and S1B ) . The two social phenotypes also did not correlate with each other ( Fig A in S2 Fig . p>0 . 05 ) . The apparent lack of correlations seemed to challenge the well-established notion that c-di-GMP imposes a tradeoff between biofilm and swarming [28 , 29] . Another explanation , however , was that the 28 strains , despite coming from the same hospital , might be phylogenetically diverse . P . aeruginosa may live asymptomatically with its human host until immune-compromising cancer therapy facilitates opportunistic infection [30]; if the 28 strains spanned a large phylogenetic distance , the tradeoff could be hidden by many genetic differences accumulated during their separate evolutionary histories . To clarify this issue , we sequenced the whole-genomes of the 28 MSKCC isolates and reconstructed their phylogeny ( Fig 2D ) . We included , for reference , the publicly available genome of PA14 and those of two other well-characterized strains , PAO1 and PA7 [31] . The phylogenetic tree confirmed features seen before—PA14 and PAO1 resided in two major clades [32] and PA7 was an outlier [33]—and revealed that the 28 isolates were indeed phylogenetically diverse from each other . Interestingly , the ability to infect a specific body site was not restricted by phylogeny: isolates from different sample types were found in both the PA14 and the PAO1 clades ( circle colors , Fig 2D ) . We then analyzed c-di-GMP levels , biofilm levels and swarming motility in the light of the reconstructed phylogeny . The sequenced genomes revealed that the strains varied little in the number of genes predicted to be in the c-di-GMP pathway ( numbers listed next to each isolate , Fig 2D ) . A statistical analysis of phylogenetic signal , the Moran I test [34] , indicated that the c-di-GMP level had a strong phylogenetic signal ( p<0 . 05; Fig A in S3 Fig ) but biofilm and swarming had not ( p>0 . 05; Fig B , C in S3 Fig ) . We then tried correlating biofilm and swarming using the method of phylogenetic generalized least squares regression ( PLSR ) [35]—a method that correlates two phenotypes after correcting for phylogeny ( see S1 Text ) . PLSR showed a significant anti-correlation ( Fig B in S2 Fig ) which would support a tradeoff between biofilm and swarming . But the anti-correlation depended on a subset of three strains—M37351 , M55212 and F30658—that were closely related and had strong phenotypic differences among them . The correlation vanished if we excluded those three strains from the analysis ( Fig C in S2 Fig ) , which indicates that the tradeoff between biofilm formation and swarming is hard to detect across large phylogenic distances . We investigated the correlation between biofilm and swarming in three groups of closely related clinical isolates after PLSR ( Fig 2D , gray shaded ) . The genomes in those three subgroups differ in 480 , 593 and 1654 SNPs , respectively . The phylogenetically-corrected values of biofilm and swarming showed strong correlations in group I and II ( Fig AB in S4 Fig ) but not in group III ( Fig C in S4 Fig ) . Other than phylogenetic distance , the correlations also depended on the phenotypic diversity observed in each groups . For example , F30658 in group II was a strong swarmer and weak biofilm-former—the opposite from the other strains of this subgroup . But all of the four strains in group III showed very similar phenotypes to each other . PLSR helped reveal the hidden correlation between biofilm and swarming , and supported that there is a tradeoff between the two co-regulated phenotypes but only among strains that are closely related and have different phenotypes . We then investigated whether the pattern of c-di-GMP levels , biofilm and swarming observed across the entire phylogenetic tree could be explained by a few genetic variants of large effect in c-di-GMP network genes , or if explaining the pattern required many genetic variants of small effect . We used LASSO technique [36] , an algorithm that searches for a small number of features to explain a set of observables ( see S1 Text ) . We selected the smallest subset of genetic variants ( the features ) as we increased a penalization , λ , for including many features ( see S1 Text ) . According to this analysis , explaining 63% of the phenotype deviance required a model with at least 18 variants in c-di-GMP network genes ( Fig 2E ) . All variants were predicted to have low effect , since even the strongest variant would only explain 23 . 3% of the phenotypic deviance ( Fig 2F ) . In summary , LASSO showed that c-di-GMP , biofilm and swarming—in addition to being uncorrelated when investigated across the entire tree—have a complex diversity that may not be explained by a small set of genetic alterations of large effect , but was more likely to result from a combination of genetic alterations of small effect . The lack of correlations between c-di-GMP and the two social phenotypes that it commands—biofilm and swarming—raised an important question: how can the c-di-GMP network co-regulate those phenotypes and , at the same time , allow them to be uncorrelated across the phylogenetic tree ? We sought to address this question with a simple theoretical model . The model considers that a bacterial cell has m biochemical sensors that can modulate intracellular c-di-GMP levels in response to environmental stimuli ( Fig 3A ) . Each sensor is either a DGC ( which synthetizes c-di-GMP ) or a PDE ( which degrades c-di-GMP ) , and we modeled their biochemical kinetics with commonly used methods ( e . g . [37] ) : Each DGC-based sensor synthetizes c-di-GMP ( C ) from its substrate—which the model assumes is non-limiting ( represented by ∅ ) —with a basal synthesis rate , Ri , basal . The rate increases to Ri , basal + Ri when the sensor binds to a cognate stimulus Xi , which we modeled as a binary variable ( Xi = 0 means the stimulus is absent , Xi = 1 means the stimulus is present ) . The reaction for DGC-based synthesis of c-di-GMP was therefore ∅→riCwhereri=Ri , basal+RiXi Similarly , a PDE-based sensor degrades c-di-GMP into a product—which we assumed does not affect the relevant kinetics ( again represented by ∅ ) —at a basal consumption rate Rj , basalC . The degradation rate goes to Rj , basalC + Rj when the sensor binds to a cognate stimulus Xj , which we also modeled as a binary variable . The reaction for PDE-based degradation of c-di-GMP was therefore C→rj∅whererj=Rj , basalC+RjXj Considering these two types of biomolecular reactions , we could write a differential equation for the dynamics of c-di-GMP inside the cell as a function of the detected stimuli . This equation considered q proteins of the DGC kind and l proteins of the PDE kind , such that q + l = m: dCdt=∑i=1q ( Ri , basal+RiXi ) −∑j=1l ( Rj , basalC+RjXj ) [Eq 1] Then , we used the common steady-state approximation ( dC/dt ~ 0 ) which assumes that the intracellular levels of c-di-GMP stabilize rapidly after sensing new stimuli . This approximation allowed us to write the following mass-balance equation relating the “basal decay” , “basal synthesis” and the “net responsive” rates: ( ∑j=1lRj , basal⏟Basaldecay ) C=∑i=1qRi , basal⏟Basalsynthesis+∑i=1qRiXi−∑j=1lRjXj⏟Netresponsive [Eq 2] With a simple variable substitution we arrived at an equation that determines c-di-GMP levels as a function of a vector of all stimuli sensed by the cell , X = {X1 , … , Xm}: C ( X ) =α+∑i=1mβiXi [Eq 3] Where α≡∑i=1qRi , basal∑j=1lRj , basal , βi≡Ri∑j=1lRj , basal if i is a DGC and βi≡−Ri∑j=1lRj , basal if i is a PDE . Then , inspired by the FleN-FleQ system , we modeled how an effector module would change its activity depending on the c-di-GMP level . The inverse regulation [29] ensures bacteria express either biofilm genes or motility genes . We modeled this process using a single binary output , Y , such that when the output is Y = 0 the bacterium expresses motility genes and when Y = 1 the bacterium expresses biofilm genes . We defined an effector setpoint CSP , which is the c-di-GMP level at which FleN-FleQ switches from expressing motility genes to expressing biofilm genes . As in previous models of bow-tie networks [4] we used a smooth sigmoidal function ( the logistic function ) for the effector activity . The probability that a cell expresses biofilm genes depends on c-di-CMP relative to the effector setpoint: P ( Y=1|X ) =logistic ( C ( X ) −CSP ) [Eq 4] Finally , Eq 4 could be re-written with a simple variable change: P ( Y=1|X ) =logistic ( β0+∑i=1mβiXi ) [Eq 5] where β0≡∑i=1qRi , basal∑j=1lRj , basal+Rd−CSP . This model explains how the decision to express biofilm or swarming genes could emerge from simple biochemical reactions ( Fig 3B–3D , S5 Fig ) . Despite its simplicity , the model can describe sophisticated information processing such as conditional gene expression . For example , a network with just two sensors ( m = 2 ) , where sensor i = 1 senses mechanical contact with surfaces and sensor i = 2 senses a chemical attractant , can be tuned to form biofilm only when it senses a surface ( X1 = 1 ) but not a chemical attractant ( X2 = 0 ) by having its β’s optimized to express biofilm genes when X1 = 1 and X2 = 0 . Importantly , the model also shows that the network behavior can be robust to changes in its biochemical components . Robustness is an important feature of biochemical networks [38] . In the c-di-GMP network this means that two different bacteria could express the same phenotype in a given environment despite having different intracellular c-di-GMP levels , as long the biochemical components were such that the values of the compounded β parameters remained unchanged . The c-di-GMP network of P . aeruginosa has potentially more than 40 DGC and PDE proteins ( Fig 2D ) . This provides many possibilities to integrate different stimuli and regulate biofilm formation or swarming in different environments—a regulatory complexity that explains the phenotypic diversity observed among the 28 clinical isolates . The next question is how does selection tune the c-di-GMP network depending on the environments experienced ? We first sought out to investigate this question using experimental evolution with the laboratory strain PA14 . In the past , we had shown that a swarming environment selected for hyperswarmer mutants with single point mutations in FleN [16] . Here , we analyzed a hyperswarmer mutant from that study—mutant FleN ( V178G ) , from hereon called strain fleN*—to understand whether its phenotype could be explained by our model . The mutant fleN* is a poor biofilm former [16] . Its specialist-swarming phenotype could be either due to having a low level of c-di-GMP or a failure of FleN-FleQ to respond to raising c-di-GMP levels since either possibility could cause the bacterium to stay locked in motility mode . We measured c-di-GMP in fleN* and the levels were the same as in the PA14 wild-type strain ( Fig 4A ) . This indicated that the FleN ( V178G ) mutation decreased the FleN-FleQ response without changing the c-di-GMP level . To explore whether fleN* could acquire new mutations that recovered its biofilm capabilities , we put this strain under a constant selection for biofilm formation using drip-flow biofilm reactor [39] ( Fig 4B ) . After growing biofilms for a few days ( see methods ) we could isolate three distinct mutants of fleN* with recovered biofilm capabilities . Two of these had mutations in the dipA gene ( DipAL505R , DipAT792P , called respectively dipA* , dipA** ) and one had a mutation in the wspF gene ( wspFdup776-791 , called wspF* ) . Interestingly , all three mutants had higher c-di-GMP levels than their fleN* ancestor ( Fig 4A ) . We also confirmed—using the Congo red binding assay—that those three mutants indeed decreased their production of extracellular polymers needed for biofilm formation ( Fig 4D ) . To summarize , all mutants had decreased swarming ( a mild decrease in dipA* , dipA** and a total loss in wspF* , Fig 4B ) , higher c-di-GMP levels than both the wild-type and the fleN* ( Fig 4A ) , lower expression of flagella genes ( Fig 4E ) , higher surface attachment ( Fig 4C ) , and higher production of extracellular matrix ( Fig 4D ) . We cloned the dipA* , dipA** and wspF* mutations into the fleN* background and confirmed that these mutations were sufficient to increase capacity for biofilm formation and reduce swarming ( S6 Fig ) . Clean deletions ( ΔdipA and ΔwspF ) caused similar changes towards more biofilm and less swarming in both the fleN* and wild-type background , indicating ( i ) that the mutations phenocopied loss-of-function and ( ii ) that they could work even in the absence of the fleN* mutation ( Fig A , B , C in S7 Fig ) . The raised levels of c-di-GMP suggested that the mutations in dipA* , dipA** and wspF* could be compensating for the decreased sensitivity of FleN-FleQ and allowing the bacteria to recover their biofilm formation . The two proteins encoded by the mutated genes—DipA and WspF—are however functionally very different . DipA has both a GGDEF and a EAL domain and its loss-of-function can increase biofilm formation and decrease biofilm dispersal [40]; results from a screen suggest that DipA acts as a PDE [41] . WspF does not interact with c-di-GMP directly but does so indirectly; it is a methyltransferase that de-methylates the transmembrane Wsp complex that thereafter activates the c-di-GMP synthase WspR [42] . We created double dipA*wspF* and dipA**wspF* mutants in the fleN* background to determine whether the mutations would conflict with each other ( Fig D in S7 Fig ) . Our evolutionary experiments produced mutants that—unlike the clinical strains—had large differences in c-di-GMP , biofilm and swarming caused by a few alleles of large effect . How does our model explain these observations ? The laboratory strain PA14 is a generalist capable of both biofilm and swarming . Our model says that the interplay between the c-di-GMP level C and the FleN-FleQ setpoint CSP determines the decision to switch the phenotype . In an environment that favors motility—such as a swarming plate—c-di-GMP would stay below the effector setpoint such that C < CSP . In an environment that favors biofilm formation—such as a solid surface—c-di-GMP would raise above the setpoint such that C > CSP ( Fig 3B ) . The fleN* hyperswarmer is a swarming specialist that forms weak biofilms despite having the same c-di-GMP level as the wild-type PA14 . According to our model , the hyperswarmer has a higher setpoint , CSP′ , which would lock the bacteria in motile-mode even when c-di-GMP levels raise to levels CSP′ > C > CSP ( Fig 3C ) . The three distinct biofilm-recovery mutants dipA* , dipA** and wspF* could compensate for a higher setpoint by producing more c-di-GMP and raising its level to C′ > CSP′ . Interestingly , the mutations dipA* and dipA** had milder phenotypes than wspF*; those strains where still capable of both biofilm and swarming despite having higher c-di-GMP levels , whereas wspF* lost its swarming entirely ( Fig 3D ) . This suggests that the two dipA mutants adjusted their c-di-GMP level to regain their generalist behavior , while the wspF mutant became a biofilm specialist ( S5 Fig ) . The mutants evolved in the laboratory experienced strong selective pressures , and their phenotypes—caused by large-effect alleles—showed strong associations: biofilm and swarming were anti-correlated ( Fig 4F , S8 Fig ) . The clinical strains showed weak phenotype associations and only small-effect alleles , suggesting that they had evolved under weak selection . Can our model help unite our clinical and laboratory observations ? The link between small-effect alleles and weak selection , well established in evolutionary theory [43] , would be difficult to test empirically: the selection experienced by the clinical isolates during their evolution occurred in the past and is now inaccessible to us . We turned to theory to investigate how the strength of selection across fluctuating environments and the architecture of the c-di-GMP network could lead to the diversity of phenotypes seen across the clinical and laboratory strains . The bow-tie model in Eq 5—which can be derived from biomolecular reaction principles—is mathematically equivalent to the equation for a logistic regression [44] , which is a discrete choice model used for classification problems in machine learning . The analogy immediately suggests that the c-di-GMP network may work as a biochemical classifier that integrates many environmental stimuli and classifies to which of the two categories—motility-favoring or biofilm-favoring—a new environment belongs . The network which gives bacteria the ability to change phenotype when they encounter a new environment results from the environmental changes , or fluctuations , experienced during their evolutionary history . Natural selection exerted in each environment works on the bacteria at the population level in a way that resembles telling bacteria—by killing them or letting them live—whether the action was favorable . How fast the environment changes relatively to the strength at which natural selection acts on the bacterial population is a critical parameter . We call this parameter n , the effective length of the evolutionary history . In the extreme case of n = 1 , selection is so strong that only the last environment matters . A value n > 1 , but still small , represents a strong selection where the fittest network consistently outperformed its competitors across a small number of environments . The larger the value of n the weaker the selection in each environment , and the fittest network is the one that consistently outperformed competitors in a long series of environments . We derived a mathematical analogy between evolution across fluctuating environments and training a logistic regression classifier to investigate how low n ( strong selection ) can produce specialist networks whereas high n ( weak selection ) favors generalists . Classifiers learn their task by training with large datasets , for example a matrix m × n of input variables X and their correct output E = ( E1 , … , En ) . The likelihood of obtaining the output Yj = Ej is P ( Yj = 1|Xj ) if Ej = 1 , and is 1 − P ( Yj = 1|Xj ) if Ej = 0 . This can be written P ( Yj=1|Xj ) Ej× ( 1−P ( Yj=1|Xj ) ) 1−Ej for brevity . The fitting criterion in a logistic regression is that the values of β = ( β0 , … , βm ) should maximize the likelihood of obtaining output E from input X across the n data points: maxβ{∏j=1nπjEj ( 1−πj ) ( 1−Ej ) }whereπij=P ( Yj=1|Xj ) =logistic ( β0+∑i=1mβiXijXi ) [Eq 6] Evolution across fluctuating environments may be described in a similar way . In our case , each environment j ∈ {1 , … , n} is either a motility-favoring environment , Ej = 0 , or a biofilm-favoring environment , Ej = 1 , and the fitness fj in each environment is the agreement between the phenotype favored Ej and the expressed phenotype Yj: fj=πjEj ( 1−πj ) ( 1−Ej ) whereπij=P ( Yj=1|Xj ) [Eq 7] A classical result from evolutionary theory states that when a diverse population experiences a series of n fluctuating environments natural selection will favor the variant with the highest fitness geometric mean across the n environments [45]: F=∏j=1nfjn [Eq 8] Under these conditions , the fittest network across n environments would be the one that made best use of the array of m stimuli sensed in each environment , Xj = ( Xj1 , … , Xjm ) , and expressed—to the extent possible—the right phenotype . This network is the one with β = ( β0 , … , βm ) that maximize geometric mean fitness across the n environments: maxβ{∏j=1nP ( Yj=1|Xj ) Ej× ( 1−P ( Yj=1|Xj ) ) 1−Ejn} [Eq 9] which is the same as the criterion for logistic regression , because maximizing the nth-root of a quantity is the same as maximizing the quantity itself . To summarize the analogy , a classical result of evolutionary theory [45] allowed us to conclude that the total set of m stimuli sensed during network evolution across n fluctuating environments corresponds to a m × n input matrix , X= ( X1T , … , XnT ) , and the phenotypes favored by each of those n environments correspond to an output vector , E = ( E1 , … , En ) T . The solution of Eq 6 and Eq 9—the set of values β that maximizes the quantities described—is the same and so natural selection across fluctuating environments is mathematically equivalent to training a machine learning classifier . The analogy above opens the way to investigate how the size of the m × n matrix determines the fitness of a network in future environments , since it is well known in statistical learning that the size of training data determines the performance of a classifier when it encounters new input data . We carried out simulations where we considered a simple scenario: fluctuating environments that selected for either biofilm or motility , and that occurred with the same probability . We generated the binary vectors of length n to represent the phenotype E favored in each environment ( Ej = 0 representing swarming selection and Ej = 1 representing biofilm selection ) and we created n × m matrices of noiseless stimuli X ( Xij = 0 in a environment favoring swarming and Xij = 1 in an environment favoring biofilm ) and then we swapped the values for a fraction 1 − η to add unbiased noise to the stimuli ( supporting material ) . We then derived the analytical solution for the best network in the limit of very long evolutionary histories ( n → ∞ ) as a function of the signal quality , η . This theoretical best network was—by definition—unbiased for biofilm or swarming since the two phenotypes were set to be equally probable . This means that the sensor activities , β1 , … , βm , should all be equal ( all stimuli are equally informative and should have the same weight on the network’s response ) , and their values should increase ( the sensors should become more sensitive ) with increasing signal quality η . We then investigated how the strength of selection determined the network by calculating the network selected with finite values of n ( Fig 5A ) . This network is the solution of fitting a logistic regression ( Fig 5B ) . In contrast to the theoretical best , the calculated network was typically biased to either biofilm or swarming ( Fig 5C ) . The bias was stronger for small n because it was more likely that the vector of evolutionary histories E with small length n had an overrepresentation of either biofilm or swarming . We then saw that the stronger the network bias was , the worse the fitness in future environments , E′ , would be ( Fig 5D ) . This result , while expected from statistical learning , has biological insight: it explains that strong selection , such as in our laboratory experiments , can select for specialist networks biased for biofilm or swarming . Weak selection , more likely outside the laboratory , reduces network bias and produces generalists . We then investigated how the number of sensors in the network , m , affected fitness ( Fig 5E , top ) . We saw—interestingly—that the future performance of a network increased with the number of sensors m , peaked at an intermediate value m ~ n/2 , and then decreased for m > n/2 ( Fig 5E , bottom ) . A network with m > n/2 had too many components and could be tuned to irrelevant features of past environments that were simply due to noise or under sampling , making it incapable of generalizing in future environments . This is related to statistical overfitting , a well-known phenomenon: the more parameters there are in a statistical model , the easier it is to overfit [46 , 47] . For the c-di-GMP network this means that the optimal number of sensors for a network depends on the strength of selection across fluctuating environments . A network with too few sensors ( m < n/2 ) cannot be properly tuned and will be disfavored by natural selection . Networks with too many sensors ( m > n/2 ) , on the other hand , can be over-tuned to the past and maladapted for the future . Optimal networks have a number of sensors m ~ n/2 . Consistent with statistical learning , their maximum achievable fitness is limited by the noise in the stimulus ( S9 Fig ) and increases with the size of the training history , n ( Fig 5E ) . The analogy between c-di-GMP signaling and a machine learning classifier explains that weak selection favors generalist bacteria; generalists integrate environmental stimuli and decide between biofilm and swarming according to the environmental fluctuations experienced in their evolutionary history . Evolution in strong selection , on the other hand , favors specialists . This is similar to how small data sets tend to produce biased classifiers . In the light of our model , we sought to exploit strong selection in laboratory environments to search for genetic alterations that might bias the c-di-GMP network towards swarming motility . According to our model , mutations that improve swarming should impact biofilm formation , and could be potentially used as targets against P . aeruginosa virulence [11] . We first noted that the wspF* strain , a biofilm specialist , had a 16 base-pair insert-repeat which functioned as a reversible DNA switch [48] . This strain , when placed under strong swarming selection for longer than 24 h , generated swarming plumes made of mutants that spontaneously lost the insert ( Fig 6A , S1 Movie ) . We repeated this swarming-plume assay with a fleN*ΔwspF strain—a biofilm-specialist that lacked the wspF gene entirely—to search for mutations that could occur elsewhere and cause the phenotype to switch from biofilm specialism back to swarming . The fleN*ΔwspF strain also generated swarming plumes when placed under swarming selection for longer than 24 h ( S2 Movie ) , although this took longer than for the wspF* ( Fig 6B; logrank test P = 0 . 03 ) . Whole-genome sequencing of one plume isolate revealed a 3 bp deletion in the gene wspA ( Δ857–859 ) . This fleN*ΔwspFwspA* mutant restored the fleN* phenotype of low biofilm and hyperswarming ( Fig 6C ) . WspA is a critical component of the surface-sensing Wsp complex ( Fig 6D–6F ) ; the switch from biofilm specialism to swarming specialism could be due to an inability of raising c-di-GMP when the bacteria touched a surface . Having found this one Wsp-disabling mutation , we asked whether strong swarming selection applied to the fleN*ΔwspF strain could reveal new Wsp-disabling mutations every time . We repeated swarming-plume experiment 89 times and we used high-throughput sequencing to target-sequence the wspABCDER operon of plume isolates . We identified 43 new distinct mutations affecting the Wsp system: 17 deletions , 5 insertions and 21 single nucleotide variants; some of these mutations occurred multiple times ( Fig 6G , Table 1 in S1 Text ) . All mutations caused the biofilm specialist to regain its swarming , and are therefore potential targets against P . aeruginosa biofilm formation . Interestingly , two plume isolates apparently had no mutations in wspABCDER . We sequenced their whole genomes to search for mutations elsewhere . Both mutants had point mutations in another predicted c-di-GMP network gene , PA14_03720 ( mutations D378G and E506A ) . This gene has a GGDEF motif but , intriguingly , a previous study had not detected an effect in biofilm or swarming in a ΔPA14_03720 mutant [41] . The point mutations that we identified in PA14_03720 thus provide an unexpected way to impact the c-di-GMP network and cause loss of biofilm specialism .
We presented empirical results and a new mathematical model that provides a new interpretation of the ubiquitous c-di-GMP network of bacteria that computes like a biochemical machine learning classifier . Our analysis of 28 P . aeruginosa clinical isolates revealed diverse levels of c-di-GMP , biofilm formation and swarming motility . The three traits were uncorrelated , and the apparent lack of associations seemed to contradict a well-known dichotomy between biofilm and swarming . Phylogenetic analysis showed evidence of a tradeoff , but only among a few closely related strains . Explaining a significant fraction of the diversity in c-di-GMP , biofilm and swarming seen in our clinical isolates required many small-effect alleles ( Fig 2E ) . In contrast , mutants evolved in strong-selecting laboratory conditions had large-effect mutations that caused switches from swarming specialism to biofilm specialism and back ( Figs 4 and 6 ) . Our mathematical model explains these mutations: Altering the input/output mapping of the c-di-GMP network can lock the bacteria in either biofilm or swarming mode . We use a classical insight from evolutionary theory—that natural selection across a series of fluctuating environments favors strategies that maximize the geometric mean fitness [45]—to investigate why strains evolved under weak selection ( most likely outside the laboratory ) have small-effect alleles , whereas strains evolved under strong selection ( as we applied in our laboratory evolutionary experiments ) have large-effect mutations . We derived a mathematical equivalence between natural selection and training a logistic regression model . This analogy is based on simplifying assumptions and is valid only when the genetic variance within the population is large; in that case selection can choose from wide range of variants and pick the best one . When genetic diversity within the population is low , evolution should resemble reinforcement learning—another learning paradigm , where data is fed online . Mutations in bacteria would correspond to “suggesting” an action , and the environment would “inform” the population whether the action was favorable by killing bacteria or letting them live . Nonetheless , the simplifying assumptions allowed us to investigate the networks with maximum geometric mean fitness and gain biological intuition on the evolution of c-di-GMP . We saw that the strength of selection determines the optimal number of input sensors ( Fig 5E ) . Our simulations also explained why networks evolved in strong selection are more likely to be biased—specialists in either biofilm or swarming . These insights helped us unify our clinical and laboratory observations . The architecture of biochemical networks determines their function [49] . The bow-tie architecture of c-di-GMP suggests a machine learning classifier whose function is to determine , from a set of stimuli , to which of two categories an environment belongs—biofilm-favoring or motility-favoring . It is likely that some of the stimuli sensed by the c-di-GMP network will be redundant; in that case their integration would improve decision-making by averaging out noise [1] . Some stimuli , however , may be complementary; in that case their integration could enable conditional decision-making . Some of those stimuli may help bacteria determine who their neighbors are to better resist cheating—a constant threat to the stability of social behaviors , including biofilm and swarming [50] . Signal integration in a bow-tie network has therefore many advantages . The reliance on a core molecule , however , has a well-known disadvantage [3]: mutations that improve one output can impair the other output ( s ) . Microbiologists had already noted this phenomenon [29] . The tradeoff also occurred in our experimentally evolved hyperswarmers , which lacked biofilm formation [16] . We saw it again here in the dipA and wspF mutants ( Fig 4F , S8 Fig ) which improved biofilm formation but decreased swarming . And we leveraged the tradeoff in the plume-isolation assay to find 45 new mutations that caused loss of biofilm specialism ( Fig 6 ) . Our network model—simple on purpose—made several notable assumptions . First , the model assumed deterministic and steady-state biochemical reactions . The model also assumed one single c-di-GMP pool within the cell; some evidence suggests there may be many pools [51] although this is under debate [52] . Our goal , however , was to demonstrate that even a simple biochemical network could compute like a machine learning classifier . Including dynamics , stochasticity and more hidden nodes in the c-di-GMP network could add even more sophisticated computation ( S10 Fig ) and the network could eventually approach the performance of a deep neural network [4] . Understanding the function and evolution of such biochemical networks is where the concepts of machine learning—already a powerful tool to interpret complex biological data [53]—could help elucidate the evolution of biological systems [54] . Our results shed light on bacterial evolution in three important ways: First , they provide a mechanism of adaptation on a range of timescales , from the second to minutes involved in the swarm/biofilm decision to the timescales involved in evolution . Second , they suggest that we may be able to estimate the evolutionary history—the number of environments that a bacterium has experienced in its evolution—from the number of sensors in a network . Our model says that well-adapted networks should have a number of sensors ( m ) that is proportional to the evolutionary history ( n ) . In our simplified model , this relationship is m = n/2 . If we know more about the stimuli and dynamics of a biochemical network such as c-di-GMP in P . aeruginosa ( m ~ = 53 ) , we should be able to calculate the effective size of the evolutionary history that P . aeruginosa has experienced . This analysis could be made across different species to compare their evolutionary histories and perhaps even predict future fitness . Third , the idea of “overfitting” to past experiences suggests network weakness that we could exploit . For this application , it will be important to know when is the environment change “extremely rapid” versus “not rapid enough” . The conventional view is that most natural environments change slowly most of the time , as natural environments tend to be smooth , punctuated by rare but large change . Many laboratory settings are “not rapid enough” as well . For example , the drip flow biofilm experiments shown here were “not rapid enough” for all cells to wash away; this was on purpose so we could obtain mutants that recovered biofilm formation . The hygienic environments in hospitals are often “not rapid enough” either , and bacteria can adapt and become resistant to antibiotics . We may already be familiar with the “overfitting” idea: Almost all of our methods to kill bacteria come from knowing that bacteria “overfit” what they experienced in the past , and we need to artificially change the environment fast , such as in a sudden rise in antibiotic concentration or ultraviolet radiation , to effectively kill bacteria . We could take advantage of new knowledge to engineer combinations of environmental stimuli that bacteria never encountered before and trigger a maladapted response—for example biofilm dispersal—in a way that treats infection but prevents resistance .
All strains were grown overnight in lysogeny broth ( LB ) at 37°C with shaking at 250 rpm . Swarming media consisted of 0 . 5% agar ( Bacto ) supplemented with 5g/L casamino acid , 1 mM MgSO4 , 0 . 1 mM CaCl2 and 1X buffer ( 12 g/L Na2HPO4 ( Fisher Scientific ) , 15 g/L KH2PO4 ( Fisher Scientific ) and 2 . 5 g/L NaCl , pH6 . 7 ) [55] . Biofilm assays were carried out in 96-well plates in 1% trypton at 25°C for 24 hours and quantified by crystal violet staining [56] . c-di-GMP measurements were obtained from colony biofilms incubated on trypton plates with 1% agar . The P . aeruginosa clinical isolates were sequenced using PacBio by the Genomics Facility at the Icahn School of Medicine at Mount Sinai ( Robert Serba , PI ) , the genomes were annotated by the PATRIC [57] and the LASSO regression was done with glmnet [58] . Isogenic clones of PA14 were sequenced using Illumina MiSeq platform and mutations were identified using breseq [59] . All data analysis and plotting was conducted in Matlab , except for the Moran test for phylogenetic signal determination conducted in R using package ‘adephylo’ [60] . Mathematical model was implemented in Matlab based on the logistic regression in function mnrfit . m . | How does evolution shape living organisms that seem so well adapted that they could be intelligently designed ? Here , we address this question by analyzing a simple biochemical network that directs social behavior in bacteria; we find that it works analogously to a machine learning algorithm that learns from data . Inspired by new experiments , we derive a model which shows that natural selection—by favoring biochemical networks that maximize fitness across a series of fluctuating environments—can be mathematically equivalent to training a machine learning model to solve a classification problem . Beyond bacteria , the formal link between evolution and learning opens new avenues for biology: machine learning is a fast-moving field and its many theoretical breakthroughs can answer long-standing questions in evolution . | [
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"microbial"... | 2017 | Bow-tie signaling in c-di-GMP: Machine learning in a simple biochemical network |
Vertebrate body designs rely on hydroxyapatite as the principal mineral component of relatively light-weight , articulated endoskeletons and sophisticated tooth-bearing jaws , facilitating rapid movement and efficient predation . Biological mineralization and skeletal growth are frequently accomplished through proteins containing polyproline repeat elements . Through their well-defined yet mobile and flexible structure polyproline-rich proteins control mineral shape and contribute many other biological functions including Alzheimer's amyloid aggregation and prolamine plant storage . In the present study we have hypothesized that polyproline repeat proteins exert their control over biological events such as mineral growth , plaque aggregation , or viscous adhesion by altering the length of their central repeat domain , resulting in dramatic changes in supramolecular assembly dimensions . In order to test our hypothesis , we have used the vertebrate mineralization protein amelogenin as an exemplar and determined the biological effect of the four-fold increased polyproline tandem repeat length in the amphibian/mammalian transition . To study the effect of polyproline repeat length on matrix assembly , protein structure , and apatite crystal growth , we have measured supramolecular assembly dimensions in various vertebrates using atomic force microscopy , tested the effect of protein assemblies on crystal growth by electron microscopy , generated a transgenic mouse model to examine the effect of an abbreviated polyproline sequence on crystal growth , and determined the structure of polyproline repeat elements using 3D NMR . Our study shows that an increase in PXX/PXQ tandem repeat motif length results ( i ) in a compaction of protein matrix subunit dimensions , ( ii ) reduced conformational variability , ( iii ) an increase in polyproline II helices , and ( iv ) promotion of apatite crystal length . Together , these findings establish a direct relationship between polyproline tandem repeat fragment assemblies and the evolution and the design of vertebrate mineralized tissue microstructures . Our findings reveal that in the greater context of chordate evolution , the biological control of apatite growth by polyproline-based matrix assemblies provides a molecular basis for the evolution of the vertebrate body plan .
Proline-rich regions occur in a wide variety of functionally significant proteins , including mucins , snow flea antifreeze proteins , prolamine storage proteins , pancreatic polypeptide hormones , neuropeptides , Alzheimer amyloid , prion proteins , and tooth enamel proteins [1] , [2] . Many proline-rich proteins contain repetitive motifs and adopt left-handed polyproline II helical conformations ( PPII ) [3] , [4] . These PPII helices are more mobile than other periodic structures , e . g . α-helices or β-sheets [5] , but nevertheless exhibit well-defined molecular backbone conformation due to the rigidity of the proline ring . The well-defined yet mobile and flexible structure of polyprolines has led to the hypothesis that such proteins may function as mineral-binding domains , protein-protein docking domains , or internal molecular spacers during the formation of biological minerals and other biocomposites [6] . Remarkably , proline-rich , tripeptide tandem repeat proteins participate on all levels of biological mineralization and include members as diverse as the Haliotis rufescens protein Lustrin A involved in the extracellular deposition of shell and pearl , the Strongylocentrotus purpuratus protein SM50 contributing to the mineralization of sea urchin teeth and spicules from magnesium calcite and protodolomite , as well as vertebrate collagen I and the tetrapod tooth enamel protein amelogenin [6] , [7] . The rise of vertebrates coincides with the emergence of revolutionary body designs that rely on hydroxyapatite as the principal mineral component of bones and teeth [8] . Vertebrates use apatites to form relatively light-weight , articulated endoskeletons and sophisticated tooth-bearing jaws , facilitating rapid movement and efficient predation . A large degree of flexibility ( Greek: apatite = deceit ) allows apatites to be readily shaped by proline-rich proteins such as collagen I and the tooth enamel protein amelogenin . Apatite mineral growth and habit in vertebrate enamel are controlled by a unique proline-rich protein , amelogenin , which forms the majority of the developing enamel matrix ( about 95% ) [9] . A recent study has shed new light on the organization of this relatively unstructured protein [10] . Other studies have indicated that amelogenin self-assembly might be mediated by a complementary relationship between the hydrophobic and PPII helical regions [11] , [12] . Difficulties in obtaining protein crystals suitable for X-ray crystallography have prompted a series of studies using circular dichroism ( CD ) , NMR , Raman spectroscopy , and molecular modeling studies [13] . Earlier CD , FTIR , and Raman spectroscopy experiments suggested mixed β-sheet/β-turn/helix and random coil structures [7] , [14] , [15] with extended β-spiral/poly-L-proline type II ( PPII ) helical structures in the midsection of amelogenin [13] . The importance of the amelogenin N-terminus for amelogenin self-assembly has been confirmed by yeast-two-hybrid studies and biochemical analysis of the two serine residues in positions 16 and 25 [16] , [17] . Based on solid state NMR data , the amelogenin carboxy-terminal domain appears to be oriented next to the hydroxyapatite crystal surface [18] . Loss of the carboxy-terminus as it occurs during amelogenin proteolytic processing has been associated with a reduced affinity to hydroxyapatite and a reduction in the ability to inhibit crystal growth [19] , [20] . Recent crystal growth studies suggest that the carboxy-terminus is important for the alignment of crystals into parallel arrays while the remainder of the molecule plays a role in the inhibition of crystals growth [21] . With the emergence of prismatic enamel in mammals , the length of amelogenin polyproline tri-peptide repeats increases significantly , suggesting that the augmentation of amelogenin proline-rich regions is governed by evolutionary trends . The augmentation in polyproline repeat length occurs within the proline-rich amelogenin peptide ( PRAP , i . e . the region from AA46–AA166 ) , which is comprised of an evolutionary “hotspot” containing a series of PXX tandem repeats [22] , [23] . The rapid evolution of the PRAP from amphibian to mammals was primarily accomplished by insertions of PXX tripeptide motifs [24] , with PXQ as the most frequent tripeptide sequence element . In these extended polyproline repeat structures , both proline and glutamine cause structural rigidity of the newly added tripeptide complexes [25] . The unique occurrence of elongated polyproline stretches and amelogenin protein assembly in the evolution of the vertebrate dentition prompted us to ask the question of whether repeat length and self-assembly dimensions were related and whether there was any association between polyproline repeat motif length and structural changes in mineral shape and matrix organization . In order to ask this question , we compared polyproline repeat length and amelogenin nanosphere dimensions between vertebrates and generated a number of biomimetic peptides . We then tested the relationship between mammalian and amphibian polyproline repeat length , nanosphere assembly , and crystal growth in a frog amelogenin overexpressing mouse model . Finally , we determined the 3D NMR structure of the amelogenin repeat region to identify unique structural motifs explaining the correlation between amelogenin self-assembly and polyproline repeat length . In the present study we are demonstrating that the unique ability of polyproline motifs to shape biological minerals lies in their ability to alter protein matrix self-assembly . We are arguing that in the greater context of chordate evolution , the biological control of apatite growth by polyproline-based matrix assemblies provides a molecular basis for the evolution of the vertebrate body plan .
PXX repeat element organization is highly conserved between mammals ( e . g . , Homo , Mus ) , reptiles ( e . g . , Elaphe , Paleosuchus ) , and amphibians ( e . g . , Xenopus , Rana ) ( Figure 1A ) . There were fewer PXX repeat elements in amphibians , while several mammalian species ( e . g . , ruminants and marsupials ) featured PXX repeat numbers exceeding those found in humans or mice ( Figure 1A , 1B ) . A comparison with known amelogenin sequences indicated that the number of PXX repeats in the frog Rana pipiens was significantly shorter than the PXX repeat number in mice , goats , or steers ( Figure 1B ) . This comparison indicates a potential trend toward increased polyproline repeat length with increasingly sophisticated enamel structures in vertebrates . In order to determine whether changes in PXX repeat length were associated with changes in supramolecular enamel protein assembly dimensions in nature , we compared repeat length and supramolecular assembly dimensions from four selected vertebrate species using atomic force microscopy ( AFM ) and dynamic light scattering ( DLS; Figure 1B–D ) . Native enamel matrix proteins from frog ( Rana pipiens ) , mouse ( Mus musculus ) , goat ( Capra hircus ) , and bovine ( Bos taurus ) were chosen to represent increasing PXX repeat length in vertebrates ( Figure 1B ) . Both the AFM and the DLS analysis demonstrated that enamel protein supramolecular assembly dimensions gradually decreased by 60% from frog to bovine , while PXX length gradually increased by 250% ( Figure 1B–D ) , suggesting an inverse correlation between polyproline repeat length and enamel protein 3D-assembly dimensions in the evolution of vertebrate enamel proteins . In order to determine the effect of polyproline designer peptides of increasing length ( Figure 1A ) on apatite crystal growth , crystals were grown in the presence of PXX polyproline designer peptides or amelogenins . Addition of PXX designer peptides to the crystallization solution resulted in the formation of needle-shaped crystallites , and longer PXX repeat motifs corresponded with increased crystal length ( PXX12: 21 . 6±6 . 5 nm , PXX24: 42 . 9±8 . 5 nm , PXX33: 102 . 1±36 . 3 nm ) . Addition of recombinant full-length amelogenin ( rM180 ) resulted in the formation of elongated crystals of 106 . 2±19 . 3 nm length . Hydroxyapatite crystals grown without any addition of protein measured 8 . 2±3 . 9 nm in length while the 33 mer glutamine/alanine replacement polypeptide PQA yielded flake-like particles with broad diffraction rings ( Figure 2A , 2D ) . There were distinct differences in diffraction patterns between crystals grown under the control of various additives ( Figure 2A ) . The control only showed diffuse diffraction patterns indicative of amorphous calcium phosphate . Both the PXX12 and the PQA sample also revealed only diffuse diffraction rings . The PXX24 treated sample featured a preferred orientation in the 002 plane and a diffuse reflection ring in the 210 plane . Both the PXX33 and the amelogenin treated samples displayed sharp rings in both the 002 and the 210 plane . There was also a very faint reflection ring in the 104 plane of the PXX24 , the PXX33 , and the amelogenin samples . These findings demonstrate that PXX designer peptides with increased length yield significantly longer apatite crystals with diffraction patterns similar to those of developing enamel apatite crystals [29] . Specifically , 12 mer polyproline repeat stretches were associated with amorphous apatite while stretches of 24 mer and above featured a crystalline mineral phase . They also document that PXX polyproline peptides alone exert a profound control on apatite crystal growth . We interpret these results to indicate that the polyproline backbone of elongated PXX repeat peptides enhances protein matrix structural rigidity , resulting in an inhibition of epitaxial apatite crystal growth on a- and b-axis surfaces while promoting apatite crystal growth in c-axis direction . In order to further understand the mechanisms by which polyproline repeat peptides affect crystal growth , we decided to test the effect of polypeptide length on protein matrix organization . In previous studies we demonstrated that the extracellular protein matrix of developing tooth enamel provides a complex supramolecular biomineralization template that directly controls enamel crystal formation [9] , [26] . Here we once more used our PXX12 , PXX24 , and PXX33 peptides to ask the question whether the length of these polypeptides affects organic matrix organization . In order to address this question , protein assemblies on coated carbon grids were studied using transmission electron micrographs ( TEM ) ( Figure 2B ) . In addition , AFMs of proteins in solution were generated ( Figure 2C ) . Our results demonstrated that protein matrix nanosphere diameters were 17 . 9±2 . 7 nmAFM ( 10 . 1±1 . 2 nmTEM ) for PXX12 , 13 . 9±2 . 6 nmAFM ( 6 . 1±0 . 8 nmTEM ) for PXX24 , and 9 . 2±1 . 9 nmAFM ( 4 . 0±0 . 5 nmTEM ) for PXX33 . Nanosphere diameters of the two controls were 27 . 3±4 . 6 nmAFM ( 13 . 3±1 . 5 nmTEM ) for the recombinant full-length mouse amelogenin control ( rM180 ) and 15 . 1±2 . 8 nmAFM ( 8 . 8±1 . 9 nmTEM ) for the 33 mer glutamine/alanine replacement polypeptide PQA ( Figure 2E ) . In average , TEM nanosphere dimensions were about 50% of their AFM counterparts , a difference that might be explained by the differences in sample preparation between the dissolved protein used for AFM and the dried TEM sample . Sample buffers did not yield any significant substructures . Both AFM and TEM data demonstrated that nanosphere diameters decreased with increasing peptide length , i . e . PXX33 nanospheres measured about half the size of PXX12 nanospheres and were double as densely packed . This apparent readiness of extended PPII helices to assume a high level of compaction might be explained by a dramatic reduction in conformational entropy in such an assembly [27] . A comparison of mouse and frog amelogenin sequences indicated that the number of prolines was 34 and 27 in mouse and Rana pipiens PRAPs , respectively , and that the major difference between mouse and frog amelogenins was a 33% higher number of PXX repeat motifs in mice versus frogs ( Figure 1A , 1B ) . Glutamine is the second most likely residue to appear in a PPII helix segment ( second to proline ) [4] and thus a likely partner to interact with prolines in the function of PPII helices . We were thus interested in testing the effect of glutamines on the macromolecular assembly of polyprolines . Remarkably , when the 5 glutamines in PXX33 were exchanged with alanine substitutes ( PQA peptide ) , nanosphere diameters about doubled and electron density distribution on micrographs was drastically altered . The 33 mer glutamine/alanine replacement polypeptide PQA did not yield any HAP crystals of measurable length ( Figure 2A ) . The loss of HAP crystal extensions underscores the importance of glutamine insertions in the overlying polyproline repeat sequence for crystal growth . The glutamine substitutions with alanine , effectively reversing the effect of extended polyproline macromolecular compaction found in PXX stretches , also indicate that glutamines play a pivotal role in the compaction of PPII helices as they occur in many biological systems , including biominerals . While hydroxyapatite crystals of mammalian enamel are organized into tightly packed rods ( prisms ) [26] , [28] , [29] , this regular organization in enamel prisms is largely absent in amphibians and reptilians [30] . On a molecular level , the emergence of prismatic enamel organization during the amphibian/reptile to mammal transition has been paralleled by a significant increase in amelogenin PXX repeat length ( Figure 1A ) [31]–[36] . We have thus hypothesized that the unique arrangement of elongated mammalian apatite crystals into prisms is a result of PXX repeat length in the PRAP . In order to examine the effect of a shortened polyproline repeat amelogenin on mouse enamel formation , Rana pipiens amelogenin expressing mice ( fAmel-x-null mice ) were generated by cross-breeding amelogenin null mice with Rana pipiens amelogenin transgenic overexpressors to prevent the normal mouse amelogenin background from interfering with the transgenic phenotype . In this model , frog amelogenins were cleaved in a very similar fashion to their murine counterparts ( Figure 3J ) . Enamel of first mandibular molar from five amelogenin null , fAmel-x-null , and wild-type mice each was analyzed and compared between groups . The phenotype in frog amelogenin overexpressors that were not crossed with null mice ( fAmel ) was less severe and thus not used for further analysis . Amelogenin null mice , which were used as a control , only featured a rudimentary mineral deposit on the surface of the underlying dentin ( Figure 3B ) . Comparison between first mandibular molar enamel of fAmel-x-null mice and wild-type controls demonstrated 50 . 3% reduced enamel thickness and grossly altered enamel prism structure including massive patches of fused crystallites , especially in the coronal half of the fAmel-x-null enamel layer ( Figure 3C , 3E versus 3D , 3F ) . We attribute this change in enamel prism pattern to a drastic reduction of PXX repeat stretches in the frog amelogenin compared to its mouse counterpart , resulting in an impairment of protein assemblies to coat and package individual elongated crystallites . The prism-less organization of fAmel-x-null enamel also somewhat resembled the prism-less structure of frog enamel ( Figure 3E versus Figure 3A ) , suggesting that amelogenins with elongated polyproline stretches might be one requirement for prismatic enamel . Enamel matrix nanosphere diameters were 20 . 9±2 . 6 nm in fAmel-x-null mice and 14 . 0±1 . 9 nm in their wild-type controls ( Figure 3G–3I ) . As a result , enamel matrix nanosphere dimensions in fAmel-x-null mice exceeded those of controls by 50 . 1% . These findings are corroborated by DLS-based comparisons demonstrating significantly larger frog amelogenin nanospheres compared to mouse amelogenin nanospheres ( Figure 1D ) . Together , these studies document that the presence of increased length of polyproline repeat stretches in mammalian amelogenins is associated with both reduced macromolecular assembly dimensions and sophisticated mammalian enamel crystal/prism structure . Alterations in prism organization as seen as a result of elongated polyproline stretches may also entail additional mechanisms not highlighted in the present study . Cellular effects such as changes in cell movement pattern or in ameloblast morphology might also contribute to the phenotype observed and other portions of the amelogenin molecule are likely to be involved in amelogenin nanosphere assembly as well , even though their exact contributions remain to be established . In order to identify unique structural features and to explore the effect of repeat motif elongation on enamel matrix organization , we have performed a series of structural analyses based on the longest PRAP-derived designer peptide PXX33 . In the absence of amelogenin X-ray crystallography data , CD , FTIR , and Raman spectroscopy studies have suggested mixed β-sheet/β-turn/helix and random coil structures [7] , [37] with extended β-spiral/poly-L-proline type II ( PPII ) helical structures in the PRAP [10]–[12] . In order to determine structural features of the PRAP-derived PXX33 polypeptide , NMR analysis was performed and chemical shifts of 20 out of 33 amino acid residues of the PXX33 peptide were completely or partially assigned ( Table S1 ) , providing a basis for subsequent NOE ( Nuclear Overhauser Effect ) analysis . Using Nuclear Overhauser Effect Spectroscopy ( NOESY ) , a total of 151 ( NOE ) definitive restricts were obtained , including 20 intra-amino acid residues and 100 neighbor amino acids [dN ( i , i+1 ) ] , 24 dN ( i , i+2 ) , 5 dN ( i , i+3 ) , and 2 dN ( i , i+4 ) NOEs . No long distance NOEs were detected ( Table S2 ) . Analysis of chemical shifts and NOE patterns did not reveal any typical α-helix or β-strand secondary structures . In order to calculate and analyze the three-dimensional structure of the PXX33 peptide at atomic resolution , NOE constrains were entered into the DYANA software package to calculate a total of 200 candidate structures . There was a fairly high backbone root mean square deviation ( RMSD ) of 7 . 51±1 . 51 Å and a heavy atom RMSD of 8 . 56±1 . 54 Å between the 200 candidate structures investigated . The absence of long distance NOEs suggest that PXX33 forms extended structures in aqueous solution while the high RMSD values imply a lack of well-defined conformations . In order to illustrate the similarities and slight variation between individual candidate structures , five structures representing lowest energy conformations were plotted together and superimposed using the MolMol software ( Figure 4A ) . Our analysis revealed significantly higher structural variability at the PXX33 N-terminus representing the PXX12 polypeptide ( Figure 4A ) . The high structural variability between various conformations in the PXX12 region might be one of the reasons for the larger size and irregular boundaries of the PXX12 nanospheres compared to their PXX24 and PXX33 counterparts . Further analysis of individual lowest energy conformations revealed three left-handed extended PPII helices ( PPII-1 , P13–P16; PPII-2 , P19–P22; PPII-3 , P28–P31 ) ( Figure 4B ) , which were identified using the following criteria: ( i ) left-handedness , ( ii ) 3 amino acid residues per turn , and ( iii ) 3 . 1 Å per residue advance ( 9 . 3 Å per turn ) . Individual PPII turns measured 8 . 8 Å ( PPII-1 ) , 9 . 3 Å ( PPII-2 ) , and 9 . 45 Å ( PPII-3 ) . These were four residue-length PPII helices in which the proline rings at the positions i and i+3 were oriented in the same direction [1] , [38] . The presence of three PPII helices in the amino acid region 13–33 and the absence of PPII helices in the PXX12 stretch might be another reason for the enormous compaction observed in PXX33 supramolecular assemblies compared to PXX12 and PXX24 counterparts as PPII helices have been associated with unusual structural compactness [38] . Several other factors may also explain the dramatic compaction of PXX33 supramolecular assemblies compared to aggregates formed by shorter polypeptides . It is widely accepted that hydrophobic free energy is a major force driving peptide-peptide interactions [39] . The surface of the larger PXX33 peptide ( Figure 4C ) is at least as hydrophobic as the smaller PXX12 peptide ( Figure 4D ) , and its increased surface area provides more contacts for interaction and more van der Waals attraction . An increased attraction between the larger peptides would be consistent with the formation of aggregates with higher density , which was observed experimentally . Another factor contributing to the reduced size of PXX33 assemblies might be their reduced mobility , especially in light of the flexibility of polyproline structures in solution . The mean thermal velocity of a peptide due to Brownian motion is inversely proportional to the square root of its mass [40] , resulting in smaller peptides in an aggregate to impact each other with higher frequency , which in turn would weaken the strength of the aggregate and reduce its density . Already Hellenistic culture knew of the enormous adaptability , variability , and flexibility of apatites , using the word απαταω ( to deceive ) in reference to the similarities between apatites and other minerals . Five hundred million years earlier , during the Ordovician , the first vertebrates took advantage of these versatile minerals as building blocks for newly designed endoskeletal backbones and teeth . The incorporation of apatites into early vertebrate body designs was likely facilitated by SPARC and SPARCL1 as ancestors of SCPP mineralization proteins that arose at the same time by tandem duplication [41] . These early SPARC proteins might have served as templates for insertion-based repeat length expansion as it is associated with the generation of intrinsically unstructured proteins [42] , [43] . The significant variation in enamel structure and polyproline repeat length among mammals ( e . g . between ruminants , dolphins , and rodents ) indicates that polyproline length not only increases from amphibians to mammals but also varies significantly among mammals , perhaps in response to various functional loads . While neither the use of apatites nor the presence of proline-repeat polypeptides are unique for vertebrate mineralized tissues , vertebrates were nevertheless first in using polyproline repeat proteins to orchestrate the deposition of apatites into endoskeletal mineralized tissues . Microstructured apatites incorporated into innovative , highly flexible body plans not only gave these comparatively diminutive creatures a survival advantage over heavily armored organisms from the same period but also provided a mineral substrate for the evolution of teeth as powerful tools facilitating predation and food apprehension [44]– . The ability of polyproline fragments alone to self-assemble and to guide apatite crystal growth in C-axis dimension raises the question about the role of the N-terminal and C-terminal amelogenin flanking domains . Here we propose that the flexible yet rigid structure of polyproline-rich assemblies provides a dynamic molecular packaging material between elongating mineral crystals . The evolution of elaborate mammalian enamel prisms as well as the design of the first vertebrate endoskeletons might thus be a result of sophisticated supramolecular polyproline matrices that insulate , guide , and package individual apatite crystals . Mirroring nature , the suitability of polyproline designer peptides to modulate apatite crystal growth emerges as a novel design concept for biomimetic enamel scaffolds and enamel tissue engineering .
Peptides ( >99% purity ) were synthesized by Genescript ( Piscataway , NJ ) . The carbon coated copper TEM grids were purchased from SPI Supplies ( West Chester , PA ) . Twelve mm coverslips were obtained from Fisher Scientific ( Pittsburgh , PA ) . D2O ( 99 . 5% ) was purchased from Cambridge Isotope Laboratories ( Andover , MA ) . Four hundred MHz NMR tubes were obtained from Kontes ( Vineland , NJ ) . Other common regents were from Sigma Aldrich ( St Louis , MO ) . The full-length mouse amelogenin coding sequence was cloned into pASK-43 ( + ) with EcoR I and XhoI restriction site at 5′ and 3′ end , respectively . BL21-DM* was used as the host bacteria to express the recombinant proteins . The bacteria were cultured at 37°C until the OD600 reached 0 . 8 and then were induced at 32°C for 4 h . The expressed proteins were absorbed onto Ni-NTA agarose column and washed with 10 column volumes of PBS and 3 column volumes of 40 mM imidazole in PBS . Then the proteins were eluted with a pH 5 . 0 gradient ( from 50 mM to 500 mM ) imidazole PBS solution . The eluted proteins were dialyzed against H2O several times to make sure the salt and imidazole were diluted at least 10 , 000 times . Subsequently , the purified proteins were concentrated to about 10 mg/ml using a Centriprep YM-3 column . One litter bacteria culture yielded about 50 mg high quality mouse full-length amelogenin protein . Based on the high percentage of amelogenins in the enamel matrix of developing teeth , enamel from unerupted teeth was dissected and collected in 1 ml 6 M guanidine solution ( pH 7 . 0 ) and incubated overnight to dissociate the enamel proteins from the enamel crystals . After centrifugation at 6 , 000 g for 15 min , the supernatant containing the amelogenin protein was dialyzed against water to remove the guanidine . Enamel proteins were then concentrated with YM-3 centricon columns . Mice were sacrificed according to UIC animal care guidelines . For scanning electron microscopy , 20 d postnatal mouse mandibles were fixed in 4% paraformaldehyde and then saggitally hemisected using an Exakt sawing device . Enamel surfaces were etched in EDTA for 5 min , rinsed thoroughly , and dried overnight . Samples were coated with gold and palladium and then examined using a JEOL JSM-6320F scanning electron microscope . All measurements in this study were statistically evaluated using ANOVA and statistical dispersion was recorded and displayed using standard deviation ( s . d . ) . HAP crystal growth experiments were performed as previously described [21] . Briefly , peptides and proteins were dissolved in DDW at a concentration of 4 mg/ml and then adjusted to pH7 . 5–8 . 0 with 20 mM NH4OH at 4°C . Carbon coated copper TEM grids were immersed into the reaction mixture containing 1 mg/ml peptide/protein , 2 . 5 mM CaCl2 , and 1 . 5 mM ( NH4 ) 2HPO4 and incubated in a moisturized container at 37°C for 2 . 5 h . Subsequently , TEM grids were quickly rinsed with DDW , blotted against filter paper , and air dried . Transmission electron microscopy was performed using a JEOL 1220 TEM . Electron diffraction patterns were collected as described earlier [29] . Briefly , patterns were obtained on 20 representative samples per group using a JEOL JEM-3010 in the diffraction mode at 300 kV and a camera length of 50 cm . Measurements were made at 90° incident to the sample . Patterns were measured for spot or ring diameter directly from the digital camera image , and the d spacings obtained were compared to those characteristic for hydroxyapatite . Droplets containing 100 µl of diluted ( 1 mg/ml ) pH7 . 5–8 . 0 peptide/protein solution were placed on carbon coated copper TEM grids and incubated in a moisturized container at 37°C for 2 h . Thereafter , TEM grids were quickly rinsed with DDW , immersed into 100 µl of freshly prepared 1% phosphotungstic acid solution for 6 min , quickly rinsed with DDW again , air dried , and analyzed using a Joel1220 TEM . The AFM measurements were carried out using an extended MultiMode AFM ( MMAFM ) integrated with a NanoScope IIIa controller ( Veeco Instruments , Santa Barbara , CA ) and a Q-Control Module ( nanoAnalytics , Muenster , Germany ) . The MMAFM was equipped with a calibrated E-type piezoelectric scanner and a glass cell for fluid TappingMode AFM ( both from Veeco ) . The silicon AFM cantilever/probe used in this study was rectangular in shape , 130 µm in length and 35 µm in width ( NSC36 , MikroMasch ) . The advertised typical force constant and resonant frequency of this cantilever/probe is 0 . 6 N/m and 75 kHz , respectively . Nominal sharpness of the probe-tip end radius is ≤10 nm . The cantilever/probes were oscillated near 30 kHz at low amplitude for fluid tapping mode AFM . Fluid damping reduces the resonant frequency of rectangular AFM cantilevers in air by approximately 50% . The AFM substrate used for protein adsorption was Grade V5 , Pelco mica ( 10×40 mm ) purchased from Ted Pella ( Redding , CA ) . The mica was freshly cleaved using adhesive tape prior to use . Stock solutions of 10–20 mg/ml protein in 40 mM Tris ( pH 8 . 0 ) were mixed and stored at 4°C and analyzed by AFM within a few days . Stock solutions were diluted typically at 1∶100 into the blank AFM imaging buffer ( 40 mM Tris , pH 8 . 0 ) during scanning and adsorption to mica was monitored . Typical AFM scan rates were 1 . 0–1 . 25 Hz for 512 data points×256 lines . The AFM images were planefit to correct for background sloping errors . The mouse amelogenin genomic fragment was obtained by PCR amplification of the BAC clone RP23-334F21 ( X-chromosome ) , containing the amelogenin promoter region . We amplified −2 . 3 kb of a region that included the promoter , exon 1 , intron 1 , and part of exon 2 . Primers 1 and 2 ( Figure S1 ) were used to amplify a region from the ApaI ( −2 , 345 ) site to the EcoRI ( −262 ) site and Primers 3 and 4 to amplify a region from the EcoRI ( −262 ) site to the ATG start codon on exon 2 including the mouse amelogenin signal peptide region . Primers 5 and 6 amplified a fragment that ranged from the first amino acids of the frog amelogenin to the stop codon based on our frog amelogenin cDNA plasmid [35] . All three fragments were cloned into the pBSKII modified vector ( Stratagene , La Jolla , CA ) containing poly A . For cross-breeding studies , we mated mouse homozygous amelogenin knockout mice with over-expressing frog amelogenin transgenic mice . These mouse amelogenin knockout and frog amelogenin over-expressing compound mice were used to study frog amelogenin function in vivo . For further analysis , enamel of first mandibular molar from five amelogenin null , fAmel-x-null , and wild-type mice each was analyzed and compared between groups . The phenotype in fAmel mice alone was less severe and thus not used for further analysis . Following sample processing for electron microscopy , 20 electron micrographs per sample from each group were collected and further processed for image analysis . Crystal dimensions were converted from pixels into nanometers based on electron micrograph reference bars . For nanosphere measurements , 5 micrographs were measured and at least 30 nanospheres in each micrographs were selected . For the mineral crystals measurements , 5 micrographs were measured and at least 10 crystal needles in each micrograph were selected . All the data were analyzed with SPSS software using the ANOVA test . All NMR measurements were performed in either 10% D2O/90% H2O or 100% D2O at 10°C on a Bruker DRX 800MHz spectrometer . The concentration of individual peptides was 5 mg/ml . Standard homonuclear 2D TOCSY , NOESY , and COSY experiments were conducted in order to generate backbone , side chain , and NOE constraint assignments . The mixing time for TOCSY and NOESY was 80 ms and 150 ms , respectively . 13C-HSQC was performed with the naturally abundant 13C isotope . Spectra were processed and analyzed using the SYBYL software package ( Tripos , MO ) . All 1H dimensions were referenced to internal 2 , 2-dimethyl-2-silapentane-5-sulfinate ( DSS ) . NOE constraints were manually classified into strong ( 2Å ) , medium ( 4Å ) , and weak ( 6Å ) groups . The sequence-specific backbone resonance assignment was achieved through a combination of 2D NOESY , TOCSY , and 13C-HSQC spectra by matching chemical shifts for a given residue or short distance NOE signals . Structure calculations were performed with the DYANA 1 . 5 program [47] , using a 40 , 000-step energy minimization procedure . All subsequent analyses of the structure and graphic representations of the three-dimensional structures were performed using MolMol [48] . | The microstructure of vertebrate bones and teeth is controlled by polyproline-rich protein matrices ( such as amelogenin ) that serve as a scaffold to control the assembly of biological apatites . In tooth enamel , amphibians have large amelogenin subunits and thin enamel while mammals have smaller amelogenin subunits in tandem with elongated crystals and complex prismatic organization . Using specific peptides and frog amelogenin overexpressed in mice , we confirmed the effect of the length of the elongated polyproline repeat on reduced matrix subunit dimensions and enhanced apatite crystal length . Three-dimensional structures solved by NMR ( nuclear magnetic resonance ) and surface modeling algorithms indicate that elongated polyproline repeat stretches in amelogenins affect the dimensions of the supramolecular matrix through an increase in polyproline II helices , resulting in a compaction of supramolecular subunit dimensions . We propose that the availability of readily shaped apatites and innovative mechanisms based on amelogenin-repeat motifsthat compartmentalize and shape biological minerals was essential for the rise of early vertebrates , enabling the manufacture of strong teeth and backbones that might have given vertebrates a decisive survival advantage in the competition for food and in the sophistication of locomotion . | [
"Abstract",
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"Methods"
] | [
"biochemistry/molecular",
"evolution",
"developmental",
"biology/developmental",
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"biology/pattern",
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] | 2009 | Elongated Polyproline Motifs Facilitate Enamel Evolution through Matrix Subunit Compaction |
Histone variants , including histone H2A . Z , are incorporated into specific genomic sites and participate in transcription regulation . The role of H2A . Z at these sites remains poorly characterized . Our study investigates changes in the chromatin environment at the Cyclin D1 gene ( CCND1 ) during transcriptional initiation in response to estradiol in estrogen receptor positive mammary tumour cells . We show that H2A . Z is present at the transcription start-site and downstream enhancer sequences of CCND1 when the gene is poorly transcribed . Stimulation of CCND1 expression required release of H2A . Z concomitantly from both these DNA elements . The AAA+ family members TIP48/reptin and the histone variant H2A . Z are required to remodel the chromatin environment at CCND1 as a prerequisite for binding of the estrogen receptor ( ERα ) in the presence of hormone . TIP48 promotes acetylation and exchange of H2A . Z , which triggers a dissociation of the CCND1 3′ enhancer from the promoter , thereby releasing a repressive intragenic loop . This release then enables the estrogen receptor to bind to the CCND1 promoter . Our findings provide new insight into the priming of chromatin required for transcription factor access to their target sequence . Dynamic release of gene loops could be a rapid means to remodel chromatin and to stimulate transcription in response to hormones .
Transcription activation relies on a choreography of local chromatin remodeling events that include posttranslational histone modifications and replacement of canonical histones by variants [1]–[4] . Chromatin immunoprecipitation ( ChIP ) studies have provided extensive information on the recruitment of these complexes by the hormone bound estrogen receptor in ERα-positive breast cancer cells [5] . The first complex to occupy promoter sequences is the ATP-dependent SWI/SNF chromatin remodeling complex and its catalytic subunit Brg1 . Its activity enables subsequent binding of a plethora of histone and protein modifying assemblies which lead to transcription initiation by polymerase II [6]–[10] . In contrast , it is less clear how local chromatin structure prepares for rapid and massive recruitment of the estrogen receptor itself in the presence of estrogen . Incorporation of histone variants constitutes a means to alter nucleosome properties and positioning at specific genomic loci . The histone H2A variant H2A . Z is frequently found within nucleosomes at regulatory sequences [11]–[14] . In particular , H2A . Z occupancy characterizes inducible and constitutive DNAseI hypersensitive sites to which nuclear receptors bind [15] . This variant is believed to induce a chromatin conformation that poises genes for transcription in human cells [16] . In yeast , H2A . Z exchange is mediated by the SWR1 complex [17] , [18] . However , in mammalian cells , the mechanisms of H2A . Z deposition are still poorly characterized and may require several distinct protein complexes depending on the cellular context ( for a review see [19] ) . Among ATP-dependent chromatin remodeling complexes the TIP48/TIP49 containing SWR1/SRCAP [20] or TIP60/p400 [21] , [22] complexes have been shown to play a role in H2A . Z deposition [17] , [18] , [23] . p400 was also reported to be required for H2A . Z incorporation into the TFF1/pS2 gene concomitant to estrogen receptor binding [24] . In an in vitro study Choi et al . demonstrated that the AAA+ family ( ATPases Associated with various cellular Activities ) members TIP48/TIP49 participate in the replacement of H2A by H2A . Z [25] . This H2A . Z exchange was facilitated by TIP60-mediated H2A acetylation . TIP48/TIP49 proteins ( also known as TIP49b and TIP49a , Rvb2 and Rvb1 , reptin and pontin ) are important for assembly and activity of the histone TIP60 acetyltransferase complex [26] . To gain a better understanding of the early steps required in estrogen receptor mediated transcription activation and the coordination between remodeling complexes and chromatin structure , we analyzed transcription of the cyclin D1 gene ( CCND1 ) in ERα-positive MCF-7 breast cancer cells . This oncogene is frequently overexpressed in human breast tumors [27] . Its down-regulation increases migratory capacity and is linked to unfavorable prognosis [28] , [29] . Cyclin D1 is a mitogenic sensor that modulates cell cycle progression . CCND1 transcription is stimulated by 17β-estradiol ( E2 ) , inhibited by antiestrogens and cell cycle regulated in ERα-positive breast cancer cells [30] , [31] . Here we show that TIP48 and H2A . Z associate with CCND1 promoter and enhancer sequences . TIP48 is required for chromatin reorganization which is initiated by release of H2A . Z and opening of a repressive promoter-enhancer gene loop enabling TIP60 and the E2 bound estrogen receptor to be loaded to stimulate CCND1 transcription .
In ERα-positive MCF-7 cells grown in steroid stripped media only basal transcription levels of the Cyclin D1 gene ( CCND1 ) were measured . Addition of E2 lead to a 2 . 5-fold increase in CCND1 mRNA levels in cells treated 6 h with 100 nM E2 ( Figure 1A ) . H2A . Z has been reported to act in concert with ER to regulate the TFF1 gene [24] prompting us to examine H2A . Z association with CCND1 regulatory elements . Analysis of ChIP-on-chip data revealed that H2A . Z was highly enriched at sequences 5′ and 3′ flanking the CCND1 gene , and largely absent from the open reading frame ( Figure 1B ) [32] . By conventional ChIP , we found that the amount of H2A . Z present at the CCND1 promoter was reduced by 50% at the TSS ( Figure 1C ) . Eeckhoute et al . identified an enhancer ( enh2 ) at the 3′ end of the CCND1 gene which acts as the primary site for ERα and cofactor binding during CCND1 transcriptional regulation [7] . We thus analyzed the chromatin organization of enh2 . Similar to the promoter , H2A . Z present at enh2 was removed during transcription activation ( Figure 1C ) . Reduced binding was not due to a decrease in H2AFZ expression in the presence of E2 ( Figure 1A ) . Chromatin modifications at the CCND1 promoter and enhancer appear to be coordinated . Removal of H2A . Z from promoter sequences upon transcription activation correlates with observations in yeast and several mammalian cells and points to a mechanism of regulation distinct from the one of the ERα target gene TFF1 [33] , [24] , [34] . Replacement of nucleosomal H2A with H2A . Z has been shown to be catalyzed by the TIP48/49 complex in vitro [25] . The TIP48/49 complex was thus a good candidate for regulating H2A . Z dynamics at CCND1 regulatory sequences . TIP48 and TIP49 are ubiquitously expressed and are often part of the same complex . In most cell types , and in particular in epithelial cancer cells such as MCF-7 cells , silencing one of the partners by interference RNA lead to degradation of the other partner [33] . Better antibody specificity and efficiency prompted us to investigate TIP48 . TIP48 was associated with the promoter and enh2 of CCND1 in non-induced ERα-positive MCF-7 cells ( Figure 1D ) . Binding of TIP48 to the CCND1 TSS and enh2 decreased rapidly following addition of 100 nM estradiol ( E2 ) . Expression of the gene coding for TIP48 was insensitive to E2 ( Figure S1A ) . To examine the relationship between TIP48 and H2A . Z , we selectively depleted TIP48 by siRNA ( Figure S1A ) . In siTIP48 transfected cells treated or not with E2 , H2A . Z binding to CCND1 was reduced compared to control , non–specific siRNA transfected cells . Levels of H2A . Z binding in the absence of TIP48 were roughly equivalent to levels in E2 treated control cells ( Figure 2A ) . Moreover , nucleosome density assessed by immunoprecipitating histone H3 was unchanged near the TSS ( Figure 2B ) . Thus , eviction of H2A . Z upon initiation of E2 stimulated transcription was not due to general chromatin decondensation around the CCND1 gene and its promoter region in particular . TIP48 appears to be necessary for recruiting H2A . Z to the CCND1 gene in MCF-7 mammary tumor cells . We thus asked whether binding of H2A . Z or its release were important for regulating CCND1 transcription . H2A . Z mRNA expression levels were reduced ∼5-fold 48 h post transfection with a smartpool siRNA directed against H2A . Z compared to control cells ( Figure S1B ) . Reduced levels of H2A . Z did not alter basal CCND1 expression levels , but impeded activation by E2 ( Figure 2C ) . Similarly , in the absence of TIP48 , basal transcription levels were conserved , while activation of CCND1 by E2 was compromised ( Figure 2C ) . H2AFZ mRNA levels were not affected by selective knockdown of TIP48 ( Figure S2A ) . Thus , Stimulation of CCND1 expression required release of H2A . Z concomitantly from both these DNA elements . Absence of activation was likely due to failure of ERα fixation to the CCND1 promoter . Under standard conditions , E2 stimulated ERα binding to both the promoter and enh2 of CCND1 ( Figure 3A ) [7] . Selective knock down of TIP48 hindered ERα binding to these sites ( Figure 3A ) . Reduced binding could not be attributed to altered or decreased ESR1 expression patterns in cells transfected with control or TIP48 siRNAs ( Figure 3B ) . Therefore , TIP48 appears to be necessary to remodel CCND1 chromatin structure for productive ERα binding in the presence of hormone . TIP48 and TIP60 have been found as part of the same complex [20] , [25] . TIP60 also cooperates with ERα and other chromatin-remodeling enzymes during estrogen-induced transcription [34] , [35] . We tested whether TIP48 and TIP60 binding to the CCND1 promoter was coordinated . In the presence of E2 , TIP60 was recruited to the CCND1 promoter ( Figure 4A ) . Upon depletion of TIP48 , TIP60 no longer associated with the CCND1 TSS ( Figure 4A ) . Cooperation between TIP48 , ERα and TIP60 binding was likely to be necessary for transcription activation . To unravel a functional link , we first over-expressed TIP60 in MCF-7 cells ( Figure S2B ) . TIP60 overexpression stimulated E2 activated CCND1 transcription nearly 5-fold compared to control untreated cells , without affecting neither basal , non-induced mRNA levels ( Figure 4B ) nor the expression pattern of the H2AFZ gene ( Figure S2C ) . In siTIP48 transfected cells , overexpression of TIP60 was no longer able to stimulate CCND1 transcription upon E2 stimulation ( Figure 4C ) , suggesting that TIP48 is required for TIP60 function . TIP60 is found in protein complexes able to acetylate histones , with a preference for lysine 5 of H2A [36] . Core histones are generally acetylated in the promoter region of transcribed genes . Acetylation of the histone variant H2A . Z was shown to characterize active genes in yeast and recently also in prostate cancer cells [13] , [37] . Using an antibody that specifically recognizes H2A . Z acetylated at 3 N-terminal lysines , we determined that a large fraction of H2A . Z bound to the CCND1 promoter and to the 3′ enh2 , was highly acetylated ( Figure 5A ) . Acetylation levels of H2A . Z did not vary following E2 induced CCND1 gene activation in control samples ( Figure 5A ) . However , because H2A . Z was released during transcription activation , the ratio of acetylated H2A . Z/total H2A . Z increased nearly 2-fold at these sites ( Figure 5B ) . In siTIP48 transfected cells , we observed a decrease in acetylated H2A . Z present at the TSS and the enh2 ( Figure 5A ) . The increased ratio of acetylated H2A . Z associated with the CCND1 gene following E2 was abolished in cells transfected with siTIP48 ( Figure 5B ) . The reduced ratio of H2A . Z acetylation thus correlated with impeded transcription activation in siTIP48 transfected MCF-7 cells ( Figure 5B and Figure 2C ) . In conclusion , failure of TIP60 to associate with CCND1 in the absence of TIP48 correlated with reduced binding of ERα ( Figure 3 ) , reduced levels of H2A . Z acetylation at the CCND1 gene ( Figure 5 ) and the inability to activate this gene by estrogen ( Figure 2C ) . Long-range chromatin interactions between ERa recognition sequences and enhancers have been proposed to regulate ERa-target genes in breast cancer cells [38] , [39] . The main enhancer regulating CCND1 is located at the 3′ end of the gene , 14 kb distant from the promoter [7] . Gene looping via promoter-enhancer crosstalk is associated with repressed , low CCND1 expression in ERa-negative , MDA-MB231 cells [40] . Thus we asked whether this loop also existed in MCF-7 cells and more importantly , whether looping was sensitive to hormone . We used a chromatin conformation capture ( 3C ) assay . The 3C method detects physical proximity between distal DNA sites by ligation of cross-linked restricted DNA fragments [41] , [42] . Ligation products between enh2 and promoter , and between enh2 and a control fragment inside the CCND1 ORF were amplified and normalized to an amplified enh2 PCR product ( see Materials and Methods ) ( Figure 6A ) . We measured significant interaction frequencies between enh2 and promoter sequences in MCF-7 cells grown in hormone-stripped media ( -E2 ) . Interaction frequencies were reduced ∼10-fold 45 min after addition of E2 to the cell culture ( Figure 6B ) . No significant amplification of ligation products between enh2 and the internal control fragment was detectable . Hence , an extragenic loop mediated by specific promoter enhancer interactions was present when CCND1 expression is low ( Figure 6B and Figure 2D ) . Upon transcription activation , gene looping is markedly reduced . It was tempting to speculate that TIP48 plays a role in regulating looping . We assessed the relative frequencies of interaction between enh2/promoter and enh2/internal control fragments in MCF-7 cells transfected or not by siTIP48 . Depletion of TIP48 had no impact on enh2/promoter contacts in the absence of E2 ( Figure 6B ) . This observation correlated with identical basal expression levels of CCND1 in control and siTIP48 transfected cells ( Figure 2C ) . 45 min after addition of E2 to the cells , the frequency of enh2/promoter interaction was 5-fold greater in siTIP48 transfected cells compared to control cells ( Figure 6B ) . Conservation of significant repressive gene looping could thus account for impeded E2 bound ERa binding to the CCND1 promoter and compromised transcription activation . We propose a model ( Figure 7 ) in which TIP48 is required at early steps during transcription activation which is initiated by release of H2A . Z and subsequent dissociation of the enhancer from the promoter . E2 bound estrogen receptor can then recognize the promoter and stimulate transcription of CCND1 .
We unraveled a role for TIP48 in initiating transcription activation of the CCND1 oncogene . Recruitment of the histone acetyltransferase TIP60 is dependent on TIP48 and H2A . Z binding to the promoter and 3′ enhancer of the CCND1 gene . We propose that low levels of CCND1 expression are regulated because the associated gene loop is transcription-dependent . This regulation is brought about by the activity of TIP48 containing complexes which locally act upon chromatin structure to release a disabling loop . Such a mechanism allows fine-tuning transcription regulation of genes pivotal for the cellular equilibrium in rapidly changing environments . Our work describes early events implicated in E2 induction of CCND1 . These events include dynamic exchange of a series of cofactors , namely the TIP48 complex and histone variant H2A . Z , recruitment of TIP60 and acetylation of H2A . Z enabling the main transcription factor , the estrogen receptor , to associate with its target sequences . TIP60 can directly interact with ERa and its acetyltransferase activity is important during transcription initiation once ERa is bound to target gene promoters [34] . TIP60 is a versatile enzyme that functions with a variety of partners in a gene and cell specific manner [34] , [43] . Selective knock-down of TIP60 by siRNA compromises activation of some , but not all ERa target genes in MCF-7 cells , as well as nuclear receptor independent genes in several cell lines ( unpublished observations ) . CCND1 was one of the genes found to be insensitive to siTIP60 [34] . This observation denotes that TIP60 can be replaced by other histone acetyltransferases in CCND1 transcription activation . Thus , dependency of early chromatin remodeling steps on TIP48 and H2A . Z may be more generally applicable to allow cofactor recruitment for productive ERa binding in stimulated transcription . We found that H2A . Z was removed from CCND1 regulatory elements while this variant had previously been shown to be recruited to the promoter of the TFF1 gene upon E2 treatment of MCF-7 cells [24] . Differences in promoter structure are a plausible explanation for divergent remodeling mechanisms . It is also likely that post-translational modifications of H2A . Z are important as shown in a recent genome wide study by Valdes-Mora et al . who found that H2A . Z acetylation at the TSS correlates with active transcription in prostate cancer cells [13] . Indeed , the level of acetylation of H2A . Z near the TSS of CCND1 was equivalent at non-activated and E2 stimulated cells . We propose that , in ERα-positive breast cancer cells , the ratio of acetylated H2A . Z/H2A . Z rather than the total amount of H2A . Z bound to the CCND1 promoter correlates with transcriptional activity . Chromatin remodeling events are crucial for hormone stimulated activation of estrogen receptor target genes . However , so far , all data available describe the recruitment of remodeling complexes and cofactors once the estrogen receptor is bound . The Brg1 subunit of the SWI/SNF complex is one of the first proteins to associate with ERa and , although transcription is no longer activated in its absence , ERa remains bound in siBrg1 transfected cells [34] . Here we demonstrate that chromatin remodeling events prior to ERa binding are essential for initiating transcription . These events depend on TIP48 and H2A . Z specific nucleosome conformation . Chromatin structure impedes ERa loading via intragenic looping . Notably , interaction between promoter and enhancer sequences forms a repressive complex . Reduced distances between 5′ and 3′ ends of gene loci have been attributed to greater chromatin density . In this case , looping does not require changes in chromatin compaction . Dynamic release of gene loops is consistent with rapid chromatin remodeling and transcription activation by hormone . Finally , addition of hormone triggers large scale chromatin remodeling . In breast cancer cells gene response to progestin is mediated by nucleosomes [44] and estradiol treatment leads to expansion of chromosome territories within minutes [45] . This latter phenomenon was also observed in ERα-negative cells ( unpublished ) suggesting that chromatin decondensation is independent of the receptor and may prepare its binding in ERα-positive cells . It is thus tempting to speculate that the signaling mechanism by which hormone addition primes chromatin triggers histone exchange and remodeling prior to ERα binding .
MCF-7 cells were purchased from ATCC and were maintained in DMEM/F12 without phenol red with Glutamax containing 50 mg/ml gentamicin , 1 mM sodium pyruvate and 10% heat-inactivated and steroid free fetal calf serum ( FCS ) ( Invitrogen ) . MCF-7 cells were treated with 10−7 M estrogen E2 ( Sigma ) for the indicated times . 5×106 MCF-7 cells were transfected with 20 nM of H2A . Z siRNA ON-TARGET plus SMARTpool , TIP48 siRNA ON-TARGET plus SMARTpool or scrambled ( scr ) siRNA ( Dharmacon Thermo Scientific ) using Interferine ( Ozyme ) . Cells were mock-transfected ( pcDNA3 . 1 ) or transfected with 1 µg of pcDNA3 . 1/TIP60 ( gift from Dr . Didier Trouche ) using the Amaxa Cell line Nucleofactor Kit V program P-020 according to the manufacturer's protocol . TIP60 siRNA [43] was purchased from Eurogentec , and transfected using Interferine ( Ozyme ) . 5×105 MCF-7 cells were seeded in 6 well plates . 72 h following siRNA transfection , total cell extracts were isolated and protein levels of H2A . Z , TIP48 and TIP60 analyzed by immunoblotting on gel SDS-page 15% using antibodies against H2A . Z ( ABCAM , ab4174 ) , TIP48 ( gift of Dr . Mikhaïl Grigoriev ) TIP60 [43] or GAPDH ( Millipore , mab374 ) . Total RNA was extracted using an RNeasy mini-kit ( Qiagen ) and eluted with 35 µl of RNAase-free water . First strand cDNA was generated using 2 µg of total RNA in a reaction containing random oligonucleotides as primers with the ThermoScript RT-PCR system ( Invitrogen ) . Real-time PCR was performed on a Mastercycler ep realplex 4 ( Eppendorf ) using the platinum SYBR Green q-PCR SuperMix ( Invitrogen ) according to the manufacturer's instructions . Amplification conditions: 1 min at 50°C , 3 min at 95°C followed by 40 cycles ( 20 s at 95°C , 20 s at 60°C , 20 s at 72°C ) . mRNA expression were normalized against expression levels of the RPLP0 ribosomal gene used as an internal control . qRT-PCR primers: H2AFZ: 5′-CCTTTTCTCTGCCTTGCTTG-3′ and 5′-CGGTGAGGTACTCCAGGATG-3′ , CCND1: 5′-GCGTCCATGCGGAAGATC-3′ and 5′-ATGGCCAGCGGGAAGAC-3′ , RPLP0: 5′-TGGCAGCATCTACAACCCTGAA-3′ and 5′- CACTGGCAACATTG CGGACA-3′ , TIP48: 5′-TGAAGAGCACTACGAAGACGC-3′ and 5′-CCTTACTACCCAGCTC CTGA- 3′ . ChIP analyses were performed as described previously [46] . Samples were sonicated to generate DNA fragments <500 bp . Chromatin fragments were immunoprecipitated using antibodies against H2A . Z ( ab4174 , ABCAM ) , acetyl H2A . Z ( ab18262 , ABCAM ) , TIP48 ( gift of Dr . Mikhaïl Grigoriev ) , ERα ( sc-543 , Santa Cruz ) , H3 ( ab1791 , ABCAM ) , TIP60 [47] or an irrelevant HA antibody ( H6908 , Sigma ) . The precipitated DNA was amplified by real-time PCR , with primer sets designed to amplify the promoter ( TSS ) and enh2 enhancer regions of the CCND1 gene ( Figure 1B ) . qRT-PCR primers: CCND1 ( TSS ) : 5′-CGGGCTTTGATCTTTGCTTA-3′ and 5′-ACTCTGCTGCTCGCTGCTAC-3′ , distal CCND1 enhancer ( enh2 ) : 5′-CAGTTTGTCTTCCCGGGTTA-3′ and 5′- CATCCAGAGCAAACAGCAG-3′ . All ChIP data are shown as percent input . 3C assays were performed essentially as described [48] , [49] , with minor modifications . MCF-7 cells were treated with E2 10−7 M for 45 mn or transfected with a scrambled control siRNA , with TIP48 SMARTpool siRNA ( Dharmacon Thermo Scientific ) , and cultured in phenol red-free DMEM containing 10% FBS-T for 72 h before cross-linking . The culture medium was removed , and cells were fixed with 1 . 5% formaldehyde for 10 min at room temperature . Cells were then washed twice with cold phosphate-buffered saline solution , and resuspended in ice-cold lysis buffer ( 10 mm Tris-HCl , pH 8 . 0 , 10 mm NaCl , 0 . 2% Nonidet P-40 , and protease inhibitor mixture ) . Nuclei were resuspended in 1 ml of Buffer B 1 . 2× buffer ( MBI Fermentas ) supplemented with SDS 0 , 3% . Triton X-100 1 , 8% was added to sequester the SDS and incubated for 1 h at 37°C . The cross-linked DNA was digested overnight with 400 units of restriction enzyme Csp6I ( MBI Fermentas ) . The restriction enzyme was inactivated by incubation at 65°C for 20 min . The reactions were diluted with ligase buffer ( 50 mm Tris-HCl , pH 7 . 5 , 10 mm MgCl2 , 10 mm dithiothreitol , 1 mm ATP , and 25 µg/ml bovine serum albumin ) , supplemented with Triton X-100 ( 1% final concentration ) . The DNA was ligated using T4 DNA ligase ( New England Biolabs , Ipswich , MA ) overnight at 16°C and an additional 100 units for 2 h at 37°C . RNase was added for 30 min at 37°C , and samples were incubated with SDS overnight at 70°C to reverse the crosslink . The following day , samples were incubated for 2 h at 45°C with proteinase K , and the DNA was purified by phenol-chloroform extractions and ethanol precipitation . Interaction between chromatin domains was assessed by real-time-PCR amplification for each predicted ligation event [48] , [50] . Primers have been designed on the digested BAC fragments , directly around the putative site of ligation for the four possibilities . BAC clones RP11-300ID ( BACPAC Resources Center at Childrens Hospital Oakland Research Institute , Oakland , CA ) containing the CCND1 gene and downstream 160-kb region were used . 40 ug of BAC was digested by Csp6I overnight and ligated . This product was purified by phenol chloroform and precipitated in order to generate 3C control templates . PCR primer efficiency was measured by amplifying 0 . 01 to 50 ng of digested BAC product and also tested on a fixed amount ( 50 ng ) of digested genomic DNA . All primers have an annealing temperature between 65 to 70°C and a product size around 150–300 bp . All primer combinations showed PCR efficiency between 90 and 100% . 3C assay results are presented as the average from three independent preparations of 3C DNA , followed by qPCR analysis in triplicate . qPCR for enh2 ( PCR primers design inside the Csp6I restriction fragment enh2 ) was used as an internal control to verify ligation events . Non-digested sample and ligation between a control fragment and enh2 were also performed ( data not shown ) . Primers used for one of the four ligation event tested: Enh2/Prom: 5′-CTGGGAGAGATGGAGCTGAG-3′ and 5′-GGTTTTGTTGGGGGTGTAGA-3′ , Enh2/ctrl: 5′-AAGCTCTCCCACAACCCATT-3′ and 5′-GTCAGCCCCACTGTTGACTC-3′ . Other primers available upon request . | Our study investigates changes in the chromatin environment at the Cyclin D1 gene that are a prerequisite for transcriptional initiation in response to estradiol . Gene expression is under control of chromatin structure . Histone variants , including histone H2A . Z , are incorporated into specific genomic sites and participate in transcription regulation . We show that H2A . Z is present at the transcription start-site and downstream enhancer sequences of CCND1 when the gene is poorly transcribed . Stimulation of CCND1 expression required release of H2A . Z concomitantly from both these DNA elements . The TIP48/reptin protein , which is part of several chromatin remodeling complexes , also associated with the CCND1 regulatory elements . Here , TIP48 promotes exchange of H2A . Z , which triggers a dissociation of the CCND1 enhancer from the promoter , thereby releasing a repressive intragenic loop . This release then enables estrogen receptor binding to the CCND1 promoter . Acetylation of H2A . Z is required for these processes . Our findings provide new insight into the priming of chromatin required for transcription factor access to their target sequence . Hence , we propose a new model for early events in transcription activation that were not shown before . Specifically , release of looping could be a rapid means to activate transcription efficiently in response to stimuli , in particular estrogen . | [
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] | 2013 | TIP48/Reptin and H2A.Z Requirement for Initiating Chromatin Remodeling in Estrogen-Activated Transcription |
Turnover of regulatory sequence and function is an important part of molecular evolution . But what are the modes of sequence evolution leading to rapid formation and loss of regulatory sites ? Here we show that a large fraction of neighboring transcription factor binding sites in the fly genome have formed from a common sequence origin by local duplications . This mode of evolution is found to produce regulatory information: duplications can seed new sites in the neighborhood of existing sites . Duplicate seeds evolve subsequently by point mutations , often towards binding a different factor than their ancestral neighbor sites . These results are based on a statistical analysis of 346 cis-regulatory modules in the Drosophila melanogaster genome , and a comparison set of intergenic regulatory sequence in Saccharomyces cerevisiae . In fly regulatory modules , pairs of binding sites show significantly enhanced sequence similarity up to distances of about 50 bp . We analyze these data in terms of an evolutionary model with two distinct modes of site formation: ( i ) evolution from independent sequence origin and ( ii ) divergent evolution following duplication of a common ancestor sequence . Our results suggest that pervasive formation of binding sites by local sequence duplications distinguishes the complex regulatory architecture of higher eukaryotes from the simpler architecture of unicellular organisms .
The importance of regulatory variations as a driving force for phenotypic evolution has been suggested some time ago [1] , [2] . However , a quantitative understanding of gene regulation has become possible only after the advent of large-scale genomic sequence and regulatory interaction data . Important building blocks are genome-wide maps of protein-DNA binding , statistical inference methods [3] , [4] , high-throughput measurements of sequence-specific binding affinities of transcription factors [5]–[8] , and cross-species comparisons of regulatory sequences and regulatory functions [9] . The resulting picture is quite diverse: Core parts of developmental regulatory networks can be conserved over large evolutionary ranges [10] , and individual promoters in flies are found to be conserved in function over large evolutionary distances [11] , [12] . Functional changes in promoters have been identified as well , but the relative roles of adaptive and near-neutral evolution remain to be clarified . The sequences in regulatory DNA regions evolve under less constraint than their functional output . This feature can be explained by wide-spread compensatory changes , which have been identified between different nucleotides within individual binding sites as well as between different sites within a promoter [11]–[17] . At the promoter level , this dynamics includes loss and gain of binding sites , the rates of which have been estimated in flies and yeast [13] , [18] , [19] . The observed site turnover is consistent with moderate negative selection acting on individual sites [13] , [20] , whereas the function of entire promoters is under stronger stabilizing selection [11] . The evolutionary constraint of regulatory sequence and function depends on the level of complexity in promoter architecture . Prokaryotes and unicellular eukaryotes have short intergenic regions , and regulatory functions are often encoded by few binding sites . The more complex cis-regulatory information in higher eukaryotes is organized into regulatory modules , which are typically a few hundred base pairs long and are spatially separated by larger segments of intergenic DNA [21] , [22] . Within modules , regulatory functions often depend on clusters of neighboring binding sites for multiple transcription factors , which are coupled by cooperative interaction [23]–[27] . Bioinformatic algorithms trace such site clusters to identify regulatory DNA regions [28]–[34] . The relative order and spacing of sites within clusters follows a regulatory “grammar” , which distinguishes functionally neutral site changes from rearrangements affecting promoter function [17] , [35]–[39] . The combinatorial complexity of this grammar ensures the specificity of regulation in the larger genomes of multicellular eukaryotes [40] , [41] . At the same time , the grammar is flexible enough to allow substantial sequence evolution in a regulatory module while maintaining its overall functional output . In addition to point mutations , sequence insertions and deletions ( indels ) play a significant role in this dynamics . Several studies have noted the prevalence of repetitive sequence elements in promoter regions and their potential influence on regulatory function [42]–[49] . In particular , a recent detailed analysis of the evolutionary rates of short tandem repeats in Drosophila has shown a net surplus of insertions , suggesting that these repeats may produce new regulatory sequence [48] . But to what extent is this actually the case ? A priori , the link between repeat evolution and regulation is far from obvious: Duplications in repeats can either be part of the neutral background evolution in regulatory sequences , or increase the spacing between existing binding sites of a regulatory module , or contribute to the formation of new sites . Disentangling these roles is subtle , because detected tandem repeats in contemporary sequence overlap with only a small fraction of binding sites , motif size and total length of most repeats are shorter than length and spacing of typical binding sites in a cluster , and repeat lifetimes are much shorter than conservation times of regulatory elements [49] . Hence , the role of repeat dynamics for regulation is an open problem: Do local duplications actually transport and produce regulatory information ? This is the topic of the present paper . We show that local duplications have left a striking signature in the fly genome: the majority of transcription factor binding sites in regulatory modules show evidence of a duplication event in their evolutionary history . We conclude that over long evolutionary times , local duplications are pervasive and crucial for the formation of complex regulatory modules in the fly genome . This mode of evolution sets the speed of regulatory evolution and facilitates adaptive changes of promoter function . We infer site duplications from their traces in the sequence of neighboring binding sites , but most duplication events predate the tandem repeats present in contemporary sequence . This distinguishes our study from comparative analysis of regulatory sequence between closely related species [45]–[49] , which can detect the insertion-deletion dynamics of contemporary repeats , but cover only a small window in the evolution of regulatory sites . The importance of binding site evolution by duplication is grounded in the biophysics of transcription factor-DNA interactions: the sequence-dependent probability of binding between factor and site depends in a strongly nonlinear way on the binding energy [3]: it takes values close to 1 in an energy range below the maximum binding energy , then drops rapidly as the energy decreases further , and is close to 0 in the energy range of non-binding sites . This nonlinearity generates strong epistatic effects for point mutations within binding sites [13] , [50] and , in turn , an asymmetry in the turnover of binding sites . Functional sites can rapidly lose their binding affinity to a factor by one or two point mutations . Rapid adaptive formation of a site , however , requires a seed sequence with marginal binding , to which positive selection for point mutations towards stronger binding can latch on . Such seeds are contained in random sequence , but at unspecific positions . Estimates of the rate of site formation based on biophysically grounded fitness models suggest that point mutations alone can explain the rapid formation of an individual site in a sufficiently large sequence interval , but not the formation of spatially confined agglomerations of sites characteristic of regulatory modules [50]–[52] . As we show in this paper , local sequence duplications generate seeds for new sites specifically in the neighborhood of functional sites . Our analysis proceeds in three steps . First , we analyze local sequence similarities in regulatory regions of the Drosophila melanogaster genome in a model-independent way . In regulatory modules , we find a significant autocorrelation in nucleotide content for distances up to about 70 bp . This autocorrelation includes the known contributions of tandem repeat sequences , but it extends to a much larger distance range . The signal turns out to be generated by local sequence clusters , a substantial fraction of which are functional transcription factor binding sites with similar sequence motifs . In the second part of the paper , we turn specifically to binding sites: we infer the evolutionary origin for pairs of neighboring sites , using a known set of validated sites and a probabilistic model with mutations , genetic drift , and selection . The model compares the likelihood of two alternative histories: a pair of sites evolves either independently or by duplication from a common ancestor sequence . The duplication is followed by diversification under selection for binding of two ( in general different ) factors . We show that the duplication pathway is the most likely history for pairs of sites with a mutual distance up to about 50 bp . Furthermore , we find evidence that this pathway is specific to regulatory modules of multicellular eukaryotes . Finally , we show that the duplication mode has adaptive potential: duplicated ancestor sites can act as seeds for the subsequent formation of a novel binding site for the same factor and , notably , even for a different factor .
The most straightforward measure of local similarity in a sequence segment is the autocorrelation function , which is defined as the difference between the likelihood that two nucleotides at a distance of base pairs are identical and mean identity of two random nucleotides , . This function is straightforward to evaluate from sequence data as given by eq . ( 2 ) in Materials and Methods . We have obtained the autocorrelation function in 346 regulatory modules of the D . melanogaster genome with length of more than bp identified by REDfly database [53]–[55] . The results are shown in Fig . 1 ( a ) . In the distance range up to about 70 bp , the function takes positive values that decay with in a roughly exponential way; this signal is clearly above the noise level . The mean identity is evaluated in a local window of 500 bp ( changing the window length affects the baseline of this function , but not its short-distance behavior ) . The autocorrelation signal is small and has several potential sources , such as multiple binding sites for similar motifs , microsatellite and minisatellite repeats at short length scales [46]–[49] , homopolymeric stretches of nucleotides characteristic of nucleosome-depleted regions [56] , or other local inhomogeneities in sequence composition . As a next step , we will characterize local sequence similarity in a more specific way: we will show that mutually correlated nucleotide pairs are not evenly distributed over regulatory modules , but occur in local clusters with a characteristic length scale of around 7 bp . This signal will be analyzed from an evolutionary point of view and be linked to cis-regulatory function . To motivate the following analysis , assume that a given sequence segment is covered by families of sites belonging to different motifs . By definition , a motif is a probability distribution of genotypes , which describes a specific set of sequence sites with consecutive base pairs and is different from the background distribution . The statistical deviation of a motif from background is measured by the relative entropy between these distributions , , which is given by eq . ( 4 ) in Materials and Methods . This quantity determines the average sequence information per site , which is often quoted in units of bits [4] . Multiplying with the number of sites for each motif and summing over all motifs produces a measure of the total sequence information contained in a genomic region . Well-known motifs in regulatory DNA are the families of binding sites for a given transcription factor . In eukaryotic systems , these sites have a typical length of about 5–10 bp and frequency distributions ( called position weight matrices ) with a typical information content bits per site; see the recent discussion by [57] . Other motifs can be defined , for example , in nucleosome-depleted sequences in eukaryotes and for repeat units in tandem repeats . If all motifs occurring in a given sequence segment were known , we could try to predict their sites and evaluate the information content directly . In the present part of the analysis , we proceed differently . We only assume that sequence motifs carry a certain information content over sites of a given length , but we make no further assumptions on position weight matrices , sequence coverage , or evolutionary origin . We can still recover part of this sequence information from those motifs that occur more than once in the sequence segment . A pair of sites of length belonging to the same motif has an average similarity information given by the mutual entropy , which measures the enhanced similarity of aligned nucleotides of the site sequences compared to the background similarity and is given by eq . ( 5 ) in Materials and Methods . Clearly , the similarity information between pairs of sites is a somewhat diluted measure of the full information content due to motifs . As a rule of thumb , the mutual entropy per site pair , , recovers about half of the sequence information per site , . For example , binding sites for the same transcription factor are strongly correlated , with a typical similarity and a similarity information bits per site pair . Here , we want to identify pairs of similar sites at a given distance and relate them to the sequence autocorrelation function discussed above . Thus , we estimate the total similarity information per unit sequence length of all strongly correlated pairs of sites with distance and length in regulatory modules . This quantity can be defined by constructing a set of site pairs for given and with the following properties: ( i ) Any pair of sites has an average mutual similarity between aligned nucleotides above a certain threshold , . ( ii ) The left sites ( and , hence , also the right sites ) of all pairs have no mutual overlaps . This condition is necessary in order to avoid overcounting of mutual similarity in overlapping site pairs . ( iii ) The sum of the mutual similarities of all pairs in the set is maximal ( see Fig . 4 for illustration ) . This condition is also used to set the similarity threshold . To identify a set of site pairs with properties ( i ) to ( iii ) , we use a dynamic programming algorithm as explained in Materials and Methods . This method allows for optimization of sequence length similar to the procedure in local sequence alignment algorithms [58] . In the maximum-similarity set , we record the average mutual similarity of aligned nucleotides in site pairs , which determines the mean information content per site pair , ( see eq . ( 5 ) in Materials and Methods ) . We also record the number of site pairs and determine the excess over the number expected by chance in background sequence , ( see Materials and Methods ) . The distance-dependent total similarity information per unit length in a sequence segment of size can then be estimated as . Our inference of is related to recent methods for prediction of unknown regulatory modules based on their enhanced sequence similarity contained in words of length [32]–[34] . But the evaluation of sequence similarity and the goals of the analysis differ: module prediction uses the total similarity in a genomic region , which in our setup is given by summation of over all distances and over different word lengths . Our analysis is limited to known regulatory modules and focuses on the dependence of on and . A specific part of this signal , obtained from sites with distance below 50 bp , will be associated below with local duplications as prevalent evolutionary mode . We evaluate the similarity information in the set of 346 regulatory modules of Drosophila melanogaster and in surrounding background sequence . The following features of local sequence similarity can be extracted: – The total information of local sequence similarity is maximal for motifs of length . Fig . 1 ( b ) shows the total similarity information of all detected site pairs in the range of up to 100 bp , , as a function of the site length . The function takes its maximum , that is , the similarity information is most significant , for . The signal falls off at shorter length scales , because typical motif sequences are only partially covered , and at larger length scales , because uncorrelated flanking nucleotides contribute negatively to the similarity information . In this sense , detected motifs cover a characteristic length of about 7 bp . A similar length scale has been observed in tandem repeats [45]–[47] . – The function takes distance-dependent positive values in the range of up to 50 bp and saturates to a positive asymptotic value for larger distances . Thus , its distance dependence is compatible to that of the sequence autocorrelation function shown in Fig . 1 ( a ) . This pattern is due to site pairs with high mutual similarity , . – Correlated binding sites explain a substantial part of the similarity information . We estimate this contribution by masking all functional sites [53]–[55] and re-evaluating the function in their sequence complement; see Fig . 1 ( c ) . Known binding sites cover about of the regulatory modules , but the signal is reduced by about , indicating that these sites are an important source of similarity information . The binding site-masked signal is comparable to its counterpart in non-regulatory intergenic sequence . – Microsatellite repeats explain only a small part of the similarity information . We identify such repeats using the Tandem Repeat Finder [59] . If we remove about of the sequence in regulatory modules as repeats , the similarity information is reduced by less than ; Fig . 1 ( c ) . This is not surprising , because our sequence similarity measure differs from that of repeat analysis . In particular , our measure is sensitive to correlated segments on larger distance scales than typical tandem repeats , because it does not require a contiguous interval of self-similar sequence in between . – Homologous regions in other fly genomes show a consistent form of . We analyze homologous regions of two other Drosophila species , D . yakuba and D . pseudoobscura ( see Materials and Methods ) . As shown in Fig . S2 , these putative regulatory modules have patterns of very similar overall amplitude and distance-dependence , with enhanced values in the range of up to 50 bp . In summary , our model-independent analysis shows that motifs with a characteristic length of about 7 bp play an important part in the distance-dependent sequence autocorrelation of Drosophila regulatory modules . The characteristic length coincides with the typical length of binding sites , and a substantial fraction of the signal can be explained by sequence correlations involving known binding sites . Therefore , we now focus the analysis on a smaller , but experimentally validated set of sites [53]–[55] . This allows us to analyze in detail the evolutionary mechanism generating the sequence similarity between neighboring sites . Binding sites are ideal objects to study the production of information by sequence evolution . The sequence motif is approximately known for about transcription factors in Drosophila , that is , we can analyze the full position-dependent sequence information of these motifs , not just the similarity information of motif pairs . Furthermore , there is a simple link between sequence statistics and evolution of binding sites: assuming the sequence distribution defines a motif at evolutionary equilibrium , its sequence information is proportional to the average fitness effect of its binding sites , , with a proportionality constant equal to the effective population size [20] , [50] , [51] , [60] . The fitness contribution of a particular binding sequence , , is proportional to its log-likelihood ratio in the distributions and . The ensemble of these fitness values defines an information-based fitness landscape for binding of a specific transcription factor . These relations between sequence statistics and fitness of binding sites quantify our intuition that specific sequences are overrepresented in a motif to the extent they confer a selective advantage over random sequences [4] . If we write the motif distribution in the product form of a position weight matrix , we obtain an approximate expression for the fitness in terms of the position-specific single-nucleotide frequencies in the motif sequence and their counterparts in background sequence: . This expression , which is in its simplest form already contained in Kimura's U-shaped equilibrium distribution for a two-allele locus [61] , is known as Bruno-Halpern model in the context of protein evolution [62] and has been used to infer fitness effects of mutations in binding sites [20] , [50]–[52] , [60] , [63] . Although this additive fitness model neglects fitness interactions between nucleotides within binding sites as well as between sites within a regulatory module , it is justified for the purpose of this study ( see below ) . The fitness landscape defines the selection coefficient of any change from a state to a state of a binding site , . Together with the effective population size and the mutation rates , these selection coefficients determine the evolutionary dynamics of binding sites . In particular , the probability of evolving from an ancestor site to a descendent site through a series of point substitutions within an evolutionary distance can be evaluated in an analytical way from the underlying substitution matrix [58] , [64] ( see Materials and Methods ) . Here , we use this quantitative sequence evolution model to infer modes of binding site evolution . For any given pair of adjacent sites and that bind transcription factors and , respectively , we want to evaluate the likelihood of two different histories of site formation . In the first mode of evolution , the sites are assumed to evolve to their present sequence states by point substitutions from independent ancestor sequences and under independent selection given by the fitness landscapes and , as illustrated in Fig . 2 ( a ) . If the selection for binding is assumed to act over a sufficiently long evolutionary time , the probability of observing the present sequence states and in this independent mode of evolution is simply . This mode of evolution can only result in distance-dependent sequence similarity arising from an increased coverage with pairs of adjacent sites with correlated motifs and ( evidence for this effect will be discussed below ) . However , it does not generate increased similarity of individual pairs of adjacent sites beyond that of their motifs . In the second mode of evolution , the sites are assumed to evolve from a common ancestor sequence by a local duplication event at a distance from the present , followed by diversification under selection given by separate fitness landscapes and : either the original site is under stationary selection for binding factor and the duplicated site has evolved the new function of binding the factor or vice versa , as illustrated in Fig . 2 ( b ) . In this mode , the present sequences and have evolved from their last common ancestor by independent substitution processes with transition probabilities and . The dynamics results in a joint probability of the form where the distribution of the ancestor sequence is given by ( see Materials and Methods ) . In this mode , distance-dependent sequence similarity arises due to common descent , causing the sequences of adjacent sites to be more similar than their motifs and . Importantly , this effect is generic and not tied to any functional properties of the transcription factors and . Fig . 2 ( c ) shows a few examples of enhanced sequence similarity in pairs of adjacent binding sites in regulatory modules of D . melanogaster . The relative likelihood of common versus independent descent for a specific pair of sites is given by the duplication score . A positive score value indicates that the pair , is more likely to have evolved by duplication from a common ancestor sequence than independently . Clearly , the information about common or independent descent comes from the similarity between the sequences and in a gapless alignment . The particular feature of site sequences is that they have evolved under selection for the binding motifs of the transcription factors and . Therefore , our score measures the similarity between the sequences and in a specific way: it gauges matches and mismatches depending on the weights of aligned nucleotides in their respective binding motifs and . For example , a match gets low score if it concurs with a common preferred nucleotide of the motifs , and high score if it goes against the preferred nucleotide of at least one of the motifs . The duplication score depends on the parameter , which we choose by a maximum-likelihood procedure ( see Materials and Methods ) . This parameter describes the expected excess similarity of site pairs related by common descent , but it is not a linear clock of divergence time . Simulated evolution of binding site histories shows that the maximum-likelihood duplication score reliably distinguishes between site pairs with common and with independent descent ( see Materials and Methods ) . Below , we use the distribution of duplication scores to infer the mode of evolution prevalent in a given class of site pairs . This likelihood analysis goes beyond the inference of the sequence similarity introduced above . It can be seen as a decomposition of the distance-dependent similarity between sites into two parts: the similarity between their motifs , and the excess similarity of the actual site pairs beyond that of their motifs . The first part reflects functional correlations within regulatory modules and is assigned to the background model . Only the second part provides evidence for common descent , which is gauged by the scoring function . Our model scores only the sequence similarity within site pairs and does not incorporate the insertions and deletions between the sites after duplication , which determine their relative distance . This is justified , because the likely divergence times of most duplicated site pairs are much longer than repeat lifetimes . If a site duplicates within a repeat , the relative distance between copies may subsequently undergo rapid evolution due to the high indel rates in these regions [46]–[49] . Given a surplus of insertions over deletions in regulatory modules , we expect the relative distance to increase on average [48] . The spacing of contemporary sites is then the result of a long-term diffusive insertion/deletion dynamics within the repeats active since duplication , most of which have decayed in today's sequence . This leaves the similarity of conserved functional sites as the most prominent long-term marker of these dynamics . Using the duplication score , we have evaluated the sequence similarity of pairs of neighboring binding sites in regulatory modules of the Drosophila melanogaster genome . These sites are experimentally validated and recorded in the REDfly database [53]–[55] ( see Materials and Methods ) . We infer the prevalent mode of evolution as a function of the distance between sites and obtain the main result of this paper: – In fly , binding sites with a distance of up to about bp are more likely to share a common ancestor than to have evolved from independent origins . Fig . 3 ( a ) shows the histogram of duplication scores for the set of binding site pairs with bp . The score distribution of these pairs is clearly distinguished from the background distribution , which is obtained from pairs of sites located in the same module at a distance bp and is associated with independent descent . We decompose the score distribution of adjacent sites in the form , attributing the excess of large scores to pairs of sites of common descent with a score distribution . Our best fit of this mixed-descent model to the data distribution has a fraction of adjacent site pairs formed by duplication; see Fig . 3 ( a ) . The total log-likelihood of the mixed-descent model relative to the background model is given by multiplying the relative entropy of the distributions and with the number of site pairs , . We estimate , providing significant statistical evidence that the prevalent mode in adjacent sites is evolution from common descent ( for details , see Materials and Methods ) . We note that this significance emerges for the ensemble of the adjacent site pairs , whereas the relative log-likelihood for duplication per site pair , , is of order one: individual site sequences are inevitably too short to reliably discriminate between the two evolutionary modes . Our conclusion that local sequence duplications generate the observed excess similarity of adjacent sites is supported by a number of further controls and a comparison with the yeast intergenic regulatory sequences: – The relative log-likelihood for duplication per site pair decreases with increasing distance between sites . In Fig . 3 ( b ) , we evaluate the relative entropy for the score distributions of site pairs with different values of mutual distance . We find a rapid decay up to about bp , that is , the score distribution becomes successively more similar to the background distribution with increasing site distance . This pronounced distance-dependance is comparable to that of the total sequence similarity shown in Fig . 1 ( c ) and is consistent with local duplications as underlying mechanism . – Similarity of neighboring sites is broadly distributed over pairs of transcription factors . We partition the 306 site pairs with a mutual distance of less than 50 bp by factor pairs and evaluate the partial score averages . We compare the distribution of these averages with the corresponding distribution of averages evaluated after scrambling the score values of the site pairs , as shown in Fig . 3 ( c ) . The two distributions are statistically indistinguishable , which shows that excess sequence similarity is a broad feature of adjacent binding sites and is not limited to a subset of sites for factor pairs with specific functional relationships . This supports our conclusion that the excess sequence similarity reflects common descent and not fitness interactions ( epistasis ) between sites . Of course , epistasis is common for binding sites in the same regulatory module , because these sites perform a common regulatory function . However , generic interactions couple the binding energies of adjacent sites , not directly their sequences . Epistatic effects generating excess sequence similarity are conceivable for specific factor pairs , but do not appear to be a parsimonious explanation for the broad similarities of adjacent binding sites we observe . – In yeast , binding site duplications are not frequent . For comparison , we have also evaluated a set of pairs of binding sites in the Saccharomyces cerevisiae genome . Fig . 3 ( d ) shows distribution of duplication scores for the set of binding sites with bp . This distribution is strongly peaked around zero ( because the maximum-likelihood value of is large , see Materials and Methods ) and indistinguishable from the distribution of the control set of random site pairs; both distributions have a negative average . As in Drosophila , most binding sites in the same intergenic region of S . cerevisiae are located within bp from each other . However , we do not observe evidence for local duplications as a mode of binding site formation in yeast . Clearly , this result does not exclude that such duplications take place , but they do not appear to be frequent enough to generate a statistically significant excess similarity of neighboring sites . This is not surprising given the differences in regulatory architecture between yeast and fly: individual sites in S . cerevisiae are more specific than in Drosophila; the average sequence information of a binding motif is bits , compared to bits [57] . Accordingly , a larger part of the regulatory functions in yeast relies on single sites , and there are no regulatory modules which would require frequent duplications for their formation . Do the inferred site duplications have adaptive potential for the formation of novel binding sites ? Here , we use the term adaptive potential to indicate that the duplication itself may be a neutral process , and selection for factor binding may latch on later to duplicated sites . The duplication of a site for a given transcription factor has obvious adaptive potential towards formation of an adjacent site for the same factor . But local duplications also have adaptive potential if the duplicated site is to evolve the new function of binding a different factor , because the binding motifs of transcription factors with adjacent sites are correlated . This correlation quantifies the ability of one factor to recognize the binding sites of another factor , including seed sites generated by sequence duplications . Specifically , we define the binding correlation of a transcription factor with another factor as the average information-based fitness to bind factor in the ensemble of A-sites . In Fig . 4 , this quantity is evaluated for all factor pairs with adjacent binding sites , together with the range of fitness of known target sites for factor and the average in background sequence ( see Materials and Methods ) . For most such factor pairs , the fitness of a typical A-site is seen to be similar to that of weak B-sites and significantly larger than the average fitness of background sequence . This binding correlation between motifs is sufficient so that an A-site duplicate can act as a seed for a B-site , which can subsequently adapt its strength by point mutations . The binding correlation is specific to factors which have adjacent binding sites; we have found no such effect in the control ensemble of all factor pairs ( most of which do not have adjacent sites ) . Furthermore , some highly specific motifs , such as hunchback , twi and z do not show binding correlations with other factors .
Local sequence duplications ( and deletions ) are a generic evolutionary characteristic of intergenic DNA and , in particular , of regulatory sequence [44]–[49] . In this study , we have established evidence for local sequence duplications as a mechanism that transports and produces cis-regulatory information . These duplications generate specific , distance-dependent sequence similarity in strongly correlated pairs of sites with a relative distance of up to about 50 bp , which account for a substantial part of the sequence autocorrelation in fly regulatory modules . In particular , they provide a parsimonious explanation for the excess sequence similarity of transcription factor binding sites , which is broadly observed in this range of relative distance . We conclude that the majority of these adjacent site pairs have evolved from a common ancestor sequence . The large amplitude of the duplication signal may be the most surprising result of this study . It far exceeds the level expected from the repeats in contemporary sequence , which cover only about 5 percent of binding sites and are typically shorter than the distance between correlated sites . Common-descent site pairs are the cumulative effect of past duplications over macro-evolutionary intervals , whose trace is conserved by selection on site functionality . This result establishes local duplication as a pervasive formation mode of regulatory sequence , which generates , for example , the known local variations in site numbers between Drosophila species . Of course , our evidence for this mode is statistical and , at this point , is confined to a limited dataset of binding sites with confirmed functionality [53]–[55] . The duplication mode appears to be specific to multicellular eukaryotes; we have not found comparable evidence in the yeast genome . Our findings are relevant for genome analysis in two ways: including local duplications should inform inference methods for binding sites as well as alignments of regulatory sequence with improved scoring of indels [46]–[49] . With such methods , it may become possible to follow the evolutionary history of binding site duplications across species . We have found evidence that local duplications can confer adaptive potential for the formation of novel binding sites , because they generate seed sequences with marginal binding specifically in the vicinity of existing sites . This mechanism is necessary , because point mutations alone can only lead to rapid loss but not to gain of new sites with positional specificity . Thus , duplications and point mutations complement each other , suggesting that typical binding sites within multicellular eukaryotes have an asymmetric life cycle: formation within a functional cluster by local duplication , adaptation of binding energy by point mutations , evolution of relative distance to neighboring sites by insertions and deletions in flanking sequence , conservation by stabilizing selection on binding energy , and loss by point mutations . The life cycle of individual binding sites interacts with other levels of genome evolution . Gene duplications with subsequent sub-functionalization have been identified as an important evolutionary mode specifically in higher eukaryotes [65] . If subfunctionalization is initialized at the level of gene regulation , it amounts to a loss of regulatory input for both gene duplicates and provides a mechanism for adaptive loss of binding sites . This process alone would lead to genomes with many genes , but few functions per gene . Maintaining regulatory complexity with multi-functional genes as observed in eukaryotic genomes [23] , [26] requires a converse evolutionary mode: gain of new functions by existing genes . At the regulatory level , this amounts to gain of regulatory input , i . e . , adaptive formation of new binding sites . Previous studies have identified regulatory modules as important units of transcriptional control , in which clusters of binding sites bind multiple transcription factors with cooperative interactions . The sites in a cluster follow a regulatory grammar resulting from natural selection acting on site order , strength , and relative distances [36]–[38] . If sequence duplications play a major role in the formation of such clusters , we may ask how much of their observed structure reflects this mode of sequence evolution , rather than optimization of regulatory function by natural selection . Local duplications generically produce descendant sites , which are weak binding sites for another factor at best , as shown in Fig . 4 . ( Significant heterogeneity in binding strength between adjacent sites is indeed observed in our sample . ) The resulting binding sequences are hardly optimal in terms of specificity and discrimination between different factors . Cooperative binding between transcription factors may have evolved as a secondary mechanism to confer regulatory function to these sequence structures . In this paper , we have argued that local sequence duplications facilitate the adaptive evolution of gene regulatory interactions . However , the adaptive potential of duplications does not imply that the duplication process itself has to be adaptive or even confined to regulatory sites . Similar to gene duplications [65] , many site duplications may be neutral and provide a repertoire of marginal regulatory links . Adaptive diversification can build subsequently on this repertoire , conserving and tuning those links that confer a fitness advantage and discarding others .
The sequence analysis of D . melanogaster is based on the cis-regulatory modules and experimentally validated binding sites collected in the REDfly v . 2 . 2 database [53]–[55] , and on the position weight matrices of Dan Pollard's dataset ( http://www . danielpollard . com/matrices . html ) . To measure the distance-dependent sequence similarity , we use the 346 known regulatory modules with length of more than 1000 bp in D . melanogaster . The analysis in D . yakuba and D . pseudoobscura is based on the 249 well-aligned homologous regions obtained from multiple alignments of 12 Drosophila species ( dm3 , BDGP release5 ) ; see Fig . S2 . For the evolutionary inference in the second part of the paper , we use only the experimentally validated binding sites contained in these modules which are not necessarily selected for high similarity to motifs or for high mutual similarity . To avoid biases in our analysis , the set of sites is truncated in three ways: ( i ) We only use binding sites for transcription factors that occur in at least two different regulatory modules , so that the position weight matrix is not biased by the sequence context of a single module . ( ii ) We use only sites that have no sequence overlap with other sites in the dataset , because our inferred fitness landscapes describe the selection for a single regulatory function [13] . ( iii ) We exclude sites in the X chromosome , which could bias the results by its high rate of recent gene duplications and the abundance of repeat sequences [66] , [67] . These conditions produce a cleaned set of 506 transcription factor binding site pairs located in 74 cis-regulatory modules . For the analysis in S . cerevisiae , we use sites and position weight matrices from the SwissRegulon database [68] . These footprints do not always match the length of their position weight matrices . To produce a set of site sequences of common length , longer footprints are cut and shorter ones joined with flanking nucleotides , such that the binding affinity is maximized . Sequence autocorrelation is a measure of enhanced mean similarity between the nucleotides of a sequence segment . The distance dependence of the autocorrelation signal provides information about the range , within which the nucleotides appearing in the sequence are correlated . In a given sequence segment , the nucleotide frequencies are given by ( 1 ) where if and otherwise . These determine the mean similarity between two random nucleotides of the segment , . The sequence autocorrelation function is then defined by ( 2 ) We evaluate this function in the 346 regulatory modules of Drosophila melanogaster genome with length of more than 1000 bp identified by REDfly v . 2 . 2 database [53]–[55] . As shown in Fig . 1 ( a ) , we find an approximate exponential decay with a characteristic length of about 100 bp as the range of sequence correlation . The mean identity is evaluated in a local window of 500 bp ( changing the window length affects the baseline of this function , but not its dependence on distance up to 100 bp ) . Information about the spatial distribution of these correlated nucleotides along the genome is contained in higher orders of sequence autocorrelation ( i . e . , reoccurrence of doublets , triplets , etc . ) . Here , we use information theory to identify such clusters of correlated nucleotides in a sequence region . We want to detect reoccurring nucleotide patterns or motifs . A motif of length is a probability distribution for sites which differs significantly from the background distribution . If we neglect correlations between nucleotides , we can write these distributions as the product of single-nucleotide frequencies , ( 3 ) and . The matrix of single-nucleotide frequencies ( 3 ) is called the position weight matrix of the motif . The sequence information of the motif is defined as the relative entropy ( Kullback-Leibler distance ) of these distributions [69] , ( 4 ) To study the sequence coverage by informative motifs , we use a reduced form of the full frequency distribution by mapping it to the mean similarity of its motif sites . Hence , even without any prior knowledge on frequency distributions , we can recover part of the sequence information for those motifs that occur more than once in the sequence segment . Two sites drawn from the motif have a mean similarity between aligned nucleotides , which is higher than the background mean similarity . The similarity information of the motif is given by the relative entropy ( 5 ) Similarity information between pairs of sites is a somewhat diluted measure of sequence information . As a rule of thumb , the mutual similarity entropy per site pair , , recovers about half of the motif information per site , . To estimate the total similarity information of all strongly correlated pairs of sites with distance and length in a sequence segment of length , we construct a set ( 6 ) of site pairs with the following properties: ( i ) The left ( and also the right ) sites of all pairs have no sequence overlap , ( 7 ) ( ii ) The mean similarity of each pair is greater than a threshold , ( 8 ) ( iii ) The sum of mutual similarities is maximal ( see Fig . S1 ) By the dynamic programming recursion ( 9 ) we obtain the sequence of partial scores with the initial condition . We then use a backtracking procedure ( see , e . g . , [58] ) to determine the set of positions and , hence , the number and the average similarity of the high-similarity pairs ( 6 ) . To estimate the expected number of pairs in background sequence , , we apply the same procedure to 1000 sequences of length , which are generated by a first-order Markov model ( 10 ) with the same single-nucleotide frequencies and conditional frequencies as in the actual sequence . We then evaluate the excess and obtain an estimate of the total information contained in the enhanced autocorrelation of motifs as given by eq . ( 5 ) , ( 11 ) We infer by maximum likelihood analysis of the total similarity information in the sequence . This method also allows for optimization of the motif length , similar to the procedure in the local sequence alignment algorithms [58] . Our evolutionary dynamics of binding site sequences for a given transcription factor is determined by the Bruno-Halpern fitness model [62] derived from the position weight matrix and the background frequencies , ( 12 ) This relationship between fitness and nucleotide frequencies is valid if binding sites are at evolutionary equilibrium under mutations , genetic drift , and selection , and background sequence is at neutral equilibrium ( accordingly , all inferred fitness values are scaled in units of the effective population size ) . The relationship of the evolutionary ensembles with the underlying thermodynamics of site-factor interactions is discussed , for example , in ref . [52] . Eq . 12 defines an information-based fitness model: the average fitness of functional binding sites equals the sequence information of the motif , ( 13 ) with and ; see eqs . ( 1 ) , ( 3 ) and ( 4 ) . We infer from the local background frequency of the region base pairs around each binding site . The rates of point substitutions within binding sites are determined by the scaled selection coefficients derived from this fitness model and the point mutation rates ( which are assigned a uniform value for simplicity ) . Here , we use the standard Kimura-Ohta substitution rates ( 14 ) which are valid in the regime ( in which subsequent substitution processes are unlikely to overlap in time ) and [61] , [70] . The matrix of these substitution rates then determines the transition probabilities ( propagators ) from an arbitrary initial sequence to an arbitrary final sequence within an evolutionary distance [58] , [64] . Given the set of transition probabilities , we obtain the joint probability for a pair of sites that bind transcription factors and , respectively , and have evolved from a common ancestor as described in the main text and in Fig . 2 ( b ) . First , we assume that the ancestor site is at evolutionary equilibrium under selection to bind factor , that is , the contemporary site has the ancestral function and has evolved a new function after duplication . This gives the contribution ( 15 ) where we have used the detailed balance condition of the substitution dynamics [64] . There is a second contribution describing the case of the ancestor under stationary selection to bind factor . Weighing these cases with equal prior probabilities , we obtain ( 16 ) In our analysis of pairs of adjacent binding sites in Drosophila , there is usually a dominant contribution from one of the terms , from which we can infer the likely function of the ancestor site . In the limit of large , the evolution from a common ancestor becomes indistinguishable from evolution by independent descent , . The duplication score ( 17 ) is a measure of sequence similarity between binding sites . This score depends on the evolutionary distance parameter . We infer the optimal value of by maximizing the likelihood ratio between the score distribution of pairs with mutual distance and the score distribution of pairs with independent origin . In D . melanogaster , we find a finite maximum-likelihood evolutionary distance and significantly positive values of the duplication score for adjacent binding sites . In S . cerevisiae , we find large values , i . e . , there is no statistical evidence for evolution by common descent . Our conclusions are largely independent of the values of used in ( 16 ) and ( 17 ) . These values should be regarded as model fit parameters for the observed sequence similarities . Energy-based fitness models [13] , [64] , which take into account the epistasis between mutations within binding sites , are required to obtain more accurate estimates of , which can be tested against phylogenetic data . Epistasis will increase the inferred values of compared to the additive ( Bruno-Halpern ) model [13] , [64] . We evaluate the score distribution of a given class of site pairs in terms of a mixture model of common and independent descent , ( 18 ) The distribution of scores for independent descent , , is obtained from pairs of sites in a common module with a relative distance bp ( Fig . 3 ( a ) , dashed line ) . This distribution is approximately Gaussian and has a width of order one , which is consistent with the simulations reported below . Because we build from sites in a common module , its score average is above that for pairs of sites located in different modules . In this way , the overall sequence similarity within modules , which depends on the local GC-content , is assigned to the background model and does not confound the evidence for common descent . The distribution is the best fit to the the large-score excess of the distribution for adjacent sites with a relative distance bp ( Fig . 3 ( a ) , violet-shaded ) . This distribution has larger mean and is broader than the background distribution , which is also consistent with the simulations reported below . Given a set of site pairs with scores described by the distribution , the log-likelihood of the mixed-descent model ( 18 ) relative to the independent-descent background model is given by ( 19 ) it equals the product of the number of sites and the relative entropy . The extensive quantity measures the statistical evidence for the mixture model based on the number and the score distribution of site pairs , whereas quantifies only the shape differences between the distributions and . We evaluate eq . ( 19 ) using the conservative estimate with ; see Fig . 3 ( a ) . We have tested our inference procedure by simulations of the sequence evolution for pairs of binding sites with common and with independent descent . For these simulations , we use four pairs of different factors , and two pairs of equal factors . For each factor pair , we obtain an ensemble of 25000 pairs of binding sites with a duplication in their evolutionary histories , as described by Eqs . ( 15 , 16 ) and Fig . 2 ( b ) . We first obtain 500 duplication events : the last common ancestor sequence is drawn with equal likelihood from the ensemble or , and the divergence time is drawn from an exponential distribution with mean . For each duplication event , we draw 50 site pairs from the distribution describing evolution under selection for binding of factors and , respectively . We then apply our scoring procedure to this set of site pairs . As for the real sequence data , we infer a single maximum-likelihood parameter by maximization of the total duplication score . As shown in Fig . S3 ( a ) , has a pronounced maximum at a value , which is close to the mean divergence time of the input data . We conclude that the constraint of a fixed does not confound the inference of common descent . We also obtain separate score distributions for sites binding the pairs of equal factors and of different factors listed above; see Fig . S3 ( b ) and Fig . S3 ( c ) . These distributions are similar and clearly distinguish duplicated site pairs from pairs with independent ancestries for both factor groups . We conclude that our method can infer common descent of binding sites , independently of their functional characteristics . We define the binding correlation for each ordered pair of factors as the average information-based fitness of A-sites for the B-factor , ( 20 ) This value is an estimate for the compatibility of the A-sites with the transcription factor and equals , up to a constant , the information-theoretic cross entropy between the distributions and . In Fig . 4 , this quantity is compared to ( i ) the sequence information of the motif , which equals the average fitness of B-sites for the B-factor by eq . ( 4 ) , ( 21 ) and ( ii ) to the average fitness of background sequence for the B-factor , ( 22 ) | Since Jacob and Monod stressed the importance of gene regulation in evolution , our understanding of the mechanisms of regulation has substantially advanced . In higher eukaryotes , genes often have complex regulatory input , which is encoded in cis-regulatory sequence with multiple transcription factor binding sites . However , the modes of genome evolution generating regulatory complexity are much less understood . This study reports a surprising finding: in fly regulatory modules , the majority of transcription factor binding sites show evidence of a local sequence duplication in their evolutionary history , which relates their sequence information to that of neighboring binding sites . Our analysis suggests that local sequence duplications are a pervasive production mode of regulatory information . This mode appears to be specific to higher eukaryotes; we have not found evidence of frequent local duplications in the yeast genome . Our results affect genomic sequence analysis , in particular , computational identification of cis-regulatory elements and alignment of regulatory DNA . At the same time , they address fundamental questions on the evolution of regulation: How much of the regulatory “grammar” observed in higher eukaryotes is due to optimization of function , and how much reflects the underlying sequence evolution modes ? What is the result and what is the substrate of natural selection ? | [
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"biol... | 2011 | Formation of Regulatory Modules by Local Sequence Duplication |
G-quadruplex or G4 DNA is a non-B secondary DNA structure that comprises a stacked array of guanine-quartets . Cellular processes such as transcription and replication can be hindered by unresolved DNA secondary structures potentially endangering genome maintenance . As G4-forming sequences are highly frequent throughout eukaryotic genomes , it is important to define what factors contribute to a G4 motif becoming a hotspot of genome instability . Using a genetic assay in Saccharomyces cerevisiae , we previously demonstrated that a potential G4-forming sequence derived from a guanine-run containing immunoglobulin switch Mu ( Sμ ) region becomes highly unstable when actively transcribed . Here we describe assays designed to survey spontaneous genome rearrangements initiated at the Sμ sequence in the context of large genomic areas . We demonstrate that , in the absence of Top1 , a G4 DNA-forming sequence becomes a strong hotspot of gross chromosomal rearrangements and loss of heterozygosity associated with mitotic recombination within the ∼20 kb or ∼100 kb regions of yeast chromosome V or III , respectively . Transcription confers a critical strand bias since genome rearrangements at the G4-forming Sμ are elevated only when the guanine-runs are located on the non-transcribed strand . The direction of replication and transcription , when in a head-on orientation , further contribute to the elevated genome instability at a potential G4 DNA-forming sequence . The implications of our identification of Top1 as a critical factor in suppression of instability associated with potential G4 DNA-forming sequences are discussed .
In addition to the canonical Watson-Crick helical duplex ( B-DNA ) , genomic DNA , especially repetitive sequences , can assume other types of structures such hairpins , Z-DNA , triplex DNA ( H-DNA ) or tetrahelical DNA structures [1]–[6] . Impediments to normal DNA metabolic processes including transcription and replication imposed by such secondary DNA structures explain the correlation between repetitive sequence elements and elevated genome instability . Genomic instability at purine-rich GAA•TTC repeats and CAG•CTG repeats , which can fold into three-stranded H-DNA [7] and a slipped hairpin structure [8] , forms the molecular basis of multiple neurodegenerative diseases , such as Freidreich's Ataxia and Huntington's disease , respectively . G-quadruplex or G4 DNA is another non-B secondary DNA structure that potentially interferes with normal DNA transactions [5] , [9]–[11] . G4 DNA contains a stacked array of multiple G-quartets , which are comprised of four guanines interacting in a planar configuration [5] , [10] . G4 DNA can be readily formed in solution by oligonucleotides containing multiple runs of guanines and by actively transcribed plasmid DNA [12] , [13] . G4 DNA-forming sequences or G4 motifs are present in the genomes of diverse organisms and conserved throughout evolution; they number >375 , 000 in the human genome and >1 , 400 in the Saccharomyces cerevisiae nuclear genome [14]–[17] . The distribution of G4 motifs is highly concentrated at telomeres , rDNA loci , immunoglobulin heavy-chain switch regions , and G-rich minisatellites and significantly correlates with nucleosome-free regions and transcription start sites ( TSSs ) [10] , [11] , [18] . In oncogenes , G4 motifs are mostly enriched in the regions flanking TSSs , which suggests that G4 DNA may be involved in transcriptional regulation [15] , [17] . G4 DNA becomes a structural barrier to transcription and replication in vitro indicating that it might play a significant role in genome instability [19]–[21] . In the absence of Pif1 , a potent G4 DNA unwinding helicase , replication forks slow down near G4 motifs present in the yeast genome strengthening the argument that an unresolved G4 structure can lead to increased genome instability [22] . G4 motifs are frequently found at unstable genomic loci including proto-oncogenes and sites of frequent translocation breakpoints [23] and at preferred mitotic and meiotic DNA break sites [17] . In some human cancers , G4 motifs have been identified at frequent breakpoints involved in chromosomal translocations including the major breakpoint region in the proto-oncogene BCL2 [24] . Chromosomal translocations involving G-rich immunoglobulin switch regions have long been observed in various cancer cell lines [25] . The identification in silico of potential G4 DNA-forming sequences at sites of genome instability , however , has not yet been fully verified by in vivo demonstration of biological relevance of G4 DNA structure . Seminal advances in understanding the genome instability induced by the repetitive , DNA secondary structure-forming sequences have been made using yeast and bacterial model systems [26]–[28] . A large tract of GAA•TTC repeats in yeast , for example , acted as a hotspot of gross chromosomal rearrangements ( GCRs ) and interstitial deletions [26] , [27] . In bacteria , CTG•CAG repeats from the Myotonic Dystrophy gene induced large deletions when the repeats were highly transcribed [29] . When a guanine-run containing a human subtelomeric minisatellite was integrated into the yeast genome , it significantly elevated GCRs [30] and resulted in frequent repeat expansion and contraction [31] . The guanine-rich yeast telomere repeats , when placed within an intron of an interstitially located gene , cause various types of chromosomal rearrangements including deletions and inversions [32] . Accumulating evidence pointing to G4 motifs as genome instability hotspots underscores the importance of defining endogenous and exogenous factors that influence the integrity of genomic loci containing these motifs . Active transcription , when oriented in the direction to place the guanine-runs in the transiently single stranded non-transcribed strand ( NTS ) , was shown to stimulate formation of G4 DNA structure both in vitro and in bacterial cells [13] . To determine the effect of G4 DNA on yeast genome stability , we previously constructed a genomic reporter assay where a potential G4 DNA-forming sequence was highly transcribed to promote secondary DNA structure formation . By normalizing the extent of genome instability occurring at this reporter construct to that occurring at the exact same sequence transcribed in inverse orientation ( that is , with the G4 motifs on the transcribed strand ) , we were able to apply a stringent control for the correlation between elevated genome instability and G4 DNA . Using this approach , we found that gene conversion recombination was significantly elevated by highly transcribing guanine-run containing sequence in a strictly strand-specific manner [33] . In the current report , we demonstrate that active transcription transforms a guanine-run containing sequence into a strong hotspot for gross chromosomal rearrangements and loss-of-heterozygosity ( LOH ) . Our data also show that the direction of replication can significantly alter the level of instability at a potential G4 DNA-forming sequence suggesting that genomic context in terms of both transcription and replication is important when considering G4 motifs as potential genome instability hotspots . Finally , we identify a critical role of Topoisomerase I ( Top1 ) in suppressing various types of genome rearrangements associated with co-transcriptionally formed G4 DNA .
We previously showed that gene conversion resulting from ectopic recombination is increased due to co-transcriptionally formed G4 DNA . In order to determine whether co-transcriptionally formed G4 DNA can also elevate gross chromosomal rearrangements ( GCRs ) , we modified the GCR reporter system previously described by Chen and Kolodner [34] , [35] . In this reporter system , the URA3 gene was integrated into the left arm of chromosome V ( CHR5 ) replacing the HXT13 gene located ∼8 . 5 kb centromere-distal to the CAN1 gene ( Fig . 1A ) . The loss of functional CAN1 or URA3 results in resistance to the drug canavanine ( Can ) or 5-Fluoroorotic acid ( 5-FOA ) , respectively . Because the first essential gene on the left arm of CHR5 , PCM1 , is located ∼60 kb from the telomere , the region containing CAN1 and URA3 genes can be lost without affecting viability of haploids . The hypothetical rate of double drug resistance ( CanR/5-FOAR ) occurring via independent mutations in CAN1 and URA3 is approximately 10−12 , which is significantly lower than the observed rate of deletion of the left arm of CHR5 ( 10−11–10−10 in a wild-type background ) [35] . Therefore , by selecting for colonies resistant to both Can and 5-FOA , GCR events resulting in simultaneous loss of CAN1 and URA3 genes are detected . We modified the GCR assay by integrating the pTET-lys2-GTOP or -GBTM cassette immediately centromere-proximal to CAN1 ( Fig . 1A ) . As previously described [36] , the pTET-LYS2 cassette contains the LYS2 gene transcribed from the heterologous tetracycline-repressible promoter ( pTET ) linked to a marker gene conferring G418 resistance ( G418R ) . Within the LYS2 ORF , at ∼390 bp from the start codon , a 760 bp fragment from the mouse immunoglobulin ( Ig ) switch Mu ( Sμ ) region was inserted to generate pTET-lys2-GTOP or -GBTM cassettes [33] . Switch regions , which are required for Ig heavy chain class-switch recombination ( CSR ) , are comprised of multiple , degenerate guanine-rich repeats of several kb in length [37] . The Sμ sequence is a model G4 motif and was previously demonstrated to form G4 DNA structures both in vivo and in vitro when highly transcribed [13] . The sequence of the Sμ fragment incorporated into the LYS2 ORF ( containing ∼17 of ( GAGCT ) nGGGGT repeats ) is shown in Figure S1 . This fragment was inserted either in the physiological ( -GTOP ) or in the inverted orientation ( -GBTM ) , placing the guanine runs on the non-transcribed ( NTS ) or the transcribed strand ( TS ) , respectively ( Fig . 1B ) [33] . Topoisomerase 1 ( Top1 ) is a highly conserved enzyme that relieves positive or negative torsional stress associated with transcription and replication [38] , [39] . Top1 functions by covalently attaching to the 3′ end of nicked DNA , which is quickly re-ligated after swiveling of the DNA strands to remove supercoiling . Although not essential for viability in yeast , replication slows down and sometimes stalls in the absence of Top1 , especially at highly transcribed regions [40] . We previously reported that disruption of Top1 leads to an increase in Sμ-induced gene conversion events [33] . In order to determine whether Top1 also plays a role in preventing GCRs initiating at G4 DNA , we deleted the TOP1 gene from strains containing pTET-lys2-GTOP and –GBTM constructs . In wild-type ( WT ) backgrounds under high-transcription conditions , the rates of CanR/5-FOAR events were 0 . 88×10−10 and 0 . 20×10−10 for pTET-lys2-GTOP and –GBTM constructs , respectively ( Fig . 1C ) . These rates are not significantly different from each other and are comparable to GCR rates previously reported in the absence of an inserted pTET-lys2 cassette [34] , [35] . Upon TOP1 deletion , the rate of GCR ( CanR/5-FOAR ) was significantly elevated for both the pTET-lys2-GTOP and -GBTM construct . Importantly , GCR occurred at a significantly higher ( ∼30-fold ) rate for pTET-lys2–GTOP , where guanine-runs are present on the NTS , compared to the pTET-lys2–GBTM construct where guanine-runs are on the TS . We tested whether active transcription of the guanine-run-containing sequence is required for the elevated GCRs by growing the top1Δ strains in medium containing doxycycline , an analog of tetracycline , which resulted in ∼60- to 200-fold reductions in the transcription rates ( Table S1 ) . Repression of transcription from the pTET promoter by doxycycline resulted in a >200-fold decrease in the rate of CanR/5-FOAR for the strain associated with the pTET-lys2-GTOP construct ( Fig . 1C ) . For the strain containing the pTET-lys2-GBTM construct , transcriptional repression also led to a significant decrease in the rate of CanR/5-FOAR . In order to determine whether the GCRs in top1Δ backgrounds initiate at the G4 DNA containing reporter construct , we carried out PCR analysis to map the GCR initiating breakpoints . For the strain containing the pTET-lys2-GTOP construct in the top1Δ background , 27 of 30 CanR/5-FOAR isolates tested had lost the portion of LYS2 gene with the Sμ fragment insertion but still retained the G418R cassette located just centromere-proximal to this region ( Fig . 1A and Table 1 ) . Thus , 90% of the GCR events occurring in this strain initiated within the ∼4 kb region comprised of the pTET-lys2-GTOP cassette between G418R cassette and CAN1 . This is proportionally greater than the GCR initiating in the same region for the pTET-lys2-GBTM construct in top1Δ strain ( 9 out 23; P<0 . 0005 by chi square analysis ) . To further characterize the chromosome breakpoints in GCR events associated with the pTET-lys2-GTOP cassette , we carried out PCR with a degenerate primer annealing to generic yeast telomere sequence ( CA16; 5′ CACCACACCCACACAC 3′ ) and a primer annealing to the 5′ untranslated region of the LYS2 gene . Out of 27 samples where the disruption of the pTET-lys2-GTOP cassette was confirmed by PCR mapping , telomere-anchored PCR products of 700 to 1900 bp were obtained for 16 samples . Subsequent sequencing of these fragments showed that , in 15 CanR/5-FOAR clones , de novo telomere additions occurred at various locations within the G4-forming Sμ fragment ( Class I events in Fig . 2 and Fig . S1 ) . Due to the high G/C content and repetitiveness of the Sμ sequence , sequencing analysis failed to identify the site of telomere addition in one of the PCR fragments . In concurrence with preferential telomere addition sites previously identified [41] , the junctions of de novo telomere addition were located at GT dinucleotides and frequently at 5 to 6 nt clusters of GT-rich sequence . Additionally , mutations , deletions/insertions , and duplications within the Sμ fragment were detected in seven of the 15 CanR/5-FOAR clones with telomere additions . We further characterized the genome rearrangements associated with G4 DNA using pulse field gel electrophoresis ( PFGE ) and microarray-based comparative genome hybridization ( array-CGH ) . PFGE showed that , in the 16 samples where de novo telomere addition at Sμ was confirmed by sequencing ( Class I ) , CHR5 was reduced in size by ∼35 kb and co-migrated with CHR8 ( Fig . 2A and Fig . S2A ) . The loss of CHR5 sequences from the left telomere to the integration site of pTET-lys2 cassette ( at 34 , 000 NT ) was confirmed by array-CGH analysis ( Fig . 2B and Fig . S2A ) . In another 8 samples ( Class II ) where CHR5 appeared smaller by only ∼15 kb ( Fig . 2A and Fig . S2B ) , array-CGH identified segmental deletions between the original location of HXT13 ( at 23 , 000 NT ) and the pTET-lys2 cassette . PCR and sequencing analysis showed that this recurrent deletion was mediated by a pair of 21 bp direct repeats introduced into the two respective sites of CHR5 as parts of plasmid constructs used for integration of the URA3Kl marker at HXT13 locus ( pUG72; Euroscarf ) and pTET promoter ( pCM225; Euroscarf ) ( Fig . S3 ) . Finally , in class III events , deletion of CHR5 sequences from the left telomere to the pTET-lys2 integration site occurred in combination with duplications of the immediate proximal sequences on the left arm , and duplication of a terminal segment on the right arm ( Fig . 2B and Fig . S2C–D ) . The two clones from class IIIa showed duplications from pTET-lys2 to the Watson-oriented YELWdelta1 dispersed long terminal repeat element ( LTR ) , and the one example of class IIIb showed a duplication extending further to the full length Crick-oriented Ty1 element insertion at the URA3 locus ( ura3-52 allele ) . All three class III clones had duplications of a segment of the right arm extending from position ∼446 , 000 NT ( containing the full length Crick-oriented YERCTy1-1 element and the Watson-oriented YERWdelta20b LTR ) all the way to the right telomere . These clones displayed longer versions of CHR5 of ∼700 kb ( migrating just below CHR10 ) and ∼770 kb ( co-migrating with CHR2 and CHR14 ) for class IIIa and class IIIb , respectively ( Fig . 2A and Fig . S2C–D ) . The class III chromosome sizes were consistent with the deletions and duplications detected by array-CGH and suggested a complex mechanism of formation . Similar array-CGH patterns were observed recently in the analyses of GCR events in yeast CHR5 as well as in humans [42] , [43] . These studies described breakpoint structures consistent with an intra-strand fold-back mechanism in which a resected free 3′ end folds back on itself , re-anneals to a microhomology region and primes break-induced DNA replication ( BIR ) . By plasmid-rescuing this regions subcloning and sequence analysis ( See Materials and Methods ) , we confirmed that the duplicated regions proximal to the Sμ breakpoints in class IIIb clone A7 and class IIIa clone A8 were comprised of inverted duplications separated by single copy regions corresponding to the original ssDNA loops , as predicted by the fold-back mechanism ( Figs . S4A and S4B ) . We were not successful in rescuing the duplicated regions from the Class IIIa clone A16 . The recovered rearrangement structures observed in the A7 and A8 clones can be explained by two different models ( Fig . S4C ) . In the first scenario , as the BIR event initiated by the intra-strand fold-back reached the Ty/LTR sequences on the left arm , it collapsed , re-annealed at the Ty/LTR sequences on the right arm ( template switching ) , and continued on to reach the right telomere to produce a stable monocentric chromosome . The second possibility is that BIR continued all the way to the right telomere forming an unstable dicentric chromosome , which was then stabilized by a secondary homologous recombination event between Ty/LTR repeats leading to loss of one of the centromeres . Although our data does not allow us to distinguish between these two possibilities , we favor the template switching model ( Model 1 in Fig . S4C ) since BIR is generally thought to be impeded by centromeric structures , and BIR template switching has been shown to be frequent in yeast [44] , [45] . Spontaneous DNA breaks in diploid cells are frequently repaired by allelic mitotic recombination using as template either a sister chromatid or a homologous chromosome . We designed an assay that can measure G4 DNA-induced mitotic recombination between chromosome III ( CHR3 ) homologs in diploids ( Fig . 3A ) . First , we integrated the URA3 gene near the telomere of the left arm of CHR3 in a haploid strain derived from YPH45 ( a S288c derivative ) . On the same arm of CHR3 , about 44 kb centromere-proximal to the URA3 integration site , pTET-lys2-GTOP or -GBTM was integrated replacing HIS4 . As described above for the CHR5 GCR assay , the pTET-lys2-GTOP and -GBTM cassettes contained the 760-bp fragment of Sμ sequence and were adjacent to an aminoglycoside phosphotransferase gene conferring resistance to the drug G418 ( G418R ) . Because the direction of replication fork movement relative to the direction of transcription can affect recombination at highly transcribed regions [46] , [47] , each cassette was integrated in two orientations relative to the nearby replication origin ARS306 . This yielded constructs in which the transcription and replication forks are co-directional ( SAME ) or in head-on orientation ( OPPO ) ( Fig . 3B ) . The direction of replication fork movement through this region of CHR3 was previously confirmed by 2D-gel analysis [36] . Heterozygous diploids were generated by mating the YPH45-derived haploid strains described above to a haploid strain derived from YJM789 , a clinically isolated strain with ∼0 . 5% sequence divergence relative to the S288c reference strain [48] . Because the YJM789-derived strain is Ura− , loss or mutation of the URA3 gene on the CHR3 from the YPH45 parent will result in resistance to 5-FOA in the heterozygous diploid cells . The types of genome rearrangements that can lead to loss of URA3 include ( a ) complete loss of YPH45 CHR3 , ( b ) partial loss of the left arm of YPH45 CHR3 , ( c ) Break Induced Replication ( BIR ) or reciprocal crossover ( RCO ) initiating between URA3 and CEN3 or ( d ) translocation/BIR events involving a heterologous chromosome . In RCO , the distal ends of the two CHR3s are exchanged without loss of genetic material . In BIR involving the homolog , the break in YPH45-CHR3 will be repaired through replication using YJM789-CHR3 from the break to telomere as template . In our assay , we cannot distinguish between these two mechanisms ( Fig . S5 ) . Because all of these events result in loss of heterozygosity ( LOH ) for the segment of CHR3 containing the URA3 marker , we hereafter refer to this assay as the LOH assay . In order to map the position of LOH in 5-FOAR isolates , we devised a PCR-based restriction fragment length polymorphism ( RFLP-SNP ) assay described in Fig . S6 . We defined the initiation of recombination point as between the last telomeric SNP site displaying LOH and the first centromeric SNP site displaying heterozygosity ( Table 2 ) . A 5-FOAR isolate was defined as resulting from recombination initiating at or near the G4 repeats ( G418-pTET-lys2-GTOP or GBTM ) , when it was homozygous for the centromere-distal SpeI site and heterozygous for the centromere-proximal NarI site and the G418S cassette was not present ( LOH class E in Table 2 and Figure 3A ) . When mitotic recombination is initiated by DNA breaks near the pTET-lys2-GTOP or –GBTM cassette , resection must extend into the region of the YPH45 CHR3 with homology to YJM789 CHR3 and , therefore , remove the pTET-lys2-GTOP or –GBTM cassette along with the G418R marker . To determine whether highly transcribed G4 motifs elevate LOH on CHR3 , we measured the rate of 5-FOAR in WT strains containing pTET-LYS2 ( no Sμ sequence ) or pTET-lys2-GTOP cassette . In either the SAME or OPPO orientations , the overall rate of LOH ( 5-FOAR ) associated with pTET-LYS2 or pTET-lys2-GTOP was not significantly different ( Fig . 4A ) . Using the RFLP-SNP assay , we identified 3/46 or 9/46 LOH events initiated at the highly transcribed pTET-LYS2 cassette when in the SAME or the OPPO orientation , respectively ( Table 3 ) . These proportions were not statistically different from those for LOH initiating at pTET-lys2-GTOP in the SAME or OPPO orientation ( by Fisher's exact test; P = 0 . 31 and 0 . 39 , respectively ) . We observed a dramatic and specific increase in the rates of gene conversion [33] and GCR ( see above ) associated with highly transcribed Sμ sequences when Top1 was disrupted in a haploid YPH45 background . Importantly , both occurred at significantly higher rates when the guanine-run containing strand was on the NTS where its single stranded nature fosters G4 DNA formation . In order to determine whether LOH on CHR3 is similarly affected by the location of G4-forming sequence on the TS vs . NTS , we compared LOH rates associated with pTET-lys2-GTOP and -BTM constructs in top1Δ/top1Δ backgrounds . There was no significant difference between overall rates of LOH events associated with pTET-lys2-GTOP and -GBTM constructs when replication was in the SAME direction ( Fig . 5A ) . However , when replication was in the OPPO orientation , the overall rate of LOH events was ∼3 fold higher for the pTET-lys2-GTOP than for the pTET-lys2-GBTM construct . The rates of LOH initiating at the pTET-lys2-GTOP/GBTM cassette in the SAME or OPPO orientation in top1Δ/top1Δ background were determined by analyzing 47–93 5-FOAR isolates by the RFLP-SNP assay ( Table 4 ) . When transcription from the pTET promoter was in the SAME orientation relative to replication originating at ARS306 , the rate of LOH initiating at the G4-containing sequence was similar whether the guanine-runs were on the NTS ( pTET-lys2-GTOP ) or on the TS ( pTET-lys2-GBTM ) ( Fig . 5B ) . However , the rate of LOH initiated near pTET-lys2-GTOP in the OPPO orientation was >20 fold higher than at pTET-lys2-GBTM in OPPO orientation and ∼4 fold higher than at pTET-lys2-GTOP in the SAME orientation . Upon Top1-disruption , when the replication and transcription is in “head-on” or OPPO orientation , the rate of loss of heterozygous SNP at NarI site ( at 78380 NT ) is also significantly elevated ( Table 4 ) . The NarI-SNP is not lost at a high rate in pTET-lys2-GBTM-containing strain , which suggests that recombination initiating at the pTET-lys2-GTOP cassette ( at 68300 NT ) are often associated with long conversion tracks resulting in the loss of NarI-SNP . This is consistent with the average length of conversion tracks associated with reciprocal crossover events , which is reported to be about 12 kb [49] . Using the RFLP-SNP assay described above , we characterized 93 5-FOAR isolates from the top1Δ/top1Δ strain containing the pTET-lys2-GTOP cassette in the OPPO orientation and identified 33 isolates with LOH events initiating near the G4 motif-containing reporter construct ( Table 4 – LOH Class E ) . Such LOH events can occur by ( 1 ) mitotic recombination with the other CHR3 in the heterozygous diploid cell via reciprocal crossover ( RCO ) or break-induced replication ( BIR ) with the homolog , ( 2 ) the partial loss of the chromosome arm , or ( 3 ) translocation to another chromosome with a short stretch of homology ( Figure 3A ) . In order to determine the types of rearrangements occurring at this locus , separation of the CHR3 homologs by PFGE was carried out for the 33 LOH Class E isolates ( Figure S7 ) . The CHR3 homologs of YPH45 and YJM789 differ in size by about 56 kb , likely reflecting different polymorphic subtelomeric gene content and retrotransposon insertions . In 32 out of 33 5-FOAR isolates analyzed , the YJM789-derived CHR3 was unchanged in size and the YPH45-derived CHR3 appeared slightly smaller than that of the parental haploid . A smaller CHR3 can be generated by RCO or BIR initiating on YPH45-derived CHR3 using YJM-789 derived CHR3 as the repair donor . The reduction in chromosome size was approximately 13 kb in 31/33 5-FOAR isolates ( Fig . S7 ) . In one of 33 isolates analyzed , YPH45-CHR3 was reduced in size by ∼24 kb . An ∼8 . 5 kb reduction was expected from the loss of hemizygous URA3 maker and the pTET-lys2-GTOP cassette with additional reduction in size resulting from the loss of other hemizygous sequences . Translocation to a heterologous chromosome was observed in 1/33 5-FOAR isolates . None of the analyzed isolates contained CHR3 shortened by ∼80 kb , which is predicted in case of the loss of left arm from the pTET-lys2-GTOP cassette to the telomere followed by a telomere addition at the break site ( Figure 3A , bottom panel ) . In our previous report regarding the rate of gene conversion events induced by the highly transcribed Sμ sequence , the pTET-lys2-GTOP cassette was integrated in the orientation and location identical to the “OPPO” construct described above for the LOH assay ( Fig . 6A ) . In this orientation , transcription from pTET promoter and replication originating at ARS306 are in the convergent or “head-on” orientation . In order to determine whether the rate of gene conversion is dependent on the relative orientation of transcription and replication , we deleted ARS306 by replacing the ARS consensus sequence ( 5′-WTTTAYRTTTW-3′ ) [50] with the gene encoding hygromycin B phosphotransferase ( Hph ) . In the resulting ars306Δ strain , replication through the pTET-lys2-GTOP cassette originates from the ARS305 located about 27 kb away and is in co-directional orientation relative to transcription [51] . As previously reported , in the gene conversion assay , recombination initiating at the pTET-lys2 cassette can be completed using a truncated lys2 gene fragment integrated on CHR15 resulting in lysine prototrophy ( Lys+ ) ( Fig . 6A ) [33] . In a top1Δ strain containing the pTET-lys2-GTOP cassette , reversing the replication orientation by deletion of ARS306 resulted in a three-fold decrease in the Lys+ rate indicating that replication-transcription conflict is a factor in elevated gene conversion initiating at co-transcriptionally formed G4 DNA ( Fig . 6B ) . The deletion of ARS306 did not significantly affect the gene conversion rates at pTET-lys2-GBTM in top1Δ background or at pTET-lys2-GTOP or –GBTM cassette in WT backgrounds .
G4 motifs have been implicated in various types of genome instability events . However , the large number of sequences predicted form G4 DNA structures have not all been validated as potential hotspots of genome rearrangements . We here focused on the level of transcription as a singularly important genomic context that distinguishes genetically unstable G4-forming sequence . At the Ig heavy chain locus , class-switch recombination ( CSR ) in activated B cells requires switch regions consisting of long repetitive sequences dense with guanine-runs [52] . In the pathogenic bacteria Neisseria gonorrhoeae , a G4 DNA-forming sequence was identified to be essential for the gene conversion occurring at the PilE locus , which facilitates evasion of host adaptive immune system by the production of variant pilin subunits [53] . Transcription of the guanine-run containing sequences is required to initiate recombination in both of these processes [54] , [55] . One possible role played by transcription is to provide the strand separation necessary for guanine-runs to fold into G4 structures . In order to determine the effect of G4 DNA on yeast genome stability , we designed our genetic assays to assess the role of transcription in biological processes . For effective formation of G4 DNA during transcription , we placed a model G4 motif from the mouse Ig switch Mu region into the highly transcribed pTET-LYS2 cassette . The guanine-runs were placed on non-transcribed strand ( -GTOP ) to promote co-interaction in single-strand context; as a negative control , genomic instability associated with the same G4 sequence was measured when transcribed in inverse orientation ( -GBTM ) where guanines on the TS interact with the nascent RNA and are not available for G4 formation ( Fig . 1B ) . In the gene conversion assay we used previously , only those recombination events initiating specifically at the reporter construct could be phenotypically selected [33] . In the current study , we designed two other genomic assays that allowed us to survey instability initiated over large genomic areas that includes our G4 motif-containing reporter construct . Loss of part of a chromosome arm initiating over a ∼20 kb region of CHR5 in a haploid background ( GCR assay ) or recombination initiating over a ∼100 kb region of CHR3 in a diploid background ( LOH assay ) yielded selectable , CanR 5-FOAR or 5-FOAR colonies , respectively ( Fig . 1A and 3A ) . By further analysis of the individual isolates , we were able to estimate the locations of GCR or mitotic recombination initiation . Importantly , this unbiased measurement of spontaneous genome rearrangements enabled us to define the conditions under which GCR and mitotic recombination resulting in LOH are specifically elevated at the highly transcribed G4 motif . Eukaryotic Top1 has multiple functions during DNA transactions [56] . Both negative and positive supercoils accumulated during transcription are removed by Top1 activity . Together with the type II topoisomerase Top2 , Top1 is recruited to the genomic regions undergoing replication [57] and has a role in relieving transcription-replication conflicts [40] . Top1 function can also adversely affect genome stability; its endo-ribonuclease activity generates unligatable single-strand breaks at ribonucleotides embedded in DNA , which leads to replication stress and accumulation of deletion mutations [58] , [59] . We have shown here that , at the co-transcriptionally generated G4 DNA , Top1 activity is required to suppress various types of genome instability . Top1 disruption greatly elevated overall GCR rates when the pTET-lys2-GTOP was present on CHR5 , with 90% of GCR breakpoints mapping to the G4 DNA-forming sequence ( Fig . 1C and Table 1 ) . This elevation was completely dependent on the level of transcription and the location of the guanine-runs on the NTS , reinforcing the conclusion that a potential G4 sequence motif is transformed by transcription into a genome instability hotspot . In the LOH assay , even though the overall mitotic recombination rate was not considerably elevated in the top1Δ/top1Δ background , a significantly higher proportion of the LOH tract breakpoints mapped to the pTET-lys2-GTOP but not to the pTET-lys2-GBTM cassette within the left arm of CHR3 ( Fig . 5 and Table 4 ) . In absence of Top1 , the rates of both gene conversion and LOH occurring at the G4-forming sequence were significantly higher when the transcription was in “head-on” or collisional orientation with replication fork movement than when it was in co-directional orientation ( Figure 5B and 6B ) . This suggests that Top1-dependent suppression of G4-associated genome instability involves its activity of resolving transcription-replication conflict in addition to its activity of resolving transcription-associated torsional stress . This also suggests that , in addition to the level of transcription , the relative orientation of replication is an important genomic context that can render certain G4 motifs genetically unstable . Accumulation of stalled replication forks at gene-rich regions have been observed in Top1-deficient cells indicating that one of the ways Top1 prevents genome instability is to prevent replication fork collapse due to collision with transcription [40] . Alternatively , the significantly higher LOH and gene conversion rates observed when the transcription and replication are in “head-on” orientation can be due to the intrinsic asymmetry in the replication process . In “head-on” or co-directional orientation , G-runs are present in the leading strand or lagging strand , respectively . It is possible that , upon encountering G4 DNA , the lagging strand synthesis is less prone to replication arrest since re-priming downstream will allow continued replication fork movement . It was previously reported that replication orientation did not have an effect when the G4-forming human subtelomeric minisatelite CEB1 was placed into the yeast genome [30] . The rates of GCR at this G4 forming sequence were elevated to similar degrees whether the G-runs were on the leading strand or lagging strand . In this experiment , CEB1 was not transcribed , supporting the argument that the orientation bias we observed with the highly transcribed Sμ is due to conflict between replication and transcription . In cultured mouse B cells , it was reported that class switch recombination ( CSR ) at Ig heavy chain locus was inhibited by camptothecin ( CPT ) treatment and significantly elevated by siRNA-mediated knock-down of Top1 [60] . It was suggested that G4 DNA formation is facilitated by reduced Top1 activity and that DNA ligation by Top1 , which requires the proper alignment of 3′ and 5′ ends of the breaks , is inhibited by interaction with DNA secondary structures resulting in Top1 cleavage complex and unresolved DNA breaks that initiate CSR . However , we demonstrated here that genome instability associated a G4 motif is stimulated by the complete absence of the Top1 protein ( top1Δ ) ( Fig . 1C , 5B and ref . [33] ) . DNA breaks initiating gene conversion , LOH or gross chromosomal rearrangements may originate from other sources such as G4-specific nucleases or from collapsed DNA replication forks . Multiple helicases including human FANCJ , PIF1 , BLM and yeast Sgs1 can unwind G4 structures in vitro [61] , [62] . In BLM-deficient cells , G4 motifs are frequently found near the transcription start sites of those genes with perturbed expression profile suggesting a role of BLM helicase in G4-mediated gene regulation [63] . Using GCR assays similar to that described above , two independent investigations into the effect of mutation of the 5′-to-3′ DNA helicase Pif1 on the rate of GCR initiated by G4-forming sequences reported dramatically disparate results [30] , [61] . Although greatly increased rates of FOAR/CanavanineR colonies were observed in both investigations , further analyses revealed that the 5-FOAR/CanavanineR colonies in yeast cells expressing mutant Pif1 arose mostly via rearrangements or partial loss of the chromosome in one case [30] but mainly via epigenetic silencing of the CAN1 and URA3 genes in another [61] . In case of the G4-associated GCR events occurring due to Top1-deficiency reported here , we demonstrated that the simultaneous resistance to 5-FOA and canavanine resulted from the loss of the region of the CHR5 containing CAN1 and URA3 genes ( Table 1 ) . In 60% of the GCR events involving the highly transcribed G4 motif , de novo telomere addition occurred within the guanine-run containing Sμ region ( Fig . 2 and Fig . S1 ) . Other types of events included 15 kb segmental deletions and complex genome rearrangements involving terminal deletions and segmental duplications ( Fig . 2B . Fig . S2 and Fig . S3 ) . When combined with co-transcriptionally formed G4 DNA , Top1 disruption significantly reshapes the genome not just through elevated non-crossover and allelic interhomolog recombination but also through gross deletions and duplications resulting in copy number variations . This result suggests that , whereas the function of Pif1 and BLM is possibly linked to the role of G4 DNA as an epigenetic and transcriptional regulator [17] , [61] , [63] , Top1 functions directly to prevent chromosomal rearrangements and gross loss of genetic information associated with the G4 DNA , particularly at highly transcribed areas . Activated transcription through G/C rich sequence can lead to formation of R-loops , which comprise of a long and stable hybrid between nascent RNA and template DNA strand [64] . R-loop accumulation and associated hyper-recombination can ensue when the mRNA packaging and export is disturbed in THO-TREX defective strains or when the degradation of RNA in RNA∶DNA hybrid is deficient due to absence of RNase H activity [65] . Accumulation of negative supercoils in Topoisomerase-deficient cells can also lead to R-loop accumulation [66] . Duquette et al reported that the combination of R-loop and G4 DNA , referred to as G-loop , is identifiable by electron microscopy when the Ig switch sequence is highly transcribed either in vitro or in bacteria [13] . At the pTET-lys2-GTOP cassette containing the Sμ sequence , therefore , the elevated genome instability could be the result of RNA∶DNA hybrid and/or G4 DNA . G-loop formation can be instigated by G4 DNA nucleation in the NTS , which lead to the stable annealing of the nascent RNA with the unpaired TS of DNA ( Fig . 7 ) . Alternatively , G-loop formation can initiate via the formation of RNA∶DNA hybrid , which leaves the NTS unpaired and free to fold into G4 structure . In this case , the higher stability of rG∶dC base pairing compared to rC∶dG could account for the greater instability we observed when the G-runs are on the NTS ( -GTOP ) [67] . During in vitro transcription , rG∶dC containing RNA∶DNA hybrid is critical for the formation of G-loop structure by Ig switch sequence [13] , and required for the transcription blockage by a guanine-run [19] . We tested whether G4-induced hyper-recombination is dependent on RNA∶DNA hybrid formation by overexpressing RNase H1 in top1Δ background . RNase H1 is an enzyme that degrades RNA hybridized to DNA and was shown to counteract the hyper-recombination phenotype associated with R-loops in THO/TREX mutant background [65] . As shown in Fig . 7B , RNase H1 overexpression did not reduce the elevated recombination at the highly expressed pTET-lys2-GTOP cassette , which suggests that RNA∶DNA hybrid is not required for the elevated recombination occurring at the G4 motif in absence of Top1 . We previously reported that , upon disruption of both RNase H1 and RNase H2 ( rnh1Δ rnh2Δ ) , the rates of gene conversion for the pTET-lys2-GTOP and –GBTM constructs were elevated by 28- and 8- fold , respectively [33] . RNase H1 overexpression led to significant decreases in the rates of gene conversion in rnh1Δ rnh2Δ backgrounds indicating that RNA∶DNA hybrid is responsible for the elevated recombination at both the pTET-lys2-GTOP and –GBTM constructs in rnh1Δ rnh2Δ mutant strains ( Fig . 7B ) . We postulate that RNA∶DNA hybrid and G4 DNA can each result in genome instability but that R-loop is not the primary cause of elevated recombination we observed for the pTET-lys2-GTOP upon disruption of Top1 . A function of Top1 other than the prevention of R-loop formation is relevant in suppressing genome instability at G4 DNA , which will require further investigation to identify . In summary , we report here the identification of Top1 as an important factor in suppressing genome rearrangements instigated by co-transcriptionally formed G4 DNA . In the absence of Top1 , LOH-inducing mitotic recombination as well as GCR is highly elevated but only when the guanine-run containing sequence is located on the NTS and is highly transcribed . The exquisitely specific effect of Top1 at G4 DNA is underscored by recent reports that , without co-transcriptionally formed G4 DNA , Top1-disruption had no significant effect on GCR or gene conversion rate and even suppressed the GCR or gene conversion occurring in cells with defects in the ribonucleotide excision repair ( RER ) pathway [68] , [69] . One possible explanation for its functional specificity is the high affinity binding of G4 DNA by Top1 demonstrated in in vitro experiments [70]–[72] . Top1 activity in suppressing G4-assoicated genome instability becomes even more important when transcription is in the collisional orientation with replication . This result suggests that , besides the transcription-conferred strand bias , the genomic location relative to a replication origin might determine which of the numerous G4 motifs so far identified in the eukaryotic genomes might be a hotspot of genome instability . The data presented in this report opens up the possibility that other factors suppressing G4-associated genome instability will be found among proteins with known physical and/or genetic interactions with Top1 .
Yeast strains used for the GCR assay and the gene conversion assay were derived from YPH45 ( MATa , ura3-52 ade2-101 trp101; [73] ) . For construction of the GCR assay , procedures for deletion of endogenous LYS2 on chromosome II and insertion of tetR′-SSN6 repressor-expressing cassette at LEU2 locus ( pCM244 from Euroscarf [74] ) were as described previously for the gene conversion assay [33] . A PCR-amplified LYS2 gene fragment was then integrated upstream ( centromere proximal ) of the CAN1 ORF on the left arm of chromosome V . The LYS2 promoter was replaced with a PCR-generated cassette from pCM225 ( Euroscarf ) containing the pTET promoter with 7 repeats of tetO and the tetR-VP16 activator coding sequence . The replacement of LYS2 with either the lys2-GTOP or -GBTM allele was carried out using the two-step allele replacement method . Finally , the loxP-flanked URA3Kl cassette was amplified from pUG72 ( Euroscarf; [75] ) to replace the HXT13 gene through one-step allele replacement . The construction of pTET-LYS2 and pTET-lys2-GTOP or -GBTM ( previously referred to as pTET-lys2-SμF and –SμR ) cassettes and the genomic integration on chromosome III in the YPH45 strain background were previously described [33] , [36] . For the loss of heterozygosity ( LOH ) assay , the YPH45-derived haploids were mated to an YJM789-derived haploid ( MATα , ura3 lys2; [48] ) . The plasmid pGAL1-RNH1 with the yeast RNH1 gene under the galactose-inducible GAL1 promoter was a gift from R . Crouch ( NCI; Bethesda , MD ) . For the GCR assay , 5 ml cultures in YEPD medium ( 1% yeast extract , 2% Bacto-peptone , 2% dextrose , and 250 µg/mL adenine hemisulfate , 2% agar for plates ) were inoculated with single colonies and grown for 3 days at 30°C . Cells were then plated either on YEPD or synthetic complete dextrose medium lacking arginine ( SCD-arg ) and containing canavanine ( 60 mg/L ) and 5-Fluoroorotic acid ( 5-FOA; 1 g/L ) . For the LOH assay , 1 ml YEPD cultures inoculated with single colonies were grown for 3 days at 30°C and plated on YEPD or SCD containing 5-FOA ( 1 g/L ) . For determination of gene conversion rates , growth and plating conditions were the same as previously described [33] . For RNH1 overexpression experiment , indicated yeast strains were transformed with pGAL1-RNH1 plasmid or pRS416 . Individual Ura+ transformants were used to inoculate 1 ml cultures in SCD-Ura media supplemented with 1% raffinose and 2% galacotse . After 4 days growth at 30°C , appropriate dilutions of the cultures were plated on YEPD or SCD-Ura-Lys . For each strain , 12 to 36 cultures were used to determine rates and 95% confidence intervals using the Lea-Coulson method of median [76] , [77] . Where indicated , rates were determined using the p0 method [78] . Characterization of GCR events using pulse field gel electrophoresis ( PFGE ) and microarray-based comparative genome hybridization ( array-CGH ) were carried out as previously described [79] . The microarrays used were Agilent custom 8x15k design ( AMID 028943 ) , with 14 , 965 unique 60 nt oligonucleotide probes , and a median genomic spacing of 774 bp . Detailed microarray probe composition and hybridization data are available upon request . The BglII-digested pAG25 plasmid [80] was integrated at a site proximal to the KanMX4 marker present in the class III clones . Genomic DNA from the clones containing the integrated plasmid was extracted and digested with EcoRV followed by re-ligation and rescue of re-circularized plasmids in E . coli . Restriction analyses of the rescued plasmids were consistent with inverted duplication structures . SacI restriction fragments containing the center of symmetry of the inverted duplicated regions were sub-cloned into a pUC18 plasmid vector and sequenced using primers positioned just outside the vector's multicloning site . The sequences of the inverted duplication breakpoints from clones A7 and A8 are shown in Fig . S4A and S4B . The secondary chromosomal rearrangements in class III had breakpoints at Ty/LTR sequences , and were consistent with the ectopic homologous recombination mechanism most often observed in yeast GCRs [81] . | Genome instability is not evenly distributed , but rather is highly elevated at certain genomic loci containing DNA sequences that can fold into non-canonical secondary structures . The four-stranded G-quadruplex or G4 DNA is one such DNA structure capable of instigating transcription and/or replication obstruction and subsequent genome instability . In this study , we used a reporter system to quantitatively measure the level of genome instability occurring at a G4 DNA motif integrated into the yeast genome . We showed that the disruption of Topoisomerase I function significantly elevated various types of genome instability at the highly transcribed G4 motif generating loss of heterozygosity and copy number alterations ( deletions and duplications ) , both of which are frequently observed in cancer genomes . | [
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] | 2014 | Topoisomerase I Plays a Critical Role in Suppressing Genome Instability at a Highly Transcribed G-Quadruplex-Forming Sequence |
Sequencing of the T cell receptor ( TCR ) repertoire is a powerful tool for deeper study of immune response , but the unique structure of this type of data makes its meaningful quantification challenging . We introduce a new method , the Gamma-GPD spliced threshold model , to address this difficulty . This biologically interpretable model captures the distribution of the TCR repertoire , demonstrates stability across varying sequencing depths , and permits comparative analysis across any number of sampled individuals . We apply our method to several datasets and obtain insights regarding the differentiating features in the T cell receptor repertoire among sampled individuals across conditions . We have implemented our method in the open-source R package powerTCR .
Recent advances in high-throughput sequencing of the T cell receptor ( TCR ) repertoire provide a new , detailed characterization of the immune system . T cells , each displaying a unique TCR , are capable of responding to presented antigens and initiating an adaptive immune response . An immune response is described by rapid proliferation of T cell clonotypes whose TCRs are specific to the antigen . In humans , it is estimated that the body is capable of producing more than 1018 different TCRs [1 , 2] , where high diversity of the TCR repertoire implies a greater range of pathogens that can be fought off . A variety of studies have been published demonstrating the value in characterizing this immune response for purposes such as describing tumor cell origin [3] and predicting response to cancer therapy and infection [4] . The applications of TCR sequencing are many , but this type of data presents new needs for analysis techniques not met by existing tools for other kinds of genomic experiments . Several groups have identified that the distribution of larger clone sizes in a sample can be approximated by a power law [5–8] , which means that the number of clones of a given size decays approximately as a power of the clone size . This heavy-tailed distribution comes as a consequence of extensively proliferated clones actively participating in an ongoing immune response . More recent work has aimed to quantify statistically the diversity of the TCR repertoire , initially through the use of various estimators borrowed from ecology , such as species richness , Shannon entropy [9] , and clonality . These estimators are known to be highly sensitive to sample size and missing observations . Given that the TCR repertoire is mostly populated by rare clonotypes , many of the clonotypes in the system are absent from any one sample . This presents a challenge to many of the ecological estimators . Model-based approaches to approximating the clone size distribution have also been proposed , with the goal of providing added stability and consequently more statistical power . Some examples are the Poisson-lognormal model [10] , Poisson mixture models [11 , 12] , and a heuristic ensemble method [13]; however , these models lack a biologically meaningful interpretation , and further do not sufficiently account for the power law-like nature of the data . That is , power law distributions are heavier-tailed than the Poisson or even the lognormal distribution , leading to systematic bias in the model fit . Previous research has also identified the imperfectness of the power law behavior for the clone size distribution below some clone size threshold [7 , 8] . To handle the imperfectness , [7 , 8] proposed to model large clones above the threshold using a type-I Pareto distribution , which is a member of the power-law distribution family , and omitted the clones with frequency below that threshold . The threshold is either user-specified or determined from the data based on a goodness-of-fit measure . Indeed , this model has certain biological basis . Through a stochastic differential equations setup that models the birth , death , selection , and antigen-recognition of cells active in the immune system , Desponds et al . [8] showed that the upper tail of the clone size distribution at equilibrium approximately follows a type-I Pareto distribution ( Fig 1A ) . Unlike the Poisson and lognormal models , parameters in this model are related to relevant actors in the immune response , and can reveal certain biological insights into immune response , such as average T cell lifetime [8] . Yet , the resulting model excludes all clones below a certain frequency threshold . However , even small clones may provide information; for example , Desponds et al . [8] indicated that the generation of new T cells affects the landscape of smaller clone sizes . Other studies have shown that low-frequency clones may support a diverse immune system and present a potential to mobilize against antigens , as in some cases having a clone size distribution highly dominated by a few clones has been correlated with unfavorable clinical outcome [14 , 15] . With this in mind , we sought a means to exhaust all available data and consider modeling the complete clone size distribution . To address this question , we propose a novel statistical tool , called powerTCR , to characterize the full distribution of the TCR repertoire . Our method models large clones that are above the threshold , where the power law begins , using the generalized Pareto distribution ( GPD ) , which contains the type-I Pareto distribution as a special case , but provides a more flexible fit . It also models the small clones below the threshold using a truncated Gamma distribution . It determines the threshold in a data-driven manner simultaneously with the characterization of clone size distribution . Our final model contains parameters that are analogous to those found in the type-I Pareto model of Desponds et al . [8] , relating our model to the biological interpretation of the dynamics of the immune system . Altogether , this allows our model to more accurately describe the shape of the clone size distribution for both large and small clones . Such a model is well suited for providing a global view of the state of the immune repertoire . It can also be employed to perform comparative analysis of healthy and compromised individuals to identify descriptors of strengths and deficiencies in the immune system .
Our goal is to model the clone size distribution of a sample immune repertoire . Fig 1A shows a typical distribution plotted using the repertoire of a Sarcoidosis patient in [16] . If the data are truly Pareto distributed , this plot would appear linear [17 , 18] . However , noting the linear behavior is only true for the far upper tail of the data , this suggests that these data are a departure from the Pareto distribution . This imperfect power law implicates the use of a heavy-tailed distribution above some threshold and a lighter-tailed distribution below that threshold . Here , we model the tail part with a GPD . The GPD , introduced by [19] , is a classical distribution typically used to model the values in the upper tail of a dataset . This formulation results in a distribution with density f ( x ) = 1 σ ( 1 + ξ x - u σ ) - ( 1 / ξ + 1 ) , ( 1 ) where u ∈ ( −∞ , ∞ ) is a threshold that typically needs to be prespecified , σ ∈ ( 0 , ∞ ) is a scale parameter , and ξ ∈ ( −∞ , ∞ ) is a shape parameter . The GPD has support x ≥ u when ξ ≥ 0 and u ≤ x ≤ u − σ/ξ when ξ < 0 . We model the bulk part with a Gamma distribution with the upper tail truncated at the threshold . The Gamma distribution has a flexible shape and can fit many different clone size distributions . The threshold and the parameters in the two distributions are estimated from the data simultaneously . This setup , where data above and below an unknown threshold are drawn from the “bulk” and “tail” distributions respectively , falls into a class of models called spliced threshold models . The typical motivation for the model is the belief that the data above and below the threshold are driven by different underlying processes . We refer the interested reader to [20] for a thorough review of the general spliced threshold model , and its applications in fields such as insurance , hydrology , and finance . Denote the proportion of data above the threshold u as ϕ . Let the bulk model distribution function be Hc ( x|θb ) and the tail model distribution function be Gc ( x|θt ) , where subscripts b and t denote the bulk and tail model parameter vectors , respectively . Then the distribution function of the model is given by F c ( x ) = { ( 1 - ϕ ) H c ( x | θ b ) H c ( u | θ b ) for x ≤ u 1 - ϕ + ϕ G c ( x | θ t , u ) for x > u ( 2 ) with corresponding density f c ( x ) = { ( 1 - ϕ ) h c ( x | θ b ) H c ( u | θ b ) for x ≤ u ϕ g c ( x | θ t , u ) for x > u . ( 3 ) Because the clone size distribution is count data that typically exhibit numerous ties in the less frequently observed clonotypes , it is appropriate to treat this as a discrete problem . We modify the model in order to account for any quantized or censored data . Let ψ and Ψ be the density and distribution function of a continuous distribution , and let d be the interval length at which the data are censored . We obtain a quantized analog of ψ by letting Pr ( X = x ) = Ψ ( x + d ) - Ψ ( x ) , x ∈ k · d , k ∈ Z . This results in a discrete model with distribution function F ( x ) = { ( 1 - ϕ ) H ( x | θ b ) H ( u - d | θ b ) for x ≤ u - d 1 - ϕ + ϕ G ( x | θ t , u ) for x ≥ u ( 4 ) and corresponding probability mass function f ( x ) = { ( 1 - ϕ ) h ( x | θ b ) H ( u - d | θ b ) for x ≤ u - d ϕ g ( x | θ t , u ) for x ≥ u . ( 5 ) where h ( x|θb ) ∼ discrete Gamma ( α , β ) , g ( x|θt ) ∼ discrete GPD ( u , σ , ξ ) , and d = 1 , which specifies that we model integer data ( see Methods for the functional form of the discrete Gamma distribution and the discrete GPD ) . This discretization step turns out to be important for accurate estimation in our scenario . See S2 Text for a comparison between the performance of the discrete and continuous models in settings resembling true clone size distributions . The relationship between the discrete Gamma-GPD spliced threshold model and the type-I Pareto model in Desponds et al . [8] , hereafter referred to as the Desponds et al . model , allows us to draw connections between some of our parameters and the dynamics of immune response underpinning their approach . First , results from [8] show that the threshold at which the power law begins is indicative of the point over which a clone’s large size can be attributed to active immune response , as opposed to noise in the body that arises from processes such as self-recognition . The threshold fitted from the data provides an objective way to narrow down which clonotypes from a sample repertoire should be interrogated further . This notion is convenient for studying factors such as CDR3 ( complementarity-determining region 3 ) amino acid motifs or specific V , D , and J genes important for combating certain antigens , which are typically determined based on a heuristic abundance cutoff . For example , [21] studies the 1 , 000 most abundant CDR3 amino acid motifs across all sampled peripheral blood mononuclear cell ( PBMC ) libraries , while [16] determines CDR3 amino acid motifs from clones that are present with 10 or more reads in a sampled repertoire . The threshold u estimated with our model , however , introduces a means to select motifs that does not rely on heuristics and automatically scales with sequencing depth . Moreover , the shape parameter ξ of the GPD is inversely related to the shape parameter αd used in the Desponds et al . model ( see Methods ) . As explained by Desponds et al . , a small αd , i . e . a large ξ , implies increased average T cell lifetime and antigenic noise strength . They further show that antigenic noise strength grows as a consequence of a higher initial concentration of antigens and a higher rate at which new antigens are introduced . Interestingly , ξ also positively correlates with the familiar clonality estimator ( 1-Pielou’s evenness [22] ) . Indeed , as ξ increases , the clone size distribution becomes heavier-tailed—that is , more skewed towards dominating clones . This trend is in line with that of the clonality estimator , which favors a more uniform clone size distribution as clonality approaches 0 and a distribution dominated by expanded clones as clonality approaches 1 . To numerically validate this relationship , we simulated the data from our model and computed the clonality ( see Methods ) . We observed a high correlation between clonality and ξ ( Spearman’s ρ ≈ 0 . 9 ) , confirming that ξ reflects the skewness towards dominating clones ( see S3 Text ) . It is worth noting that our model acquires a theoretical gain via the threshold stability property of the GPD [23] . That is , for any generalized Pareto distributed data , the shape parameter ξ remains constant regardless of changes in u . In our context , this means that at decreasing sequencing depths , though the threshold u would decrease due to fewer cells being sampled , the shape parameter ξ in principle would be stable against the variation in sequencing depth . We will demonstrate this gain in stability on a murine tumor dataset . See Methods for our extension of the threshold stability property to the case of the discrete GPD . In the following sections , we inspect four different datasets using our model . We compare our results to results from the Desponds et al . model to demonstrate the practical and theoretical benefits of our approach . We also make comparisons to results from the widely used richness , Shannon entropy , and clonality estimators . See Methods for information on computation of competing methods . The expression of major histocompatability complex II ( MHC-II ) proteins in tumors correlates with boosted anti-tumor immunity . As part of a study of how MHC-II expression impacts tumor progression and functional plasticity of T cells [24] , the CDR3 of TCRβ-chains of tumor infiltrating lymphocytes ( TILs ) were sequenced from breast cancer tumor tissue from six BALB/c mice [25] . Three of the mice were grafted with MHC-II expressing tumor cells and three control animals received parental MHC-II-negative cells . Samples were collected at 21 days after the date of treatment . Table A in S4 Text summarizes the number of unique clonotypes observed and the total number of reads in each sample . Sarcoidosis is an inflammatory disease that typically is accompanied by an accumulation of activated CD4+ T cells in the lungs . A particularly acute form of Sarcoidosis , called Löfgren’s syndrom ( LS ) , occurs with additional , more severe symptoms . A known signature of LS is the bombardment of the lungs with CD4+ T cells , which is expected to significantly alter the entire landscape of the TCR repertoire . We applied our method to TCR repertoire data of LS and non-LS Sarcoidosis patients [29] , originally described in [16] . In this study , bronchoscopy with the bronchoalveolar lavage was performed on a cohort of 9 LS and 4 non-LS individuals and prepared for TCR α− and β–chain sequencing . We compared the TCR distribution between LS and non-LS Sarcoidosis patients using our method and the competing methods . In order to visualize closeness of samples , we generated a distance matrix using JSD between fitted distributions using our method and the Desponds et al . model . The estimated parameters are in Tables E and F in S4 Text respectively . We then applied non-metric multidimensional scaling ( MDS ) to the distance matrix and plotted the first two coordinates . For the ecological estimators , we simply plotted centered and scaled estimates . As shown in Fig 4A , results from our model cluster LS patients into a tight group distinct from non-LS patients , bolstering the claim that LS patients exhibit a signature immune response . On the other hand , competing methods fail to uncover any pattern ( Fig 4B and 4C ) . We applied our method to data collected during a clinical trial of 13 glioblastoma patients receiving autologous tumor lysate-pulsed dendritic cell ( DC ) vaccine therapy [30] , first detailed in [31] . Three intradermal injections were administered to patients at biweekly intervals . TCRβ-chains from PBMC samples were sequenced for the patients prior to vaccinations and two weeks following the final injection . Patients were followed up with and their time to progression ( TTP ) and overall survival ( OS ) were recorded . TTP was defined as the time from the first DC vaccination until MRI-confirmed tumor progression . OS was calculated as the time from the first DC vaccination until the patient’s death from any cause . We investigated whether current tools using TCRs sequenced only from blood samples indicate anything about patients’ survival time and time to progression . We first fit our model to the pre- and post-treatment samples . In both cases , we classified the patients into two groups using the hierarchical clustering based on our model , the Desponds et al . model , and the richness , Shannon entropy , and clonality estimators . No clear grouping with respect to either TTP or OS could be observed from any clustering on the pre-treatment samples , whether by the model-based methods or the selected estimators ( see S7 Text ) . However , among post-treatment samples , our method tends to cluster together patients with better clinical outcome ( Fig 5A ) . This may indicate that the DC therapy alters the landscape of the TCR repertoire into a form that promotes favorable clinical outcome . We do , however , cluster one patient ( ID: 33296 ) with low TTP and OS in the group with overall higher TTP and OS . Interestingly , this misplaced patient had the lowest estimated TIL count and tumor/PBMC overlap of the entire cohort ( S4 Text , Table G ) . Tumor/PBMC overlap was defined as the total number of reads of shared CDR3s normalized by total reads in the tumor and PBMC samples . Similarly , patient 17232 displayed among the best clinical outcome but clustered with lower-performing patients . Patient 17232 had the highest TIL count and level of tumor/PBMC overlap in the whole cohort . This information taken as a whole suggests that , while the clone size distribution found in blood may indicate something about a patient’s response to treatment , it still does not guarantee that T cells will infiltrate the tumor , an important factor for clinical benefit [32] . S8 Text highlights the clone size distributions of these two patients against all others . Notably , inferred thresholds ( minimum u = 4 , maximum u = 6 ) on this dataset are much lower than on other datasets . This is likely because this dataset contains less deeply-sequenced samples than the others , which consequently reduces the threshold . Noting that clones with size at or above the estimated threshold are considered active participators in the immune response , we sought to investigate whether any relationship existed between clinical outcome and the proportion of more highly stimulated cells . We defined the proportion of highly stimulated cells to be the total number of reads at or above the threshold , normalized by the total number of reads in the entire repertoire ( S3 Text , Table I ) . We found correlations between this measure and both TTP ( Spearman’s ρ = 0 . 54 ) and OS ( Spearman’s ρ = 0 . 80 ) . Rank scatterplots for these correlations are in Fig 5B . The positive correlation we uncovered suggests that this statistic could be a useful tool to quantify the antigen-specificity of the sample . Risk factors for type 1 diabetes ( T1D ) are known to be heritable , yet genes alone are not sufficient explanation for drivers of the disease . Studies of monozygotic twins have revealed that , given one twin has T1D , the other will only have it at most half of the time [33] . The CD4+ T cell is viewed as the initiator of T1D as dysregulation of CD4+ antigen-recognition drives the autoimmune disease . Seeking out apparently non-heritable determinants of T1D , [34] conducted a deeper investigation of the CD4+ T cell . Briefly , the authors obtained PBMCs from 14 volunteer healthy donors ( HDs ) and 14 recently diagnosed patients with T1D . The cells were sorted using flow cytometry into distinct T cell subsets ( true naïve; TN , central memory , CM; regulatory , Treg; and stem cell-like memory , Tscm ) and TCRβ-chains were sequenced . The authors conducted a thorough analysis , finding shorter CDR3 sequence lengths and lower overall repertoire diversity among patients with T1D . However , on a per-individual basis , the authors were unable to uncover a relationship between repertoire diversity and disease status . Since the the spliced threshold model provides a new means to probe this complex data , we applied our approach to complement the original analyses .
We have developed a model , the discrete Gamma-GPD spliced threshold model , and demonstrated its utility on several datasets . As shown in our analyses , several biologically relevant descriptive features can be obtained from our model . One is the tail shape parameter ξ , a measure of the weight of the upper tail of the clone size distribution , where a heavier tail of the fitted model implies a more dominated distribution of expanded clones . Another is the proportion of total reads at or above the estimated threshold , a possible measure of intensity of the immune response . The third is the estimated threshold , which is a useful guide to objectively identify CDR3 motifs for downstream analysis . This could involve denoting motifs as only those CDR3s found in TCRs with frequencies at or above the estimated threshold for a given sample , or it could mean studying TCR gene usage among that same group of clonotypes . Though the dynamics driving our model form a compelling argument for this interpretation of the threshold , we acknowledge that further biological validation on more datasets is still needed to confirm this . Similar to other estimators , our model requires that a repertoire be adequately sampled . Without adequate sampling , the differentiating features between TCR repertoires will be masked [8] , and the estimated model parameters will not be reliable . Given the immense diversity of the TCR repertoire , one should in general be cautious about using any method to make inference about a sample TCR repertoire when few cells are sequenced . With sufficient samples , though , the spliced threshold model provides the user a meaningful high-level view of the TCR repertoire . The diversity of the TCR repertoire and its responsiveness to stimuli provide a high-dimensional biomarker for monitoring the immune system and its adaptivity . Robust assessment of the clone size distribution through TCR sequencing is important for understanding this diversity . The discrete Gamma-GPD spliced threshold model is a flexible model that effectively captures the shape of the clone size distribution . It is especially appropriate since the heavy-tailed GPD is a good fit to model the highly expanded clones that dominate many TCR repertoire samples . The method also provides a means to comparatively analyze a collection of TCR repertoire samples while maintaining convenient theoretical properties and interpretations . Compared with existing approaches , our method is more flexible , utilizes the full clone size distribution , is less sensitive to sequencing depth , and identifies the threshold in a data-driven manner . The parameters estimated from our method are biologically relevant and instructive to the dynamics of immune response . Our results on multiple datasets also show that the spliced threshold model is powerful in a range of scenarios for comparing TCR repertoires across samples , revealing potential trends in the landscapes of clone size distributions of affected immune systems .
We use maximum likelihood to estimate the parameters of our model . First , we more explicitly specify the form of our distribution . Letting x ∼ Gamma ( α , β ) , we write the probability mass function of a discrete Gamma distribution as h ( x ) = 1 Γ ( α ) [ γ ( α , β ( x + 1 ) ) - γ ( α , β x ) ] for α > 0 , β > 0 , x ∈ Z , and where γ ( α , βx ) is the lower incomplete gamma function γ ( α , β x ) = ∫ 0 β x t α - 1 e - t d t . If x ∼ GPD ( u , σ , ξ ) , we write the probability mass function of a discrete GPD as g ( x ) = ( 1 + ξ x - u σ ) - 1 / ξ - ( 1 + ξ x + 1 - u σ ) - 1 / ξ for u ∈ ( −∞ , ∞ ) , σ ∈ ( 0 , ∞ ) , and ξ ∈ ( −∞ , ∞ ) . The discrete GPD has support x ≥ u when ξ ≥ 0 and u ≤ x ≤ u − σ/ξ when ξ < 0 , where x ∈ Z . In all analyses presented here , we make no assumptions on the sign of ξ , although empirically we tend to observe ξ > 0 . To proceed , we employ a profile likelihood approach . Let u be the threshold , θb be the bulk parameter vector {α , β} , θt be the tail parameter vector {σ , ξ} , and θ be the parameter vector {θb , θt} . Let also h and H be the density and distribution function of a discrete Gamma distribution , respectively , and let g be the density of a discrete GPD . Then the complete data likelihood is given by L ( { θ , u } ∣ x ) = ∏ i = 1 n [ ( 1 - ϕ ) h ( x i ∣ θ b ) H ( u - 1 ∣ θ b ) 1 ( x i ≤ u - 1 ) + ϕ g ( x i ∣ θ t , u ) 1 ( x i ≥ u ) ] and the profile likelihood of the model at u is denoted as L p ( u ) = max θ L ( θ ∣ x , u ) . A grid search over a suitable range of thresholds u⋆ = ( u1 , … , uk ) may be implemented to maximize the profile likelihood . In this study , we adopted an approach similar to those of [19] and [40] , searching for thresholds at or above the 75% quantile of the sample . The estimated parameters are then u ^ = arg max u ⋆ ∈ u ⋆ L p ( u ⋆ ) , θ ^ = argmax θ L ( θ ∣ u = u ^ ) , and ϕ ^ = n u n , where n is the total number of clones and nu denotes the number of clones with size greater than or equal to the threshold . The Desponds et al . model was fit as previously described [8] . Briefly , the model has density f ( x ) = α d u α d x α d + 1 ( 6 ) and distribution function F ( x ) = 1 - ( u x ) α d ( 7 ) where u > 0 is the threshold and αd > 0 is a shape parameter . For each sample TCR repertoire , a grid of potential thresholds u⋆ = ( u1 , … , uk ) was constructed by considering every unique clone size in the repertoire . Then , for each ui , the shape parameter is estimated as α ^ d = n i [ ∑ j = 1 n i ln x j u i ]- 1 ( 8 ) where ni is the number of clones with size larger than the threshold ui . Once this value is computed for every threshold in u⋆ , the threshold and corresponding α ^ were chosen to minimize the Kolmogorov-Smirnov statistic . The ecological estimators [9 , 22] were computed as follows . For a sample X , let S ( X ) be the sample richness , defined as the number of unique clonotypes in X , and let pi be the number of cells of clonotype i normalized by the total number of cells in the sample . Then , the Shannon entropy of X is H ( X ) = - ∑ i = 1 S ( X ) p i ln p i ( 9 ) and the clonality of X is C ( X ) = 1 - H ( X ) ln S ( X ) . ( 10 ) The Desponds et al . model , which is a type-I Pareto distribution , and the “tail” part of our model , which is a GPD , are closely related . In fact , the GPD contains the type-I Pareto distribution as a special case . We can write the distribution function of y , where y ∼ GPD ( u , σ , ξ ) , as F ( y ) = 1 - ( 1 + ξ y - u σ ) - 1 / ξ . ( 11 ) Now , let x ∼ GPD ( u , u α d , 1 α d ) . Then F ( x ; u , u α d , 1 α d ) = 1 - [ 1 + 1 α d ( x - u u / α d ) ] - α d = 1 - [ 1 + ( x - u u ) ] - α d = 1 - ( u x ) α d which is exactly the distribution function of a type-I Pareto distribution with threshold u and shape αd ( Eq 7 ) . Of course , this exact relationship only holds when σ = u α d . Nevertheless , αd and ξ perform the same function in their respective distributions , adjusting the weight of the tail . This relationship always holds—a larger ξ ( smaller αd ) implies a heavier-tailed distribution , while a smaller ξ ( larger αd ) implies a lighter-tailed distribution . We conjecture that ξ , the shape parameter of the GPD , positively correlates with clonality . We numerically validated this claim using a simulated cohort of 48 clone size distributions . That is , we generated samples of n = 20 , 000 clonotypes , where our 48 parameter settings were derived from every combination of α ∈ {3 , 5 , 10} , ξ ∈ { . 25 , . 5 , . 75 , 1 . 1} , and ϕ ∈ {0 . 1 , 0 . 15 , 0 . 2 , 0 . 25} . We chose β = 0 . 15 , σ = α β , and u = ⌊Qα , β ( 1 − ϕ ) ⌋ in each simulation , where Qα , β is the quantile function of the Gamma distribution with mean α β . To adjust for the effect of sample size on clonality , we downsampled the simulated data so that each sample contained the same number of reads ( 415 , 989 total reads per sample ) . We computed the clonality of each simulated TCR repertoire on these adjusted datasets . The relationship between a pair of TCR repertoires can be elucidated by evaluating the distance between their fitted spliced threshold models . Several methods to compare densities are available . We propose measuring the distance between each pair of distributions using Jensen-Shannon distance ( JSD ) [41] . This metric is a symmetric and smoothed adaptation of the well-known Kullback-Leibler divergence that does not require the distributions under comparison to share the same support . Given discrete distributions P and Q , the JSD between P and Q is J S D ( P , Q ) = 1 2 [ ∑ i ( P i ln P i M i ) + ∑ i ( Q i ln Q i M i ) ] , ( 12 ) where M i = 1 2 ( P i + Q i ) . The resulting distances allow analysis and visualization via MDS or hierarchical clustering of the samples . Throughout our study , we use Ward’s method for hierarchical clustering . The threshold stability property of the GPD is well-established [23] . Here , we show that the property also holds for the discrete GPD . Let X ∼ discrete GPD ( u , σ , ξ ) and denote its distribution function as F with Fc as its continuous analog . Then we can write P ( X - u ≤ x + 1 | X ≥ u ) = P ( u ≤ X ≤ x + u + 1 ) P ( X ≥ u ) = F ( x + u + 1 ; u , σ , ξ ) - F ( u ; u , σ , ξ ) 1 - F ( u ; u , σ , ξ ) = F c ( x + u + 2 ; u , σ , ξ ) - F c ( u + 1 ; u , σ , ξ ) 1 - F c ( u + 1 ; u , σ , ξ ) = ( 1 + ξ σ ) - 1 / ξ - ( 1 + ξ x + 2 σ ) - 1 / ξ ( 1 + ξ σ ) - 1 / ξ = 1 - ( 1 + ξ x + 1 σ + ξ ) - 1 / ξ = F c ( x + 1 ; 0 , σ + ξ , ξ ) = F ( x ; 0 , σ + ξ , ξ ) . This states that if X ∼ discrete GPD ( u , σ , ξ ) , then X − u ∼ discrete GPD ( 0 , σ + ξ , ξ ) . Or , for our application , consider a clone size distribution , where clones larger than some threshold u are distributed according to the discrete GPD . At decreasing sequencing depths , this estimated u decreases , implying naturally that the size a clone in the sample must achieve to be considered “expanded” decreases . Still , while u shrinks , the threshold stability property states that ξ remains constant . | A more detailed understanding of the immune response can unlock critical information concerning diagnosis and treatment of disease . Here , in particular , we study T cells through T cell receptor sequencing , as T cells play a vital role in immune response . One important feature of T cell receptor sequencing data is the frequencies of each receptor in a given sample . These frequencies harbor global information about the landscape of the immune response . We introduce a flexible method that extracts this information by modeling the distribution of these frequencies , and show that it can be used to quantify differences in samples from individuals of different biological conditions . | [
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... | 2018 | powerTCR: A model-based approach to comparative analysis of the clone size distribution of the T cell receptor repertoire |
Experimental studies have revealed evidence of both parts-based and holistic representations of objects and faces in the primate visual system . However , it is still a mystery how such seemingly contradictory types of processing can coexist within a single system . Here , we propose a novel theory called mixture of sparse coding models , inspired by the formation of category-specific subregions in the inferotemporal ( IT ) cortex . We developed a hierarchical network that constructed a mixture of two sparse coding submodels on top of a simple Gabor analysis . The submodels were each trained with face or non-face object images , which resulted in separate representations of facial parts and object parts . Importantly , evoked neural activities were modeled by Bayesian inference , which had a top-down explaining-away effect that enabled recognition of an individual part to depend strongly on the category of the whole input . We show that this explaining-away effect was indeed crucial for the units in the face submodel to exhibit significant selectivity to face images over object images in a similar way to actual face-selective neurons in the macaque IT cortex . Furthermore , the model explained , qualitatively and quantitatively , several tuning properties to facial features found in the middle patch of face processing in IT as documented by Freiwald , Tsao , and Livingstone ( 2009 ) . These included , in particular , tuning to only a small number of facial features that were often related to geometrically large parts like face outline and hair , preference and anti-preference of extreme facial features ( e . g . , very large/small inter-eye distance ) , and reduction of the gain of feature tuning for partial face stimuli compared to whole face stimuli . Thus , we hypothesize that the coding principle of facial features in the middle patch of face processing in the macaque IT cortex may be closely related to mixture of sparse coding models .
The variety of objects that we see everyday is overwhelming and how our visual system deals with such complexity is a long-standing problem . Classical psychology has often debated on whether an object is represented as a combination of individual parts ( parts-based processing ) or as a whole ( holistic processing ) [1] . Experimental studies have revealed evidence of both types of processing in behaviors [1 , 2] and in neural activities in higher visual areas [2–5] , somewhat favoring holistic representation for faces and parts-based representation for non-face objects [1 , 2 , 5] . However , a theoretical question is: how could a single system reconcile such two seemingly contradictory types of processing ? Although a number of studies on computational vision models showed remarkable performance in visual recognition [6–10] , success in modeling higher visual areas [11 , 12] , or account for behavioral experiments on holistic face processing [12 , 13] , none of these studies offered insight into the tension between parts-based and holistic processing in a comparative manner with neurophyisology . In this study , we address this question in a novel theoretical framework , called mixture of sparse coding models . We assume two separate sparse coding models , one dedicated to encode face images and the other to encode non-face object images , that perform competitive interaction . Sparse coding is well known for its close relationship with representations in early visual areas [14–22]; we transfer this technique to the study of higher visual representations . That is , exploiting the fact that sparse coding to image data of a specific category can yield parts-based feature representations ( cf . [23 , 24] ) , we constructed two separate category-specific representations for faces and objects analogously to the formation of specialized subregions for faces and objects in the inferotemporal ( IT ) cortex [25 , 26] . Furthermore , we combined the two sparse coding models into a mixture model and modeled neural activities in terms of Bayesian inference . Then , we found that this framework gave rise to a form of holistic computation: not only recognition of the whole object depends on the individual parts , but also recognition of a part depends on the whole . This is in fact a Bayesian explaining-away effect: an input image is first independently interpreted by each sparse coding submodel , but then the one offering the better interpretation is adopted and the other is dismissed . For example , even if a part of an input image is a potential facial feature ( e . g . , a half-moon-like shape ) , that feature would not be recognized as an actual facial feature ( e . g . , a mouth ) if the whole image is a non-face object ( Fig 1B ) . We discovered that our model had a close relationship with computation known for a region of the macaque IT cortex called the face-selective middle patch , as documented by Freiwald et al . [4] . First , our model cells in the face submodel exhibited prominent selectivity to face images over non-face object images in a similar way to actual face-selective neurons , and this selectivity was crucially dependent on the above-mentioned explaining-away effect . Second , these model cells reproduced a number of tuning properties of face neurons in the middle patch . In particular , our model face cells tended to ( a ) be tuned to only a small number of facial features , often related to geometrically large parts such as face outline and hair , ( b ) prefer one extreme for a particular facial feature while anti-prefering the other extreme , and ( c ) reduce the gain of tuning when a partial face was presented compared to a whole face . We quantified these properties and compared these with the experimental data at the population level [4]; the result showed a good match . Thus , we propose the hypothesis that regions of the IT cortex representing objects or faces may employ a computational principle similar to mixture of sparse coding models .
To investigate the computational principles underlying face and object processing in the IT cortex , we designed a multi-layer network model illustrated in Fig 1A . The network had the architecture that received an image of 64 × 64 pixels , processed it with a fixed bank of standard energy detector models , and fed the results to two sparse coding models , called face submodel and object submodel ( each with 400 model neurons ) , which were then combined into a mixture model to perform competitive interaction as explained later . Each energy detector computed the squared norm of the outputs from two Gabor filters for the input image ( Fig 1A , inset ) . The two filters had the same center position , orientation , and spatial frequency , but had phases different by 90° . The entire bank of energy detectors had all combinations of 10 × 10 center positions ( in a grid layout ) , 8 orientations , and 3 frequencies; thus , the output of this stage had a total of 2400 dimensions ( see the section on Model details in Methods ) . In the actual visual cortex , inputs to IT areas are presumably computed between V1 and V4 and this computation must be much more complex than the energy detector bank in our model . However , some important aspects should still be reflected by this simple operation since a large number of V4 neurons are known to be orientation-selective [27]; moreover , this simple assumption was sufficient to reproduce certain response properties of face neurons as shown in what follows . In training the mixture model , we assumed , for simplicity , that the class label of each input image , either “face” or “object , ” was given ( Fig 1C ) . This allowed us to use a naive learning procedure that separately trained each face or object submodel with an existing sparse coding method . Specifically , we used publicly available face and object image datasets in which the faces or objects were properly aligned within each image frame [24 , 28 , 29] ( see the section on Data preprocessing in Methods ) . Then , for each image class k , which was either 1 ( face ) or 2 ( object ) , we learned the basis matrix Ak and the mean vector bk by sparse coding of the corresponding set of images that were processed by the energy model . ( The basis matrix and mean vector were used for determining the responses of the model neurons to an input as explained below . ) Classical mixture models are usually trained with an unsupervised learning method without class labels [30] . However , such learning is generally not easy and not our main interest here since we focus on inference , i . e . , on computation of evoked responses , not on learning or plasticity . ( We come back to this point in the Discussion section . ) To perform sparse coding learning , we adopted our previously developed approach based on independent component analysis ( ICA ) [22] , which is known to be a good approximation of sparse coding [31] and for which efficient algorithms exist . In this approach , an important step was to drastically reduce the input dimensions , from 2400 to 100 dimensions here , by principal component analysis ( PCA ) before performing ICA . This is , in fact , a simple modification of a standard preprocessing used in any classical sparse coding or ICA methods . However , we have previously discovered that such strong dimension reduction has an effect of spatial pooling [32] and thereby produces much larger basis patterns than without it [22] . In the present case , we later show that weaker dimension reduction resulted in representations of overly small features , which led to a loss of discriminative power . After this step , to regain enough components from the reduced dimensions , we used overcomplete ICA [33] , estimating 400 components from 100 dimensions . ( See the section on Learning details in Methods ) . Once the network was trained , the response properties of the model neurons were tested using various input images . In this phase , we never explicitly gave class information on each input image , but rather let the network estimate it by Bayesian inference , which worked in the following three steps ( Fig 1D ) . Step 1 is similar to inference in the classical sparse coding [31] , where the responses in each submodel are estimated so as to minimize the reconstruction error and maximize the sparsity at the same time . One difference is , however , that the sparsity constraint here is on the difference from the mean vector bk . We assume here a non-zero mean since the mean of face images is not zero and such stimulus usually elicits non-zero responses of actual face neurons , while the classical sparse coding assumes a zero mean since the mean of natural image patches is a blank , gray image , and such stimulus evokes no response of V1 neurons . The last two steps in our inference are a major departure from the classical sparse coding , where step 2 computes the posterior probability indicating how well each submodel interprets the input and step 3 multiplies the responses in each submodel by the corresponding posterior probability . Note that , because of the normalization of the probabilities in step 2 , the multiplication in step 3 results in a competitive behavior of the two submodels . Thus , even if the input contains a feature that can potentially activate some units in a submodel , such units may eventually be deactivated when the whole input was not interpreted well by this submodel compared to the other submodels ( Bayesian explaining-away effect ) . Finally , to compare with neural responses later , we passed the response value of each unit ( after step 3 ) to the smooth half-wave rectifying function h ( a ) = log ( 1 + exp ( a ) ) , which always produces non-negative values . Although we presented above the mixture model and its inference computation in an informal and procedural way , these can be formalized rigorously within a probabilistic generative model . Generally , the motivation for such formalization is to regard visual recognition as a process of inferring hidden causes in the external world that generate a natural image . Our model can be seen as one such approach: all the computations described above can be derived from Bayesian inference of posterior probabilities in a statistical framework of mixture of sparse coding models . The details can be found in the section on Theory of mixture of sparse coding models in Methods . We proceed to show the representation in our model obtained by the learning procedure described so far . The basis matrix Ak of each submodel defines its internal representation and each column vector of the matrix ( basis vector ) exposes the specific feature represented by each unit . Fig 2 shows the basis vectors of three example units in the face submodel . Each unit is visualized as a set of ellipses corresponding to the energy detectors , where their underlying Gabor filters have the indicated center positions ( in the visual field coordinates ) , orientations , and spatial frequencies ( inversely proportional to the size of the ellipse ) . The color of the ellipse indicates the weight value normalized by the maximal weight value . For readability , we show only the ellipses corresponding to the maximal positive ( excitatory ) weight and the minimal negative ( inhibitory ) weight at each location . Although this visualization approach may seem a bit too radical , it did not lose much information: we confirmed by visual inspection that the local weight patterns for most units had only one positive peak and one negative peak at each position and frequency and the patterns of orientation integration did not have notable changes across frequencies . In Fig 2 , we can see that unit #1 represented a face outline either on the left ( excitatory ) or on the right ( inhibitory ) ; unit #2 represented mainly eyes ( excitatory ) ; unit #3 mainly represented a mouth ( excitatory ) and nose ( weakly inhibitory ) . Fig 3 shows the basis vectors of 32 randomly selected units from ( A ) the face submodel and ( B ) the object submodel . The representations in these two submodels were qualitatively different: face units represented local facial features ( i . e . , facial parts like outline , eye , nose , and mouth ) and object units represented local object features . Next , we show a series of comparisons between the response properties of our model and the experiments conducted by Freiwald et al . [4] on the region in monkey IT cortex called the face middle patch . As mentioned above , due to the Bayesian explaining-away effect in the mixture model , model face units exhibited selectivity to face images and object units to object images . We measured the responses of our model units to natural face and object images that were separate from the training images ( without explicitly giving class labels ) . The left panel of Fig 4A shows the responses ( y ^ k in step 3 of Bayesian inference ) of the face units ( top ) and object units ( bottom ) to face images , where the images were sorted by the response magnitudes , separately for each unit . The right panel similarly shows the responses of the same units to object images . We can see that the face units were prominently responsive to many face images while indifferent to non-face object images; the object units had the opposite property . To quantify such face selectivity , we calculated the face-selectivity index for each unit , which was defined as the ratio between the difference and the sum of the mean response to faces and the mean response to objects ( where the baseline , i . e . , the response to a blank image , was subtracted from each response value ) . Fig 4D ( blue ) shows the distribution of face-selectivity indices for the face units . The result indicates almost no unit with index between −1/3 and 1/3 , which is consistent with the experimental data [4 , Figure 1b] . Such vivid selectivities disappeared when the mixture computation was removed . Fig 4B shows the analogous responses of the face and object units immediately after performing sparse coding ( y ^ k in step 1 ) ; the face units became almost equally responsive to object images to face images . Indeed , Fig 4D ( yellow ) shows that the face-selectivity indices of those units became substantially lower by the removal of mixture , with a majority falling between −1/3 and 1/3 . To gain more insight into the underlying computations , see the distributions of face posterior probabilities ( r1 in step 2 ) for face and object images in Fig 4C: faces and objects were clearly discriminated . In fact , those posterior probabilities modulated the response of each unit representing a part ( step 3 ) , which resulted in prominent face selectivity . ( Note that the discrimination capability did not automatically arise from step 3 since it actually depended on proper training of both submodels; see the section on “Control simulations . ” ) Further , Fig 4E shows that the images that elicited the largest responses of the face units were mostly faces in the mixture model ( blue ) , whereas it was not the case in the model without mixture ( yellow ) . Thus , even though the face units by themselves could detect accidental features similar to facial parts , the mixture computation ensured that they responded only when the whole input was a face image . In other words , face selectivity can be interpreted as a form of holistic processing in our mixture model . We next turn our attention to tuning properties to facial features . The experiment by Freiwald et al . [4] used cartoon face stimuli for which facial features were controlled by 19 feature parameters , each ranging from −5 to +5 . The authors recorded responses of a neuron in the face middle patch while presenting a number of cartoon face stimuli whose feature parameters were randomly varied . Then , for each feature parameter , they estimated a tuning curve by taking the average of the responses to the stimuli that had a particular value while varying other parameters ( “full variation” ) . We simulated the same experiment and analysis on our model ( see the section on Simulation details in Methods; see also S3 Fig for examples of cartoon face images . ) . To illustrate tuning to facial features in our model , Fig 5 shows the tuning curves of the face units in Fig 2 to all 19 feature parameters . Each unit was significantly tuned to one to nine feature parameters ( where significance was defined in terms of surrogate data; see Methods ) . Some tunings clearly reflected the corresponding parts in the basis representations . Unit#1 was tuned only to the face direction , preferring the left as opposed to the right . Unit#2 mainly showed tuning to eye-related features , in particular , preferring narrower inter-eye distances and larger irises . Unit#3 mainly showed tuning to mouth- and nose-related features , in particular , preferring smily mouths and longer noses . Even in the whole population , most units were significantly tuned to only a small number of features similarly to the experiment [4] . Fig 6A shows the distribution of the numbers of tuned features per unit , which were on average 3 . 6 and substantially smaller than 19 , the total number of features . The face neurons in the monkey face middle patch were also tuned to only a small number of features , i . e . , 2 . 6 on average [4 , Figure 3c] ( replotted in red boxes in Fig 6A ) . Fig 6B shows the distribution of the numbers of significantly tuned units per feature . The distribution strongly emphasizes geometrically large parts , i . e . , face aspect ratio , face direction , feature assembly height , and inter-eye distance . The shape of the distribution has a good match with the experimental result [4 , Figure 3d] ( replotted in Fig 6B ) , though iris size seems much more represented in the monkey case . A prominent property of the experimentally obtained tuning curves was preference or anti-preference of extreme facial features [4]; our model reproduced this property as well . For example , Fig 5 shows that many tuning curves were maximum or minimum at one of the extreme values ( −5 or +5 ) . For the entire population , Fig 7A shows all significant tuning curves of all face units , sorted by the peak feature values . To quantify this , Fig 7B shows the distributions of peak and trough feature values; the extremity preference index ( the ratio of the average number of peaks in the extreme values to the number of peaks in the non-extreme values ) was 9 . 1 and the extremity anti-preference index ( analogously defined for troughs ) was 12 . 0 . These indicate that the tendency of preference or anti-preference of extreme features generally held for the population . This result is in good agreement with the monkey experiment [4] , which also reported distributions of peak and trough values that were biased to the extreme values [4 , Fig . 4a] ( the extremity preference indices were 7 . 0 , 5 . 5 , and 7 . 1 , and the extremity anti-preference indices were 12 . 6 , 13 . 7 , and 12 . 1 for three monkeys; the average distribution is replotted in Fig 7B ) . In addition , the experimental study even observed monotonic tuning curves [4] , which were also found in our model as in Fig 5 . To quantify this for the population , Fig 7C shows the distribution of minimal values of the significant tuning curves preferring value +5 pooled together with the tuning curves preferring value −5 that have then been flipped; the distribution has a clear peak at value −5 . Further , for each minimal value in Fig 7C , the average of the tuning curves ( normalized by the maximum response ) with that minimal value is given in Fig 7D; the averaged tuning curve for minimal value −5 has a monotonic shape . These indicate that tuning curves preferring one extreme value tended to anti-prefer the other extreme value and be monotonic . This result is consistent with the experimental data , which also showed a distribution of minimal values that was peaked at −5 [4 , Fig . 4d] ( replotted in Fig 7C ) and a monotonic averaged tuning curve corresponding to minimal value −5 [4 , Fig . 4d , inset] . We discuss later why the model face units acquired such extremity preferences . We have explained above the face selectivity property as a form of holistic processing in the mixture model . On the other hand , the experimental study investigated holistic face processing in the IT cortex by using partial face stimuli and inverted face stimuli [4] . To gain insight into these experiments , we also conducted simulations of the same experiments in our model . To simulate the experiment with partial faces [4] , we estimated two kinds of tuning curves in addition to the one used so far ( “full variation” ) , namely , the responses to full cartoon faces where one feature was varied and the other were fixed to standard ones ( “single variation” ) and the responses to partial faces where only one feature was presented and varied ( “partial face” ) . ( See the section on Simulation details in Methods ) . Fig 8 compares tuning curves in ( A ) full variation vs . single variation , ( B ) full variation vs . partial face , and ( C ) single variation vs . partial face . Overall , the shapes of the tunings were similar for all three kinds ( average correlation 0 . 94 to 0 . 95 ) . However , the gain of each tuning function ( the slope of the fitted linear function ) tended to drop after the removal of most of facial features ( Fig 8C ) ; the average gain ratio was 2 . 0 , which was close to 2 . 2 , the experimentally reported number [4 , Fig . 6c] . This effect was not only because typical face units represented a combination of two features or more , but also because partial faces looked less face-like than full faces: Fig 8E shows lower face posterior probabilities for the partial face condition than the full variation condition . Indeed , such drop was weakened when the mixture computation was removed: the average gain ratio was 1 . 5 when the same comparison was made for the responses of model face units without the mixture computation , i . e . , using only step 1 in Bayesian inference ( Fig 8D ) . In addition to these , note that the tunings curves in full variation were slightly reduced compared to those in single variation ( Fig 8A and 8B ) ; a similar tendency can be observed in the experimental result [4 , Fig . 6c] . This reduction in the model was because the face images used in the single variation condition took standard feature values for most parameters and such face images looked more face-like than others ( giving slightly larger face posterior probabilities than the full variation condition; Fig 8E ) . To simulate the experiment with inverted faces [4] , we presented , to the model , the same set of full cartoon faces except for their vertical inversion and estimated tuning curves for each facial feature in the same way ( full variation ) . As a result , we found that the number of units that were tuned to each facial feature was more or less similar to the original model ( Fig 8F , left ) . However , the tuning curves for assembly height tended to be inverted , whereas those for most other features did not ( Fig 8F , right; for eye eccentricity , only two units had significant tunings and they happened to have a highly negative correlation between the upright and inverted cases ) . These results were consistent with the experiment [4 , Figure 7ad] . However , we also observed that the overall responses of the model face units to inverted faces were much lower compared to upright faces ( a somewhat similar tendency can be discernible in the experimental report [4 , Figure 7bc] ) . This was because the mixture model could not classify well the inverted faces as faces since the face submodel was trained only with upright face images; consequently , the face posterior probabilities were generally low for inverted faces ( Fig 8E , violet ) . Taken together , our result indicates that feature tuning for inverted faces could be explained by representation of individual parts of upright faces , although whole inverted faces may not be recognized as faces . Interaction between feature parameters was limited , though present . For each pair of feature parameters , a 2D tuning was estimated by averaging the responses to a pair of parameter values while varying the remaining parameters . Then , the 2D tuning for a pair of parameters was compared to another 2D tuning predicted by the sum of two ( full-variation ) 1D tunings for the same parameters or by the product of these . The distributions of correlation coefficients are given in Fig 9; the averages were both 0 . 90 , which was similar to the experimental result ( averages 0 . 88 and 0 . 89 ) [4 , Figure 5b] . How much do our results depend on the exact form of model ? To address this question , we modified the original model in various ways and conducted the same analysis ( Figs 10 , 11 and 12; S1 and S2 Figs ) . First , we already showed that , when we omitted the mixture computation and simply used a sparse coding model of face images , the model units were deprived of selectivities to faces vs . objects ( Fig 4 ) . However , tuning properties to facial features did not change much . Fig 10 shows that the distributions of the number of tuned features per unit , of the number of tuned units per feature , of the peak feature values , and of the trough feature values for the modified model ( cyan curves ) are all similar to the original model ( blue curves ) . Therefore , while the selectivities were from the mixture model , the tuning properties were produced by the sparse coding . Next , we varied the strength of dimension reduction of the outputs of the energy detector bank before performing sparse coding learning ( the original model reduced the dimensionality from 2400 to 100 ) . Three observations were made . First , consistently with our previous observation in our V2 model [22 , 32] , overall feature sizes tended to decrease while the reduced dimensionality was increased . Fig 12 shows example face and object units in the case of 300 reduced dimensions; compare these with Fig 3 . ( When we further increased the reduced dimensionality , we obtained quite a few units with globally shaped , somewhat noisy basis representations . These seemed to be a kind of “junk units” that are commonly produced when the amount of data is insufficient compared to the input dimensionality . ) Second , as the reduced dimensionality increased , face posterior probabilities ( as in Fig 4C ) were substantially decreased for face images ( Fig 11 ) ; the face images could barely be discriminated in the case of 300 reduced dimensions . Meanwhile , face posterior probabilities remained low for object images . This seemed to happen because the object submodel now learned to represent spatially very small and generic features so that it could give sufficiently good interpretations not only to object images but also to face images . This justified our model construction approach that performs strong dimension reduction before sparse coding learning . Third , Fig 10A and 10B shows that the number of tuned features per unit and the number of tuned units per feature decreased in the case of 300 reduced dimensions ( red curve ) . This was due to the weakened selectivity rather than the size decrease of feature representations since the effect disappeared when the mixture computation was omitted ( yellow curve ) . As an additional control simulation , we varied the number of units ( 200 or 800 ) in each submodel of the mixture model while keeping the other conditions . In either case , we observed no discernible difference in the results from the original model ( S1 Fig ) . We also examined a single sparse coding model ( with no mixture model ) trained with face and non-face images all together . In this model , we found almost no unit having face selectivity that was as vivid as in the original model; even for the units that gave average responses larger to faces than non-faces ( which were only less than 10% of the whole population ) , selectivity to face images was rather weak , with face-selective indices mostly less than 1/3 ( S2A Fig ) . However , such weakly face-selective units showed tuning properties similar to the original model ( S2B Fig ) . Taken together , the response properties of those units were comparable to the sparse coding model trained only with faces without mixture model ( Figs 4B and 10 , cyan curves ) .
In this study , we proposed a novel framework called mixture of sparse coding models and used this to investigate the computational principles underlying face and object processing in the IT cortex . In this model , two sparse feature representations , each specialized to faces or non-face objects , were built on top of an energy detector bank and combined into a mixture model ( Fig 1 ) . Evoked responses of units were modeled by a form of Bayesian inference , in which each sparse coding submodel attempts to interpret a given input by its code set , but the best interpretation explains away the input , dismissing the explanation offered by the other submodel . The model units in our face submodel not only exhibited significant selectivity to face images similarly to actual face neurons ( Fig 4 ) , but also reproduced qualitatively and quantitatively tuning properties of face neurons to facial features ( Figs 5 to 9 ) as reported for the face middle patch , a particular subregion in the macaque IT cortex [4] . Thus , computation in this cortical region might be somehow related to mixture of sparse coding models . While sparse coding produced parts-based representations in each submodel ( Figs 2 and 3 ) , the mixture model produced an explaining-away effect that led to holistic processing ( Fig 4E ) . This combination was key to simultaneous explanation of two important neural properties: tuning to a small number of facial features and face selectivity . That is , although the former property could be explained by sparse coding alone ( Fig 10 ) , the latter could not ( Fig 4B ) presumably since facial parts could accidentally be similar to object parts . However , when the sparse coding submodels for faces and objects were combined in the mixture model , the individual face units could be activated only if the whole input was interpreted as a face . In this sense , our theory interprets the face selectivity property as a signature of holistic processing . ( It should be noted that the face selectivity may not be considered an “emergent” property of the model in the same sense as the tuning properties , since some kind of enhanced selectivity might well be expected by the introduction of a mixture model . ) We also linked our model with more classical experiments on holistic processing by reproducing the tuning properties for partial or inverted faces ( Fig 8 ) . However , we could not prove the necessity of the mixture computation in these cases since the results without mixture were still consistent , albeit more weakly , with the experimental data . Having explained known response properties , we can draw a few testable predictions of unknown properties from our theory . First , since face selectivity depends on the computational progress of stimulus interpretation as a face or as an object , we can predict delayed suppression in responses of face-selective neurons to non-face stimuli . Second , since face selectivity depends on the failure of stimulus interpretation as an object , we can predict loss of selectivity of face-selective neurons after deactivation of the object-selective region by muscimol injection or cooling . Among the reported properties of face neurons in the monkey IT cortex , preferences to extreme features ( in particular , monotonic tuning curves ) were considered as a surprising property [4] since they were rather different from more typical bell-like shapes such as orientation and frequency tunings . We showed that our model explained quite well such extremity preferences ( Fig 7 ) . It is intriguing why our model face units had such property . First , we would like to point out that the facial features discussed here are mostly related to positions of facial parts and such features can be relatively easily encoded by a linear function of an image . This is not the case , however , for orientations and frequencies since encoding these seem to require a much more complicated nonlinear function , perhaps naturally leading to units with bell-like tunings . Second , we could speculate that the extremity preferences may be really necessary due to the statistical structure of natural face images , irrelevant to any particular details of our model . Indeed , even when we perform a very basic statistical analysis of principal components of face images ( so-called eigenfaces , e . g . , [34] ) , they look like linear representations of certain facial features , maximal in one extreme and minimal in the other extreme . However , this seems to be a rather deep question and fully answering it is beyond the scope of this study . The results shown here relied on all computational components in mixture of sparse coding models , including inference computation of each sparse coding submodel and suppressive operations using computed posterior probabilities . Since these computations seem to be difficult to implement only with simple feedforward processing in the biological visual system , a natural assumption would be some kind of recurrent computation possibly involving feedback processing . While quite a few biologically plausible implementations have been proposed for sparse coding inference , e . g . , [31 , 35] , we prefer here not to speculate how the mixture computation might be implemented , in particular , whether class information as in the top layer in our model might be represented explicitly in some cortical area or implicitly as some kind of mutual inhibition circuit between the face-selective and the object-selective regions in IT . Related to the previous point , it would also be interesting whether or not similar results could be reproduced by a deep ( feedforward ) neural network model [6–12] . Note that , although face-selective units , tuning properties to head orientation , or behavioral properties on holistic face processing ( such as the face inversion effect ) have been discovered in some models [11–13 , 36] , no tuning properties to facial features like here have been reported yet . We particularly wonder whether the face-selective units in such models represent facial parts , since such parts are sometimes impossible to recognize correctly without any surrounding context if the input image does not contain enough detail , e . g . , Fig 1B . While it is mathematically true that such nonlinear context-dependent computation could also be arbitrarily well approximated by a feedforward model , whether this can be achieved by a network optimized for image classification needs to be investigated empirically . In any case , however , we think that top-down feedback processing as formulated in our model would be a simpler and biologically more natural way of performing such computation . Since we trained each submodel of our mixture model separately by face or object images , our learning algorithm was supervised , implicitly using class labels ( “face” or “object” ) . This choice was primarily for simplification in the sense of avoiding the generally complicated problem of unsupervised learning of a mixture model . We do not claim by any means that face and object representations in the IT cortex should be learned exactly in this way . Nonetheless , the existence of such teaching signals may not be a totally unreasonable assumption in the actual neural system . In particular , since faces can be detected by a rather simple operation [37 , 38] , some kind of innate mechanism would easily be imaginable . This may also be related to the well-known fact that infant monkeys and humans can recognize faces immediately after eye opening [39 , 40] . Early work on sparse coding concentrated on explaining receptive field properties of V1 simple cells in terms of local statistics of natural images [14 , 15] , following Barlow’s efficient coding hypothesis [41 , 42] . The theory was subsequently extended to explain other properties of V1 complex cells [17–19] and V2 cells [20–22] . The present study continues this approach to investigate higher visual representations , though a novel finding here is that an additional mechanism , a mixture model , is necessary to explain the neural properties discussed here . On the other hand , in computer vision , sparse-coding-like models have also been used for feature representation learning . In particular , the classical study on ICA of face images [34] may be related to the construction of our face sparse coding submodel , although the previous study reported global facial features as the resulting basis set [34] . ( Because of this , it was once argued that parts-based representations require the non-negativity constraint [23] . However , it seems that such completely global ICA features may have been due to some kind of overlearning and , indeed , local feature representations were obtained when we used enough data as in Fig 3; we also confirmed this in the case with raw images . ) Another relevant formalism is mixture of ICA models [43] . Although the idea is somewhat similar to ours , their full rank assumption on the basis matrix and the lack of Gaussian noise ( reconstruction error ) terms make it inappropriate in our case because the strong dimension reduction was essential for ensuring the face selectivity ( Fig 11 ) . Our model presented here is not meant to explain all the properties of face neurons . Indeed , the properties explained here are a part of known properties of face neurons in the middle patch , which is in turn a part of the face network in the monkey IT cortex [25 , 44 , 45] . In the middle patch , face neurons are also tuned to contrast polarities between facial parts [46] . In more anterior patches , face neurons are tuned to viewing angles in a mirror-symmetric manner or invariant to viewing angles but selective to identities [47] . Further , all these neurons are invariant to shift and size transformation as usual for IT neurons [47] . Explaining any of these properties seems to require a substantial extension of our current model and is thus left for future research . Finally , since most detailed and reliable experimental data on the IT cortex concerns face processing , we hope that the principles , such as presented here , found in face processing could serve to elucidate principles of general visual object processing .
Our hierarchical model began with a bank of Gabor filters . The filters had all combinations of 10 × 10 center locations ( arranged in a square grid within 64 × 64 pixels ) , 8 orientations ( at 22 . 5° interval ) , 3 frequencies ( 0 . 25 , 0 . 17 , and 0 . 13 cycles/pixels ) , and 2 phases ( 0° and 90° ) . The Euclidean norm of each Gabor filter with frequency f was set to f 1 . 15 ( following 1/f spectrum of natural images ) and the Gaussian width and length were both set to 0 . 4/f . As a face image dataset , we used a version of Labeled Faces in Wild ( LFW ) [28] where face alignment was already performed using an algorithm called “deep funneling” [29] . By this alignment , faces had a more or less similar position , size , and ( upright ) posture across images . The dataset consisted of about 13 , 000 images in total . Each image was converted to gray scale , cropped to the central square region containing only the facial parts and hairs , and resized to 64 × 64 pixels . Since many images still contained some background , they were further passed to a disk-like filter , which retained the image region within 30 pixels from the center and gradually faded the region away from this circular area . Finally , the pixel values were standardized to zero mean and unit variance per image . As an object image dataset , we used Caltech101 [24] . We removed four image categories containing human and animal face images ( Faces , Faces_easy , Cougar_face , and Dalmetian ) . The objects within the images were already aligned . The dataset consisted of about 8 , 000 images in total . Like face images , each image was converted to gray scale , cropped to square , resized to 64 × 64 pixels , passed to the above mentioned disk-like filter , and standardized per image . For each class , we reserved 1 , 000 images for selectivity test and used the rest for model training . To train the mixture model , we first processed the images with the energy detectors and then subtracted , from each data x , the dimension along the mean x ¯ of all ( face and object ) data: x ← x - x ¯ x ¯ ⊺ x ∥ x ¯ ∥ 2 ( 5 ) Although this operation was not quite essential , this had the effect of a linear form of contrast normalization suppressing a part of inputs with prominently strong signals; in fact , we observed that , without this operation , some elements of mean vectors bk estimated as below became outrageously large . Then , for each submodel for image class k , we learned the basis matrix Ak and the mean vector bk in the following two steps: For overcomplete ICA , we used the score matching method for computational efficiency [33] . Formally , let dk be the vector of top 100 eigenvalues ( from PCA ) sorted in descending order , Ek be the matrix of the corresponding ( row ) eigenvectors , and Rk be the weight matrix estimated by the overcomplete ICA . Then , using the filter matrix defined as W k = R k diag ( d k ) - 1 / 2 E k , ( 6 ) the basis matrix can be calculated as Ak = ( Wk ) # ( # is the pseudo inverse ) and the mean vector as b k = W k x ¯ k ( where x ¯ k is the mean of all data of class k ) . Note that the signs of the filter vectors obtained from ICA are arbitrary; for the present purpose , we adjusted each sign so that all elements of bk became non-negative . A mixture of sparse coding models is similar to a classical mixture of Gaussians [30] in that it describes data coming from a fixed number of categories , but different in that each category is defined by a sparse coding model [14] . Formally , we assume an observed variable x : R D , a ( discrete ) hidden variable k: {1 , 2 , … , K} , and K hidden variables y h : R M ( h = 1 , 2 , … , K ) . Intuitively , x represents a ( processed ) input image , k represents the index of an image class ( submodel ) , and yh represents features ( responses ) for the class h . We define the generative process of these variables as follows ( see Fig 13 for the graphical diagram ) . First , an image class k is drawn from a pre-fixed prior πh : [0 , 1] ( where ∑h πh = 1 ) : P ( k ) = π k ( 7 ) We call k here the generating class . Next , features yk for the class k are drawn from the Laplace distribution with mean vector b k : R M and a pre-fixed standard deviation λ ( common for all dimensions ) P ( y k ∣ k ) = L ( y k ∣ b k , λ ) = ∏ m 1 2 λ exp ( - | y m k - b m k | λ ) ( 8 ) and an observed image x is generated from the features yk by transforming it by the basis matrix A k : R D × M , with a Gaussian noise of a pre-fixed variance σ2 added: P ( x ∣ y k , k ) = N ( x ∣ A k y k , σ 2 I ) ( 9 ) Here , Ak and bk are model parameters estimated from data ( see the section on Learning details above ) . Features yh for each non-generating class h ≠ k are drawn from the zero-mean Laplacian P ( y h ∣ k ) = L ( y h ∣ 0 , λ ) ( 10 ) and never used for generating x . Altogether , the model distribution is rewritten as follows: P ( x , y 1 , y 2 , … , y K , k ) = N ( x ∣ A k y k , σ 2 I ) L ( y k ∣ b k , λ ) [ ∏ h ≠ k L ( y h ∣ 0 , λ ) ] π k ( 11 ) Since data are generated from the mixture of K distributions each of which is a combination of a Laplacian and a Gaussian similar to the classical sparse coding model [31] , we call the above framework mixture of sparse coding models . However , we depart from standard formulation of mixture models or sparse coding in two ways , motivated for modeling face neurons . First , since the feature variable yh for the non-generating classes h ≠ k are unused for generating x , a standard formulation would simply drop the factor Eq ( 10 ) , leaving yh unconstrained . However , our goal here is to model the responses of all ( face or object ) neurons for all stimuli ( faces or objects ) . In fact , actual face neurons are normally strongly activated by face stimuli , but are deactivated by non-face stimuli , which is why our model uses a zero mean for non-generating feature variables . Second , the classical sparse coding uses a zero-mean prior [31] , which is suitable for natural image patch inputs since their mean is zero ( blank image ) and this evokes no response like V1 neurons . However , the mean of face images is not zero and such mean face image usually elicits non-zero responses of actual face neurons . Therefore our model uses a prior with potentially non-zero mean bk on the feature variable yk for the generating class . Given an input x , how do we infer the hidden variables yh ? Since evoked response values of neurons that are experimentally reported are usually the firing rates averaged over trials , we model these quantities as posterior expectations of the hidden variables . Since exact computation of those values would be too slow , we use the following approximation ( see the derivation in the section on Approximating posterior later ) . Note that , in eq ( 12 ) , the feature variables for non-selected classes are always exactly zero: y ^ h ( k ) = 0 for h ≠ k . ( 15 ) Therefore , even though an alternative approach would be to model neural responses by the MAP estimates of feature variable for the best image class , this may be too radical since responses becoming absolutely zero are a little unnatural . The Bayesian inference described in the section on Model can be derived from steps 1 to 3 above in a straightforward manner using the model definition Eq ( 11 ) and the property Eq ( 15 ) . Given an input x , we intend to compute the posterior expectations of each yh: E [ y k ∣ x ] = ∑ k ∫ ∫ ⋯ ∫ y k P ( y 1 , y 2 , … , y K , k ∣ x ) d y 1 d y 2 ⋯ d y K ( 16 ) Direct computation of this value is not easy . Note , however , that , from the definition of the model ( eq 11 ) , the posterior distribution has a single strong peak for each class k , with variances more or less similar across all classes . Therefore we approximate the posterior probability by P ( y 1 , y 2 , … , y K , k ∣ x ) ≈ δ ( y 1 = y ^ 1 ( k ) , y 2 = y ^ 2 ( k ) , … , y K = y ^ K ( k ) ) r k ( 17 ) where y ^ h ( k ) is the MAP estimate of yh when the selected image class is k ( eq 12 ) and rk is the relative peak posterior probability for the class k ( eq 13 ) . Here , δ ( ⋅ ) is the delta function that takes infinity for the specified input value and zero for other values . Substituting the approximation Eq ( 17 ) into eq ( 16 ) yields eq ( 14 ) . Cartoon face images were created by using the method described by Freiwald et al . [4] . Each face image was drawn as a linear combination of 7 facial parts ( outline , hair , eye pair , iris pair , eyebrows , nose , and mouth ) . The facial parts were controlled by 19 feature parameters: ( 1 ) face aspect ratio ( round to long ) , ( 2 ) face direction ( left to right ) , ( 3 ) feature assembly height ( up to down ) , ( 4 ) hair length ( short to long ) , ( 5 ) hair thickness ( thin to thick ) , ( 6 ) eyebrow slant ( angry to worried ) , ( 7 ) eyebrow width ( short to long ) , ( 8 ) eyebrow height ( up to down ) , ( 9 ) inter-eye distance ( narrow to wide ) , ( 10 ) eye eccentricity ( long to round ) , ( 11 ) eye size ( small to large ) , ( 12 ) iris size ( small to large ) , ( 13 ) gaze direction ( 11 x-y positions ) , ( 14 ) nose base ( narrow to wide ) , ( 15 ) nose altitude ( short to long ) , ( 16 ) mouth-nose distance ( short to long ) , ( 17 ) mouth size ( narrow to wide ) , ( 18 ) mouth top ( smily to frowny ) , and ( 19 ) mouth bottom ( closed to open ) . Note that the first three parameters globally affected the actual geometry of all the facial parts , while the rest locally determined only the relevant facial part . See S3 Fig for example images . Following the method in the same study [4] , we estimated three kinds of tuning curves: ( 1 ) full variation , ( 2 ) single variation , and ( 3 ) partial face . For full variation , a set of 5000 cartoon face images were generated while the 19 parameters were randomly varied . For each unit and each feature parameter , a tuning curve at each feature value was estimated as the average of the unit responses to the cartoon face images for which the feature parameter took that value . The tuning curve was then smoothed by a Gaussian kernel with unit variance . To determine the significance of each tuning curve , 5000 surrogate tuning curves were generated by destroying the correspondences between the stimuli and the responses . Then , a tuning curve was regarded significant if ( 1 ) its maximum was at least 25% greater than its minimum and ( 2 ) its heterogeneity exceeded 99 . 9% of those of the surrogates , where the heterogeneity of a tuning curve was defined as the negative entropy when the values in the curve were taken as relative probabilities . For single variation , a tuning curve for a feature parameter at each value was estimated as the response to a cartoon face image for which the feature parameter took that value and the other were fixed to standard values . The standard parameter values were obtained by a manual adjustment with the stimuli used in the experiment [4 , Suppl . Fig . 1] . For partial face , cartoon face images with only one facial part ( hair , outline , eyebrows , eyes , nose , mouth , or irises ) were created . Each tuning curve for each feature parameter was obtained similarly to single variation , except that only the relevant facial part was present in the stimulus . | Does the brain represent an object as a combination of parts or as a whole ? Past experiments have found both types of representation; but how can such opposing notions coexist in a single visual system ? Here , we introduce a novel theory called mixture of sparse coding models for investigating the possible computational principles underlying the primate visual object processing . We constructed a hierarchical network combining two sparse coding modules that each represented one feature set , of either facial parts or non-facial object parts . Competitive computation between the modules , formalized as Bayesian inference , enabled parts to be recognized with a strong top-down influence from the category of the whole input . We show that the latter computation is crucial to explain in detail neural selectivity and tuning properties that were experimentally reported for a particular face processing region called the middle patch . Thus , we offer the first theoretical account of neural face processing in relation to parts-based and holistic representations . | [
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"cogniti... | 2017 | A mixture of sparse coding models explaining properties of face neurons related to holistic and parts-based processing |
Nervous system function requires proper development of two functional and morphological domains of neurons , axons and dendrites . Although both these domains are equally important for signal transmission , our understanding of dendrite development remains relatively poor . Here , we show that in C . elegans the Wnt ligand , LIN-44 , and its Frizzled receptor , LIN-17 , regulate dendrite development of the PQR oxygen sensory neuron . In lin-44 and lin-17 mutants , PQR dendrites fail to form , display stunted growth , or are misrouted . Manipulation of temporal and spatial expression of LIN-44 , combined with cell-ablation experiments , indicates that this molecule is patterned during embryogenesis and acts as an attractive cue to define the site from which the dendrite emerges . Genetic interaction between lin-44 and lin-17 suggests that the LIN-44 signal is transmitted through the LIN-17 receptor , which acts cell autonomously in PQR . Furthermore , we provide evidence that LIN-17 interacts with another Wnt molecule , EGL-20 , and functions in parallel to MIG-1/Frizzled in this process . Taken together , our results reveal a crucial role for Wnt and Frizzled molecules in regulating dendrite development in vivo .
Correct dendrite development is essential for the establishment of neuronal connectivity and , in sensory neurons , for the detection of external stimuli . However , the complexity and variety in morphology of dendrites has made the study of their development more challenging than that of axons . Previous findings have shown that some axon guidance molecules can also regulate dendrite development , often with opposing effects . For example , the guidance cue Slit can simultaneously repel axons and enhance dendrite branching and outgrowth in cortical neurons [1] . Similarly , Semaphorin 3A , a guidance molecule that acts through the Neuropilin-1 receptor , functions as both a chemorepellent for cortical axons and a chemoattractant for dendrites within the same neurons [2] . The differential response of axons and dendrites to Semaphorin 3A is mediated by asymmetric localization of a soluble guanylate cyclase to the dendrites [2] . In cultured hippocampal neurons , local elevation of cAMP and reduction of cGMP in undifferentiated neurites promotes axon formation and suppresses dendrite formation , whereas the reciprocal levels of these molecules have the opposite effects [3] . Interestingly , local upregulation of cAMP in a single neurite results in long-range inhibition of cAMP levels in all other neurites , suggesting a mechanism for the development of one axon and multiple dendrites and indicating that dendrite formation in this context is secondary to axon formation [3] . More recently , in vivo studies have uncovered molecules that regulate dendrite development independently of the axon . Sensory neurons in the head of C . elegans develop by anchoring their dendritic tips to the nose while the cell body migrates away , extending a dendrite ( retrograde extension ) [4] . In the C . elegans tail motor neuron , DA9 , the extracellular guidance cue UNC-6/Netrin controls the final extension of the dendrite in an axon-independent manner through its interaction with the receptor UNC-40/DCC [5] . In a different highly branched mechanosensory neuron , PVD , the cell-autonomous activity of the EFF-1 fusogen promotes branch retraction to retain a precise patterning of arbors during dendrite development [6] . In a Drosophila sensory neuron ( vch'1 ) , correct orientation of the dendrite is regulated by Netrin-A and its receptor Frazzled and is mediated by a migrating cap cell , which drags the tip of the dendrite into position [7] . In all these cases , however , the cell-intrinsic molecules involved in the initial stages of dendrite formation remain elusive . Wnt morphogens and their Frizzled receptors are highly conserved molecules with diverse functions in nervous system development [8] , [9] . In rat and mouse hippocampal neurons , Wnt molecules promote dendritic arborization [10] , [11] , whereas in Drosophila neuronal activity regulates the remodeling of dendritic branches in a Wnt-dependent manner [12] . In C . elegans there are five Wnt ligands , ( LIN-44 , EGL-20 , CWN-1 , CWN-2 , and MOM-2 ) and four Frizzled receptors ( LIN-17 , MIG-1 , CFZ-2 , and MOM-5 ) . The posteriorly expressed Wnt ligand , LIN-44 , regulates neuronal polarity , axon guidance , axon termination , and synapse formation , acting mainly as a repellent through the LIN-17/Frizzled receptor on neurons in the posterior of the animal [13]–[17] . Another posteriorly expressed Wnt ligand , EGL-20 , controls cell migration and axon guidance of different cells along the anterior-posterior axis of the worm [18]–[20] . CWN-1 and CWN-2 , which are expressed more broadly along the anterior-posterior axis , affect neurons in the mid-body and the head of C . elegans , regulating neuron migration , axon guidance , nerve-ring placement , as well as the outgrowth and pruning of neurites [21]–[25] . In this study , we show that LIN-44/Wnt initiates and guides the development of the dendrite in the PQR oxygen sensory neuron , through a mechanism that occurs prior to and independently of the formation of the axon . In contrast to its role as a repellent in synapse formation and axon termination , in the context of PQR development LIN-44 acts as an attractant that is specific for the outgrowth of the dendrite . The effect of LIN-44 is mediated through the LIN-17 receptor , which functions in a cell-autonomous manner . We also identify EGL-20/Wnt and MIG-1/Frizzled as crucial molecules in PQR dendrite development . Taken together , these findings show for the first time that Wnt signals and Frizzled receptors can promote dendrite-specific outgrowth in developing neurons in vivo .
PQR is an oxygen-sensory neuron with its cell body positioned in the posterior lumbar ganglion on the left side of the animal [26] . PQR extends a single axon anteriorly along the ventral nerve cord and a single dendrite posteriorly towards the tail ( Figures 1D , 2A ) . The tip of the dendrite , which is part of the left phasmid sensory organ , protrudes with its sensory cilia into the pseudocoelom . PQR is born post-embryonically , facilitating investigation of its development in newly hatched larvae . A gcy-36::GFP reporter was used as a selective marker for PQR , allowing visualization of its dendrite during development , starting at the L1 stage ( see Materials and Methods ) . PQR arises as a descendant of the QL neuroblast , and subsequently migrates towards the tail . We observed that upon reaching its final destination , at 5 . 5–6 . 5 h after hatching , PQR assumed a rounded or elliptical shape , without any neurites ( Figure 1A ) . At 6 . 5–7 h , dendrite formation began with lamellipodia-like extensions emerging on the dorsal-posterior region of the cell body , which had become elliptical or triangular in shape ( Figure 1B ) . At this stage , no other projections were present , indicating that dendrite outgrowth is initiated before outgrowth of the axon . At 7–7 . 5 h , the dorsal-posterior protrusion thinned and extended into a developing dendrite with a growth cone at its distal tip , and the cell body became rounder in shape ( Figure 1C ) . At the same time , the axon began to emerge from the ventral-anterior side of the cell , appearing as a small neurite that , unlike the dendrite , did not present a large growth cone at its tip . By 7 . 5 h , both the dendrite and axon were visible and continued to extend to their final positions until 18 h after hatching ( L2/L3 ) ( Figure 1D ) . PQR subsequently maintained its morphology throughout adulthood ( Figure 2A ) . Overall , our analysis demonstrates that the PQR dendrite forms by growth cone crawling and is initiated prior to axon outgrowth . We next used a candidate gene approach to discover the molecules regulating dendrite development in PQR . We found that animals mutant for LIN-44/Wnt presented severe defects , with PQR dendrites that were short , absent , or misrouted in the anterior direction ( Figure 2B–D , and quantified in 2E ) . The axon , however , appeared morphologically normal . These defects could arise from a dendrite-specific effect or a change in neuronal polarity whereby the identity of the neurites is compromised . To distinguish between these two possibilities we investigated whether there were any changes in the location of the presynaptic sites of PQR , which are normally on the axon . rab-3 encodes for a vesicle-associated Ras GTPase , which localizes to presynaptic densities [27] , [28] . Using a YFP::RAB-3 fusion protein expressed specifically in PQR ( Pgcy-36::YFP::RAB-3 ) , we found that the presynaptic sites in lin-44 mutants were largely located on the axon as in wild-type animals ( Figure 2F ) . This suggests that the identity of the neurites is unchanged and that the PQR defect of the lin-44 mutant is dendrite-specific . Next , we tested if the PQR dendrite defect of lin-44 mutant animals could arise from an abnormal cell division in the precursor cell . However , we found that the asymmetric cell divisions of the PQR precursor occurred normally in the lin-44 mutant animals ( Figure S1 ) , precluding this possibility . Finally , we investigated whether the absent and short dendrite phenotypes we observed were generated either by excessive pruning or by direct outgrowth failure . Examination of early stages of PQR development in lin-44 mutants revealed that the dendrite often failed to form or fully extend ( Table S1 ) ; we also observed animals with dendritic growth cones developing abnormally on the anterior side of the neuron , which would explain the anteriorly misrouted dendrites observed in adults ( Table S1 ) . Thus , our results indicate that LIN-44 acts at very early stages of PQR development by regulating proper formation of the growth cone and its extension . The Wnt ligand LIN-44 is expressed in close proximity to the PQR neuron from four hypodermal cells ( hyp-8 , -9 , -10 , and -11 ) in the tip of the tail [29] , a position posterior to the PQR dendrite ( Figure 3A ) . As the PQR dendrite grows towards the source of LIN-44 , we hypothesized that this molecule might act instructively as an attractive cue for the developing dendrite . Alternatively , LIN-44 may act as a permissive cue , whereby its positional information is not essential for correct dendrite development . To distinguish between these two possibilities , we expressed LIN-44 ectopically from regions anterior to the PQR cell body in lin-44 mutant animals , using a version of LIN-44 genomic DNA that had been engineered to contain a secretion signal sequence to ensure proper secretion from cells that do not normally produce LIN-44 [16] . Transgenic lines were generated to express LIN-44 from the myo-2 promoter [30] in the pharynx ( Pmyo-2::LIN-44 ) , or from a short fragment of the cwn-1 promoter [21] in the intestine and head neurons ( Pcwn-1::LIN-44 ) ( Figure 3A and Figures S2 , S3 ) . When compared to lin-44 mutant animals , transgenic animals expressing LIN-44 anterior to PQR displayed a decrease in the proportion of normal dendrites and an increase in the proportion of dendrites that were misrouted in the anterior direction , towards the ectopic source of LIN-44 ( Figure 3B and Figures S2 , S3 ) . On the contrary , expression of LIN-44 from its endogenous promoter ( Plin-44::LIN-44 ) provided strong rescue of the PQR dendrite defect of lin-44 mutant animals ( Figure 3B ) . We next examined the ectopic expression of LIN-44 from the myo-2 promoter in the wild-type background and found that it altered the normal development of the PQR dendrite ( Figure S4 ) . Thus , the worsening of dendrite defects observed when LIN-44 is ectopically expressed from anterior regions suggests that LIN-44 has an instructive role in PQR dendrite development , whereby it acts as an attractive cue to direct the outgrowth of the dendrite . In wild-type C . elegans , the four tail hypodermal cells hyp-8 , -9 , -10 , and -11 express LIN-44 throughout embryogenesis and larval stages [29] . In order to define the time period in which LIN-44 is required for normal PQR dendrite development we eliminated larval production of LIN-44 by laser ablation of the hyp-8 , -9 , -10 , and -11 hypodermal cells . Remarkably , in adult animals that were laser-ablated as newly hatched L1 larvae , the PQR dendrite appeared to be largely unaffected ( Figure 4A ) even though the ablations were performed several hours before PQR is born in the mid-L1 stage . This result indicates that LIN-44 expression from these hypodermal cells during embryogenesis is sufficient for the correct development of the PQR dendrite . To further define the temporal requirement of LIN-44 we next utilized an inducible heat shock promoter to express LIN-44 ( Phsp16-2::LIN-44 ) in a lin-44 mutant background at specific times during development . Heat shock-induced LIN-44 expression in newly hatched L1 animals partially rescued PQR dendrite defects ( Figure 4B and Figure S5 ) . However , when animals were heat shocked later , at the time of dendrite outgrowth , no such rescue effect was observed ( Figure 4B ) , suggesting that LIN-44 expression is required prior to PQR dendrite outgrowth . The hsp16-2 promoter drives expression broadly throughout the body of the animal , in cells that are both anterior and posterior to PQR [31] . Thus , the dendrite rescue we observed in heat shocked animals could indicate that LIN-44 plays a permissive role , or that the ligand is produced more efficiently from regions posterior to PQR . To further investigate this we expressed Phsp16-2::LIN-44 into a wild-type background and found that the ectopic expression of LIN-44 generated PQR defects similar to those of lin-44 mutants , confirming the instructive role of this molecule ( Figure S6 ) . Taken together , these results suggest that a molecular pattern of LIN-44 generated prior to PQR formation , during embryonic development and early L1 , is both necessary and sufficient to instruct PQR dendrite outgrowth hours later , at which time the source of LIN-44 expression becomes dispensable . LIN-17 is a Frizzled molecule known to function as a receptor for LIN-44 in a variety of developmental processes [14] , [16] , [17] , [29] , [32]–[35] . We found that lin-17 mutants had defects resembling those of lin-44 , with PQR dendrites that were short , absent , and misrouted anteriorly ( Figure 5A ) . lin-17 mutants also presented a strong migration defect [18] , [19] , with a high percentage ( 60% to 90% ) of PQR neurons mispositioned in anterior regions of the body . Thus , our analysis was performed on those animals in which PQR was correctly positioned in order to eliminate any possible effect that the aberrant location may have had on PQR dendrite development . Importantly , lin-17 mutants , like lin-44 mutants , appeared to have largely normal localization of presynapses to the axon , as visualized using the YFP::RAB-3 fusion protein expressed specifically in PQR ( Pgcy-36::YFP::RAB-3 ) , eliminating the possibility of a switch in neurite identity ( Figure 2F ) . In addition to testing known alleles of lin-17 , we also performed a forward genetic screen and isolated a previously uncharacterized allele , vd002 , consisting of a G to A transition in position 490 of the lin-17 gene that resulted in a cysteine residue being replaced by a tyrosine residue ( Figure 5A ) . The isolation of this mutant from an unbiased screen further supports the significance of lin-17 in this process . To investigate whether there might be a genetic interaction between lin-17 and lin-44 with respect to PQR dendrite development , we next examined lin-17 lin-44 double mutants and found that the dendrite defects were qualitatively and quantitatively similar to those of lin-17 mutants ( Figure 6A ) . This indicates that these two molecules function in the same genetic pathway with respect to PQR dendrite development and strongly suggests that LIN-44 acts as a ligand for LIN-17 in this process . LIN-17 is expressed extensively and dynamically in several cells of the tail region including PQR ( Figure S7 ) [35] . Wnt signaling through the LIN-17 receptor could occur cell-autonomously within PQR or could result from interactions with the surrounding cells . We first tested whether LIN-17 acts cell-autonomously by expressing the wild-type lin-17 cDNA from the gcy-36 promoter , which is transcriptionally active in PQR during the final stages of its migration . This transgene failed to rescue the dendrite defects , despite being tested at a range of different concentrations ( see Materials and Methods ) . We therefore questioned whether LIN-17 might be required in PQR at earlier stages , before the gcy-36 promoter is transcriptionally active . To test this possibility we used the egl-17 promoter that is highly and selectively expressed in the precursors of PQR during the L1 stage [36] , [37] to drive LIN-17 expression from the time PQR was born . Wild-type LIN-17 cDNA expressed by the egl-17 promoter ( Pegl-17::LIN-17::YFP ) strongly rescued the PQR dendrite defects of lin-17 mutants , to levels similar to that of the endogenous promoter ( Plin-17::LIN-17::YFP ) ( Figure 5B ) . These results suggest that LIN-17 regulates dendrite development in a cell-autonomous fashion and is required very early in development , before or during PQR migration . The PQR dendrite is ensheathed by PHso2L , a glia cell of the left phasmid sensillum; this sensillum comprises two socket cells ( PHso1L , PHso2L ) , a sheath cell ( PHshL ) , and two sensory neurons ( PHAL and PHBL ) [26] . Recent results in different systems have demonstrated a role of the support cells in regulating dendrite development [4] , [7] . To determine if similar mechanisms were in place for PQR development , we next performed cell-ablation experiments whereby we selectively eliminated the socket cells or the socket cells together with the sheath cells . PQR morphology in ablated animals was largely normal , with only a small number of animals presenting short dendrites when left and right phasmid socket cells were ablated ( 3/15 ) or when left phasmid socket and left sheath cells were ablated ( 2/19 ) . We never observed the penetrance and variety of defects of the lin-17 mutants . These results indicate that glial cells play a minor role in only the final stages of dendrite extension and suggest that LIN-17 does not have an effect on the PQR dendrite through these support cells ( Table S2 ) . In addition , ablations of the phasmid neurons PHA and PHB also had no effect on PQR dendrite development ( Table S2 ) , thereby providing further evidence that the function of LIN-17 in PQR dendrite development is unlikely to be mediated by the surrounding cells . To further understand how LIN-17 acts on the PQR dendrite , we then asked at what stage in PQR development LIN-17 was visible on the cell membrane and how LIN-17 was distributed in PQR . Using a LIN-17::YFP functional fusion protein expressed under the control of the egl-17 promoter , we observed faint , relatively uniform localization of LIN-17 on the membrane of the QL . a cell as it was dividing into QL . aa and PQR ( unpublished data ) . Following this division , the membrane-localized LIN-17::YFP in PQR decreased until it was barely visible at the time at which PQR had completed its posterior migration ( unpublished data ) . This reduction in LIN-17::YFP appeared to be independent of down-regulation by the egl-17 promoter and is consistent with our previous results suggesting an early role for LIN-17 in regulating PQR dendrite outgrowth . We suggest that ubiquitous membrane-localization of LIN-17 may be required to detect the posterior source of Wnt ligand , which acts as the directional signal for the PQR dendrite . Multiple Wnt ligands and Frizzled receptors are known to function in basic developmental processes in C . elegans and have frequently been shown to have redundant or synergistic roles . Although lin-44 mutants present striking PQR dendrite defects , 32% of these animals still have the ability to sprout a normal PQR dendrite , suggesting the involvement of other molecules in this process . We therefore tested three other Wnt molecules–EGL-20 , CWN-1 , and CWN-2–for possible roles in PQR dendrite formation . EGL-20 is expressed around the PQR cell body , in a group of epidermal and muscle cells near the anus [13] , [20] , and CWN-1 and CWN-2 are expressed to a greater extent anteriorly in the intestine , body wall muscle , and neurons in the midbody and head regions , anterior to the PQR cell body [13] , [22] , [38] . No significant dendrite defects were observed in cwn-1 or cwn-2 single mutants . The cwn-1 cwn-2 double mutant presented a higher percentage of ectopic processes from the cell body , and dendrite branching , compared to the single mutants , but no absent-dendrite or dendrite-misrouting defects were observed ( Table S3 ) . This suggests that these molecules are less directly involved in development of the PQR dendrite , but are important to prevent the formation of ectopic processes . Although the loss of cwn-1 alone caused no significant dendrite defects on PQR , when combined with the lin-44 mutation it was able to enhance the dendrite misrouting defects of lin-44 mutants ( Table S3 ) . Thus , CWN-1 might have a minor and redundant role in PQR dendrite development . As previously described , egl-20 mutants have a very strong Q cell migration defect [18]–[20] resulting in 97%–98% of animals having anteriorly positioned PQR neurons . Restricting our analysis to those animals with PQR correctly positioned , we found that only 7% of egl-20 animals developed a normal , full-length dendrite , whereas the rest presented qualitatively similar defects to those of lin-44 and lin-17 animals , with absent , short , and anteriorly misrouted PQR dendrites ( Figure 6B ) . egl-20 mutants presented a higher proportion of anterior dendrites , as compared to lin-44 mutant animals ( Figure 6B ) , but the PQR dendrite phenotype of the egl-20 lin-44 double mutant did not display a significant worsening of defects when compared to the egl-20 single mutant . This suggests that egl-20 and lin-44 may interact to regulate PQR dendrite formation ( Figure 6B ) . Furthermore , the egl-20 lin-17 double mutant was no worse than either of the single mutants ( Figure 6C ) , suggesting that LIN-17 may act as a receptor for both EGL-20 and LIN-44 . Taken together , the above results indicate that egl-20 and lin-44 are the major regulators of PQR dendrite outgrowth , and appear to genetically interact , whereas cwn-1 plays only a minor role in the process . To determine the possible roles of other Frizzled receptors , we also studied PQR dendrite formation in cfz-2 and mig-1 mutants . cfz-2 mutants showed no significant defects , whereas mig-1 mutants presented 50% normal PQR dendrite ( Figure 6D , Table S3 ) . Thus , LIN-17 appears to be the main Frizzled receptor regulating PQR dendrite formation . To analyze functional redundancy among the Frizzleds , we tested whether mig-1 could enhance the lin-17 defect . In the mig-1 lin-17 double mutant , there was almost a 2-fold increase in the absent-dendrite phenotype ( Figure 6D ) , indicating a possible parallel role of mig-1 in PQR dendrite formation .
Several studies across different model systems have shown that Wnts can act instructively as both attractants and repellents in neurodevelopmental processes such as axon guidance , synapse formation , and neurite outgrowth [13] , [16] , [17] , [23] , [41]–[43] . Conversely , Wnt molecules can also act in a permissive manner , as non-spatial cues [14] , [15] , [20] , [22] . Our results suggest that posteriorly expressed LIN-44 acts as an attractive cue for the PQR dendrite . Ectopic expression of LIN-44 from the anterior side of PQR increases the tendency for dendrites to emerge and grow anteriorly , towards the source of LIN-44 . This role of LIN-44 as an attractant in PQR dendrite development differs from its role as a repellent signal for synaptic clustering in the dorsal section of the DA9 motor neuron [16] , highlighting the distinct effect of LIN-44 on these neighbouring neurons . The partial rescue of PQR dendrite defects by ubiquitous expression of LIN-44 from the heat shock promoter could suggest a permissive role for LIN-44 . However , a possible alternative interpretation is that local asymmetry of the ligand is generated , providing rescue when the concentration is higher on the posterior side of PQR . This conclusion is supported by the observations that a higher concentration of ligand ( increased length of heat shock ) is unable to increase the rescue , and that in the wild-type background heat shock-directed expression causes dendrite defects . To be fully functional , Wnts must undergo post-translational modifications , sorting in the endoplasmic reticulum , and secretion from the cells where they are expressed [44] . It is possible that cells that do not normally express LIN-44 have lower efficiency in regulating the proper maturation and secretion of this Wnt molecule . Hence LIN-44 expression from the heat shock promoter may provide functional , secreted LIN-44 with variable efficiency depending on the tissue of expression . Wnt patterning occurs during embryogenesis , at a time when many neurons are born . Our observation that PQR forms a normal dendrite following ablation of the tail hypodermal cells at the time of hatching suggests that embryonically expressed LIN-44 provides spatial information needed by the developing PQR several hours later . However , PQR remains receptive to heat shock misexpression of LIN-44 up until the dendrite begins developing . It is not known how stable Wnts are in C . elegans; however , in Drosophila the Wnt Wingless ( Wg ) and the morphogen Decapentaplegic ( Dpp ) are stable for about 3 h [45] , [46] . Wnts can also function at long distances . In C . elegans , for example , EGL-20 has been shown to direct cell migration across half the animal's body length [20] , [46] . Similarly in Drosophila , Wg can cover 10–20 cell diameters away from its source in the developing wing [47] , [48] spreading over a distance of about 50 µm in 30 min [46] . Our results showing an effect of LIN-44 when expressed in the pharynx from the promoter myo-2 in a region far from PQR also suggest a potential long range effect for this ligand . Emerging evidence suggests that dendrites of sensory neurons are shaped in a variety of ways . In contrast to dendrite development by retrograde extension , or towing by associated cells [4] , [7] , we and others [49] have observed that the dendrite of PQR forms by growth cone crawling , a mode of development more commonly seen in axons . In LIN-44 mutants , this growth cone often fails to form , preventing the outgrowth of a dendrite . Our results demonstrate that LIN-17 , a receptor for LIN-44 , cell-autonomously regulates the initiation and outgrowth of the PQR dendrite . To our knowledge , a ligand-receptor pair that can specifically affect the development of a dendrite in this manner has not previously been described . Interestingly , phasmid glia associated with the PQR dendrite do not have a major effect on its development . It has previously been shown that lin-44 and lin-17 mutants have defects in phasmid socket glia that arise due to disrupted polarity of the T cell precursor [29] , [35] , [50] . However , the aberrant structure of the phasmid in these mutants does not seem to be the main cause of dendrite defects , as ablation of these cells did not reproduce the mutant phenotypes . Notably , glia appear to have some involvement in the final extension of the dendrite , as some ablated animals had short dendrites . This is reminiscent of a previous study in which it was demonstrated that ablation of the sheath glia associated with the CEP sensory neuron in the head of C . elegans resulted in a failure of the sensory dendrite of this neuron to fully extend [51] . Different lines of evidence suggest that LIN-17 , like LIN-44 , may be required early in development to promote normal dendrite outgrowth . Cell-specific LIN-17 expression can rescue lin-17 dendrite defects if induced very early , before PQR is born , but has no such effect when induced later , once the cell has almost completed its migration . Furthermore , LIN-17::YFP expression from the rescuing egl-17 promoter appeared to become extremely faint or absent by the time the dendrite began to develop . This raises the interesting possibility that levels of LIN-17 receptor on the PQR cell surface are temporally regulated to elicit the appropriate response to Wnt ligands . We propose a model in which the LIN-17 receptor , present at low levels on the membrane of the PQR cell from the moment it is born , detects a posterior source of LIN-44 that signals the dendrite to emerge from the posterior side of the cell ( Figure 7A , B ) . This initial specification of the site of dendrite outgrowth appears to be an important determinant of the subsequent direction of dendrite outgrowth . The tendency for lin-44 and lin-17 mutant dendrites to grow anteriorly from the PQR cell , rather than from random orientations ( including dorsal or ventral ) , may imply the presence of an intrinsic anterior-posterior bias of the site and direction of PQR dendrite outgrowth controlled by Wnts and Frizzleds , or the existence of a dorso-ventral dendrite outgrowth controlled by other guidance molecules still unknown . In C . elegans , Wnts are expressed in different regions along the anterior-posterior axis . These different Wnts have often been shown to have distinct effects on cells that are located in proximity to the respective source of Wnt expression . Our genetic studies suggest that , similar to LIN-44 , the posteriorly expressed Wnt ligand EGL-20 also acts through the LIN-17 receptor to regulate PQR dendrite development ( Figure 7C ) , which could explain why lin-17 defects are more severe than those of lin-44 . However , whether EGL-20 plays an instructive role in this process remains unclear . Previous studies have also shown that both LIN-44/Wnt and EGL-20/Wnt can function through LIN-17/Frizzled; however , whether Frizzled receptors can simultaneously bind multiple Wnts , or whether Wnts can form homo- or hetero-dimers , remains unknown . The Wnt molecules CWN-1 and CWN-2 are both expressed more broadly in the body wall muscle , intestine , ventral cord neurons , and some head neurons [13] , [21] , [22] , [38] . Although these Wnts do not appear to directly regulate PQR dendrite development , our observation that a significant proportion of cwn-1 and cwn-2 mutants present ectopic processes on PQR suggests an indirect role in neurite pruning . This is consistent with recent findings that identify CWN-1 and CWN-2 as key regulators of developmental pruning of the head neuron AIM [21] . The MIG-1 receptor appears to act synergistically in a parallel pathway to LIN-17 ( Figure 7C ) . Notably , the increase in the percentage of the absent dendrite phenotype of the lin-17 mig-1 double mutant compared with the lin-17 mutant suggests a role for MIG-1 in regulating the ability of the neuron to send out a dendrite , regardless of its direction . Wnt morphogens have diverse functions in developmental processes across species , yet how they act with such precision on a single cell within a closely wired nervous system remains enigmatic . As we and others have shown , spatio-temporal organization of Wnts and their Frizzled receptors must be tightly orchestrated . The challenge now will be to gain insight into how these molecules are patterned and how they can be interpreted differently by individual cells .
Nematodes were cultured using standard methods [52] . All experiments were performed at 18°C except where otherwise noted . The following mutations were used: LGI , lin-17 ( n677 ) , lin-17 ( n671 ) , lin-17 ( n3091 ) , lin-17 ( vd002 ) , lin-44 ( n1792 ) , mig-1 ( e1787 ) ; LGII , cwn-1 ( ok546 ) ; LGIV , egl-20 ( n585 ) , cwn-2 ( ok895 ) ; LGIV , cfz-2 ( ok1201 ) . Transgenes used were: kyIs417[Pgcy-36::GFP , Podr-1::dsRed] , kyIs403[Podr-1::dsRed2 , Pflp-18::UNC-43g::dsRed2 , Pgcy-36::YFP::RAB-3 , Pgcy-36::mCFP] , vdEx127[Phsp16-2:LIN-44 ( 10 ng/µl ) , Pcoelomocyte::GFP ( 25 ng/µl ) ] , wyEx806[Plin-44::signal sequence:: flag::GFP::lin-44 genomic coding::lin-44 3′UTR , odr-1::GFP] , vdEx224[Pcwn-1::signal sequence::flag::GFP::lin-44 genomic coding ( 20 ng/µl ) , Pcoelomocyte::GFP ( 30 ng/µl ) ] , vdEx235 ( Pmyo-2::signal sequence::flag::GFP::lin-44 genomic coding ( 20 ng/µl ) , Pcoelomocyte::GFP ( 30 ng/µl ) ] , vdEx251[Podr-1::dsRed ( 30 ng/µl ) , Pegl-17::LIN-17::YFP ( 20 ng/µl ) , Pgcy-36::mCherry ( 0 . 5 ng/µl ) ] , vdEx133[Plin-17::LIN-17::YFP ( 10 ng/µl ) , Pchs-2::dsRed ( 2 ng/µl ) , pSM ( 10 ng/µl ) ] , vdEx265[ Plin-17::mCherry ( 20 ng/µl ) , Pegl-17::GFP ( 50 ng/µl ) ] . The kyIs417 strain was generated in Cori Bargmann's lab , the kyIs403 strain was provided by Manuel Zimmer and Cori Bargmann , and the wyEx806 strain was provided by Kang Shen . Standard molecular biology methods were used . All constructs were cloned into pSM ( a kind gift from Steve McCarroll and Cori Bargmann ) , a derivative of pPD49 . 26 ( Andrew Fire ) . The Pmyo-2::GFP::LIN-44 and Pcwn-1::GFP::LIN-44 constructs were generated by cloning a myo-2 promoter and a 170 bp fragment of the cwn-1 promoter [21] into FseI/AscI sites of pSM . A sequence encoding signal sequence::flag::GFP::LIN-44 genomic DNA ( modified from the wyEx806 transgene [16] ) was cloned downstream of each promoter into BamHI/NheI sites . The Plin-17::LIN-17::YFP rescue plasmid was generated by inserting a HindIII/NheI digested 6 . 5 kb fragment of the lin-17 promoter upstream of a LIN-17::YFP clone ( Pitr-1 pB::LIN-17::YFP [a gift from Kang Shen] ) . The Pegl-17::LIN-17::YFP rescue plasmid was made by inserting a 5 . 4 kb NotI/FseI digested fragment of the egl-17 promoter upstream of LIN-17::YFP ( digested from Plin-17::LIN-17::YFP plasmid and cloned into NheI/PspOM1 sites ) . Pegl-17::mCherry was generated by digesting mCherry from a pSM mCherry clone and inserting into KpnI/PspOMI sites behind the egl-17 promoter . Pgcy-36::mCherry was created by inserting 1 . 1 kb gcy-36 promoter into pSM mCherry . The Plin-17::mCherry clone was generated by cloning a BamHI/NheI digested 6 . 5 kb fragment of the lin-17 promoter into pSM mCherry . A Pgcy-36::LIN-17::YFP expression construct was unable to rescue dendrite defects in lin-17 ( n677 ) mutants when injected at concentrations of 0 . 2 , 0 . 5 , 1 , 2 , and 10 ng/µl . We analyzed PQR development in synchronized populations of anesthetized larvae ( L1 stage ) in a kyIs417 ( Pgcy-36::GFP ) background . Animals were synchronized by collecting newly hatched animals , from a plate containing only eggs , every 10 min using M9 buffer . Synchronized animals were transferred to fresh plates and grown for 5–9 h at 22°C before imaging . Developmental stages were characterized in synchronized populations , with little variation among animals . PQR morphology was scored at L4 or adult stages . Mutations in mig-1 , lin-17 , and egl-20 caused PQR migration defects , resulting in anterior ( and in some cases posterior ) mis-positioning of PQR . Given that this would cause PQR to be in a different position in relation to its normal surroundings , and importantly the source of LIN-44 , we chose to score dendrite defects only in those animals where PQR had migrated to its normal position . The PQR dendrite was scored as short if it was less than three cell bodies in length . Wild-type and lin-44 ( n1792 ) mutant animals carrying the Phsp16-2::LIN-44 transgene were maintained at 18°C . As development at this temperature is slower than at 22°C as in Figure 1 , dendrite outgrowth occurs at ∼8 h rather than ∼6 . 5 h . L1 animals were heat shock-induced at 33°C in a water bath for 30 min ( or longer , where indicated ) at different stages of development as indicated , following which they continued to grow at 18°C . Transgenic animals ( lin-44; Phsp16-2::LIN-44 ) and non-transgenic controls ( lin-44 ) were scored at the L4 stage or as young adults . Animals were mounted on 4% agar pads and immobilized using tetramisole hydrochloride ( 0 . 01%–0 . 03% ) . Epifluorescence was used to visualize animals with a Zeiss Axioimager Z1 and a Zeiss Axioimager A1 microscope . A Photometrics camera , Cool snap HQ2 , was used for imaging . Metamorph software was used to analyze the collected Z stacks . Developing stages of PQR were imaged using a Zeiss LSM510META confocal microscope and Zen 2008 software . An antifading agent , Dayco , was used in addition to tetramisole hydrochloride . Laser ablations were performed in L1 animals carrying the kyIs417 transgene using a MicroPoint Laser System Basic Unit attached to a Zeiss Axio Imager A1 ( Objective EC Plan-Neofluar 100×/1 . 30 Oil M27 ) . Animals were ablated 0–1 h after hatching and were scored at the L4 stage . For ablations of phasmid glia and phasmid neurons , ablation success was determined at the L4 stage by soaking animals in DiI on slides for 2 h prior to scoring ( DiI stains the phasmid neurons when these cells and the phasmid structure are unaltered [53]–[55] ) . Statistical analyses were performed using Primer of Biostatistics 3 . 01 . Error of proportions was used to estimate variation within a single population . The Student's t test was used in all cases , except in those with multiple comparisons , for which the Bonferroni t test was used . | Neurons have distinct compartments , which include axons and dendrites . Both of these compartments are essential for communication between neurons , as signals are received by dendrites and transmitted by axons . Although dendrites are vital for neural connectivity , very little is known about how they are formed . Here , we have investigated how dendrites develop in vivo by examining an oxygen sensory neuron ( PQR ) in the nematode C . elegans . Using a genetic approach , we have discovered that Wnt proteins , a group of highly conserved secreted morphogens , interact with their canonical Frizzled receptors to control the development of the PQR dendrite . We show that Wnt molecules act as attractive signals to determine the initiation and direction of dendrite outgrowth . Interestingly , Wnt proteins act specifically on the dendrite without affecting the axon , suggesting that outgrowth of the dendrite can be regulated by distinct processes that are independent of axon formation . We predict that similar mechanisms may be in place in other species owing to the conserved roles of Wnt and Frizzled molecules in development . | [
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... | 2011 | LIN-44/Wnt Directs Dendrite Outgrowth through LIN-17/Frizzled in C. elegans Neurons |
With the advent of whole exome sequencing , cases where no pathogenic coding mutations can be found are increasingly being observed in many diseases . In two large , distantly-related families that mapped to the Charcot-Marie-Tooth neuropathy CMTX3 locus at chromosome Xq26 . 3-q27 . 3 , all coding mutations were excluded . Using whole genome sequencing we found a large DNA interchromosomal insertion within the CMTX3 locus . The 78 kb insertion originates from chromosome 8q24 . 3 , segregates fully with the disease in the two families , and is absent from the general population as well as 627 neurologically normal chromosomes from in-house controls . Large insertions into chromosome Xq27 . 1 are known to cause a range of diseases and this is the first neuropathy phenotype caused by an interchromosomal insertion at this locus . The CMTX3 insertion represents an understudied pathogenic structural variation mechanism for inherited peripheral neuropathies . Our finding highlights the importance of considering all structural variation types when studying unsolved inherited peripheral neuropathy cases with no pathogenic coding mutations .
Charcot-Marie-Tooth ( CMT ) disease is the collective name given to a group of clinically and genetically heterogeneous inherited peripheral neuropathies that affect both motor and sensory neurons . Over 80 genes have been associated with CMT and other related inherited peripheral neuropathies , which account for up to 80% of CMT cases [1–4] . In our Australian cohort , after extensive whole exome sequencing ( WES ) analysis of multiple family members , a proportion of these unsolved families also have no detectable protein-coding mutation in the exome . This suggests that point mutations and small insertions/deletions of non-coding DNA and DNA structural variations may account for some of the unsolved cases . CMTX3 , a subtype of X-linked CMT , is one such locus which has remained unsolved after extensive molecular analyses . The CMTX3 locus was initially mapped to the long arm of chromosome X in two American families [5] . The locus was confirmed and refined to a 5 . 7 Mb region on chromosome Xq26 . 3-q27 . 3 in a large United Kingdom/New Zealand family ( CMT623 ) [6] and an Australian family ( CMT193-ext ) [7] . Affected males from these two families generally presented a slightly milder phenotype than the more common X-linked CMT subtype , CMTX1 . However the degree of severity varied . Onset of disease generally started in the first decade , initially presenting in the lower limbs . Sensory symptoms included marked pain and paraethesia in hands and feet as well as sensory loss . Tremor in hands and spastic paraparesis was not observed . Nerve conduction velocities data suggested these patients have an intermediate CMT . Female carriers were considered asymptomatic with normal nerve conduction velocities , however the observation of subtle clinical signs including high-arched feet , weakness in foot dorsiflexion and loss of ankle reflexes suggested female carriers may present very mild symptoms [6] . The two families carry the same CMTX3 haplotype , suggesting they share an identical genetic mutation inherited from a common ancestor . Genotype analysis of one of the original American families ( US-PED2 ) initially suggested this family also carried the distal portion of the CMTX3 haplotype [7] . However , re-examination of family US-PED2 by whole exome sequencing ( WES ) identified a known BSCL2 mutation ( c . 263A>G , p . Asn88Ser ) as the genetic cause of disease in the family [8] . Mutation screening families CMT623 and CMT193-ext excluded all coding sequences mapping within the 5 . 7 Mb locus for pathogenic mutations [6 , 9] . Therefore , we employed whole genome sequencing ( WGS ) to interrogate the disease locus for pathogenic non-coding single nucleotide variants and structural variations in these families .
Two affected males and an unaffected male control from each of the families CMT623 and CMT193-ext ( i . e . four patients and two controls ) underwent WGS . An average of 134 Gb of sequence was generated for each individual . On average , 96% of total reads mapped to the reference genome and all samples had a minimum depth of coverage ( DOC ) of 44X across the whole genome ( Table 1 ) . The CMTX3 locus had an average DOC of 24X , which reflected the males being hemizygous for chromosome X . Patient and control sequence alignments revealed the presence of split-reads at Xq27 . 1 ( Table 2 ) . The four affected males consistently showed split reads at the genomic location chrX:139 , 502 , 948 . The corresponding paired ends for the split reads mapped both upstream and downstream of the suggestive breakpoint at chromosome Xq27 . 1 . Split-reads at this location were not identified in the two unaffected males . The unaligned sequences of these split-reads mapped to two genomic regions ( chr8:145 , 768 , 312 and chr8:145 , 848 , 158 ) . These genomic positions are located 78 kb apart on chromosome 8q24 . 3 and represent the boundaries of the DNA region that has been inserted into chromosome Xq27 . 1 in the CMTX3 patients . Patient WGS data also showed split-reads on chromosome 8 that contained Xq27 . 1 sequence and paired with reads anchoring to these two locations on chromosome 8q24 . 3 . Further analysis also identified discordant paired ends in which one read pair mapped to Xq27 . 1 and the other read pair mapped to 8q24 . 3 . This was observed in all four patients and absent from the two control samples . Table 2 summarizes the number of split-reads and discordant paired ends identified for each patient . Based on these data we predicted that a 78 kb sequence from 8q24 . 3 had been inserted into chromosome Xq27 . 1 in CMTX3 patient DNA . To determine whether the entire 78 kb region from chromosome 8q24 . 3 had been duplicated and inserted into Xq27 . 1 we assessed the DOC across the genomic interval chr8:145 , 700 , 000–145 , 900 , 000 ( Table 3 ) . Control males showed a uniform DOC across the entire 200 kb region with a mean DOC of 40X . The affected males , however , showed a 1 . 6-fold increase in DOC ( mean DOC of 64X ) within the boundaries of the insertion breakpoints ( chr8:145 , 768 , 312–145 , 846 , 158 ) . The DOC for the genomic regions immediately flanking the 8q24 . 3 insert sequence were similar to the controls ( Fig 1A ) . These data suggested that patients with CMTX3 carry an extra copy of the 78 kb region from chromosome 8q24 . 3 through the interchromosomal insertion event at the CMTX3 locus . We next assessed whether the interchromosomal insertion segregated with the disease in our two distantly related families using a multiplex PCR genotyping assay ( Fig 1B ) . Genotyping results for a subset of family members from CMT623 are shown ( Fig 1C ) . The different sized amplicons were confirmed via Sanger sequencing ( S1 Fig ) . The 78 kb insertion segregated in 55 individuals ( 25 affected males and 30 carrier females ) from families CMT623 and CMT193-ext . The 78 kb insertion was not seen in the 50 unaffected members ( 30 males , 20 females ) from families CMT623 and CMT193-ext that were available for testing . All individuals were clinically diagnosed and genotyped for the CMTX3 haplotype prior to this study . The 8q24 . 3 interchromosomal insertion was absent in 627 control X chromosomes from neurologically normal females ( n = 252 ) and males ( n = 123 ) . Sanger sequencing the amplicons spanning the insertion breakpoints confirmed the WGS predictions ( Fig 2A and 2B ) . The 8q24 . 3 sequence inserted directly between the genomic locations chrX:139 , 502 , 948–139 , 502 , 949 . For the proximal breakpoint , the exact location of the end sequence from chromosome X and start position of the 8q24 . 3 insertion sequence could not be unambiguously defined due to a 2 bp overlap ( AA ) in the sequence ( Fig 2A ) . For the purposes of defining breakpoints , we have designated the chromosome 8 insertion start position as chr8:145 , 768 , 312 . The distal breakpoint is more complex ( Fig 2B ) . The 8q24 . 3 insertion sequence ends at position chr8:145 , 848 , 158 followed by a small insertion from chromosome 12q13 . 12 , which maps within an intron of the FAIM2 gene . A total of 19 bp from the small insertion sequence maps to 12q13 . 12 however the first 10 bp also overlap with chromosome 8 ( green sequence , Fig 2B ) . Adjacent to the 12q13 . 12 insertion , the first 12 bps of chromosome X at the distal breakpoint are inverted . There is also a single nucleotide variant ( T>G ) at chrX:139 , 502 , 968 and a single nucleotide deletion at chrX:139 , 502 , 976 ( Fig 2B ) . These variants appear to be unique to the two CMTX3 families and have not been reported in variant databases including the 1000 Genomes Project [10] or dbSNP [11] . The 8q24 . 3 insertion region is 77 , 856 bp and contains a partial transcript of the ARHGAP39 gene ( exons 1–7 ) encoded on the negative strand ( Fig 2C ) . The duplicated 8q24 . 3 sequence has inserted into an intergenic region of Xq27 . 1 with the nearest flanking genes being LOC389895 ( located 329 kb downstream proximal to the 78 kb insertion ) and SOX3 ( located 84 kb distal to of the insertion ) ( Fig 2C and 2D ) . Based on the genomic architecture of the CMTX3 interchromosomal insertion , we hypothesized two possible mechanisms that could lead to peripheral neuropathy: 1 ) overexpression of the partial ARHGAP39 transcript due to 8q24 . 3 trisomy; or 2 ) transcriptional dysregulation of one or more genes mapping within the CMTX3 locus . Aberrant splicing with the ARHGAP39 partial transcript may also be a possible mechanism . However this is unlikely , as the inserted ARHGAP39 partial transcript is predicted to be transcribed on the negative strand and the nearest downstream gene , LOC389895 , is a single exon gene transcribed from the positive strand ( Fig 2C ) . Copy number variations ( CNVs ) that result in the duplication or deletion of a gene is a well-known cause of CMT neuropathy , indicating that peripheral nerves are sensitive to gene dosage . A 1 . 5 Mb duplication on chromosome 17p12 [12 , 13] , resulting in trisomy of the PMP22 gene [14–17] , causes the most common form of CMT ( CMT1A ) . This was the seminal example of a CNV causing disease . The reciprocal 1 . 5 Mb 17p12 deletion causes hereditary neuropathy with liability to pressure palsies ( HNPP ) [18] . Although relatively rare [19–21] , a small number of individual cases describing whole and partial gene duplications or deletions for other CMT loci including MPZ [21–23] , GJB1 [24–26] , MFN2 [27] , and NDRG1 [28] have also been reported . Currently there are no interchromosomal insertions reported as a cause of CMT . To assess whether the CMTX3 insertion affects gene expression , quantitative RT-PCR analysis was used to assess the mRNA expression levels of candidate genes in patient and control lymphoblasts . No difference in ARHGAP39 expression was observed between the patient and controls ( Fig 3A ) . This suggested that trisomy of the ARHGAP3 partial transcript is unlikely the underlying cause of neuropathy . Large rearrangements disrupting non-coding DNA sequences are likely to cause disease by dysregulating the transcriptional expression of one or more nearby genes [29] . Duplication of a 186 kb sequence located 3 kb distal to the PMP22 gene [30 , 31] , harboring Schwann cell-specific transcription factor binding sites [32] , was found to cause CMT1A by dysregulating PMP22 expression [30 , 31] . Non-coding DNA structural variations can disrupt the interaction between a gene and its functional non-coding DNA sequences ( such as promoters , enhancers and silencers ) or introduce new interactions , resulting in dysregulated temporal and spatial gene expression [29 , 33 , 34] . Recent studies have shown that regulatory elements and their target genes cluster within local chromatin interaction domains or “topologically associated domains” [35] . Genomic rearrangements that physically disrupt the boundaries of these domains introduce ectopic interactions between regulatory elements and genes that can cause disease [29] . However , based on Hi-C profile data from human embryonic stem cells [35] the 78 kb sequence from 8q24 . 3 appears to have inserted into a topologically associated domain without disrupting the boundaries ( S2 Fig ) suggesting that if the CMTX3 mutation dysregulates a nearby gene it is likely through some other mechanism . To explore the possible mechanism of transcriptional dysregulation of one or more genes mapping within the CMTX3 locus , we assessed the expression of SOX3 and FGF13 . Large DNA interchromosomal insertions at the Xq27 . 1 locus have been previously reported to cause a range of phenotypes [36–40] and these two genes are known to be dysregulated in patients with other Xq27 . 1 interchromosomal insertions [38 , 40] . SOX3 encodes the sex determining region Y-box 3 transcription factor . In an XX sex reversal patient carrying a 774 kb interchromosomal insertion from chromosome 1q25 . 3 , an increase in SOX3 expression was observed in the patient lymphoblasts [40] . SOX3 expression however was not detected in the control lymphoblasts . In both our patient and control lymphoblast cell lines , SOX3 mRNA expression could not be detected ( S3 Fig ) . These results reflect previous reports of SOX3 expression in control lymphoblasts [40] and it is likely that SOX3 is silenced by methylation in lymphoblasts [41] . Unlike the 1q25 . 3 interchromosomal insertion , the presence of the 8q24 . 3 interchromosomal insertion does not appear to affect SOX3 expression in lymphoblasts . FGF13 encodes the fibroblast growth factor 13 protein that is part of the fibroblast growth factor homologous family [42] . Hypertrichosis patients carrying a 389 kb interchromosomal insertion from chromosome 6p21 . 1 showed reduced FGF13 expression in patient hair follicles [38] . We observed a 3-fold increase in expression in lymphoblast cells from the CMTX3 patient ( Fig 3B ) . Although the assay could not distinguish between the different FGF13 isoforms , our preliminary finding demonstrates that the 8q24 . 3 interchromosomal insertion dysregulates FGF13 expression in CMTX3 patient lymphoblasts . We hypothesize that if similar dysregulation of FGF13 gene expression were to be observed in patient neurons this could be the underlying cause of disease in CMTX3 patients . It is also possible that the observed dysregulation of FGF13 is a benign , bystander effect of the 78 kb interchromosomal insertion . Further gene expression studies on FGF13 and the remaining genes mapping to the CMTX3 locus , will be required to fully determine the pathogenic consequence of the CMTX3 8q24 . 3 insertion . There have been six large interchromosomal insertions previously reported; each originating from unique genomic regions and ranging from 124–774 kb [36–40] . These interchromosomal insertions have been shown to cause hypoparathyroidism [36] , hypertrichosis [37 , 38] , ptosis [39] , and XX male sex reversal [40] . CMTX3 is the fifth disease phenotype to be associated with an Xq27 . 1 interchromosomal insertion , clearly suggesting there is a recurrent mutation mechanism at the Xq27 . 1 locus . There are several mutation mechanisms that give rise to structural variations ( recently reviewed in [43 , 44] ) . We propose that this recurring mutation mechanism is possibly due to double stranded DNA breaks occurring in the 180 bp palindrome sequence at Xq27 . 1 [37] followed by incorrect repair of the DNA break through microhomology-mediated break-induced replication [45 , 46] . For most of the interchromosomal insertions , including the CMTX3 insertion , at least one breakpoint is located near the center of the 180 bp palindrome sequence , close to where the hairpin loop is predicted to form ( Fig 4 ) [37–40] . Hairpin loops are susceptible to double stranded DNA breaks due to endonuclease activity and are common hotspots for translocations [47] . Since the chromosome X breakpoints of these interchromosomal insertions localize within this hairpin structure , this suggests that hairpin formation of the palindrome sequence and endonuclease activity may be the initial process of the recurrent mutation mechanism . Microhomology-mediated break-induced replication ( MMBIR ) coupled with fork stalling and template switching ( FoSTeS ) has been proposed as an alternative model for the formation of genomic rearrangements that cannot be explained by non-allelic homologous recombination [45 , 48 , 49] . In this model , microhomology-induced template switching occurs where nearby single-stranded DNA is used as template to repair DNA breaks . Depending on the template , this results in the formation of deletions , duplications , triplications inversions or translocations that are flanked by minimal sequence homology of 2–6 bp at the breakpoints [45] . Further complexity at the genomic rearrangement breakpoints , involving small deletions and/or small insertions of unlinked or unknown sequences , are also commonly observed and is likely due to multiple template-switching events occurring during the repair process [49] . Sequencing the breakpoints of the CMTX3 rearrangement revealed an additional 19 bp from chromosome 12q13 . 12 , an inversion of 12 bp from chromosome Xq27 . 1 and microhomology between chromosome X and chromosome 8 sequence as well as between the chromosome 8 and chromosome 12 sequence ( Fig 2A and 2B ) . Microhomology , small deletions at the Xq27 . 1 sequence and additional small inserted sequences , from unlinked ( i . e . from another chromosome ) or unknown sources , also feature in the other disease-associated interchromosomal insertions at Xq27 . 1 [36–40] suggesting these insertions arose through MMBIR/FoSTeS . Since each unique DNA insertion causes different disease phenotypes this suggests that the inserted genomic sequence is important . Based on the varying gene dysregulation observed for patients with hypertrichosis [38] , XX sex reversal [40] and CMTX3 , we predict the disease specificity from each interchromosomal insertion into Xq27 . 1 arises from the introduction of DNA regulatory elements that interact with the nearby genes in a tissue-specific manner . Unsolved Mendelian diseases mapping to the Xq27 . 1 region should therefore be assessed for large interchromosomal insertions using WGS analysis . With 20% of our CMT families remaining genetically unsolved after WES [2] , finding the causes of disease in these families is an important goal for inherited peripheral neuropathies . Our discovery suggests that structural variation involving non-coding DNA may explain a portion of the unsolved families . It also highlights the importance of looking beyond CNV when analyzing the genome for structural variation . Although the CMTX3 mutation represents trisomy of 8q24 . 3 , given that this does not result in a dosage change for ARHGAP39 , it is likely that the insertion itself underlies the peripheral neuropathy . WGS provides a powerful tool to detect the full spectrum of DNA variation including all classes of structural variations [50 , 51] . Given that structural variations are found throughout the general population [52 , 53] distinguishing pathogenic and benign structural variations will be difficult without large families to confirm segregation . In time , improved annotation of benign genomic rearrangements in SV databases , that go beyond CNV and map the location and orientation of all SV subtypes , will assist in delineating pathogenic structural variations in patients . Pathogenic structural variations identified in families that are large enough for segregation analyses , as we have shown for the CMTX3 mutation , will provide genomic landmarks in which WGS data from smaller families can be mined for structural variation sequencing signatures ( such as split reads and discordant paired ends ) . This strategy will , however , have limited use if structural variations causing inherited peripheral neuropathy prove to be rare private mutations . With decreasing WGS costs and improved sensitivity of WGS alignment algorithms , we predict that more structural variations are likely to be identified as the pathogenic cause of CMT . However , we acknowledge that the detection of these mutations in both the research and clinical diagnostic settings will be a challenge with no immediate solution . In conclusion , we have provided compelling data supporting the likely genetic cause of CMTX3 neuropathy as a 78 kb interchromosomal insertion at Xq27 . 1 [der ( X ) dir ins ( X;8 ) ( q27 . 1;q24 . 3 ) ] . Based on genealogy studies we believe this founder insertion originated prior to the early 1800s in a Scottish family . Our discovery is the first neuropathy caused by an Xq27 . 1 interchromosomal insertion . We propose that large structural variations involving non-coding DNA , similar to the CMTX3 mutation , may account for a proportion of the unsolved CMT cases .
Participating family members gave informed consent according to the protocols approved by the Sydney Local Health District Human Ethics Review Committee , Concord Repatriation General Hospital , Sydney , Australia ( reference number: HREC/11/CRGH/105 ) . Genomic DNA was extracted from peripheral blood using the PureGene Kit ( Qiagen ) following manufacturer’s instructions . Extractions were performed by Molecular Medicine Laboratory , Concord Repatriation General Hospital ( Sydney , Australia ) . Genomic DNA samples ( 3 μg ) were dispatched to NextCODE ( Massachusetts , USA ) who outsourced WGS of samples to Macrogen ( South Korea ) . Paired-end ( 101 bp ) sequencing was performed on a HiSeq 2000 sequencer ( Illumina ) following standard protocols . Raw WGS data was returned to NextCODE who performed the following bioinformatics analyses . Access to all pipeline output files and visual representation of WGS data was made available through the Sequence Miner ( NextCODE ) application . Primers ( X . F: 5’-CTCCAGCTTTGTTCTTTGGAC-3’; X . R: 5’-TCACCAACATTTCCAATCTCC-3’; 8 . F: 5’-CAAACCCAATTCAGGTCCAG-3’; 8 . R: 5’-GCCTAGGAGGTGTCCCTTTC-3’ ) were designed to amplify wild type chromosome X and the distal and proximal breakpoints of the 8q24 . 3 interchromosomal insertion . Multiplex PCR was performed in a 15 μl reaction containing 25 ng genomic DNA , 1X MyTaq Red Mix ( Bioline ) , 8 pmol primer X . F , 8 pmol primer X . R , 2 pmol primer 8 . F and 4 pmol primer 8 . R . All PCR thermocycling was performed on an Eppendorf MasterCycler using a touchdown cycling protocol . Specific cycling temperatures are available on request . Amplicons were size fractionated on 1 . 5% ( w/v ) agarose gel at 40 V/cm . Amplified DNA was purified using the Isolate PCR and Gel Kit ( Bioline ) after gel electrophoresis following manufacturer’s instructions . Purified amplicons were submitted to Garvin Molecular Genetics ( Sydney , Australia ) for Sanger sequencing . Patient EBV-transformed lymphoblast cell lines were prepared using standard procedures at Genetic Repositories Australia ( Sydney , Australia ) . Sex and aged matched controls were obtained from the Genetic Repositories Australia . Lymphoblasts were maintained in RPMI 1640 ( Invitrogen ) supplemented with 10% fetal bovine serum ( Scientifix ) and 2 mM L-glutamine ( Gibco ) . Total RNA was isolated from patient lymphoblast cells using Trizol ( Life Technologies ) according to the manufacturer’s instructions . RNA was eluted in 50 μl RNAse-free water , DNase-treated with Turbo DNase ( Life Technologies ) and stored at -80°C until required . RNA ( 1 μg ) was converted to cDNA using iScript cDNA Synthesis Kit ( Biorad ) following manufacturer’s protocols . Isolated cDNA ( 100 ng ) was subjected to quantitative RT-PCR analysis using TaqMan Gene Expression Assays ( Invitrogen ) following manufacturer’s protocols . Quantitative RT-PCR was performed on a Step One Plus ( Applied Biosystems ) and relative fold difference was calculated using the comparative Ct method [55] . Target gene expression was determined relative to the housekeeping gene 18S . For each RNA extraction ( n = 3 per sample ) , quantitative RT-PCR reactions were performed in triplicate . The 1000 Genomes Project , http://www . 1000genomes . org The 3D Genome Browser , http://promoter . bx . psu . edu/hi-c/index . html dbSNP , http://www . ncbi . nlm . nih . gov/SNP/ NextCODE , https://www . nextcode . com OMIM , http://www . omim . org UCSC , https://genome . ucsc . edu | Next generation sequencing technologies have greatly advanced disease gene discovery for Charcot-Marie-Tooth ( CMT ) disease and related inherited peripheral neuropathies . However , many families with CMT remain unsolved after all protein-coding sequences have been interrogated through whole exome sequencing . The pathogenic mutations in these unsolved families may be non-coding point mutations , small indels or large structural variations involving thousands to millions of base pairs . In two large , distantly related families with X-linked CMT , all known protein-coding sequence variants were tested and no causal variant was found . Using whole genome sequencing we identified a 78 kb 8q24 . 3 insertion at chromosome Xq27 . 1 as the likely underlying cause of neuropathy in these two families . This is the first report of a large insertion causing CMT and highlights an understudied disease mechanism for inherited peripheral neuropathy . | [
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"chromosome... | 2016 | Whole Genome Sequencing Identifies a 78 kb Insertion from Chromosome 8 as the Cause of Charcot-Marie-Tooth Neuropathy CMTX3 |
Visceral leishmaniasis is a potentially fatal infectious disease caused by the protozoan parasite Leishmania infantum/chagasi in the New World , or by L . donovani or L . infantum/chagasi in the Old World . Infection leads to a variety of outcomes ranging from asymptomatic infection to active disease , characterized by fevers , cachexia , hepatosplenomegaly and suppressed immune responses . We reasoned that events occurring during the initial few hours when the parasite encounters cells of the innate and adaptive immune systems are likely to influence the eventual immune response that develops . Therefore , we performed gene expression analysis using Affymetrix U133Plus2 microarray chips to investigate a model of early infection with human monocyte-derived macrophages ( MDMs ) challenged with wild-type L . chagasi parasites , with or without subsequent co-culture with Leishmania-naïve , autologous T-cells . Microarray data generated from total RNA were analyzed with software from the Bioconductor Project and functional clustering and pathway analysis were performed with DAVID and Gene Set Enrichment Analysis ( GSEA ) , respectively . Many transcripts were down-regulated by infection in cultures containing macrophages alone , and the pattern indicated a lack of a classically activated phenotype . By contrast , the addition of autologous Leishmania-naïve T cells to infected macrophages resulted in a pattern of gene expression including many markers of type 1 immune cytokine activation ( IFN-γ , IL-6 , IL-1α , IL-1β ) . There was simultaneous up-regulation of a few markers of immune modulation ( IL-10 cytokine accumulation; TGF-β Signaling Pathway ) . We suggest that the initial encounter between L . chagasi and cells of the innate and adaptive immune system stimulates primarily type 1 immune cytokine responses , despite a lack of classical macrophage activation . This local microenvironment at the site of parasite inoculation may determine the initial course of immune T-cell development .
Visceral leishmaniasis ( VL ) is a potentially fatal infectious disease caused by the protozoan parasites Leishmania chagasi/infantum in the New or in parts of the Old World , or by L . donovani in other regions of the Old World . [1] Infection leads to a variety of outcomes ranging from asymptomatic infection to active disease , which is characterized by fevers , cachexia , hepatosplenomegaly and suppressed immune responses . Without treatment , most symptomatic patients die . [2] Investigations into the mechanism underlying the immunosuppression during acute VL have demonstrated defective antigen-specific proliferation and IFN-γ responses to parasite antigen , [3]–[5] high expression of IL-10 in the spleen and serum of symptomatic VL patients[6]–[10] and high serum levels of IL-4 , TGF-β and IL-2 receptor . [11]–[13] In vitro infection with Leishmania parasites suppresses macrophage microbicidal responses and IFN-γ pathway signaling , [14]–[17] suggesting that these suppressive changes begin at the earliest stages of infection . Whether this defect in macrophage responses to Leishmania infection is communicated to local adaptive immune cells is not known . We reasoned that events occurring during the initial few hours when the parasite encounters cells of the innate and adaptive immune systems are likely to influence the eventual immune response that develops . We hypothesized that the parasite would cause unique changes in gene expression in both innate and adaptive cells of the immune system encountered early in infection . To test this hypothesis , we analyzed gene expression with an in vitro model using human monocyte-derived macrophages ( MDMs ) challenged with L . chagasi promastigotes with or without subsequent co-culture with Leishmania-naïve , autologous T-cells . Gene expression analysis of RNA harvested from both MDMs alone and the MDM-T cell co-cultures indicated a surprising type 1 inflammatory cytokine response during the earliest stages of parasite invasion into the host .
A Brazilian isolate of L . chagasi ( MHOM/BR/00/1669 ) was maintained in hamsters by serial intracardiac injection of amastigotes . Parasites were grown as promastigotes at 26°C in liquid hemoflagellate-modified minimal essential medium and used within 3 weeks of isolation . [18] Parasite sub-cultures were used on day 7 of growth for infections . On day zero , venous blood was drawn from four healthy , US resident adult male volunteers ages 24–64 in accordance with the human subjects guidelines approved by the University of Iowa Institutional Review Board . None of the donors have been exposed to Leishmania . Written consent was obtained from all donors . Only male donors were used to eliminate , as a variable in the analysis , the known effects of gender on VL . [2] , [19] PBMCs were isolated from venous blood by density gradient sedimentation on Ficoll-Paque Plus ( GE Healthcare , Uppsala , Sweden ) and cultured in RPMI 1640 ( Gibco ) with 20% autologous serum in 60 ml Teflon wells ( Savillex Corporation ) at 37°C in 5% CO2 . Serum was obtained from the volunteers using BD Vacutainer Serum Plus Blood Collection tubes ( Becton Dickson ) . On day 6 , human MDMs were purified by adherence to tissue culture plates ( Corning ) that had been pre-coated with poly-L-lysine ( 0 . 1 mg/ml; Sigma ) . After 4 hours culture in RPMI 1640 with 10% heat-inactivated fetal calf serum ( Sigma ) , 2 mM L-glutamine , 100U/ml penicillin and 100 µg/ml streptomycin ( Gibco ) [RP-10] at 37°C , 5% CO2 , non-adherent lymphocytes were rinsed off . MDMs were infected with stationary phase L . chagasi parasites at a 10∶1 parasite:MDM ratio . Plates were immediately centrifuged at 60 g for 4 minutes at 4°C to synchronize the infections . After one hour , non-adherent parasites were rinsed off and cells were maintained in RP-10 . PBMCs were again isolated from the same donor on day 7 , and CD3+ cells were isolated by negative selection using either a cocktail of antibody-coated beads ( anti-CD14 , anti-CD19 , anti-CD56; Miltenyi Biotec ) or the Pan-T-Cell Isolation Kit II ( Miltenyi Biotec ) according to manufacturer's instructions . A small aliquot was fixed and stained for flow cytometry analysis with an anti-CD3-PE conjugated antibody ( Miltenyi Biotec ) to assess enrichment . The resultant enriched population should contain a mixed population of both Leishmania-naïve CD4+ and CD8+ T cells , but should be depleted of monocytes , dendritic cells , NK cells and NKT cells . Negative selection routinely resulted in a population of cells that was >90–95% CD3+ ( data not shown ) . Autologous T cells were added to the infected macrophage cultures at an estimated 3∶1 T cell:MDM ratio , and the plates were again spun at 4°C and 60 g for 4 minutes . Macrophages were removed from the wells with citric saline [0 . 135M KCl , 0 . 015M Trisodium citrate ) ; Fischer] for 5 minutes at 37°C . Infection efficiency was evaluated by manually counting ≥200 macrophages in cytospin preparations ( Cytospin 4 , Thermo-Shandon Fisher ) stained with Diff Quik ( Protocol Hema 3 , Fisher Scientific ) . Experiments in which ≥70% macrophages were infected were used for RNA extraction . In the infection replicates used for the microarray studies , we averaged a percent infection ( mean±SEM ) of 79 . 9±7 . 2 for the infected macrophage only replicates and a percent infection of 76±7 . 7 for the infected macrophage-T-cell co-culture replicates . Total RNA was isolated using Trizol ( Invitrogen ) as specified in the manufacturer's instructions . RNA was treated with DNaseI and further cleaned using the Qiagen RNeasy mini-kit ( Qiagen , Hilden , Germany ) . RNA for microarrays was harvested from uninfected or infected macrophages and from uninfected or infected macrophage – T-cell co-cultures at 4 hours after the initiation of co-culture ( 28 hours after initiation of infection ) . RNAs for validation experiments were harvested after 4 hours , 24 hours , 2 days and 3 days of co-culture . RNA quality was assessed using the Agilent Model 2100 Bioanalyzer ( Agilent Technologies , Palo Alto , CA ) . cRNA was generated from five µg of total RNA by using the Affymetrix GeneChip one-cycle target labeling kit ( Affymetrix , Inc . , Santa Clara , CA ) according to the manufacturer's recommended protocols . The resultant biotinylated cRNA was fragmented and hybridized to the GeneChip Human Genome U133 Plus 2 . 0 Array ( Affymetrix , Inc . ) . The arrays were washed , stained , and scanned using the Affymetrix Model 450 Fluidics Station and Affymetrix Model 3000 scanner using the manufacturer's recommended protocols by the University of Iowa DNA Core Facility . Each sample and microarray underwent standard quality control evaluations for cRNA amplification of more than 4-fold , percentage of probe sets reliably detecting between 40 and 60 percent present call , and a 3′-5′ ratio of GAPDH gene less than 3 . Raw data analysis was performed using code written in R and software from the open-source Bioconductor Project . [20] Preliminary data quality control assessments were performed with affyQCReport . The raw fluorescence data were background adjusted , normalized and converted to expression-level data using gcRMA . [21] , [22] For each donor , the log2 gcRMA expression data from the uninfected sample were subtracted from the log2 gcRMA expression data from the infected sample and then these paired data were analyzed with RankProd[23] , [24] using the one-class model . Macrophage-only data were analyzed separately from the macrophage-T cell co-culture data . Both raw and normalized data have been deposited with ArrayExpress ( http://www . ebi . ac . uk/microarray-as/aer/ ? #ae-main[0] ) under accession number E-MEXP-1290 . Briefly , RankProd rank orders all probe sets within each replicate by expression level and then calculates an ‘RP-Value’ for each probe set based on the amount that a particular probe set appears at the top or at the bottom of the ranked list . The RP-Value for each probe set then increases if the probe set is consistently present at the top or the bottom of the list . The software then re-sorts all the probe sets based on RP-Value , taking into account all pair-wise comparisons and adjusts for multiple hypothesis testing via permutation of the replicate labels . For each probe set , a “Percent False Positive” ( PFP ) value is calculated as an estimate of the false discovery rate . [23] , [25] The cutoff for significance was chosen to be all genes with a PFP rate of 5% or less . These genes , adjusted by permutation for multiple hypothesis testing , had less than a 5% chance of representing a false positive signal of statistically significant differential expression . After significant genes were identified , annotation and functional clustering was performed using DAVID . [26] The raw list of AffyIDs for each condition ( MDM vs . MDM-T cell co-culture; up vs . down regulated by infection ) was submitted as a “Gene List” to DAVID and then the data were analyzed using the “Functional Annotation Clustering” tool using the “Highest” classification stringency setting . Pathway analysis was performed using Gene Set Enrichment Analysis , v . 2 . [27] GSEA takes a list of genes and tests whether , within that queried list of genes , there is statistically significant enrichment or not of pre-defined groups of genes , or “gene sets . ” Gene sets examined through GSEA include canonical metabolic and signaling pathways , groups of genes previously identified and validated to be up- or down-regulated when cells are given a particular stimulus , or genes present at a similar physical location ( e . g . within a particular cytoband ) . This type of analysis can detect subtle changes present in the data . For example , if several key members of a particular signaling pathway are all up-regulated by 5% these changes may not be detected by traditional analyses , although these changes could biologically represent a substantial increase in the net “flux” of the signaling pathway . GSEA settings were default except for 1000 permutations , “phenotype” permutation and calculating differential expression based on the mean expression value for each phenotype . cDNA was generated from the cleaned up total RNA samples using the Superscript III First Strand Synthesis System kit ( Invitrogen ) using random hexamer primers and an RNaseH-treatment step following the manufacturer's instructions . TaqMan real-time PCR gene expression assays were purchased from Applied Biosystems , Inc . ( ABI ) and were performed according to the manufacturer's instructions . Data were analyzed using the Δ ( ΔCt ) method . [28] Supernatants from three independent infections , each from a separate individual , were assayed . The supernatants were stored at −20°C before use . Cytokine levels in the supernatants were assessed using a panel and controls samples , from Lincoplex ( Millipore , Billerica , MA ) , according to the manufacturer's instructions . Data were generated on a BioRad Bio-Plex Assay Reader 200 ( BioRad ) . RT-PCR and cytokine data were plotted as the mean of three independent experiments . Statistical significance was assessed with GraphPad Prism , v . 5 using a 1-way ANOVA and Tukey's Multiple Comparison Test or a 2-way ANOVA test .
To test our hypothesis that the parasite induces unique changes in gene expression in both innate and adaptive cells of the immune system encountered early in infection , we examined gene expression in four parallel conditions: ( a ) Uninfected MDMs , ( b ) Infected MDMs , ( c ) Uninfected MDMs co-cultured with autologous T-cells and ( d ) Infected MDMs co-cultured with autologous T-cells . A box plot of the log2 ratios of the Uninfected expression values subtracted from the Infected expression values for all probe sets plotted separately for each donor demonstrated that although there was slightly more variation present in the MDM-only ratios for Donor 4 , overall there did not appear to be substantial inter-donor variation ( Figure 1 ) . Most genes did not appear to change markedly from zero as evidenced by the narrow range of most of the inter-quartile boxes . Although the analyses presented below were generated employing all 4 donors' data , the analyses were also tested leaving donor 4 out and did not differ substantially ( data not shown ) . To generate the RNA samples , we only used infections where there was >70% infected macrophages at the 4 hour co-culture time point ( MDM: 79 . 9±7 . 2% infection; MDM-T-cell: 76±7 . 7;see Methods ) . For subsequent experiments over longer time intervals , the percent of infected cells stayed roughly constant over the time intervals measured ( see figure legends ) . Data generated from the analysis of RNA from cultured MDMs alone were analyzed separately by RankProd from the data from MDM-T cell co-cultures . The numbers and identity of genes either up-regulated or down-regulated in infected samples compared to uninfected samples that had a particular estimated PFP were computed . In both culture conditions , statistically significant differentially-regulated genes were considered if genes met a 5% PFP cutoff . Out of 54 , 675 total probe sets , in the MDM only condition RankProd identified 9 probe sets with a PFP cutoff of ≤5% that were up-regulated by infection with L . chagasi and 72 probe sets that were down-regulated . In the MDM-T cell co-culture condition , RankProd identified 116 probe sets with a PFP cutoff of ≤ 5% that were up-regulated by infection with L . chagasi and 19 down-regulated probe sets . A complete list of all probe sets identified by RankProd is provided in Table S2 . After identification of the differentially expressed probe-sets using RankProd , the lists of AffyIDs for the up- and down-regulated probe-sets were submitted to the DAVID bioinformatics website to functionally cluster the various probe-sets by Gene Ontology categories and other functional annotation . [26] Tables 1 and 2 show a selected list of the identified differentially expressed genes grouped by functional annotation . In the MDM cultures ( Table 1 ) , infection led to many more down-modulated than up-regulated transcripts . In contrast to macrophage infection with bacterial pathogens , [29] Leishmania infection of MDMs did not lead to up-regulation of transcripts encoding proteins characteristic of classical inflammation . Infection with L . chagasi down-modulated several transcripts encoding proteins involved in cellular regulation . Down-modulated gene transcripts included those corresponding to YAF2 , which encodes a protein that binds the transcription factor YY1 , eIF2C3 , an initiation factor belonging to the PIWI family that is essential for mammalian cell siRNA-mediated gene silencing , [30] Cdc42 , whose protein product is involved in actin regulation , a guanine nucleotide exchange factor , and a gene involved in autophagy . There was also significant down-modulation of transcripts encoding classical inflammatory receptors ( IL-1R2 and CSF2 ) , [29] and Peroxiredoxin 6 , a protein involved in redox cycling and oxidative defense . [31] Transcripts encoding proteins associated with classical macrophage activation , such as TNF-α , IL-10 , MIP-1-α , IP-10 , IL-6 , iNOS , MHC II and CIITA , [32] were not differentially regulated . The MDM-T cell co-culture condition provided a model of the initial interaction between Leishmania-infected macrophages and circulating T cells , using a mixed population of peripheral blood-derived Leishmania-naïve T cells . [33] We chose an early time point ( 4 hours of co-culture; 28 hours total infection time ) to study the gene expression initiated by this initial contact . In contrast to our study of infected MDMs , the addition of T-cells to the co-culture increased the proportion of genes that were differentially regulated ( Table 2 ) . Of note , the mRNA encoding the early activation T-cell marker CD69 was significantly induced in the co-culture replicates , suggesting that we were able to successfully extract mRNA from both the infected macrophages as well as the added T cells . Among the many transcripts that were up-regulated by co-culture were those that encoded proteins that promote acute inflammation , including chemokines that attract neutrophils ( CXCL-2 , 3 ) and resting T cells/NK cells ( CXCL-10 ) . The latter , also called IP-10 , promotes Th1-type immunity . Cytokines and interleukins expressed uniquely in the infected co-culture condition included IL-1α , IL-1β , and IL-6 which are pro-inflammatory , and IFN-γ and IL-2 which are both produced by and promote the development of a Th1-type cells . Consistently , the mRNA for STAT1 , a key signaling molecule in the IFN-γ pathway , also increased . Transcripts encoding other chemokines ( CCL-8 , 20; CXCL-9 , 11 ) were also induced in the co-culture . As such , the “flavor” of transcripts induced uniquely in co-cultures with the addition of T-cells to infected MDMs reflects an environment favorable for the development of type 1 immune cytokine responses . Similar to the results in the MDM-only condition , transcripts for several isoforms of the MT-1 gene were highly up-regulated in co-cultures containing infected MDMs . Due to their up-regulation either with or without T-cells present , we presume these most likely reflect changes in MT gene expression occurring in the infected MDMs , although it is possible that these transcripts were also up-regulated in the co-cultured T-cells . [34] In order to validate some of the above data using a more quantitative method , we performed TaqMan-based reverse transcriptase-PCR experiments on 6 differentially regulated genes . The relative mRNA expression levels were verified using the same RNA samples that had been analyzed in microarrays for two donors ( Figure 2 ) . With the exception of SNX13 which yielded equivocal results , the direction and magnitude of calculated change comparing infected samples to uninfected samples corresponded to the change predicted by the microarray expression data for both donors . SNX13 had been identified as an up-regulated gene by microarray analysis , but did not change appreciably above baseline by RT-PCR . In addition to the six transcripts illustrated in Figure 2 , we also validated by RT-PCR the up-regulation of the metallothionein gene , MT1M . The relative mRNA levels of this gene were increased roughly six-fold after four hours of co-culture ( data not shown ) . In addition to the functional clustering using DAVID , we investigated the data using the pathway software , Gene Set Enrichment Analysis ( GSEA ) . [27] We examined separately the MDM only and the MDM-T cell co-culture data sets . Selected results from this analysis are represented in Table S1 . When corrected for multiple hypothesis testing ( FDR q-value <0 . 02[25] ) , the MDM only data did not show statistically significant enrichment of any gene sets , consistent with infected MDMs exhibiting a “quiescent” phenotype . In contrast , the MDM-T cell data demonstrated enrichment of several gene sets annotated to be canonical immune cytokine signaling pathways as well as groups of genes up-regulated when different cell types are stimulated with a variety of conditions such as hypoxia , proliferation or cytokines . Of particular interest , IL-6 related gene sets appear several times on the list as KRETZSCHMAR_IL6_DIFF , BROCKE_IL6 and IL6_PATHWAY . The former two gene sets include genes that are differentially regulated when multiple myeloma cells are treated with recombinant IL-6 . [35] The IL-6 Pathway and additionally the IL-12 Pathway gene sets ( Biocarta ) were both found to be enriched within the infected MDM-T cell co-culture data set . IL-6 mRNA itself , but not IL-12 subunits , were up-regulated in co-cultures ( see Table 2 ) . Furthermore , the GATA3 pathway ( Biocarta ) and TGF-β signaling pathway ( Biocarta ) were also enriched in the MDM-T-cell microarray data indicating that some genes not belonging to inflammatory pathways were induced . GATA3 is a transcription factor and “master regulator” of Th2 differentiation , [36] and TGF-β suppresses both Th1 and Th2 effector cell development . [37] , [38] In the primary data , IL-4 mRNA was only up-regulated 1 . 7-fold in contrast to IFN-γ mRNA which went up almost 3-fold . In aggregate , these data suggest the infection induced primarily a type 1 immune cytokine activation phenotype , but other modulatory factors ( i . e . TGF-β ) may be secondarily activated . To determine the duration of transcript and protein up-regulation , selected cytokines associated with inflammatory responses were measured . We chose IFN-γ and IL-6 , whose transcripts were up-regulated according to microarray data ( Table 2 ) . We also examined IL-10 , a cytokine that promotes progressive VL disease , based upon the hypothesis that important modifying factors are secondarily up-regulated in response to the initial type 1 response . [10] The relative mRNA abundance at time points between 4 h of co-culture ( i . e . 28 hours of infection ) and 3d of co-culture were measured using TaqMan based RT-PCR gene expression assays and normalized to GAPDH ( Figure 3A , Δ ( ΔCt ) method ) . The IFN-γ mRNA peaked at 24 h at roughly three-fold above background ( p<0 . 05 ) and then declined back to baseline . IL-6 mRNA showed a trend toward increased expression during infection , although the changes were not statistically significant . IL-10 mRNA did not demonstrate any significant change , consistent with our microarray findings . Changes in the levels of IFN-γ , IL-6 and IL-10 in the supernatants of MDM-T cell co-culture wells were measured at the same time points ( Figure 3B ) . In all cases , infected co-cultures showed significant accumulation of cytokine ( p<0 . 05 ) comparing 4h of co-culture to 3d of co-culture , whereas there was no similar accumulation of cytokine in uninfected wells . IFN-γ cytokine accumulation peaked at about 4-fold above uninfected cells at 72 hours whereas IL-10 accumulated to approximately 2 . 5-fold above uninfected cells . The mean percent infection levels for these replicates were 83 . 2±2 . 7 , 81 . 1±1 . 8 , 71 . 2±1 . 3 and 63 . 8±7 . 3% at 4 , 24 , 48 and 72 hours of co-culture , respectively . Thus , despite the fact that the levels of IFN-γ increased out to 3d of co-culture , the percent of infected cells stayed roughly constant over the same time interval .
Our study was designed to test the hypothesis that a unique immune response to L . chagasi/infantum is initiated early during the initial interactions between the first immune system cells that encounter the parasite . These include macrophages and T-cells , elements of the innate and adaptive immune systems , respectively . To that end , we used microarrays to examine early gene expression patterns in purified Leishmania-naïve human T cells during their first encounter with infected human macrophages . The data suggested that macrophages exhibit a quiescent phenotype 24 hrs after infection with Leishmania , but Leishmania-naïve T cells respond to infected MDMs primarily with an inflammatory or a type 1 immune cytokine response . These in vitro data suggest that the initial microenvironment created at the site of Leishmania infection may be conducive to development of a type 1 adaptive immune response . Immune responses during symptomatic VL are dominated by suppression of antigen-specific IFN-γ responses[3] and patients have high levels of the suppressive cytokines IL-10 and TGF-β in their serum with a negative Montenegro reactions . [8]–[10] , [13] , [19] Whether this immunosuppression initiates early in the process of macrophage:T cell interactions is not fully known . Several studies have previously profiled transcriptional responses of phagocytic cells to Leishmania infection . Buates and Matlashewski[14] showed that in L . donovani infected BALB/c macrophages , ∼40% of the examined genes in a modified array are down-regulated 4 hours after infection . Rodriguez et al . [39] reported that murine macrophages exhibited a novel non-classical , non-alternative activation profile at early time points after L . chagasi infection . More recently , Chaussabel et al . [40] used microarrays to compare gene expression in human macrophages or dendritic cells infected for 16 hours with a variety of parasitic and bacterial pathogens including L . major and L . donovani . These authors showed that Leishmania infection invokes the expression of a novel set of genes that is Leishmania species-specific . Notably , L . major-infected macrophages down-regulated IFN-γ induced genes yet overall induce a stronger inflammatory profile than does L . donovani . Both Leishmania species induce IL6 gene expression . Prior studies of PBMCs incubated with species of Leishmania causing cutaneous leishmaniasis , called in vitro priming systems , demonstrated the prominent production of type 1 cytokines ( IL-12 and IFN-γ ) but lower levels of type 2 cytokines such as IL-5 . [41] In contrast , infection of PBMCs with L . donovani , which causes visceral leishmaniasis , inhibits the of production of pro-inflammatory cytokines such as IL-1 or TNF-α[42] and leads to interruption of IFN-γ signaling pathways . [43] During the current study , we pursued a global analysis of gene expression shortly after human peripheral blood derived macrophages first encounter Leishmania-naïve T-cells . Based on previously published work from our laboratory , promastigotes convert to amastigotes by 24–48 hours after infection of human macrophages . [44] Furthermore , during infection of murine macrophages with wild-type L . chagasi , fusion of developing phagosomes with lysosomes is delayed for 24–48 hours . [45] At the 28 hour post infection time point examined in this assay , it is therefore reasonable to assume that most if not all parasites will have converted to amastigotes and reside within phagolysosomes . Examination of genes expressed in the infected-macrophages-only condition revealed more genes were down-regulated than were induced . Macrophage activation patterns can be divided into classical , alternative , and Type II activation . Alternatively activated macrophages up-regulate IL-1RA , mannose receptor ( MRC1 ) , scavenger receptor ( CD36 ) , the low-affinity IgE receptor ( CD23 ) and exhibit high arginase activity . Type II macrophages up-regulate sphingosine kinase 1 ( SPHK1 ) , LIGHT , TNF-SF14 , FIZZ1 and IL-10 , have high NO . production but remain arginase low . [32] , [39] , [46] Of the above characteristic macrophage activation transcripts , none were significantly up- or down-regulated after 28 hours of L . chagasi infection . It is possible that some of the above defining macrophage activation markers would have been up- or down-regulated if samples had been taken earlier after the initiation of infection . Nonetheless , the time point chosen for this study captured macrophages harboring converted intracellular amastigotes[44] and the MDM alone condition allowed us to compare background gene expression by infected macrophages with the macrophage-T cell co-culture condition . To our surprise , and in contrast with the quiescent phenotype of the infected-macrophages , four hours after the addition of Leishmania-naïve T cells to infected macrophages , multiple genes characteristic of a type 1 immune cytokine response were up-regulated . Highlights of up-regulated transcripts included IFN-γ , STAT-1 , IL-1α , IL-1β , TNF-α and IL-6 . Further bioinformatics analysis using GSEA confirmed the fact that genes and pathways initiated by pro-inflammatory cytokines and chemokines were up-regulated . Additionally , using GSEA we also observed enrichment of the TGF-β pathway; a cytokine that is suppressive of both Th1-type and Th2-type immune responses . [37] , [38] Although we cannot discern exactly which cell type ( MDM or T cell ) contributed most highly to the above transcripts , comparison with the MDM alone conditions suggests that at least some of the cytokines such as IFN-γ may have been derived from T-cells . It should be emphasized that other cell types such as dendritic cells and NK cells should have been largely excluded from the co-culture through the positive selection on the purification column . Direct measurements of mRNA and protein levels in the co-cultured infected macrophages and T cells showed that the steady state abundance of both the mRNA and protein of IFN-γ and IL-6 accumulated significantly by 3d in the co-cultures ( Figure 3 ) . Although IL10 mRNA did not appreciably increase at 3d of infection , we observed a significant increase in levels of IL-10 in culture supernatants after 3 days of co-cultivation ( Figure 3 ) . The infection-induced accumulation of IL-6 and IFN-γ would be predicted by their respective mRNA abundance , whereas the accumulation of IL-10 was unexpected . This finding could reflect that we missed a transient peak of IL-10 mRNA during MDM infection . Nonetheless since the IL-10 pathway was not enriched using GSEA , it is reasonable to hypothesize that the accumulation of IL-10 could be a secondary , modifying response to the increase in IFN-γ and IL-6 rather than a primary response to Leishmania infected macrophages . The same scenario could be hypothesized for TGF-β . The present data suggest that the initial interactions of L . chagasi-infected macrophages with the adaptive immune system results primarily in up-regulation of type 1 immune cytokine responses . There was little evidence for type 2 activation , as IL-4 was only up-regulated slightly less than two-fold in the primary microarray co-culture data and characteristic type 2 chemokine receptors such as CCR3[47] were , in fact , down-regulated . It remains to be determined at what point L . chagasi parasites begin to tip the balance of immunity away from a curative type 1 , IFN-γ response to cause symptomatic disease . | Visceral leishmaniasis ( VL ) is a potentially fatal vector-borne infectious disease that leads to a variety of outcomes ranging from asymptomatic infection to symptomatic disease . In northeast Brazil , the etiological agent of VL is the protozoan Leishmania chagasi/infantum . Active VL is characterized by fevers , weight loss , hepatosplenomegaly and eventually immune suppression . Without treatment , most symptomatic patients die from secondary bacterial or viral super-infection . We hypothesized that a unique immune response to L . chagasi is initiated early during the initial interactions between the immune system cells that first encounter the parasite . These include macrophages and T-cells , elements of the innate and adaptive immune systems , respectively . We studied an in vitro model of these interactions in which human monocyte-derived macrophages were challenged with L . chagasi , and subsequently cultured with Leishmania-naïve , autologous T cells . Using microarray chips , we examined changes in global gene expression induced by these early interactions . Infection did not elicit a classical inflammatory program in macrophages . However , co-culture of infected macrophages and autologous T cells exhibited a pattern of gene expression , including many markers of acute inflammation or a type 1 immune response . These data suggest that early changes at the site of parasite infection would be conducive to the development of a protective type 1 response , followed by modulation of this same response . | [
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... | 2008 | Macrophage and T-Cell Gene Expression in a Model of Early Infection with the Protozoan Leishmania chagasi |
Developing intervention strategies for the control of parasitic nematodes continues to be a significant challenge . Genomic and post-genomic approaches play an increasingly important role for providing fundamental molecular information about these parasites , thus enhancing basic as well as translational research . Here we report a comprehensive genome-wide survey of the developmental transcriptome of the human filarial parasite Brugia malayi . Using deep sequencing , we profiled the transcriptome of eggs and embryos , immature ( ≤3 days of age ) and mature microfilariae ( MF ) , third- and fourth-stage larvae ( L3 and L4 ) , and adult male and female worms . Comparative analysis across these stages provided a detailed overview of the molecular repertoires that define and differentiate distinct lifecycle stages of the parasite . Genome-wide assessment of the overall transcriptional variability indicated that the cuticle collagen family and those implicated in molting exhibit noticeably dynamic stage-dependent patterns . Of particular interest was the identification of genes displaying sex-biased or germline-enriched profiles due to their potential involvement in reproductive processes . The study also revealed discrete transcriptional changes during larval development , namely those accompanying the maturation of MF and the L3 to L4 transition that are vital in establishing successful infection in mosquito vectors and vertebrate hosts , respectively . Characterization of the transcriptional program of the parasite's lifecycle is an important step toward understanding the developmental processes required for the infectious cycle . We find that the transcriptional program has a number of stage-specific pathways activated during worm development . In addition to advancing our understanding of transcriptome dynamics , these data will aid in the study of genome structure and organization by facilitating the identification of novel transcribed elements and splice variants .
Wuchereria bancrofti , Brugia malayi and Brugia timori are mosquito-borne filarial nematode parasites that cause the tropical disease lymphatic filariasis ( LF ) . The manifestation of the disease ranges from swelling of the lymph nodes to elephantiasis and hydrocele . LF is a major cause of clinical morbidity and disability , leading to significant psychosocial and psychosexual burden in endemic countries . B . malayi is the primary organism for the study of LF because it has a tractable lifecycle that can be replicated in a laboratory setting . Like other filarial nematodes it is a heteroxenous parasite alternating between arthropod vectors and vertebrate hosts . Filarial nematodes are dioecious and reproduce sexually via copulation . Inseminated adult female worms are ovoviviparous and release live larvae ( microfilariae ) into the lymph , where they eventually circulate in the bloodstream to be taken up by mosquitoes during blood feeding . After a microfilaria ( MF ) successfully penetrates the midgut of a susceptible vector , it migrates to the thoracic muscles , and develops intracellularly through two molts to achieve the developmentally arrested third-stage larva ( L3 ) that exits the mosquito proboscis during bloodfeeding and subsequently penetrates the mammalian host . Once L3s enter the definitive host , they undergo two additional molts and mature to adults in the lymphatics . Characterization of the transcriptional program over the complete lifecycle is necessary to clearly understand the development of the parasite and could help devise better target strategies for control . From the standpoint of possibly designing drug-based or vaccine interventions that prevent infection or curtail parasite transmission , there is particular interest in understanding the biology of the L3 to L4 transition in the mammalian host , and the reproductive biology of filarial worms . The completion of the draft genome of B . malayi [1] has ushered in the possibility to use whole-genome gene expression profiling . With that goal in mind , we used next-generation sequencing to comparatively analyze the transcriptome of seven B . malayi lifecycle stages: eggs & embryos , immature MF ( of less than 3 days of age ) , mature MF , L3 , L4 , adult male and adult female . We find that the transcriptional program has a number of stage-specific pathways activated during worm development and that a number of these are potential targets for drugs or vaccines .
All animal work was conducted according to relevant national and international guidelines outlined by the National Institutes of Health Office of Laboratory Animal Welfare , and was approved under UWO Institutional Animal Care and Use Protocol 0-03-0026-000246-4-6-11; and UWM Research Animal Resource Center Protocol V00846-0-10-09 . Brugia malayi adults and MF were obtained from the peritoneal cavities of patently infected dark-clawed Mongolian gerbils ( Meriones uguiculatus ) by peritoneal flush with prewarmed RPMI media ( Fisher Scientific , Piscataway , NJ ) . MF were purified by centrifugation through Ficoll-Paque® lymphocyte isolation media ( Amersham Pharmacia Biotech , Piscataway , NJ ) , and washed in PBS three times prior to flash freezing at −80°C . Adult worms were separated by gender , washed three times in RPMI , and flash frozen . Egg and embryo preparations were made by repeated cutting of 10 female worms with a scalpel to release eggs and embryos into a small volume of cold RPMI . The sample was examined microscopically and pieces of uterine tissue were removed using watchmaker's forceps . The sample was washed three times in cold RPMI prior to flash freezing . Immature MF ( ≤3 days old ) were generated and purified as previously described [2] . L4s were isolated from gerbils 12–13 days post peritoneal infection and were processed as described for adult worms . L3s were obtained from the NIAID-NIH Filariasis Research Reagent Resource Center at University of Georgia , Athens , GA . Total RNA was isolated from the majority of samples using a previously described protocol [2] that combines organic extraction with Trizol LS ( Invitrogen , Carlsbad , CA ) and column purification ( RNAqeous-Micro® , Applied Biosystems , Foster City , CA ) . Samples were treated with DNase I ( Ambion , Austin , TX , USA ) according to the manufacturer's instructions , and the absence of background DNA confirmed by using a portion of each sample in a PCR designed to amplify the B . malayi GPX gene [GenBank:X69128] ( data not shown ) . Isolation of RNA from L3s often produces low yields therefore we used a modified protocol employing homogenization of tissue combined with organic extraction in RNAzol [3] followed by cleaning , concentration and DNase treatment using a Zymo Research RNA column ( Zymo Research Corp , Orange , CA ) . For all samples RNA integrity was confirmed visually by agarose gel electrophoresis ( data not shown ) and purity and concentration determined spectrophotometrically ( NanoDrop ND-1000 , ThermoFisher Scientific ) ; samples were stored at −80°C . Total RNA was lyophilized under vacuum for transport on dry ice to the Wellcome Trust Sanger Institute Genome Facility . Polyadenylated mRNA was purified from total RNA using oligo-dT dynabead selection followed by metal ion hydrolysis fragmentation with the Ambion RNA fragmentation kit . First strand synthesis , primed using random oligonucleotides , was followed by 2nd strand synthesis with RNaseH and DNApolI to produce double-stranded cDNA using the Illumina mRNA Seq kit . Template DNA fragments were end-repaired with T4 and Klenow DNA polymerases and blunt-ended with T4 polynucleotide kinase . A single 3′ adenosine was added to the repaired ends using Klenow exo- and dATP to reduce template concatemerization and adapter dimer formation , and to increase the efficiency of adapter ligation . Adapters ( containing primer sites for sequencing ) were then ligated and fragments size-selected ( 200–275 bp ) by agarose gel electrophoresis . DNA was extracted using a Qiagen gel extraction kit protocol but with dissolution of gel slices at room temperature ( rather than 50°C ) to avoid heat induced bias . Libraries were then amplified by PCR to enrich for properly ligated template strands , to generate enough DNA , and to add primers for flowcell surface annealing . AMPure SPRI beads were used to purify amplified templates before quantification using an Agilent Bioanalyser chip and Kapa Illumina SYBR Fast qPCR kit . Libraries were denatured with 0 . 1 M sodium hydroxide and diluted to 6 pM in a hybridization buffer to allow the template strands to hybridize to adapters attached to the flowcell surface . Cluster amplification was performed on the Illumina cluster station or the Illumina cBOT using the V4 cluster generation kit following the manufacturer's protocol . A SYBRGreen QC was performed to measure cluster density and to determine whether to pass or fail the flowcell for sequencing . This was followed by linearization , blocking and hybridization of the R1 sequencing primer . The hybridized flowcells were loaded onto the Illumina Genome Analyser IIx for 54 cycles of sequencing-by-synthesis using Illumina's v4 or v5 SBS sequencing kit then , in situ , the linearization , blocking and hybridization step was repeated to regenerate clusters , release the 2nd strand for sequencing and to hybridize the R2 sequencing primer followed by another 54 cycles of sequencing to produce paired end reads . These steps were performed using proprietary reagents according to manufacturer's recommended protocol ( https://icom . illumina . com/ ) . Data were analyzed using the RTA1 . 6 or RTA1 . 8 Illumina pipeline and submitted to Array Express ( http://www . ebi . ac . uk/arrayexpress/ ) under the accession number E-MTAB-811 . Each lane of Illumina sequence was assessed for quality based on %GC content , average base quality and Illumina adapter contamination . To assess the quality of the lane , the mean base quality at each base position in the read was computed over all reads from the lane . To assess %GC content of the reads a frequency distribution of values was plotted . For a single sample in a lane , a GC plot with a normal distribution around the expected GC for the organism would be expected . Any lanes containing a contamination could therefore be identified by the presence of multiple peaks in the %GC plot . To screen for adapter contamination , the sequence reads were aligned to the set of Illumina adapter sequences using BLAT v . 34 with default parameters [4] . Any reads matching these sequences were reported as being contaminated with adapter sequence . Sequence reads from each lifecycle stage were aligned to the genome assembly [GenBank:DS236884–DS264093] using TopHat v1 . 0 . 14 , a splice junction mapper built upon the short read aligner Bowtie [5] , [6] . The pipeline utilized exon records in the genome annotation [1] to build a set of known splice junctions for each gene model , complementing its de novo junction mapping algorithm . Default parameters were used except for the following: minimum intron length was set to 50; minimum isoform fraction filter was disabled; closure-search , coverage-search , microexon-search and butterfly-search were enabled for maximum sensitivity . The resulting alignment files were converted to BAM format and low quality alignments with mapping quality scores less than 5 were removed before downstream analyses [7] , [8] . No replicate samples were sequenced and all data were combined per lifecycle stage . Reads aligned to exonic regions were enumerated for each gene model using the HTSeq package ( v0 . 4 . 7 ) in Python ( www-huber . embl . de/users/anders/HTSeq ) . Reads overlapping more than one gene model were counted as ambiguous with the mode parameter set as “union” . Following Mortazavi et al . [9] , transcript abundance estimates were computed as RPKMs ( Reads Per Kilobase of exon model per Million mapped reads ) with the following modifications: ( i ) a set of paired-end reads were counted as one in compiling sequence counts to represent a single sampling event and ( ii ) TMM ( trimmed mean of M ) -normalized values were used in place of the nominal library size to account for compositional biases [10] . The correction factors for TMM-normalization ( i . e . , the weighted trimmed mean of M values to the reference ) were calculated using the Bioconductor edgeR package [11] . The weights were from the delta method on binomial data , and the library whose upper quartile is closest to the mean upper quartile was used as the reference . Differential expression analysis was performed in edgeR by fitting a negative binomial model to the sequence count data . Using the quantile-adjusted conditional maximum likelihood method , dispersion parameters were estimated for each gene as a measure of the overall stage-to-stage variability to facilitate between-gene comparisons . All hypothesis testing was carried out using exact test for the negative binomial distribution with a common dispersion term for all genes . P-values less than 0 . 01 were considered significant . Dispersion parameters were estimated directly from the count data for comparisons contrasting a single stage or two related stages relative to all other stages . For comparisons between pairs of lifecycle stages , a common dispersion value of 0 . 2 was used , which is equivalent to allowing within-stage variations in expression levels of up to 45% . This value was chosen based on the level of variability observed between the immature and mature MF samples . Because longer transcripts give more statistical power for detecting differential expression between samples [12] , Gene Ontology ( GO ) analysis was performed using the goseq package that adjusts transcript length bias in deep sequencing data [13] . GO annotation was retrieved from the UniProtKB-GOA database [14] , and statistically over-represented GO terms in a given gene list were identified using the Wallenius non-central hypergeometric distribution . Hierarchical clustering analysis was performed using GeneSpring GX ( Agilent Technologies ) . RKPM values for each gene were baseline transformed to the median of all samples , and hierarchically clustered with centroid linkage using Pearson's uncentered correlation coefficient as distance metric .
In total , 104 million paired-end reads ( 2×54 bp ) were generated from polyA-tailed mRNA using the Illumina Genome Analyser IIx ( Table S1 ) . Sequence reads were aligned to the genome assembly using TopHat [5] , and the number of reads aligned to each gene model was summed yielding relative transcript levels for individual genes . Approximately 50% of the sequenced reads were mapped to the reference genome after low quality alignments were removed; 10% of which were aligned to genomic regions outside of the current gene models . Sequencing depth varied between the lifecycle stage libraries , affecting gene model coverage and the distribution of the read counts per gene model for each library ( Figure 1 and Figure S1 ) . Overall , in each library , 8 , 000–10 , 000 genes ( equivalent to 70 to 90% of the currently annotated gene models ) had 5 or more mapped reads . Sequence counts were RPKM ( Reads Per Kilobase of exon model per Million mapped reads ) -transformed and TMM ( trimmed mean of M ) -normalized to assist in the interpretation of transcript abundance comparisons between stages and genes [9] , [10] . For statistical inferences , however , raw read counts were directly used . Further analysis of our sequence data from a genomics perspective , covering issues related to missing , incomplete or incorrect gene models of the 2007 assembly [1] will be published elsewhere ( in preparation ) . Our sequencing libraries contained reads that map to the Wolbachia genome [GenBank:AE017321] . However , the study was not adequately designed such that one could quantitatively analyze these reads in a biologically meaningful way . Abundance estimates ( inferred from read counts ) of these transcripts most likely deviate substantially from their true in vivo levels . Poly-A selection directly affects the relative abundance of non-poly-A Wolbachia transcripts with respect to B . malayi transcripts . Moreover , the nature and extent of the biases introduced by oligo-dT method to the relative abundance levels among the non-poly-A species ( with respect to each other ) is not well understood , and one cannot assume that these biases would remain uniform among different sample preparations . Another layer of uncertainty stems from the possibility that these “Wolbachia” sequences were transcribed from the B . malayi nuclear genome rather than the endosymbiont as a consequence of the past horizontal gene transfer events , leading to a differential capture of ( presumably ) poly-A tailed “Wolbachia” transcripts of the B . malayi nuclear origin . However , given the incomplete draft nature of the B . malayi genome assembly and the inherent difficulty in mapping short reads originating from multiple loci that are similar in sequences , it remains challenging to rigorously test this hypothesis in silico . To investigate the global transcriptional differences between stages and between genes during development , a negative binomial ( NB ) based model [11] was fit to sequence count data . First , the degree of between-stage differences was assessed globally using a multidimensional scaling ( MDS ) of all-against-all comparisons in the NB model ( Figure 2 ) . The resulting sample relations appear consistent with the expected biological differences between the samples . The MDS plot indicates that , in relative terms , the transcriptome profiles of the immature and mature MF are more similar to each other than either is to other stages . Likewise , the eggs & embryos sample is closely related to the adult female sample , part of which consists of the germ-line cells . Interestingly , this plot also shows how different the transcriptome profiles of adult male and female worms are to each other . Next , we made between-gene comparisons in terms of overall transcriptional variability across stages . It is generally hypothesized that while some genes are expressed constitutively , genes with specific developmental functions are expressed at specific stages . To quantify the level of transcriptional variation for each gene across the seven lifecycle stages , the NB dispersion parameters were estimated for each gene , and used as a measure of the extra-Poisson , stage-to-stage variability . Genome-wide distribution of the dispersion parameter estimates suggests that the level of transcriptional variation is not uniform across all genes ( Figure S2 ) . Although the majority of genes show low to moderate levels of variation , certain groups of genes exhibit a significantly greater level of variation . Approximately 25% of genes have NB dispersion parameter values larger than 1 . After ranking by dispersion , genes were partitioned into quarters and designated as Q1 through Q4 in the order of decreasing variability . To examine genes displaying life stage dependent transcriptional patterns in greater detail , the top 25% most variable genes according to the NB dispersion ( i . e . , Q1 ) were subjected to an unsupervised hierarchical clustering ( Figure 3A ) . The resulting heatmap and dendrogram suggest that there are four major transcriptional patterns , each of which corresponds to an increased transcript abundance in ( i ) female and/or eggs & embryos , ( ii ) male , ( iii ) microfilariae , or ( iv ) late larval stages . The transcriptional patterns identified through the clustering analysis largely recapitulate the sample relations revealed in the MDS plot ( Figure 2 ) . To classify genes into these broad but distinct co-expression groups in a statistically robust manner , we performed a series of exact tests for the NB distribution using raw read counts for all genes ( Figure 3B ) . Relying solely on the “shape” of expression patterns derived from RPKM values , without considering how many reads contributed to each pattern , may lead to false-positive findings . We first identified genes preferentially transcribed during single stages by performing exact tests contrasting each individual stage relative to the mean of all other stages . The resulting gene lists were augmented by additional exact tests to include genes displaying increased transcript abundance in two ( related ) stages with respect to all other stages . At the level of p-value<0 . 01 , mutually-exclusive , non-redundant gene lists were compiled for each group . In total , we cataloged 2 , 430 genes into groups with distinct life stage dependent transcriptional patterns . Comparing the gene lists to the highly variable genes in the Q1 group suggests that members of the four main expression groups account for ∼80% of the top 25% most variable genes ( Figure 3C ) . Genes that are highly variable in transcript abundance , yet are not assigned to any of the four main groups ( n = 563 ) likely display complex transcriptional patterns falling outside of the four categories . In addition , five direct pairwise comparisons were made between relevant stages to gain further insights into the transcriptomic features associated with ( 1 ) sex differences , ( 2 ) intrauterine reproductive processes , ( 3 ) MF maturation , and ( 4 ) late larval development ( Figure 3D ) . Cross-referencing with the previously defined coexpression groups ( Figure 3B ) indicates that stage specificity is not homogeneous within each group of differentially transcribed genes , highlighting the complexity of the relative transcriptome differences among the lifecycle stages examined in the study . The results outlined above are described in further detail in the following sections . We identified and compared statistically overrepresented GO terms in groups of genes that differ in their level of transcriptional variation over the lifecycle ( i . e . , Q1 to Q4 ) to investigate specific gene sets and functional categories distinctly associated with high levels of transcriptional variation ( Table S2 and Figure S2 ) . This analysis identified ‘structural constituent of cuticle’ ( GO:0042302 ) as the most significantly overrepresented GO category among Q1 genes that exhibit high levels of between-stage transcriptional variation . Forty-six cuticle collagen genes are annotated with this GO term , and thirty-three of these have distinct lifecycle stage dependent transcriptional patterns ( 18 late larval , 12 female/eggs , 2 male and 1 microfilarial; Dataset S1 ) . Additional GO terms overrepresented among Q1 genes include those related to serine type endopeptidase inhibitor ( serpin ) , structural molecule , and kinase/phosphatase activity . By contrast , GO categories associated with protein metabolism , such as translation , protein transport and proteasome complex are significantly overrepresented among genes displaying relatively little transcriptional variation over lifecycle stages ( i . e . , Q2-4 ) . Although transcript levels of 990 genes are significantly higher during larval stages , 886 and 554 genes display elevated transcript abundance in adult male , and adult female and/or eggs & embryos , respectively ( Figure 3B ) . A direct pairwise comparison of male versus female transcriptome further indentified 1 , 279 genes with male-biased expression and 651 genes with female-biased expression ( Figure 3D ) . At the level of GO categories , structural molecular activity and those associated with protein phosphorylation and dephosphorylation are prominent among genes preferentially transcribed in adult male . A closer look at individual genes with male-biased expression reveals that major sperm proteins are largely responsible for driving the statistical significance of structural molecular activity ( GO:0005198 ) in these comparisons . By contrast , structural constituents of cuticle ( collagens ) , transcription factor/regulator activity , nuclear receptor activity and serpin activity constitute a main theme of the overrepresented functional categories among genes preferentially transcribed in adult female and/or eggs & embryos . In an effort to elucidate female germline-enriched transcripts and gain insight into intrauterine reproductive processes , the transcriptome profile of a library enriched for eggs and embryos was compared with that of whole adult female ( Figure 3D ) . However , because the eggs & embryos transcriptome is inherently a subset of the adult female transcriptome , this pairwise comparison is almost subtractive in nature and is likely biased against identifying transcripts enriched in germline tissues . On the contrary , detection of female transcripts either not expressed or expressed at lower levels in eggs and embryos likely remains unaffected by this asymmetric sample relation . For this reason , we used the adult male transcriptome profile as an additional reference point to better identify genes showing a germline-enriched expression pattern . We performed a Venn diagram analysis with three datasets: ( 1 ) genes with enriched expression in adult female relative to eggs & embryos , ( 2 ) genes with enriched expression in eggs & embryos relative to adult male , and ( 3 ) genes with enriched expression in adult female and/or eggs & embryos relative to all other stages ( Figure S3 ) . We considered genes belonging to the first set to exhibit somatic tissue-enriched expression pattern , and those belonging to either of the last two sets , but excluded from the first set , to exhibit germline-enriched expression pattern . Based on these criteria , 788 and 239 genes show enriched expression in female germline and somatic tissues , respectively . GO term overrepresentation analysis indicates that functional categories , such as transcription factor activity , DNA binding , regulation of transcription and nuclear receptor activity are more frequently found among genes displaying germline-enriched expression . On the contrary , genes implicated in chloride transport , lipid binding , and proteolysis are overrepresented among those with somatic tissue-enriched expression pattern ( Table S3 ) . Interestingly , structural constituents of cuticle ( GO:0042302 ) is overrepresented among both genes with germline-enriched and somatic tissue-enriched expression patterns . A closer look at individual genes reveals that mutually exclusive subsets of collagens are overrepresented in each gene set . When compared across all stages , transcript levels of 148 genes are distinctly elevated during the MF stage . Overrepresented GO terms in this group include zinc ion binding , nucleic acid binding , chitinase activity , and proteolysis ( Figure 3B and Table 1 ) . Most notably , among these are 44 genes that encode proteins with C2H2-type zinc finger domains . There are 195 zinc finger protein genes annotated in the B . malayi draft genome , some of which have high transcript levels in stages other than MF ( i . e . , 3 late larval , 17 male and 6 female/eggs ) . In a similarly biased manner , 3 out of 4 endochitinase genes identified in the current B . malayi genome show transcriptional increase during MF stages . Diverse classes of proteases are also represented in this gene set ( e . g . , cathepsin L-like proteases including Bm-cpl-6 , papain cysteine protease family , metalloprotease I , aspartyl protease and trypsin-like protease ) . Direct comparison of immature and mature MF ( IM and MM ) indicates that 126 genes show differential transcript abundance between the two samples ( Figure 3D ) . Many different metabolic genes are found in the IM overexpressed gene set , while the endochitinases are overrepresented in the MM . We identified 842 genes displaying increased transcript abundance during L3 and/or L4 stages relative to other lifecycle stages ( Figure 3B ) . Functional categories overrepresented among these genes include structural components of the cuticle , oxidoreductase activity , serpin activity , chloride transport , hedgehog receptor activity , glycogen biosynthetic process , and proteolysis . As suggested by the last GO category , various proteases ( e . g . , metalloprotease , papain family peptidase , zinc carboxypeptidase family and cathepsin-like cysteine proteases , including Bm-cpl-1 , 4 and 5 ) are prominently represented in this gene set , a pattern similarly found in the MF transcriptome . A pairwise comparison of the transcriptomes of late larval stages indicates that 342 genes have elevated transcript levels in L3s , and 155 in L4s . At the level of functional categories , cysteine-type peptidase activity ( e . g . , cathepsin-z and -L like proteases ) and serpin activity are overrepresented among L3-enriched transcripts , whereas structural constituents of the cuticle and cellular component organization are overrepresented among L4-enriched transcripts ( Table S3 ) . In addition , our data indicate that abundant larval transcripts ( Alt1 . 2 and Alt2 ) show increased abundance in L3s relative to L4s .
Using high-throughput sequencing , we have undertaken a comprehensive genome-wide survey of the developmental transcriptome of the human filarial parasite B . malayi . Although deep sequencing data are highly informative in identifying novel transcribed elements and splice variants that help improve genome annotation [15] , the present study aims to characterize transcriptome changes along the progression of the parasite's lifecycle . Transcriptome changes mediating cuticular molting likely represent one of the most notable developmental transitions in RNA expression . Like all nematodes , Brugia spp . have five lifecycle stages that are punctuated by molting of the collagenous cuticle . The tightly regulated process of molting involves cell signaling within the hypodermis to cue secretion of the new collagenous cuticle , shedding of the old cuticle and proteolytic remodeling of the new cuticle [16] , [17] . Analysis of overrepresented GO terms highlights structural cuticle components , extracellular matrix components and cysteine-peptidase inhibitors , among others , in genes with high levels of transcriptional variation over the lifecycle ( Table S2 ) . In particular , the cuticle collagen gene family displays distinct dynamic transcriptional patterns over the course of the lifecycle , likely reflecting compositional variation in cuticular structure among different life stages . Besides these structural components , genes displaying the most dramatic transcriptional variation in our data set are likely associated with developmental processes that differ between the larval and the adult stages and/or between the genders ( e . g . , gametogenesis ) . By contrast , genes constitutively expressed over the developmental period studied frequently have predicted cellular functions related to protein expression , modification and transport , possibly representing core cellular processes that are essential to the survival of cells independent of the lifecycle stage . The present study indicates that genes exhibiting adult male enriched transcriptional pattern ( relative to adult female and/or other stages ) show strong statistical bias towards GO categories related to cytoskeleton , structural molecule activity , protein phosphorylation and dephosphorylation ( Table 1 ) . Many of these gene sets and functional categories are highly represented among classes of male-enriched transcripts in parasitic nematodes [18] , [19] , [20] , [21] and have been identified in the Caenorhabditis elegans male and hermaphrodite germline as being involved in spermatogenesis [22] . Nematode sperm are unique in that they utilize a nematode-specific cytoskeletal element , major sperm protein , for ameboid motility . It is hypothesized that because mature nematode sperm lack ribosomal elements , the phosphorylation and dephosphorylation of molecules by a host of enzymes within the differentiated cells could promote maturation and pseudopod extension [22] . Seven of the genes found to be differentially expressed in male worms in our study were also found in a microarray comparison of adult male and female worms [23] , and were shown by in situ localization to be expressed either in sperm or vas deferens tissue of adult male worms and not in gravid adult female worms [24] . If we compare our RNA-seq data with recent microarray work comparing gene expression in adult male and female B . malayi [19] , 515 of our 1 , 276 ( 40% ) genes with male-biased expression match with male up-regulated genes found in the microarray comparison , and 150 out of the 651 ( 23% ) genes with female-biased expression match the microarray findings . In filarial nematodes , fertilization is internal and gravid females hold oocytes , sperm , zygotes , developing embryos , and MF in their uteri . Structural constituents of cuticle , transcription factor activity , DNA binding , and regulation of transcription emerged as notable themes in our analysis of overrepresented functional categories among genes with increased transcript levels in adult female and/or eggs & embryos ( Table 1 ) . These are likely relevant in the context of embryogenesis . Pairwise comparison of adult female with adult male presents us with a similar but more expanded view on features of genes displaying female-enriched expression ( Table S3 ) . Further comparisons with genes displaying germline-enriched expression patterns suggest that many of the female-biased transcripts , and more importantly , the majority of the above mentioned functional categories are attributable to the characteristics of the germline transcriptome . For instance , 33 out of 34 genes annotated with transcription factor activity ( e . g . , nuclear hormone receptors and homeobox domain containing proteins ) that are enriched in female and/or eggs & embryos , have a distinctly germline-enriched expression pattern . Bm-fab-1 ( Bm1_33050 ) , an embryonic fatty acid binding protein transcript previously found to be female-associated by differential display PCR and whose protein localizes to embryos [25] , [26] also exhibits a germline-enriched expression pattern . Much of our current information on molecular aspects of filarial reproduction comes from microarray and PCR-based transcriptome comparisons between whole adult male and female worms . These studies were based on the assumption that gender-associated transcripts arise from the reproductive organs and their contents . Our data suggest that such an assumption is not wholly unreasonable but may not always hold true . Out of 651 female-enriched transcripts we identified ( in comparison to male ) , 82 display somatic tissue-enriched expression patterns , and it is likely that some of these transcripts are truly not derived from the germline tissues . Spatial expression patterns have not been confirmed for the majority of gender-associated B . malayi genes , and a growing body of research on nematode neurobiology and extracellular signaling lends support to the idea that some gender-associated genes can be expressed in non-reproductive tissues . For example , free-living and parasitic nematodes use gender-specific receptors to sense environmental signals , as demonstrated by the presence of anterior chemosensors in male worms that specifically bind female pheromones [27] , [28] . Nematodes also store fat in intestinal cells , which may act as endocrine organs involved in germline signaling and are triggered by activation of intestinal cell nuclear receptors by lipophilic hormones [29] , [30] , [31] . On the other hand , these observations are not inconsistent with the possibility that some somatic tissue derived transcripts play an essential role in embryonic development or intrauterine reproductive processes . The current study suggests that components incorporated into the embryonic cuticle and the eggshell membrane may be in some part maternal in origin . This interpretation is supported in at least one case where MF sheath protein transcripts in Brugia are detectable by in situ hybridization only in adult female tissues and not in eggs or embryos [24] , while the encoded protein is found on the surface of in utero sheathed MF but not in maternal tissues [32] . Other notable transcripts showing enrichment in female somatic tissues in our study include Juv-p120 excretory/secretory proteins and astacin proteases ( Bm1_30065; Bm1_13915 ) . Homologs of the latter in C . elegans , nas-4 and nas-9 are found in pharyngeal marginal cells , and in the hypodermis and reproductive tract , respectively [33] . Their functions are unknown but the localizations suggest roles in cuticle and eggshell remodeling . After expulsion from females , developmentally arrested Brugia MF must undergo a maturation process within the mammalian host to become infective to the mosquito vector [2] , [34] , [35] . Brugia MF are sheathed in a remnant of the eggshell membrane that is acellular and insoluble , and is composed of chitin and a variety of cross-linking proteins , lipids and polysaccharides [36] , [37] , [38] , [39] . Our data indicate that a large number of transcripts representing DNA-binding proteins with zinc finger motifs as well as several endochitinase transcripts are significantly elevated in MF . Proteomic analysis also revealed a significant enrichment of zinc finger proteins in this stage of the lifecycle [21] . Although the precise role of these DNA binding proteins is unknown , it is tempting to speculate on their involvement in maintaining the developmentally arrested state of circulating MF . Transcriptional increase in proteases and chitin-associated enzymes in MF could be important in the process of casting off the chitinous sheath during or after mosquito midgut penetration [35] , [40] , [41] . Immunolocalization studies have shown that in sheathed MF , chitinase is stored in the inner body of the MF and secreted to the surface to degrade the sheath upon mosquito infection [42] . Microfilarial maturation is accompanied by transcriptional transitions and changes in the composition of the microfilarial surface [2] , [35] . Despite the remarkable change in infectivity , our data suggest that transcriptional differences between IM and MM are relatively small; it is the least pronounced of all pairwise comparisons made in this study ( Figure 2 and 3D ) . Genes involved in ATP synthase activity , tRNA production and cytoskeleton are overrepresented among those that show transcriptional change between IM and MM ( Table S3 ) . Although it is difficult to further characterize the exact nature of these changes due to a high proportion of genes with no functional annotation , we hypothesize that a metabolic shift is likely part of the maturation process in anticipation of the transition from the blood of a homeothermic host to the inhospitable midgut and hemocoel of the poikilothermic mosquito vector . It is important to consider that both populations of MF used in this experiment were derived from the peritoneal cavities of infected gerbils . Although we have previously shown a dramatic difference in mosquito infectivity between peritoneally-derived immature and mature MF [2] , [34] , [35] , it is clear that intraperitoneally-derived MF , regardless of age , are considerably less infective than those found in circulating blood [43] . It is possible that the transcriptional profile of mature circulating MF differs from those that are derived from the peritoneal cavity . Following the introduction of L3s into the peritoneal cavity of gerbils , the L3 to L4 transition requires no migration and occurs approximately 8 days post infection ( unpublished ) . This particular lifecycle transition is of great interest to researchers trying to identify parasite molecules that mediate interactions with the host immune system , and that could be exploited with vaccines to confer protective immunity , or with drugs to prevent infection . Antigens that historically have been of interest in this regard are the ALT ( abundant larval transcript ) family of potentially secreted larval acidic proteins found predominately in L2 and L3 stages [44] , [45] , [46]; the L3 cystatin cysteine protease inhibitor family , Bm-SPN-2 , TGF-β homologues , macrophage inhibition factor and Bm-VAL-1 [46]; troponin , tropomyosin and cuticular collagens [47]; Onchocerca volvulus activation associated secreted protein ( Ov-ASP-1 ) [48] , onchocystatin ( Ov-CPI-2 ) [49] and Ov-SPI-1 [50] , and B . malayi glutathione-s-transferase [51] . One hypothetical protein found to be L3 specific in our experiment , Bm1_38105 , was also highly ranked as a potential drug target [52] . In the present study , the transcriptome of developmentally arrested , vector-derived L3s was compared to that of peritoneally-derived L4s at 12–13 days post infection . Comparing our RNA-seq data to a recent microarray experiment [53] that assessed transcriptomes of vector-derived L3s to cultured and irradiated L3s , shows that 29 genes are shared , and likely constitute genes required for L3 survival in mosquitoes . These include Alt-2 and Alt1 . 2 proteins , cathepsin L precursors , Bm-col-2 , cystatin , microfilarial surface associated protein , metabolic proteins and BmSERPIN . The differential expression of cathepsins Bm-cpl-1 , 4 , and 5 in vector stage L3s is supported by EST sequences and these genes are grouped phylogenetically into a distinct clade ( Ia ) separate from other nematode cathepsin-like proteases [54] . There is strong evidence that these proteins play important roles in the L3 to L4 molt , because targeting the cpl-1 gene in O . volvulus by RNAi decreased the rate of molting [55] , and suppression of the cathepsin L-like cysteine protease transcript by injection of siRNA or dsRNA into infected mosquitoes carrying L2 and L3 stages of B . malayi retarded worm growth , disrupted development and resulted in cuticular sloughing [56] . It is important to point out that the L4s we used were from the peritoneal cavity of gerbils , and did not follow the normal behavioral pathway of intradermal passage and migration to the lymphatics . It is possible that the transcriptional profile of intraperitoneally-derived L4s is different than that of worms found in lymphatics; indeed Chirgwin et al . [57] showed different transcriptional profiles for three L3 genes at 3 days post infection in groups that had been injected intradermally and allowed to migrate naturally to the popliteal lymph node in the gerbil model , and those that were confined to the peritoneum . In this study we provide a detailed overview of the molecular repertoires that define and differentiate distinct lifecycle stages of the parasite , extending and complementing previously published work on stage-specific gene expression [2] , [19] , [21] , [24] , [53] , [58] , [59] . Inclusion of seven different developmental stage samples uniquely allows us to place specific between-stage transcriptional differences into the broader context of the transcriptomic landscape during the lifecycle of B . malayi . It is important to emphasize , however , that this is just an overview of observations and that these data will be mined by the community to provide specific information on particular gene sets to bring these deep sequencing data into more complete biological context . Because expression dynamics is an important consideration in the genome-wide assessment of candidate targets for control [52] , [60] , [61] , our comprehensive analysis of transcript abundance over developmental time is a valuable addition to a growing body of genomic and post-genomic resources that guide and support the concerted efforts to develop better intervention strategies . | Lymphatic filariasis , also known as elephantiasis , is a tropical disease affecting over 120 million people worldwide . More than 40 million people live with painful , disfiguring symptoms that can cause severe debilitation and social stigma . The disease is caused by infection with thread-like filarial nematodes ( roundworms ) that have a complex parasitic lifecycle involving both human and mosquito hosts . In the study , the authors profiled the transcriptome ( the set of genes transcribed into messenger RNA rather than all of those in the genome ) of the human filarial worm Brugia malayi in different lifecyle stages using deep sequencing technology . The analysis revealed major transitions in RNA expression from eggs through larval stages to adults . Using statistical approaches , the authors identified groups of genes with distinct life stage dependent transcriptional patterns , with particular emphasis on genes displaying sex-biased or germline-enriched patterns and those displaying significant changes during larval development . This study presents a first comprehensive analysis of the lifecycle transcriptome of B . malayi , providing fundamental molecular information that should help researchers better understand parasite biology and could provide clues for the development of more effective interventions . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
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] | 2011 | A Deep Sequencing Approach to Comparatively Analyze the Transcriptome of Lifecycle Stages of the Filarial Worm, Brugia malayi |
Centromeres typically contain tandem repeat sequences , but centromere function does not necessarily depend on these sequences . We identified functional centromeres with significant quantitative changes in the centromeric retrotransposons of wheat ( CRW ) contents in wheat aneuploids ( Triticum aestivum ) and the offspring of wheat wide hybrids . The CRW signals were strongly reduced or essentially lost in some wheat ditelosomic lines and in the addition lines from the wide hybrids . The total loss of the CRW sequences but the presence of CENH3 in these lines suggests that the centromeres were formed de novo . In wheat and its wide hybrids , which carry large complex genomes or no sequenced genome , we performed CENH3-ChIP-dot-blot methods alone or in combination with CENH3-ChIP-seq and identified the ectopic genomic sequences present at the new centromeres . In adcdition , the transcription of the identified DNA sequences was remarkably increased at the new centromere , suggesting that the transcription of the corresponding sequences may be associated with de novo centromere formation . Stable alien chromosomes with two and three regions containing CRW sequences induced by centromere breakage were observed in the wheat-Th . elongatum hybrid derivatives , but only one was a functional centromere . In wheat-rye ( Secale cereale ) hybrids , the rye centromere-specific sequences spread along the chromosome arms and may have caused centromere expansion . Frequent and significant quantitative alterations in the centromere sequence via chromosomal rearrangement have been systematically described in wheat wide hybridizations , which may affect the retention or loss of the alien chromosomes in the hybrids . Thus , the centromere behavior in wide crosses likely has an important impact on the generation of biodiversity , which ultimately has implications for speciation .
Centromeres , which are located at the primary constriction of the chromosome , are required for the accurate segregation of chromosomes and serve as the sites for kinetochore assembly during mitosis and meiosis . The main DNA components of the centromere are highly repetitive , such as the 171-bp α-satellite repeat in humans and 150- to 180-bp simple tandem repeats in some flowering plants [1–5] . Long-terminal repeat ( LTR ) retrotransposons , also known as centromeric retrotransposons ( CRs ) , are often intermingled with tandem repeats and are enriched in plant centromeric regions [6–11] . The highly conserved function of the centromere is correlated with its epigenetic features , including the histone H3 variant CENH3 in plants ( CENP-A in mammals ) [12–15] , phosphorylation of histone H2A at Thr-133 [16] and H3 phosphorylation at Ser-10 [17 , 18] . Despite the conserved centromere function , centromeric repeat sequences apparently evolved rapidly in some species under specific circumstances . This phenomenon is known as the "centromere paradox" [13] . Centromeric sequences are highly variable between different species and different chromosomes and even between the same centromeres from different ecotypes or varieties [5 , 11 , 19 , 20] . Most of the centromeric tandem repeats in plants , such as CentO in rice ( Oryza sativa ) , CentBd in Brachypodium distachyon , and CentC in maize ( Zea mays ) , are likely to be species-specific [4 , 5 , 21] . Several wild Oryza species lack CentO and instead possess genome-specific satellite repeats [22] . Similarly , little homology was found between the centromeric sequences of the potato ( Solanum tuberosum ) and its wild relative S . verrucosum [23] . Moreover , centromeres showed diversity in the repeat-less and repeat-based sequences on different chromosomes of S . verrucosum [20] . Eukaryotic centromeres carrying novel satellites may have evolved from neocentromeres that experienced insertion and/or extensive amplification of satellite repeats [20 , 24] . Previous studies revealed that recent segmental duplication , abundant rearrangements , and reshuffling occurred in CEN4 and CEN8 of rice and that the changes in CEN8 seemed to appear after the divergence of the O . sativa subspecies japonica and indica from a common ancestor [24 , 25] . An analysis of centromere retention or loss indicated that the major events during the evolution of maize from a supposed tetraploid ancestor ( Sorghum bicolor ) were chromosomal rearrangements , such as insertions and translocations , resulting in dysploidy and reduced chromosome numbers [26] . Despite the observation that substantial variations in centromeres occurred during evolution , the relationship between centromere variations and species evolution remains uncertain . In most eukaryotes , the centromeric sequences alone are insufficient to maintain a functional centromere [27] . In humans and plants , many newly formed centromeres are devoid of typical centromeric sequences , and their formation was likely determined by epigenetic mechanisms [28–32] . Additionally , the centromere activity of dicentric chromosomes is independent of centromeric sequences . Many stable dicentric chromosomes in maize , including A-A and A-B centromeres ( the centromere of the supernumerary B chromosome contains B-specific repeats ) , contain one active and one inactive centromere , as determined by examining the epigenetic modifications [18 , 33] . Furthermore , the inactive centromere recovered its activity by switching its epigenetic features under certain circumstances [34] . The essential structural and functional components for the core chromatin of centromeres include pericentromeric heterochromatin and active transcription of centromeric DNA [35–38] . The epigenetic components , rather than the DNA sequences , are essential for the establishment of centromere function . However , it remains a mystery why most functional centromeres contain highly repetitive sequences . Allopolyploid wheat , either tetraploid or hexaploid , originates from interspecific hybridizations that trigger striking chromosomal rearrangements , genome reorganization , and chromatin remodeling in the parental genomes [39–43] . Wheat also has the capacity to hybridize with its wild relatives , which provides a broader gene source for wheat germplasm enhancement through addition , translocation , and substitution lines containing alien chromosomes [44–46] . In fact , wheat appears to prefer alien chromosomes or fragments from specific genomes [47] . However , the mechanisms regulating the stable transmission of these alien genomic sources in defined genetic backgrounds are still unclear . A previous study indicated that the size of the maize centromere was expanded in oat ( Avena sativa ) -maize addition lines , which may be a key factor for the survival of neocentromeric chromosomes in natural populations [48] . As such , an understanding of the adaptation of centromeres to "genome shock" and their evolutionary history in the wide wheat hybrid will require additional studies . Due to their repetitive structures and low sequence conservation , it is difficult to compare centromeric sequences across different species . Complete centromeres on partial chromosomes have been sequenced in rice and maize [8 , 49–51] . In wheat , only partial centromere sequences have been released from published bacterial artificial chromosome ( BAC ) sequences [49 , 52–54] . Here , we observed that the content of classical centromeric retrotransposon sequences was reduced or apparently lost in both aneuploid wheats ( 4D , 1B , 5D chromosomes ) and their wild relatives , such as Th . intermedium and Th . elongatum , when hybridized with wheat ( Fig 1 and Table 1 ) . With new developments in wheat genome sequencing , we first uncovered the detailed sequences in the new centromere of the 4DS chromosome in wheat aneuploids by ChIP ( chromatin immunoprecipitation ) -sequencing with wheat CENH3 antibodies . Additionally , for Th . intermedium , which does not have a sequenced reference genome , we developed a new ChIP-dot-blot strategy ( see Methods ) to identify the novel centromeric sequences in the wheat-Th . intermedium addition line TAI-14 . We also detected the expansion of centromeric sequences and the formation of multiple centromeres in wheat and its wide hybrid offspring ( Fig 1 and Table 1 ) . Finally , we provide a detailed analysis of centromere variations and offer some new insights into centromere evolution in wheat and its wild relatives ( Fig 1 ) .
The loss of canonical centromere sequences can be induced by breakage , rearrangements and radiation at plant centromeres [31 , 32] . Here , we observed the elimination of centromeric sequences in both wheat aneuploids and their wide hybrids . Compared with normal centromeres in the T . aestivum Chinese Spring background , weaker fluorescence in situ hybridization ( FISH ) signals from the CRW probes were detected in the ditelosomic lines 5DL , 5DS and 1BS [55] ( Fig 2A–2C , 2E–2G and 2I–2K and S1 Fig ) . Thus , significant reductions of centromeric sequences can frequently occur in allopolyploid wheat . However , CENH3 immunostaining revealed that functional centromeres were present in these three lines ( Fig 2D , 2H and 2L ) . Additionally , in the ditelosomic line 4DS [55] , we were unable to detect any CRW signals with FISH in the centromere or the chromosome arms , which stands in stark contrast to the normal chromosome 4D ( Figs 3A–3C and S1 ) . However , the epigenetic marks of active centromeres , including CENH3 and H2A phosphorylation at Thr-133 and H3 phosphorylation at Ser-10 , were correctly loaded on the short arm of the 4D chromosome , suggesting that a de novo centromere had formed that lacked the canonical centromeric sequences ( Fig 3D–3F ) . The wheat-Th . intermedium addition line TAI-14 was generated from hybrids between T . aestivum Xinshuguang 1 and amphidiploids zhong2 ( 2n = 56 ) [56] . The CRW sequences were heterogeneously distributed in the centromeric region of the 42 chromosomes of Th . intermedium , although some FISH-detected signals were very weak ( S2A Fig ) . However , there were no detectable CRW signals on the Th . intermedium-derived chromosome in TAI-14 ( Fig 4A ) . These chromosomes had functional centromeres , as revealed by the presence of CENH3 ( Fig 4B ) . Additionally , most copies of CRW in the two alien chromosomes from Th . elongatum were eliminated in the derivatives of the Chinese Spring nulli-tetrasomic lines N6AT6B ( 2n = 42 ) × wheat-Th . elongatum amphidiploid 8802 ( 2n = 42 , AABBE1E2 ) ( Fig 5B ) . The genome of 8802 consists of 28 chromosomes from T . durum Kekeruite ( 2n = 28 ) and 14 chromosomes from Th . elongatum AE31 ( 2n = 28 ) [57] . No obvious CRW FISH signals were detected on the chromosomes of Th . elongatum in the addition line derived from T . durum Kekeruite × 8802 ( Fig 5A ) . However , the chromosomes that lack CRW sequences were stably transferred to the next generation , indicating that functional centromeres were formed on these chromosomes . Without a reference genome sequence for Th . intermedium , it is difficult to characterize the sequences involved in the de novo formation of centromere in TAI-14 . We designed a new strategy to isolate the neocentromere sequences based on CENH3-ChIP and dot-blot methods ( see Methods ) . The CENH3-ChIP-enriched DNAs in the control Chinese Spring ( abbreviated as CS ) and TAI14 were further analyzed by dot-blotting . The signals from the dot-blots that were significantly different between CS and TAI14 ( e . g . , signals present in TAI14 and not in CS ) were treated as potential elements involved in de novo centromere formation . Two sequences , TAI-14-1 and TAI-14-2 , were identified as new centromeric sequences for the new centromere in TAI-14 ( Fig 4C and 4D ) , and both sequences showed homology to known retrotransposons by alignment to a BAC genome sequence in wheat ( S3 Fig ) . TAI-14-1 was widely dispersed on nearly all of the chromosomes , whereas TAI-14-2 was mainly detected in the pericentromeric regions of the wheat chromosomes ( Fig 4C and 4D ) . We observed that TAI-14-2 was located at both the centromeric and pericentromeric regions on different chromosomes in Th . intermedium ( S1B Fig ) . Interestingly , some chromosomes of Th . intermedium that showed less CRW distribution were accompanied by more TAI-14-2 sequences occupancy in the centromeric region ( S2D and S2E Fig ) . This result suggests that CRW and TAI-14-2 may be complementary centromeric sequences in some chromosomes of Th . intermedium . The total loss of CRW sequences and the presence of CENH3 in the 4DS ditelosomic line suggest that a new centromere formed on the 4DS chromosome ( Fig 3 ) . Because sequenced genomes published for Ae . tauschii ( wheat D genome donor ) and CS [58 , 59] , we performed a ChIP-seq analysis with CENH3 antibodies and identified potential sequences in the new centromere on the 4DS chromosome . Because the genomes were not completely assembled , we chose a mapping strategy that equally mapped all reads to multiple loci . The raw reads of the 4DS and CS ( as control ) samples were mapped to the wheat D and CS genomes , respectively , using BWA software ( S1 Table ) [60] . Using the sequence of the CS genome as a reference , we identified 107 scaffolds on the short arm of the 4D chromosome , with different CENH3 enrichments between 4DS and CS . The sizes of the 107 scaffolds ranged from 1 , 594 to 32 , 269 bp , and these scaffolds were combined into a 994-kb region containing only 11 genes that code for ribosomal and photosystem proteins ( Fig 6 , S2 Table ) . Furthermore , we selected one of the assembled scaffolds , IWGSC_CSS_4DS_scaff_2287721 ( 3665 bp ) , as a FISH probe and confirmed its localization in the 4DS de novo centromere ( Fig 7A ) . We mapped this scaffold to the genome of Ae . tauschii using BLASTN [61] and identified a 68-kb fragment ( Scaffold 33994 ) that contained most of the sequences of the scaffold that showed mapping differences between the 4DS and CS ( Fig 6 ) . Both of the sequences , the homologous 3 , 665-bp sequence from the CS genome and the 68-kb sequence from the D genome , contained many transposable elements and similar GC levels ( 48 . 05% and 52 . 90% , respectively , Fig 6 ) . Due to the incomplete genome sequence , we tentatively suggest that the partial sequences of the 994-kb region in the wheat CS genome and the 68-kb region in wheat D genome may underlie de novo centromere formation in 4DS . In addition , the same strategy that was used to isolate the neocentromere sequences on TAI-14 was employed with 4DS to better understand the sequences in the 994-kb region . A 769-bp fragment ( named 4DS-1 ) near the original centromere was identified as a candidate sequence in the 4DS de novo centromere ( Figs 7B and S4A ) . 4DS-1 was present at multiple locations on the chromosomes from the A and D genomes . FISH detection showed that it was localized to sites near the normal centromere on the 2A , 7A , 7D and 2D chromosomes ( S4B and S4C Fig ) . However , FISH signals of 4DS-1 were not observed in the chromosomes from the B genome ( Figs 7B and S4 ) . 4DS-1 was mapped to two scaffolds ( Scaffold 10770 and Scaffold 28550 ) from the 4DS chromosome and showed mapping differences between 4DS and CS , which confirmed that 4DS-1 was a part of the de novo centromeric sequence in the 4DS ditelosomic line ( S5 Fig ) . A previous study showed that the transcripts of centromeric sequences can function as essential components of centromere structure and activity [37] . Histone modifications , such as methylation and phosphorylation , are important regulators of centromere stability and activity [35 , 38] . For sequence 4DS-1 in the 4DS de novo centromere , we tested whether there were changes in transcription and the transcripts that interacted with CENH3 via RT-qPCR and RNA-CENH3-ChIP . We selected two fragments ( ~300 bp ) of the 4DS-1 sequence , termed 4DS-1-1 and 4DS-1-2 . Compared with CS , the transcripts of 4DS-1-1 and 4DS-1-2 were slightly but not significantly decreased in 4DS ( Fig 7C ) . However , the amounts of the 4DS-1-1 and 4DS-1-2 transcripts that were associated with CENH3 were remarkably increased in 4DS compared with CS ( Fig 7D ) . These results suggest that increased transcription of the corresponding sequences may accompany de novo centromere formation in 4DS . We also checked the possible changes in six histone modifications between the normal and de novo centromeres via immunoassay . No significant signals for the euchromatin marks H2AZ and H3K4me3 were detected in either centromere ( S6A and S6B Fig ) , and enrichment of the euchromatin-related histone mark H3K4me2 was discernible for both centromeres ( S6C Fig ) . Compared with non-centromeric chromosome ends , both centromere types revealed a reduction in the heterochromatin marks H3K27me2 and H3K27me3 ( S6D and S6E Fig ) , whereas there were no obvious differences in H3K9me2 in the two centromere types ( S6F Fig ) . In general , there were no significant differences in the accumulation of most euchromatic or heterochromatic histone markers between the normal and de novo centromeres . We crossed the hexaploid amphidiploid 8802 with the T . aestivum Chinese Spring nulli-tetrasomic lines to establish new substitution lines for the chromosomes from the E genome . All chromosomes from 8802 have only one centromere , as determined by the FISH signals of CRW ( S7A Fig ) . Chromosomes with two regions containing CRW sequences were identified in the F1 hybrids of the nulli-tetrasomic ( 3 , 5 and 6 homologous groups ) lines × 8802 ( S7C–S7F Fig ) . In the F5 generation of the hybrids between nulli-tetrasomic line N6AT6B ( 2n = 42 ) × 8802 , chromosomes containing two regions with centromeric sequences were inherited from the F1 generation , but only one region was functional ( Fig 8A and 8E ) . In addition to the two-locus centromere , a three-locus centromere was discovered on an alien chromosome in the F5 generation ( Fig 8B ) , and the middle centromere region was shown to act as the functional centromere ( Fig 8F ) . Furthermore , two different three-locus centromeres were observed in the F6 generation ( S8C and S8D Fig ) . The repetitive sequences pAs1 and pSc119 . 2 were used to karyotype the chromosomes with two- or three-locus centromeres . The chromosomes with two centromeric regions originated from the 2E chromosome of 8802 , but the three centromeric regions were produced by the combination of sequences from two different arms of chromosomes 2E and 5E , rather than direct inheritance from any chromosome ( Figs 8B , 8D and S7B ) . Furthermore , two different three-locus centromeres were observed in the progeny . One progeny contained an isochromosome 2ES , and the other progeny contained a chromosome produced from the wheat 6BL chromosome and the Th . elongatum 2E chromosome ( S8A and S8B Fig ) . However , neither was stably transferred to the next generation . Multi-centric chromosomes are frequently formed in the hybrids of wheat and related species , such as Th . elongatum , Th . poticum , Th . intermedium , Agropyron cristatum , Hordeum vulgare and S . cereale ( S9 Fig ) . However , unlike the two-locus centromere in the hybrids of N6AT6B × 8802 , both centromeres in these dicentric chromosomes were active , which caused chromosomal loss in the next generation . Heterochromatin alterations and chromosomal rearrangements associated with centromere changes have been reported in derivatives of wheat-rye hybrids [62] . The chromosomes containing altered centromeres were lost in the next generation . Here , we discovered another wheat-rye hybridization-promoted centromeric retrotransposon expansion in different rye addition lines . These changes can be stably transmitted to offspring . The rye addition lines were generally obtained from successive backcrossing between wheat and triticale . A novel octoploid triticale ( 2n = 56 ) was generated by hybridization between T . aestivum Mianyang 11 ( 2n = 42 ) and S . cereale Kustro ( 2n = 14 ) . In the wheat and octoploid triticale hybrids , a novel chromosome emerged after the joining of the two 2R chromosomes ( 2R-2R ) . Compared with the normal centromeres of chromosomes 2R and 2RL , the centromere in the 2R-2R chromosome was drastically expanded and was much larger than the 2RL arm ( Figs 9A , 9D and S10 ) . Further analysis showed that this large centromere consisted of two normal centromeres from chromosome 2R and a centromere-like region between them with dispersed pAWRC . 1 ( rye-specific centromeric retrotransposon ) sequences [63] ( Fig 9A ) . Dispersed centromeric retrotransposons may function as a part of an active centromere , as 2R-2R was broken into smaller fragments after self-pollination ( Fig 9B and 9E ) . In the progeny , we detected a new 2R chromosome ( smaller than the canonical 2R ) that retained a region with dispersed pAWRC . 1 sequences and was approximately half the size of 2R-2R ( Figs 9B , 9E and S10B ) . A novel chromosome 6R contained pAWRC . 1 sequences in a region near the functional centromere in the 6R addition line ( Figs 9C , 9F and S10B ) . However , these regions did not have centromere activity in these progeny ( S11 Fig ) .
Functional centromeres without classic centromeric sequences have been reported in humans , fungi , and plants [29–32 , 64 , 65] . Here , we found that most neocentromeres in wheat and its addition lines consist of genomic sequences that have loose resemblance to the sequences of normal centromeres . The sequences involved in the de novo centromere formation in the ditelosomic lines 4DS and TAI-14 were located at the chromosome arms adjacent to the native centromere . The 4DS-1 sequence was located very near the centromeres of chromosomes 4D , and sequence TAI14-2 was detected in the pericentromeric region before the new centromere had formed ( Figs 7B and S4 ) . The preferential localizations of the de novo centromeres in wheat and its addition lines were similar to some of the newly formed centromeres in chicken , Candida albicans and a wheat-barley addition line [66 , 67] . However , in contrast to wheat , human neocentromeres can originate at multiple positions on different chromosomes through the binding of essential kinetochore proteins [30] . In addition , neocentromeres also have been observed at multiple locations on chromosome arms in other plants [29 , 32 , 65] . This difference suggests that the de novo centromere formation was not dependent on location and occurred under appropriate epigenetic conditions , as indicated by previous studies [28 , 31 , 32] . We observed that CENH3 , H2AT133ph and H3S10ph were deposited in all of the newly formed centromeres in wheat and its wide hybrids ( Figs 2D , 2H , 2L , 3D–3F and 4B ) . With the exception of CENH3 or CENP-A , other histone modifications in the centromeric region are also critical elements for centormere stability and activity . CENP-A nucleosomes are interspersed with the H3K4me2 nucleosomes within the centromeric chromatin of humans and flies [68 , 69] . Enrichment of H3K9me2 and H3K9me3 , but not H3K4me2 , has been observed in maize centromeres [70 , 71] . Furthermore , H3K4me2 , H4ac and H3K27me1 were enriched in the centromeric chromatin of rice [72] . We observed that H3K4me2 was slightly enriched in the normal and newly formed centromeres of wheat , similar to previous reports in humans , flies and rice . However , weak signals for H3K27me and H3K9me2 were observed in these two centromeres , which were different from the centromeres of maize and rice ( S6 Fig ) . Based on our results , the histone modifications in the new centromeres are similar to the normal centromeres , highlighting that possibility that unique histone modifications in wheat centromeric regions may be required for de novo centromere formation and stability . Noncoding RNAs from the centromeric sequences directly interact with the kinetochore and recruit CENPC to centromeres [73] . In maize , transcripts of centromeric retrotransposons and repeat sequences have been associated with the CENH3 protein , as detected by ChIP with anti-CENH3 antibodies [74] . Our results showed that the expression of neocentromeric sequence 4DS-1 was greatly elevated during the process of de novo centromere formation ( Fig 7C and 7D ) . The transcripts of centromeric sequences may serve as structural and regulatory components of the centromere . In fact , its transcription process , rather than the transcripts themselves , may have facilitated CENP-A deposition and nucleosome assembly at the centromere [35 , 37] . De novo centromere formation in 4DS increased the expression of the 4DS-1 sequence , which may provide the RNAs that regulate CENH3 deposition and recruit epigenetic elements [35 , 37] . For cereal centromeres , two common sequences , described as cereal centromeric sequence ( CCS1 ) and Sau3A9 , were first reported in Brachypodium and Sorghum bicolor [75 , 76] . In most cereals , these centromeric sequences represent parts of the Ty3/gypsy retroelement , e . g . , ‘cereba’ of barley , implying that cereals maintain conserved retroelements in their centromeric regions [77] . However , the tandem repeats in the centromere are species-specific [77] . We observed that the pericentromeric sequence TAI-14-2 of wheat was located in the centromeric region of several chromosomes of the wild relative Th . intermedium ( S2 Fig ) . The centromeric sequences may undergo insertion and amplification during species differentiation [20 , 25 , 51] , providing insight into the mechanism of differential centromeric sequences change in wheat and its wild relatives . Centromeres on different chromosomes of one species may evolve rapidly and are independent of each other [20] . The novel centromeric sequence 4DS-1 displayed a specific localization pattern in the different chromosomes of the wheat subgenomes , and its chromosomal location was very near the centromeres of chromosomes 7A , 7D , 2A and 2D ( Figs 7B and S4 ) . However , the sequence was not readily detectable on chromosomes 2B and 7B , and it was not found in other chromosomes , such as 4A and 4B . Similar to the situations in rice , maize , and potato , the centromeres on different chromosomes experienced differentiation by sequence loss and insertion [20 , 25 , 51] . Centromeric protein complexes , which are necessary for proper chromosome segregation , can be formed by the speciation factors HMR and LHR to mediate hybrid sterility and incompatibility in Drosophila [78] . During hybridization , the intragenomic conflicts of different centromeres may cause incompatibilities in hybrids [79 , 80] . These results strongly suggest that centromere divergence has an important impact on the generation of biodiversity [78] . To adapt to highly variable centromere DNA sequences , the centromere proteins undergo rapid evolution to maintain a functional centromere [81] . We observed that the new centromeric DNA sequences are highly variable between different genomes and chromosomes , which may allow the centromeric proteins to mediate intragenomic incompatibility and genomic specificity in the nascent hybrids . Although detailed reports are not available , it is likely that centromere sequence diversity has an important impact on speciation . An understanding of the diversity of centromeric sequences and its link to speciation and genomic stability deserve further analysis . Multiple centromeres , holocentromeres and neocentromeres formation implies that centromere positions may be alterable rather than permanently fixed [11 , 31 , 32 , 82–84] . Wide hybridization can trigger chromosomal rearrangements and genome reorganization , accompanied by centromere alterations [62 , 85] . Unstable di-centromeres and multiple centromeres have been associated with the formation of inter- and intra-chromosomal translocations in wheat-rye hybrids [62] . Our results demonstrated that multi-locus centromere formation , centromere expansion , and canonical centromeric sequence elimination may yield novel chromosomes in wheat and its wide hybrids ( see summary Fig 1 ) . Chromosomes with two regions containing centromeric sequences were observed in the F1 hybrids of three null-tetra lines and 8802 ( S7 Fig ) . During meiosis I of the F1 generation , several univalents of the E genome in amphidiploid 8802 may experience centromere breakage . Centromere misdivision , which depends on the orientation of the univalent , may occur across the either centromere or the pericentric chromatin , but chromosome fragments containing centromeric and pericentric regions may survive [86] . This observation suggests the possibility that chromosomal fragments containing centromeric and pericentromeric regions were rejoined to a novel chromosome and induced the formation of two- and three-locus centromeric regions . Centromere inactivation allows dicentric chromosomes with only one functional centromere to be stably transmitted to the next generation ( Fig 8E ) , similar to stable dicentric A-A and A-B chromosomes in maize [18 , 33 , 34] . However , the chromosome containing a three-locus centromere still suffered from centromere breakage , which led to its structural alterations in the progeny . As a consequence of a translocation in the nulli-tetrasomic line N6AT6B , a chromosome with a three-locus centromere included the 2E chromosome from 8802 and the 6B chromosome from N6AT6B ( S8D Fig ) . Similar to rye and maize [18 , 86 , 87] , intrachromosomal recombination and centromere breakage likely promoted the formation of multi-locus centromeres and novel chromosomes in wheat and its wide hybrids . Retrotransposons can be activated during wide hybridization [88] . Interspecific hybrids triggered the amplification of centromeric satellite repeats and retrotransposons [85] . In a wheat-rye hybrid , we observed two chromosomes that had likely lost their telomeres and fused into one chromosome , as previously suggested [89] . The wide hybridization affected the stability of the centromeric retrotransposons and the activation of rye-specific retrotransposons in the 2R-2R and 6R addition lines , causing centromeric sequence expansion ( Fig 9A and 9C ) . These unstable centromeres may subsequently lead to chromosome breakage and different progeny with expanded centromeres , suggesting that centromere variants may trigger the formation of novel chromosomes . Total centromere size has been postulated to be positively correlated with genome size rather than chromosome size [90] . Centromere domains of several maize chromosomes ( average size 1 . 8 Mb ) expanded to 3 . 6 Mb in the background of the oat genome [48] . However , the expanded centromere on chromosome 2R-2R may instead be a general response to genomic stress following the wheat-rye hybridization , rather than an adaptation to the wheat centromere size . In summary , we observed that the elimination , rearrangement and expansion of centromeric sequences affect chromosome morphology and maintenance in wheat wide hybrids . De novo centromere formation promotes the accurate segregation of chromosomes that experienced centromere sequence elimination . The new centromeres have low sequence homology but high epigenetic similarity to normal centromeres . The highly variable centromeric sequences between genomes and chromosomes may facilitate genomic specificity and differentiation in hybrids . The centromeric sequences involved in de novo centromere formation are mainly retrotransposon-like sequences , and their RNAs are transcribed at high levels . Multiple centromeres and centromere sequence expansion strongly influence centromere activity and cause chromosome breakage and rearrangements . More importantly , centromere variations in the ditelosomic lines 4DS , 1BS , 5DL , 5DS and TAI-14 may specifically affect the size and DNA organization of normal chromosomes . Thus , these may be useful for future studies of chromosome sorting and sequencing .
The hexaploid amphiploid 8802 ( AABBEE ) originated from hybrids between T . durum and Th . elongatum [57] . The addition lines of wheat ( Chinese Spring ) -Th . poticum , Th . intermedium and Agropyron cristatum were produced by our laboratory . The addition lines of wheat ( Mianyang 11 ) and S . cereale ( Kustro ) were produced by Dr . Shulan Fu , Sichuan Agriculture University , Chengdu , China . The nulli-tetrasomic lines and the ditelosomic lines 4DS , 1BS , 5DL and 5DS were kindly provided by Dr . Perry Gustafson , University of Missouri , Columbia , MO , USA . The root tips were prepared for the FISH experiments and probes as previously described [91] . The CRW and other genomic DNAs were labeled with Alexa Fluor-488-dUTP ( green ) or Alexa Fluor-594-5-dUTP ( red ) as needed . The root tip samples of the different wheat lines were treated with the same conditions , and with an equal amount of probe . The FISH images were acquired using an epifluorescence Olympus BX61 microscope ( Olympus China Inc , Beijing , China ) with the same exposure time and were processed with Adobe Photoshop CS 3 . 0 . For analysis , the fluorescence was quantified using ImageJ [92] . At least 20 cells from three different plants were counted in each of the different wheat lines . Significant differences were calculated using Microsoft Excel and Student’s t-test ( two-tailed ) . The root tips were fixed with 4% formaldehyde in 1×PBS for 1 h , and the metaphase chromosomes were prepared as previously described [17] . The wheat-specific CENH3 antibodies were produced in our laboratory . The phH2AThr-133 and phH3Ser-10 antibodies were described previously [16 , 17] . Chromatin immunoprecipitation ( ChIP ) was performed according to a previously described method [93] . Approximately 20 g of fresh leaf tissue from Chinese Spring and 4DS was prepared for CENH3-ChIP ( CENH3 antibody used as described above ) . ChIP-seq was conducted according to a previously described method [32] . Using an Illumina HiSeq2000 platform , the enriched DNA samples were sequenced to generate paired-end 100-bp sequence reads . RNA-ChIP was performed using a method that was similar to ChIP [93] . RNase activity was inhibited using Recombinant RNase inhibitor ( RRI ) in the RNA-ChIP process . The RNA was extracted from the sample using TRIzol reagent . The RNA was reverse-transcribed into cDNA using M-MLV reverse transcriptase ( Promega ) and random primers ( New England Biolabs ) . The qPCR protocol was performed as described [94] . Nearly 14 , 000 Mbp of raw ChIP-seq paired-end 100-bp reads were mapped to the wheat CS and wheat D genomes using BWA software [60] . All mappable reads were randomly assigned a locus from the possible options , and the duplicated reads were removed . The reads per million ( RPM ) values of every scaffold in the genome were calculated to show the normalized enrichment . We selected the scaffolds that had enriched reads in both 4DS and the control , with the RPM ratios between two samples ≥20 , or scaffolds that only had enriched reads in 4DS , with read counts ≥20 . IGV Tools was used to visualize the normalized read distribution of each scaffold [95] . The anti-CENH3 ChIP-seq data were deposited in the Gene Expression Omnibus ( GEO ) database under number GSE63752 . The ChIPed DNAs from CS and 4DS were amplified by Dop-PCR ( primer sequence was 5’-CCGACTCGAGNNNNNNATGTG G-3’ ) [96] . The DOP-PCR product was used as the DNA target , and the ChIPed DNAs were used as probes . The dot-blot protocol was performed as described [97] . The differences in the sequences between CS and 4DS that were determined by the dot-blot hybridization were selected as candidate centromeric sequences for FISH . | Chromosomal rearrangements during the formation of wheat aneuploids and their wide hybrids caused reduction , elimination or expansion of the centromeric retrotransposon sequences and the formation of multiple centromeres . Centromere function was not affected by centromeric sequence elimination , which was revealed by the de novo formation of centromeres on the rearranged chromosomes . Several retrotransposon-like elements near the former centromeres were embedded in the newly formed centromeres , and there were no obvious changes in six histone modifications between normal and new centromeres . The DNA sequences associated with the new centromeres are transcribed at a higher level after centromere formation . Chromosomes containing the neocentromeres can be stably transferred to the next generation . Chromosomes carrying two- or three-locus centromeres are unstable , which induces the formation of novel chromosomes through centromere breakage in wheat-Th . elongatum hybrid derivatives . The centromere-specific sequences on dicentric chromosomes are expanded to the chromosome arms in wheat-rye hybrids , and these sequences may function as a part of the active centromere to cause chromosome breakage in the next generation . Centromere variation and activity in wheat aneuploids and its wide hybrids may be associated with chromosome stability , rearrangements , and novel chromosome formations . | [
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... | 2016 | De Novo Centromere Formation and Centromeric Sequence Expansion in Wheat and its Wide Hybrids |
Protozoan parasites of the genus Giardia are highly prevalent globally , and infect a wide range of vertebrate hosts including humans , with proliferation and pathology restricted to the small intestine . This narrow ecological specialization entailed extensive structural and functional adaptations during host-parasite co-evolution . An example is the streamlined mitosomal proteome with iron-sulphur protein maturation as the only biochemical pathway clearly associated with this organelle . Here , we applied techniques in microscopy and protein biochemistry to investigate the mitosomal membrane proteome in association to mitosome homeostasis . Live cell imaging revealed a highly immobilized array of 30–40 physically distinct mitosome organelles in trophozoites . We provide direct evidence for the single giardial dynamin-related protein as a contributor to mitosomal morphogenesis and homeostasis . To overcome inherent limitations that have hitherto severely hampered the characterization of these unique organelles we applied a novel interaction-based proteome discovery strategy using forward and reverse protein co-immunoprecipitation . This allowed generation of organelle proteome data strictly in a protein-protein interaction context . We built an initial Tom40-centered outer membrane interactome by co-immunoprecipitation experiments , identifying small GTPases , factors with dual mitosome and endoplasmic reticulum ( ER ) distribution , as well as novel matrix proteins . Through iterative expansion of this protein-protein interaction network , we were able to i ) significantly extend this interaction-based mitosomal proteome to include other membrane-associated proteins with possible roles in mitosome morphogenesis and connection to other subcellular compartments , and ii ) identify novel matrix proteins which may shed light on mitosome-associated metabolic functions other than Fe-S cluster biogenesis . Functional analysis also revealed conceptual conservation of protein translocation despite the massive divergence and reduction of protein import machinery in Giardia mitosomes .
Since the single endosymbiotic event leading to establishment of mitochondria approximately 2 billion years ago [1 , 2 , 3] these organelles have undergone massive changes and have evolved into highly specialized and essential subcellular compartments in all eukaryotes [4 , 5] , with only one possible exception identified so far [6] . These changes comprise a dramatic size reduction , nuclear transfer of organelle genomes , and a renewal of the proteome , which is synthesized almost entirely as precursor proteins on cytosolic ribosomes [7 , 8 , 9 , 10 , 11 , 12 , 13 , 14] and imported from the cytoplasm [15] . Mitochondria have been remodeled and/or restructured to very different degrees in different species . Mitochondria-related organelles ( MROs ) , i . e . hydrogenosomes and mitosomes [16 , 17 , 18 , 19 , 20] in some protists lacking canonical mitochondria represent extreme forms of reduction and/or divergence . The potential of highly diverged organelle-specific pathways as targets for intervention has sparked research into the evolution of MROs in single-celled organisms of all five eukaryotic supergroups [21 , 22] . Notably , the microaerophilic protozoan pathogens Entamoeba histolytica [20] and Giardia lamblia [23 , 24] , as well as intracellular parasites such as Cryptosporidium parvum [25] and Encephalitozoon cuniculi [26] harbor mitosomes . Interestingly , recent investigation of MROs in Spironucleus salmonicida , a diplomonad and the closest relative of G . lamblia belonging to the Excavata super-group , revealed that these organelles are in fact hydrogenosomes [27] . Although it has been demonstrated that G . lamblia mitosomes do not produce hydrogen , this sheds a completely new light on the evolution of MROs in diplomonads . Proliferating G . lamblia trophozoites contain 20–50 double membrane-bounded 100 nm spherical mitosomes [23 , 24] devoid of an organelle genome [28 , 29 , 30 , 31] . Although not proven experimentally , G . lamblia mitosomes are likely essential due to a subset of conserved mitochondrial proteins required for iron- sulphur ( Fe-S ) protein maturation [23 , 32 , 33 , 34 , 35] . Yeast genetic experiments suggested that Fe-S protein maturation , the only function currently ascribable to G . lamblia mitosomes , is in fact the minimal essential function of mitochondria [36] . Hence , these organelles have also attracted considerable interest as cell biological models to study extreme reductive evolution of MROs [23 , 37 , 38 , 39 , 40 , 41 , 42] . However , due to massive , albeit selective sequence divergence in G . lamblia , conventional data mining strategies for identification of mitosome proteins based on homology-based in silico searches fall short [26 , 28 , 32 , 43 , 44 , 45 , 46 , 47] . Moreover , classical , organelle enrichment-based proteome analyses approaches have had only limited success owing to the small size of the organelles and the omnipresence of contaminating endoplasmic reticulum ( ER ) and cytoskeleton elements in mitosome fractions [33 , 48 , 49] . Nevertheless , there is unambiguous experimental evidence for the functional conservation of the mitosomal protein import machinery [20 , 23 , 24 , 49] . The small subset of structurally conserved mitosome proteins such as G . lamblia IscU , ferredoxin , Cpn60 , IscS and mtHsp70 are imported by transit peptide-dependent and -independent mechanisms [23] . However , the predicted components of the TOM/TIM import apparatus are diverged beyond recognition by state-of-the-art homology search tools . Indeed , the protein repertoire of the mitosomal outer membrane and its networks are scarcely characterized: only one subunit of the translocon in the outer mitochondrial ( TOM ) complex , a highly diverged Tom40 homologue ( GlTom40 ) , and [50] more recently a giardial Tim44 homologue [49] , have been identified . Furthermore , there is no information on how mitosome homeostasis is achieved in terms of organelle size and number . To address questions concerning protein networks at mitosomal membranes in association with mitosome homeostasis and to account for the extreme sequence divergence in G . lamblia , we implemented novel experimental approaches . We were successful to tag two outer membrane organelle proteins with GFP to show that these small organelles are immobilized , distinctive entities with no appreciable inter-organelle exchange or network character . Using a giardial TOM40 homolog as a starting bait we generated information on protein-protein interactions at the outer membrane as well as expanding the organelle proteome by identifying novel components . By using interactome targets validated by subcellular localization as baits for subsequent reverse co-IP rounds , we were able to extend this initial interactome beyond the outer membrane , including dually localized endoplasmic reticulum ( ER ) and mitosome proteins , as well as identifying previously described and novel imported organelle proteins . In addition to identification of two components with a role in mitosome morphogenesis and homeostasis the combined data revealed a core organelle membrane interactome composed of only 3 tightly-associated proteins . Furthermore , we tested constraints for import of nuclear-encoded mitosome proteins and could show conservation of this mechanism even in the highly diverged and reduced Giardia mitosome .
G . lamblia WB ( line C6; ATCC catalog number 50803 ) trophozoites were grown and harvested using standard protocols [51] . Encystation was induced with the two-step method as described previously [40 , 52] . Transgenic parasites were generated according to established protocols by electroporation of linearized pPacV-Integ-based plasmid vectors prepared from E . coli as described in [42] . After selection for puromycin resistance , transgenic G . lamblia cell lines were cultured without puromycin . All sequences of oligonucleotide primers for PCR used in this work are listed in S1 Table . For cloning of C-terminally hemagglutinin ( HA ) -tagged proteins in Giardia , a vector PAC-CHA was designed on the basis of the previously described vector pPacV-Integ [42] , where additional restriction sites were inserted [53] . A cyst wall protein 1 promoter ( pCWP1 ) -driven G . lamblia ferredoxin ( fd ) -human dihydrofolate reductase ( DHFR ) chimeric gene was generated by fusing two genes by overlapping PCR: i ) an intron-less fd mitosomal targeting signal ( MTS ) ( MTSfdΔint ) open reading frame ( ORF ) was generated using primer pair 33 ( S1 Table ) with G . lamblia cDNA as template , ii ) a DHFR_HA minigene was generated using primer pair 34 ( S1 Table ) with a cloned human DHFR cDNA as template . The fused product was digested with SpeI and PacI and inserted in a PAC vector to yield construct pCWP1_MTSfdΔint-DHFR_HA . A pCwp1_ MTSfdΔint-DHFR_Neomycin resistance construct ( without HA tag ) was generated for protein import block assays . Primer pair 35 ( S1 Table ) was used on pCwp1_ MTSfdΔint-DHFR_HA as a template . The amplified product was digested with NsiI and PacI and ligated into a vector containing a neomycin resistance cassette [51] . G . lamblia WBC6 and transgenic trophozoites expressing C-terminally HA tagged bait proteins were harvested and subjected to immunofluorescence assay to confirm correct subcellular distribution of bait proteins . Parasites were collected by centrifugation ( 900 x g , 10 minutes , 4°C ) , washed in 50 ml of cold phosphate buffer saline solution ( PBS ) and adjusted to 2 x107 cells . ml-1 in PBS ( VWR Prolabo ) . The appropriate formaldehyde concentration for cross-linking ( 2 . 25% ) was determined by a titration assay ( S2 Fig ) . For the co-immunoprecipitation ( co-IP ) assays , 109 parasites were resuspended in 10 ml 2 . 25% formaldehyde ( in PBS ) supplemented with 1 mM phenylmethylsulfonyl fluoride ( PMSF; SIGMA , Cat . No . P7626 ) and incubated for 30 minutes at room temperature ( RT ) . Cells were pelleted , washed once with 10 ml PBS , and quenched in 10 ml 100 mM glycine in PBS for 15 minutes at RT . The collected cells were then resuspended in 5 ml RIPA lysis buffer ( 50 mM Tris pH 7 . 4 , 150 mM NaCl , 1% IGEPAL , 0 . 5% sodium deoxycholate , 0 . 1% SDS , 10 mM EDTA ) supplemented with 2 mM PMSF and 1 x Protease Inhibitor cocktail ( PIC , Cat . No . 539131 , Calbiochem USA ) and sonicated twice using a Branson Sonifier with microtip ( Branson Sonifier 250 , Branson Ultrasonics Corporation ) with the following settings: 60 pulses , 2 output control , 30% duty cycle and 60 pulses , 4 output control , 40% duty cycle . The sonicate was incubated on a rotating wheel for 1 h at 4°C , aliquoted into 1 . 5 ml tubes and centrifuged ( 14 , 000 x g , 10 minutes , 4°C ) . The soluble protein fraction was mixed with an equal volume detergent-free RIPA lysis buffer supplemented with 2% TritonX ( TX ) -100 ( Fluka Chemicals ) and 40 μl anti-HA agarose bead slurry ( Pierce , product # 26181 ) . After binding of tagged proteins to the beads at 4°C for 2 h on a rotating wheel , beads were pulse-centrifuged and washed 4 times with 3 ml Tris-Buffered Saline ( TBS ) supplemented with 0 . 1% TX-100 at 4°C . After a final wash with 3 ml PBS the loaded beads were resuspended in 350 μl PBS , transferred to a spin column ( Pierce spin column screw cap , product # 69705 , Thermo Scientific ) and centrifuged for 10 s at 4°C . Elution was performed by resuspending beads in 30 μl of PBS . Dithiothreitol ( DTT; 100mM; Thermo Scientific , Cat . # RO861 ) was added and samples were boiled for 5 min followed by centrifugation ( 14 , 000 x g , 10 minutes , RT ) . SDS-PAGE and immunoblotting analysis of input , flow-through , and eluate fractions was performed on 4%-12% polyacrylamide gels under reducing conditions , ( molecular weight marker Cat . No . 26616 , Thermo Scientific , Lithuania ) . Transfer to nitrocellulose membranes and antibody probing were done as described previously [54] , using anti-HA ( dilution 1:500; Roche ) followed by anti-rat antibodies coupled to horseradish peroxidase ( dilution 1:5000; Southern Biotech ) . Gels for mass spectrometry ( MS ) analysis were stained using Instant blue ( Expedeon , Prod . # ISB1L ) and de-stained with sterile water . Stained gel lanes were cut into 8 equal sections . Each section was further diced into smaller pieces and washed twice with 100 μl of 100 mM ammonium bicarbonate/ 50% acetonitrile for 15 min at 50°C . The sections were dehydrated with 50 μl of acetonitrile . The gel pieces were rehydrated with 20 μl trypsin solution ( 5 ng/μl in 10 mM Tris-HCl/ 2 mM CaCl2 at pH 8 . 2 ) and 40 μl buffer ( 10 mM Tris-HCl/ 2 mM CaCl2 at pH 8 . 2 ) . Microwave-assisted digestion was performed for 30 minutes at 60°C with the microwave power set to 5 W ( CEM Discover , CEM corp . , USA ) . Supernatants were collected in fresh tubes and the gel pieces were extracted with 150 μl of 0 . 1% trifluoroacetic acid/ 50% acetonitrile . Supernatants were combined , dried , and the samples were dissolved in 20 μl 0 . 1% formic acid before being transferred to the autosampler vials for liquid chromatography-tandem MS ( injection volume 7 to 9 μl ) . Samples were measured on a Q-exactive mass spectrometer ( Thermo Scientific ) equipped with a nanoAcquity UPLC ( Waters Corporation ) . Peptides were trapped on a Symmetry C18 , 5 μm , 180 μm x 20 mm column ( Waters Corporation ) and separated on a BEH300 C18 , 1 . 7 μm , 75 μm x 150 mm column ( Waters Corporation ) using a gradient formed between solvent A ( 0 . 1% formic acid in water ) and solvent B ( 0 . 1% formic acid in acetonitrile ) . The gradient started at 1% solvent B and the concentration of solvent B was increased to 40% within 60 minutes . Following peptide data acquisition , database searches were performed using the MASCOT search program against the G . lamblia database ( http://giardiadb . org/giardiadb/ ) with a concatenated decoy database supplemented with commonly observed contaminants and the Swissprot database to increase database size . The identified hits were then loaded onto the Scaffold Viewer version 4 ( Proteome Software , Portland , US ) and filtered based on high stringency parameters , i . e . 95% for peptide probability , a protein probability of 95% , and a minimum of 2 unique peptides per protein . Where indicated in the text , slightly relaxed filtering parameters were applied . Proteins identified in both bait-specific and control datasets were considered of interest if they were at least 5-fold enriched in the bait-specific datasets ( in terms of spectral counts ) based on high stringency parameters . Access to raw MS data is provided through the ProteomeXchange Consortium on the PRIDE platform [55] . Analysis of primary structure and domain architecture of putative mitosomal hypothetical proteins was performed using the following tools and databases: MITOPROT ( https://ihg . gsf . de/ihg/mitoprot . html ) and PSORTII ( http://psort . hgc . jp/form2 . html ) for subcellular localization prediction , TMHMM ( http://www . cbs . dtu . dk/services/TMHMM/ ) for transmembrane helix prediction , SMART ( http://smart . embl-heidelberg . de/ ) for prediction of patterns and functional domains , pBLAST for protein homology detection ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ? PAGE=Proteins ) , HHPred ( http://toolkit . tuebingen . mpg . de/hhpred ) for protein homology detection based on Hidden Markov Model ( HMM-HMM ) comparison , and the Giardia Genome Database ( http://giardiadb . org/giardiadb/ ) to extract other/organism-specific information , e . g . expression levels of the protein , predicted molecular size and nucleotide/protein sequence . For functional domains predicted by SMART we used an e-value of 10e-5 as cutoff , and for protein homologies predicted by pBLAST we accepted alignment scores above 80 . However , since G . lamblia homologs for eukaryotic proteins are highly diverged , we also considered functional domain predictions associated to a lower e-value . Alignment scores between 50 and 80 were accepted only when pBLAST predictions were consistent with HHPred output . Preparation of chemically fixed cells for immunofluorescence and analysis of subcellular distribution of reporter proteins by wide-field and confocal microscopy were done as described previously [42 , 54] . Nuclear labelling was performed with 4' , 6-diamidino-2-phenylindole ( DAPI ) . The HA epitope tag was detected with a monoclonal anti-HA antibody coupled to FITC ( dilution 1:50; Roche ) whereas GlIscU was detected with a self-made antibody ( dilution 1:300 ) followed by an anti-mouse antibody coupled to Alexafluor 594 ( dilution 1:300; Molecular Probes ) . To avoid any possibility for cross reaction , co-labelling experiments for IFA were performed by incubating first with the anti-GlIscU antibody , followed by the AF594-conjugated anti-mouse secondary antibody , and direct detection of the HA epitope tag with a FITC-conjugated rat anti-HA monoclonal antibody as a final step . Transgenic G . lamblia trophozoites expressing GFP-GlTom40 or Gl29147-GFP were harvested and prepared for imaging in PBS supplemented with 5 mM glucose ( Cat . No . 49139 , Fluka ) , 5 mM L-cysteine ( Cat . No . C6852 , Sigma ) and 0 . 1 mM ascorbic acid ( Cat . No . 95209 , Fluka ) at pH 7 . 1 . FRAP and time-lapse series were performed as described previously [54 , 56] . Transgenic trophozoites ectopically expressing wild type G . lamblia dynamin related protein ( GlDRP ) ( ORF Gl50803_14373 ) or the constitutively active ( GTP-locked ) GlDRP-K43E variant under the control of the CWP1 promoter [56] were harvested 3 h post induction and analyzed by transmission electron microscopy ( TEM ) as described previously [56] . For sub-cellular fraction experiments , 4 . 106 GlDRP-HA and GlDRP-K43E-HA- expressing transgenic cells were lysed by freeze-thawing and supernatant ( soluble fraction ) and pellet ( membrane fraction ) were prepared by centrifugation at 14’000 x g for 10 minutes at 4°C . The HA-tagged proteins were detected by SDS-PAGE and Western blot using a rat anti-HA mAb ( clone 3F10 , Roche ) as described previously [54] . The MTSfdΔint-DHFR fusion ( see also above under “Constructs” ) was expressed under the control of the inducible CWP1 promoter in a background transgenic line constitutively expressing HA- tagged 17030 ( cell line Cwp1_MTSfdΔint-DHFR/Gl17030HA ) . DHFR expression was induced using the 2-step method [40] for 4 h and “chased” for 24 h by placing the cells again in standard growth medium in the presence or absence of 1 μM methotrexate ( MTX ) . Total cell lysates were separated by SDS-PAGE and Western blot to detect processed and unprocessed forms of the Gl17030HA reporter . Subcellular distribution was analyzed by immunofluorescence assay ( IFA ) using wide field microscopy .
Mitochondria in higher eukaryotes are highly dynamic organelle networks that move in the cell via microtubules and microfilaments and undergo constant fission and fusion to meet the energy requirements of the cell [57 , 58] . IFA and TEM analyses suggest that G . lamblia mitosomes are very small spherical organelles with no evidence of network formation . In addition , the mitosome population in each cell can be divided into peripheral mitosomes ( PM ) distributed randomly in the cytoplasm and what has been dubbed the central mitosome complex ( CMC ) [23] . The latter consists of a grape-like cluster of individual organelles of the size and shape of peripheral mitosomes that is closely and permanently associated to the basal body complex between the two nuclei [23] . Interestingly , these organelles remain spatially distinct despite their close proximity . The motility of this central cluster is highly constrained and restricted to ordered segregation with the duplicated basal body complex during cell division [23] . Because green fluorescent protein ( GFP ) imported into the mitosome matrix is not fluorescent [23] , GFP-tagging of mitosomes has not been possible until now . Martinkova et al . [59] have shown that mitosomes in trophozoites can be labeled for live cell microscopy using HaloTag markers [60] . However , no quantitative information on the spatial dynamics of peripheral mitosomes in the cytoplasm was presented in this report . We investigated organelle dynamics in living cells by performing time lapse microscopy of cells expressing GFP-tagged mitosome reporters for the outer membrane . Conditional expression of N-terminally GFP-tagged GlTom40 with 3 h of induction followed by “chasing” newly-synthesized GFP-Tom40 into mitosomes over 2–3 h in normal conditions was found suitable for labeling organelles in living cells ( Fig 1A and 1B ) . Tracking of individual organelles over a period of >30 min showed no significant cytoplasmic movement or changes in number or morphology ( Fig 1C ) , suggesting that organelles neither move randomly nor are they transported directionally in the cytoplasm along cytoskeleton structures . To test whether mitosome outer membrane proteins are exchanged between organelles we performed FRAP experiments on cells conditionally expressing GlTom40-GFP . Since GlTom40-GFP is membrane-anchored , FRAP addresses the question whether mitosomes are isolated organelles and whether they form membrane continuities which would allow exchange of outer membrane proteins . No recovery of fluorescence in bleached CMC or PM organelles was detected ( Fig 1D–1G ) suggesting that peripheral and CMC organelle membranes remain distinct . Despite intensive research in the field of MROs , little is known regarding factors required for their division . Dynamin-related proteins ( DRPs ) are implicated in mitochondrial and hydrogenosomal division in higher eukaryotes and in protozoa such as Trypanosoma brucei [61 , 62] and Trichomonas vaginalis [63] . G . lamblia harbors a single DRP ( ORF Gl50803_14373 ) [56] with a previously documented role in trafficking of cyst wall material , and endocytic and exocytic organelle homeostasis [56] . To test for a hitherto unrecognized role of GlDRP in determining mitosome morphology and/number , we used a dual cassette expression vector [54] to express constitutive C-terminally myc-tagged GlTom40 as a reporter for mitosomes and inducible C-terminally HA-tagged wild-type ( GlDRP-HA ) or GTP-locked ( GlDRP-K43E-HA ) variants in trophozoites . In trophozoites expressing GlDRP-HA ( Fig 2A–2C ) , IFA analyses demonstrated the typical random cytoplasmic distribution of PMs i . e . “dispersed” [23] . However , cells expressing the GTP-locked variant GlDRP-K43E-HA ( Fig 2D–2F ) presented a “clustered” mitosome phenotype , indicative of enlarged organelles . Consistent with this phenotype and in line with previous reports [56] , the subcellular distribution of HA-tagged GlDRP remained mostly cytosolic ( Fig 2B ) . Conversely , GlDRP-K43E-HA showed a punctate distribution ( Fig 2E ) and significant signal overlap with GlTom40-myc ( Fig 2F ) , suggesting selective accumulation of GlDRP-K43E-HA on mitosome membranes . We tested whether this marked association of ectopically expressed GlDRP-K43E with organelle membranes compared to the wild type DRP variant in IFA could be corroborated in cell fractionation experiments . Separation by SDS-PAGE and immunoblot analysis revealed that GlDRP-HA was almost equally distributed between the “cytosolic” and “membrane” fraction , whereas the mutated variant GlDRP-K43E-HA was detected only in the “membrane” fraction ( Fig 2G ) . These data were consistent with the microscopical analysis in Fig 2E and suggest increased association of GlDRP-K43E-HA with organelle membranes compared to wild-type GlDRP-HA . To characterize the nature of the GlDRP-K43E-HA-dependent phenotype in more detail , we performed transmission electron microscopy of induced transgenic cells . Cells expressing the GlDRP-K43E-HA variant frequently presented elongated and tubular mitosome structures ( Fig 2I and 2J ) compared to cells expressing wild type GlDRP-HA ( Fig 2H ) . Taken together , these data show how mitosomes are immobilized in the cell and present no measurable outer membrane exchange in the conditions tested . Their morphogenesis is perturbed following conditional ectopic expression of a dominant-negative GTP-locked GlDRP variant , suggesting a previously unappreciated role for this GTPase in the maintenance of mitosome integrity and organelle morphogenesis in G . lamblia . The aberrant mitosome morphology after conditional expression of GlDRP-K43E points towards mitosome-associated machinery at the organelle’s surface involved in organelle homeostasis . Despite efforts aimed at defining the protein content of mitosomes in Giardia [33 , 49 , 50] , the composition of this organelle’s outer and inner membrane proteome remains sparsely characterized , with the exception of a highly diverged putative Tom40 homologue ( GlTom40; ORF Gl50803_17161 ) and a structurally-conserved Tim44 [49 , 50] . To generate a robust mitosome outer membrane proteome we focused on GlTom40 as a point of origin and developed a tailored co-IP protocol with an HA-tagged variant as “bait” . A transgenic line GlTom40-HA constitutively expressing the epitope-tagged bait protein was generated; exclusive mitosome localization of the bait protein in transgenic cells was confirmed by IFA in co-labelling experiments with a newly-made anti-GlIscU antibody ( Fig 3A and S1A Fig ) . To ensure solubilization of mitosomal membranes while avoiding disruption of Tom40-associated protein complexes , we used carefully titrated , formaldehyde-based cross-linking [64] to stabilize predicted protein-protein interactions in co-IP experiments during extraction with the option to reverse covalent bonds ( S2 Fig; see also in Materials and Methods ) . Following MS analysis and data filtration using a control dataset obtained from non-transgenic cells ( ctrl . co-IP ) we identified a total of 52 proteins , 46 exclusive and 6 enriched in the GlTom40 co-IP dataset ( Fig 3B ) . This protein set was parsed and subdivided into different metabolic and/or functional categories ( Fig 3C ) . In the mitosomal protein category few detected four previously identified mitosome proteins namely: mitochondrial HSP70 ( ORF Gl50803_14581 ) , oxidoreductase 1 ( GlOR1; ORF Gl50803_91252 ) , proteins Gl50803_9296 and Gl50803_14939 , recently named MOMP35 [33 , 49] . We extracted additional information from the GlTom40 co-IP data by relaxing stringency parameters to ( 95_1_95 ) , obtaining a total of 150 proteins ( FDR 3 . 4% ) . Of these , 109 hits were exclusive to the expanded GlTom40 co-IP dataset which contained 3 additional annotated mitosome proteins namely , chaperonin 60 ( Cpn60; ORF Gl50803_103891 ) , GlQb-SNARE 3 ( putative Sec20 , ORF Gl50803_5161 ) and GlIscU ( NifU-like protein; ORF Gl50803_15196 ) . Limited chemical cross-linking in co-IP assays expands the range of discovery beyond primary interactions with the bait . We therefore performed an initial validation of the predicted GlTom40 interacting proteins in this dataset by subcellular localization of ectopically expressed , epitope-tagged candidates to mitosomes . We selected 13 of the 109 candidate Mitosomal Outer Membrane Tom40 interacting proteins ( MOMTiP; Table 1 ) based on their spectral counts with high stringency parameters and/or protein domains identified with HHPred ( S2 Table ) and engineered endogenous promoter-driven , C-terminally HA-tagged variants for all . IFA analysis of corresponding transgenic lines showed mitosomal localization for 8 candidates ( Fig 4A–4H ) , of which 4 proteins of unknown function ( MOMTiP-5 to 8 ) presented dual localization ( mitosome and ER ) ( Fig 4F–4I ) . The five remaining proteins of this set of 13 candidates ( MOMTiP- 9–13; Fig 4J–4N ) showed dispersed patterns of subcellular distribution and were not considered mitosome proteins . Fig 4O shows a consolidated depiction of a first GlTom40-centered mitosomal outer membrane interactome , which includes the 8 proteins localized to mitosomes described above , as well as 4 previously identified matrix proteins and 3 newly validated hypothetical proteins comprised in the list of GlTom40 interacting proteins . Taken together , the imaging data are in agreement with the protein-protein interaction data , and support limited chemical crosslinking as a suitable method to stabilize protein complexes during co-IP . IFA analysis of MOMTiP-1 to 13 indicated that the majority of these proteins are associated to mitosomes , thereby providing preliminary validation of the selected 13 candidates of the primary GlTom40-specific co-IP dataset . To further test the robustness of this primary interactome and expanding it beyond the mitosomal membrane , we performed a first reverse co-IP experiment using MOMTiP-1 ( ORF Gl50803_29147 ) as bait . MOMTiP-1 was chosen because it presented the largest spectral count with high stringency parameters in the GlTom40 dataset and localized unequivocally to mitosomes ( S1B Fig ) . MOMTiP-1 is a Giardia-specific mitosome-localized protein of unknown function . In silico analysis using TMHMM robustly detected a 22 amino acid-long transmembrane helix in the N-terminal part of the protein followed by a large C-terminal domain predicted to be exposed to the cytosol on the mitosomal surface . To track this protein in vivo , we engineered MOMTiP-1 constructs for live cell imaging using GFP reporters . We have shown previously that GFP only fluoresces if exposed to the cytoplasm and never after import into mitosomes [23] . Therefore , the brightly fluorescing and mitosome-localized MOMTiP-1-GFP fusion supports the predicted topology for MOMTiP-1 as a type 1 transmembrane protein with respect to the outer mitosomal membrane . Surprisingly , many cells expressing MOMTiP-1-GFP showed a mitosome morphology dubbed “string” phenotype suggestive of extensive elongation of organelles to large tubules ( Fig 5A; left ) . In many cases , virtually all PMs had been replaced by a single long organelle with a diameter that corresponded to that of an individual mitosome . Although the “string” mitosome phenotype was compatible with survival of the parasites , many trophozoites appeared to be delayed or even arrested in cytokinesis and had a typical heart-shaped appearance ( Fig 5A; middle ) previously observed in cells which are unable to complete cytokinesis [66] . Because the tubular organelles ran through the non-divided part connecting both daughter cells , we postulated that inability to divide mitosomes impairs completion of cytokinesis . Co-IP with an HA-tagged variant of MOMTiP-1 yielded a large dataset of 221 exclusive hits ( Fig 5B ) which included GlTom40 detected at high stringency parameters , thereby confirming the strong interaction between GlTom40 and MOMTiP-1 . The 221 MOMTiP-1 co-IP specific hits and an additional 20 enriched candidates were parsed according to different metabolic and/or functional categories ( Fig 5B ) . In addition to GlTom40 , the dataset contained several known mitosomal proteins , including matrix proteins HSP70 and GiOR1 , cysteine desulfurase ( IscS; Gl50803_14519 ) , Cpn60 , [2Fe-2S] ferredoxin ( Gl50803_27266 ) and NifU-like protein , along with all 8 hypothetical proteins previously identified in the GlTom40 co-IP dataset and 4 additional non-annotated candidate mitosome proteins ( Fig 5C–5F ) . Similarly to MOMTiP-1 , one of these ( Gl50803_17276 ) is also predicted to carry a TMD close to its N-terminus . Furthermore , this dataset contained two axoneme-associated GASP-180 proteins ( Gl50803_137716 and Gl50803_16745 ) [67] detected with high stringency parameters , in line with association of the CMC to basal bodies . Taken together , a first reverse co-IP analysis using the single-pass transmembrane MOMTiP-1 provided robust validation of the experimental approach used to identify mitosome membrane proteins , and has expanded the predicted mitosomal membrane and import machinery interactome to 22 proteins ( Fig 5G ) . Reverse co-IP using MOMTiP-1 as bait demonstrated that this protein and GlTom40 are strong interaction partners . We analyzed the intersection of their respective datasets to identify common candidate interaction partners and identified 27 proteins with high reliability ( Fig 5H ) , 10 of which localized to mitosomes ( Fig 4 ) . Given MOMTiP-1’s predicted topology , strong interaction with GlTom40 and the interactome overlap , we postulated that MOMTiP-1 and GlTom40 exist in a core complex mostly likely involved in protein translocation across the outer mitosomal membrane . To characterize other components of this core interactome of the outer mitosomal membrane and to move beyond individual complexes to explore the boundaries of the growing protein interactome network ( Fig 5G ) , we performed a series of additional reverse co-IP experiments using HA-tagged Qb-SNARE 4 ( MOMTiP-7 ) , GlIscS , protein Gl50803_9296 ( MOMTiP-4 ) and protein Gl50803_14939 ( MOMTiP-3 ) as baits . MOMTiP-7 ( Qb-SNARE 4 ) , MOMTiP-4 and MOMTiP-3 were chosen because they were identified either exclusively or in both the GlTom40- and MOMTiP-1 co-IP datasets , suggesting they may reside in the mitosomal outer membrane and could thus serve as tools for a lateral and outward expansion of this compartment’s interactome . On the other hand , GIIscS was chosen to extend the mitosomal proteome inwards towards the organellar matrix . Correct mitosomal localization for all 4 HA-tagged variants had been previously confirmed by IFA ( Fig 4 and S1C–S1F Fig ) . Evidence from extensive primary and reverse co-IP data combined with IFA analysis led us to postulate that GlTom40 , MOMTiP-1 and MOMTiP-3 exist in an outer membrane core complex , likely involved in protein import . We probed the functional conservation of mitosomal import across the GlTom40 translocon with respect to the corresponding process in bona fide mitochondria by adapting the DHFR-folate analogue system [49 , 73] to G . lamblia . Pre-sequence directed DHFR is a classical substrate used in protein translocation studies due to its ability to fold irreversibly upon binding a folate analog , e . g . MTX . Complexed with MTX , DHFR becomes unsuitable as a substrate for import and blocks translocons , which results in a general blockage of organelle protein import [73] . Transfection of MTSfdΔint-DHFR into a Gl17030-HA background , i . e . a transgenic line expressing an HA-tagged MTS-directed mitosomal reporter , allowed testing of the general effects of MTX-induced import block . We reasoned that the presence of MTX in MTSfdΔint-DHFR expressing cells could lead to an import block due to jamming of the translocase . Localization of the reporter by IFA showed an increased cytosolic Gl17030-HA signal after addition of 1 μM MTX ( Fig 7B ) compared to parasites exposed to the solvent alone ( Fig 7A ) . This suggested accumulation of the reporter in the cytosol in cells exposed to MTX as a result of a generalized import block . To test this we measured the ratio of the slightly larger Gl17030-HA reporter precursor protein and the imported and therefore processed form without the MTS by SDS-PAGE and Western blot using anti-HA antibodies . Consistent with the IFA data , unprocessed Gl17030-HA was strongly increased in the drug treated sample , whilst only the processed form was present in untreated controls ( Fig 7C ) . Taken together the data support functional conservation of the highly diverged protein import machinery in G . lamblia mitosomes .
Following its identification as a prominent GlTom40 interaction partner , the single pass membrane protein MOMTiP-1 was the first bait protein selected for reverse co-IP to expand the GlTom40 interactome . MOMTiP-1 as bait pulled down GlTom40 with the most abundant peptide counts . GFP-tagging and detection of MOMTiP-1::GFP on mitosomes suggests a membrane topology in line with characterized mitochondrial receptor proteins such as Tom20 [75] . Further support for MOMTiP-1’s membrane topology may derive from a definition of membrane orientation using alternative methods such as in situ proximity ligation or protease protection assays . The latter approach has proven useful in the determination of membrane topology for other mitosomal candidate proteins in Giardia [49] . The identification of MOMTiP-1 provides an exciting lead; however , a detailed functional characterization of this protein is required to provide independent evidence for the exact nature of this interaction and to test the hypothesis that MOMTiP-1 is a component of the GlTom40 complex with a receptor function . So far 20 proteins have been validated by localization to mitosomes , allowing for a significant expansion of the GlTom40/MOMTiP-1 interactome . This protein’s predicted topology combined with its exclusive mitosomal localization and the size and composition of its interactome , supports MOMTiP-1 as a GlTom40 accessory protein with a potential receptor function for protein import . To test this hypothesis , we engineered a truncated HA-tagged version of MOMTiP-1 consisting only of the predicted C-terminal domain ( residues 31–133; C-MOMTiP-1 ) . Ectopic expression of C-MOMTiP-1 showed a distinct cytosolic localization by IFA ( S5A Fig ) . Native co-IP of C-MOMTiP-1 and analysis of the bait-specific dataset with medium stringency parameters ( 95_1_95 ) identified only 2 mitosomal proteins ( Gl50803_16424 and MOMTiP-8 ) ( S5B Fig ) . These data show that the soluble cytoplasmic MOMTiP-1 variant does not recapitulate the interaction properties of the full-length membrane-anchored protein , suggesting that capture of imported matrix proteins may require incorporation of the putative receptor domain into a fully-assembled TOM complex , complete with ancillary factors . MOMTiP-3 was exclusively identified in the GlTom40 and the MOMTiP-1 co-IP datasets , suggesting that these 3 proteins may function in a tightly-knit complex , likely involved in protein import across the outer mitosomal membrane . TMHMM predicts two TMDs at MOMTiP-3’s N-terminus , followed by a large C-terminal domain . Powerful HMMER-based searches across several eukaryotic lineages , including the closely related diplomonad Spironucleus salmonicida [76] , yielded no orthologues for MOMTiP-3 , neither was there any predicted functional information available . Nevertheless , analysis of protease protection assays for this protein showed that MOMTiP-3 localizes at the outer mitosome membrane with its C- terminus in the cytosol [49] . These data , in combination with data on MOMTiP-1 predicted topology and interactomes developed herein , support a model for GlTom40 , MOMTiP-1 , and MOMTiP-3 for a minimized mitosomal import apparatus whose core import machinery is composed of only these 3 proteins . The dramatic perturbation of mitosomal homeostasis observed when either MOMTiP-1 ( this work ) or MOMTiP-3 [49] were constitutively overexpressed supports the hypothesis for their belonging to the same complex . Protein translocation across the outer mitosomal membrane through this highly reduced import apparatus would be conserved in its mechanism , given that MTX-induced complexing of mitosome-targeted DHFR caused accumulation of unprocessed i . e . untranslocated mitosome reporters ( Fig 7C , [49] ) . Incidentally , these data also confirm that mitosome membrane translocation requires pre-proteins to remain in an unfolded state [49] . Co-IP data combined with imaging of tagged variants identified 6 proteins with dual localization at mitosomes and ER ( Fig 8 ) . Contact between these organelles would serve at least two major functions , i . e . replication of mitosomes and transport associated to lipid biosynthesis . Thus far , we have identified five mitosome proteins with dual localization potentially involved in inter-organelle communication ( Fig 4F ) . One of them is a transmembrane Qb-SNARE 4 ( MOMTiP-7 ) [65] identified in GlTom40 and MOMTiP-1 co-IP datasets . For their biogenesis , mitochondria and MROs rely on lipid transfer from the ER , the central site for phospholipid synthesis in the cell [77 , 78] . SNAREs are best known for mediating membrane fusion in vesicular transport [79] whereas in the context of mitochondria and the ER , they function as components of so called ER-mitochondria encounter structures ( ERMES ) . In addition to being associated to mitochondrial protein import [80 , 81] , ERMES fulfills an essential function in inter-organelle lipid transport [80] . Phosphatidylserine is shuttled from the ER to mitochondria through the ERMES complex where it is converted to phosphatidylethanolamine ( PE ) by a decarboxylation reaction that generates most if not all PE in mitochondria [80 , 82] . Unlike in the hydrogenosome-containing T . vaginalis [83] , ERMES homologs have not been identified in G . lamblia , possibly due to extensive sequence divergence . Thus , whether this function is preserved in Giardia mitosomes is not known however , organelle biogenesis would necessarily depend on ER-derived lipids which are transported to mitosomes either by carrier proteins or via membrane contact sites . The latter requires tethering complexes to facilitate phospholipid exchange between the two organelles . Given that MOMTiP-7 is predicted to be a SNARE , we explored the idea that this protein is part of a larger complex mediating ER-mitosome interaction . Co-IP of MOMTiP-7 specifically detected , in addition to outer membrane proteins such as GlTom40 , MOMTiP-1 and MOMTiP-3 , 3 hypothetical proteins , two of which , MOMTiP-8 and MOMTiP-5 ( both predicted soluble proteins ) , localized both to the ER and to mitosomes . In addition , a domain in MOMTiP-8 has similarity to a yeast “Maintenance of mitochondrial morphology” protein 1 ( Mmm1 ) of the ERMES complex . Moreover , HHpred analysis revealed a link between MOMTiP-5 and a beta barrel lipid binding protein MLN64 ( e-value 0 . 0006 ) in H . sapiens which facilitates cholesterol transport to mitochondria [84] . These preliminary data support the existence of an outer mitosomal membrane-associated complex in G . lamblia mitosomes possibly involved in generating ER—mitosome membrane contact sites ( Fig 8 ) . We had previously shown that replication and inheritance of the CMC is coordinated in a cell cycle-dependent manner , whereas PMs divided stochastically [23] . The lack of a system to track organelles in living trophozoites precluded addressing the question directly whether mitosomes were motile and constituted a dynamic network of organelles with measurable exchange . Development of two GFP-tagged reporters GFP-GlTom40 and MOMTiP-1-GFP ( this study ) allowed for time-lapse experiments to follow individual organelles in a cell . However , we found no evidence for motility of organelles , neither in the CMC nor in PMs , even after prolonged observation ( 1 . 5 h ) . This is consistent with the lack of motor proteins such as kinesins and dyneins in any of the mitosomal protein interactomes we generated . Moreover , FRAP experiments revealed no exchange of GFP-tagged membrane proteins between organelles during the period of observation ( Fig 7F and 7G ) , which further corroborated the relative isolation of mitosomes within the cytosol . The lack of mitosomal motility and contact complicates investigation of their replication and morphogenesis . The two most plausible scenarios for this are currently the following: i ) PMs are released from the CMC , which continuously produces new organelles by elongation and fission to maintain a constant number of organelles in a cell-cycle independent manner; ii ) PMs and the CMC organelles replicate independently in a cell-cycle independent and -dependent manner , respectively [23] . Although time-lapse microscopy experiments did not provide evidence in support of either scenario , conditional expression of a dominant-negative , constitutively active GlDRP-K43E revealed a distinct morphogenesis phenotype ( see also below ) indicative of an organelle replication defect . As one of the key players in the regulation of mitochondrial fission , DRPs are mechano-enzymes conserved from yeast to vertebrates [85 , 86 , 87 , 88] . G . lamblia harbors a single dynamin homologue GlDRP shown to play a major role in this parasite’s endocytic pathway and stage conversion [56 , 89 , 90] . Transgenic parasites expressing the GlDRP-K43E variant exhibited larger and fewer mitosomes , compared to cells expressing the wild type GlDRP variant ( Fig 6 ) . This is in line with the dominant-negative effect on mitochondrial fission elicited by the corresponding mutation in DRPs in other organisms . To our knowledge , this is the first report on the involvement of GlDRP in mitosome homeostasis , supporting the ( at least partial ) functional conservation of mitochondrial and MRO fission [91 , 92 , 93 , 94] . The notion that G . lamblia mitosome fission is functionally conserved is further substantiated by the identification of MOMTiP-6 which presents dual localization to mitosomes and the ER . HMMER-based predictions relate MOMTiP-6 to human mitochondrial fission protein ( Fis1 , e-value 6 . 3E-05 ) which participates in the recruitment of dynamin-related protein 1 ( Drp1 ) to the mitochondrial surface for organelle fission [95 , 96] . The distinctive “string” mitosome phenotype in cells expressing MOMTiP-1-GFP clearly demonstrated that mitosomes can assume an elongated , tubular morphology , which is a prerequisite for organelle division and replication . The implication is that G . lamblia mitosomes retain at least the machinery for fission in which the mechano-enzyme GlDRP and outer mitosomal membrane elements such as MOMTiP-1 and 3 [49] play central roles .
We used an iterative approach based on co-IP experiments to generate a GlTom40-centered interactome network . Ultimately this strategy should allow building a combined proteome , which delineates the full complement of organelle proteins , peripherally associated factors , as well as interfaces with the ER and the cytoskeleton . Although this strategy requires numerous rounds of sequential co-IP and validation , it is highly informative because it produces interaction data in addition to identifying novel organelle proteins . Combined with testing of epitope-tagged variants of candidate proteins for organelle localization as a straightforward validation criterion , serial co-IPs allow for unambiguous definition of the organelle-specific proteome , as well as interfaces with other cellular structures . This strategy also led to the discovery of MOMTiP-1 , a strong GlTom40 interaction partner which plays a role in mitosomal morphogenesis . Together with GlDRP ( this work ) and MOMTiP-3 ( MOMP35; [49] ) , these are the only proteins so far known to affect mitosomal homeostasis in G . lamblia . | Organelles with endosymbiotic origin are present in virtually all extant eukaryotes and have undergone considerable remodeling during > 1 billion years of evolution . Highly diverged organelles such as mitosomes or plastids in some parasitic protozoa are the product of extensive secondary reduction . They are sufficiently unique to generate interest as targets for pharmacological intervention , in addition to providing a rich ground for evolutionary cell biologists . The so-called mitochondria-related organelles ( MROs ) comprise mitosomes and hydrogenosomes , with the former having lost any role in energy metabolism along with the organelle genome . The mitosomes of the intestinal pathogen Giardia lamblia are the most highly reduced MROs known and have proven difficult to investigate because of their extreme divergence and their unique biophysical properties . Here , we implemented a novel strategy aimed at systematic analysis of the organelle proteome by iterative expansion of a protein-protein interaction network . We combined serial forward and reverse co-immunoprecipitations with mass spectrometry analysis , data mining , and validation by subcellular localization and/or functional analysis to generate an interactome network centered on a giardial Tom40 homolog . This iterative ab initio proteome reconstruction provided protein-protein interaction data in addition to identifying novel organelle proteins and functions . Building on this data we generated information on organelle replication , mitosome morphogenesis and organelle dynamics in living cells . | [
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"organelles... | 2016 | An Interactome-Centered Protein Discovery Approach Reveals Novel Components Involved in Mitosome Function and Homeostasis in Giardia lamblia |
Protein domains are basic functional units of proteins . Many protein domains are pervasive among diverse biological processes , yet some are associated with specific pathways . Human complex diseases are generally viewed as pathway-level disorders . Therefore , we hypothesized that pathway-specific domains could be highly informative for human diseases . To test the hypothesis , we developed a network-based scoring scheme to quantify specificity of domain-pathway associations . We first generated domain profiles for human proteins , then constructed a co-pathway protein network based on the associations between domain profiles . Based on the score , we classified human protein domains into pathway-specific domains ( PSDs ) and non-specific domains ( NSDs ) . We found that PSDs contained more pathogenic variants than NSDs . PSDs were also enriched for disease-associated mutations that disrupt protein-protein interactions ( PPIs ) and tend to have a moderate number of domain interactions . These results suggest that mutations in PSDs are likely to disrupt within-pathway PPIs , resulting in functional failure of pathways . Finally , we demonstrated the prediction capacity of PSDs for disease-associated genes with experimental validations in zebrafish . Taken together , the network-based quantitative method of modeling domain-pathway associations presented herein suggested underlying mechanisms of how protein domains associated with specific pathways influence mutational impacts on diseases via perturbations in within-pathway PPIs , and provided a novel genomic feature for interpreting genetic variants to facilitate the discovery of human disease genes .
Protein domains are the structural , evolutionary , and functional units of proteins . Because domains are the basic building block of protein structure and an evolutionary module [1] that increases the protein repertoire by duplication , recombination , and divergence [2] , domain-centric annotation of proteins on function , phenotypes and diseases has been one of major research goals in computational biology [3] . A previous study reported that molecular function annotation can be transferred by sequence homology with only 35% accuracy for pairs of multi-domain proteins [4] . Given that majority of the eukaryotic proteins contain multiple domains , simple homology-based method would not provide reliable functional annotations for proteins in multi-cellular organisms including humans . Moreover , sequence-based annotation transfer is even less accurate for biological processes than for molecular functions [5] . Although biological processes and pathways are not exactly equivalent , we often refer to both as pathways . The lower reliability of sequence-based annotation for pathways are partly due to the fact that many domains are pervasive among diverse pathways . For example , the ‘winged helix-turn-helix DNA-binding’ domain occurs in many DNA-binding proteins that are involved in diverse pathways . Nevertheless , some domains may be associated with certain pathways with high specificity . Therefore , domain-based annotation of pathways requires a quantitative method which can incorporate not only sequence similarity but also specificity of domain-pathway associations . Human complex diseases are generally viewed as pathway-level disorders . Given that a large portion of disease-associated genes are also strongly associated with specific pathways [6] , protein domains associated with specific pathways may provide functional insights for the study of human diseases . Genome-wide investigations of disease-associated genetic variations have recently revealed many disease-associated genes . The observed associations between diseases and pathways triggered a boom in pathway-based analyses of disease-associated variants derived from genome-wide association studies ( GWASs ) and whole exome sequencing ( WES ) [7 , 8] . More recently , domain-level distribution of pathogenic variants revealed high concentrations for particular domains [9–13] , which implies that particular classes of domains are highly implicated in human diseases . Therefore , we hypothesized that pathway-specific domains could be highly informative for human diseases . Here , we present a network-based scoring scheme to quantify pathway specificity of protein domains , which can be used to identify domains associated with specific pathways . We first generated domain profiles for human proteins then constructed a co-pathway protein network based on the associations between domain profiles . Based on the score , we classified human protein domains into pathway-specific domains ( PSDs ) and non-specific domains ( NSDs ) . Interestingly , we observed a significant enrichment of disease-associated mutations for PSDs , where mutations tend to disrupt interfacing domains that mediate within-pathway protein-protein interactions ( PPIs ) and to have a moderate number of domain interactions . These results suggest that mutations in PSDs are likely to disrupt within-pathway PPIs , resulting in pathway disorders . Finally , we demonstrated the utility of pathway-specific domains in predicting disease-associated genes with experimental validations in zebrafish .
We previously found that human protein interactions can be accurately retrieved by associations between domain profiles with a scoring scheme based on information theory , WMI , which assigns more weight to rarer domains in calculating the MI [14] . The resultant domain-based network ( Fig 1A ) was highly predictive for proteins that operate the same human GOBP pathways . Using a Bayesian statistics framework , we assigned LLSs [15] to the links of the co-pathway network . Because the network links were based on domain-sharing patterns among proteins for the same pathway , domains enriched for a pathway also likely connect to other proteins for the same pathway ( Fig 1B ) . We therefore measured domain-pathway associations ( Fig 1C ) based on the network connections from a domain to the member proteins of the pathway . However , overall strength of the network connections for a domain-pathway association does not guarantee their specificity . We thus accounted for the distribution of each domain across pathways using the Gini Index ( GI ) . In summary , the network-based scoring scheme PS quantifies pathway-specific associations for each protein domain ( Fig 1D ) . We calculated PS of human protein domains derived from the InterPro database for GOBP pathways . To assess accuracy of domain-pathway associations , we compiled gold-standard domain-pathway associations between InterPro domains and GOBP pathways derived from InterPro2GO [16] annotations , as these are based on manual curation . We found that only 27% of InterPro domains have annotated GOBP terms by InterPro2GO . We observed strong positive correlations between PS and the likelihood of gold-standard domain-pathway associations , in which approximately 16 , 000 associations between 4 , 506 InterPro domains and 386 GOBP pathways were more than twice as likely to be gold-standard associations as would be expected by random chance ( S1 Fig ) . The significance of the agreements with gold-standard associations was also assessed by Fisher’s exact test . We observed similarly high correlations between PS and gold-standard data , where the top 16 , 000 domain-pathway associations significantly overlapped ( P ≤ 0 . 01 ) with gold-standard data ( Fig 2A ) . We defined 4 , 506 InterPro domains from the top 16 , 000 significant domain-pathway associations as pathway-specific domains ( PSDs ) and the remaining 3 , 856 InterPro domains as non-specific domains ( NSDs ) . The PS threshold for the division between the two domain classes ( P ≤ 0 . 01 ) was 0 . 056 . Recently , investigations into the protein domain-level landscapes of cancer somatic mutations have revealed domains that are enriched for somatic and germline mutations , and domain-level mutational hot spots , which facilitate the identification of novel cancer genes and functional mutations , and provide mechanistic insights regarding mutational consequences [9–13] . To investigate the relationship between disease susceptibility and pathway specificity at the domain level , we compared the frequencies of disease-associated germline variants between PSDs and NSDs derived from the following databases: GWASdb [17] , a database of human genetic variants from GWASs; SwissVar [18]; and ClinVar [19] . We calculated the normalized variation rate ( NVR ) , which represents the probability of a variant occurring in a PSD or NSD normalized to the number of variants observed in both types of domains . Notably , we observed an approximately 1 . 5-fold higher NVR in PSDs than in NSDs for all three pathogenic variant sets ( Fig 2B ) . We found that the observed enrichment of PSD for disease-associated variants was not significantly affected by moderate changes in PS threshold for defining PSDs ( S3A Fig ) . In contrast , variants with neutral effects derived from the HumVar neutral training set for PolyPhen-2 [20] exhibited similar NVRs between PSDs and NSDs . We also performed a similar analysis using cancer somatic mutations from the TCGA for several cancer types including breast cancer [21] , and found no significant differences in NVRs between PSDs and NSDs . Notably , germline cancer variants from the GWASdb set exhibited an approximately 1 . 5-fold higher NVR for PSDs than for NSDs ( 1 . 5 for PSDs and 0 . 6 for NSDs ) . These results indicate that PSDs are more susceptible to the diseases by inherited genetic variants , but not by somatic mutations . To provide mechanistic insight for the higher frequency of pathogenic variants in PSDs than in NSDs , we investigated the relationship among the disease-associated mutations , the pathway specificity of the domains , and the domain-level interaction network . Our analysis was motivated by the following three recent observations: ( i ) the majority of disease-associated variants exert pathogenic effects via perturbations of PPIs rather than on protein folding or stability [22]; ( ii ) disease-associated variants are enriched in PPI-interfacing domains [23–25]; and ( iii ) the majority of disease genes are not essential and do not encode hub proteins [26] . For example , a recent large-scale characterization of disease-associated mutations revealed that most missense disease mutations do not severely altered protein structure or stability , but rather that they tend to perturb PPIs in the majority of the wildtype proteins [22] . In the study , missense disease mutations were divided into three classes by the effects on molecular interactions or “edgotype” [27]: no apparent detectable change in interactins ( “quasi-WT” ) , partial loss of interactions ( “edgetic” ) , and apparent complete loss of interactions ( “quasi-null” ) . Importantly , two-thirds of the tested disease mutations belonged to the edgetic or quasi-null classes . These observations suggest that many mutation-disease associations may be understood via mutational effects on PPIs . Thus , we compared the frequencies of each disease mutation class between PSDs and NSDs and found that edgetic and quasi-null disease mutations exhibited >2 . 5-fold higher NVR for PSDs than for NSDs ( Fig 2C ) . We also found approximately 2-fold enrichment of PSD for nonsynonymous variants affecting on physical protein interactions recently published by IMEx consortium [28] . We confirmed that moderate changes in PS threshold for defining PSDs did not significantly influence enrichment of PSD for nonsynonymous variants affecting on physical protein interactions by IMEx ( S3B Fig ) . In contrast , the NVR of the quasi-WT class of mutations was approximately 1 . 5-fold higher for PSDs than for NSDs , which was similar to the fold change for other disease-associated variant sets ( see Fig 2B ) , indicating that PSDs are enriched for disease mutations that cause loss of wildtype PPIs . PPIs are mediated by domain-level interactions . Therefore , these results suggest that PSDs are more important than NSDs for PPIs , whose perturbations can result in phenotypic changes . To further investigate the impact of PSDs on phenotype via PPIs , we next performed domain-level network analyses based on the human structural interaction network ( hSIN ) [25] , which mapped 135 , 166 interactions between 590 interfacing domains , of which 345 and 245 were PSDs and NSDs , respectively . We compared the ratios ( log base 2 ) of PSDs to NSDs for groups of human structural interaction network ( hSIN ) interfacing domains for different ranges of domain interaction connectivity . Given that domain-level network degree is not evenly distributed , we defined groups of domains for comparisons not by equal degree interval but by similar group size . We found that PSD is enriched—indicated by positive log2 ( Domain Ratio ) score—for interfacing domains with a moderate number ( 2–120 ) of domain interactions , whereas PSD is depleted—indicated by negative log2 ( Domain Ratio ) score—among interfacing domains with either a single interaction or more than 121 interactions ( Fig 2D ) . To explain the observed higher frequency of PSDs among interfacing domains with a moderate number of domain interactions , we proposed a model of mutational consequences via the disruption of interfacing domains with different degrees of connectivity ( Fig 2E ) . Mutations on interfacing domains with a single protein interaction ( green nodes ) may result in the functional failure of a single protein and no detectable pathogenic effect . Consequently , these mutations are not detected among patients . If mutations occur in a hub-interfacing domain ( blue node ) , the interactions toward many proteins involved in diverse pathways may be disrupted , which may result in the functional failure of the global system . In this case , mutations would generally cause lethal phenotypes , resulting in purifying selection of the mutation . In contrast , mutations on interfacing domains with a moderate number of domain interactions ( yellow nodes ) , which likely corresponds to the range of the pathway size , disrupt the interactions of proteins within that pathway , which may result in the functional failure of local systems . Because the majority of disease genes are enriched for pathways [6] , these mutations are likely to cause the functional failure of disease-associated pathways , and can be detected in patients . Therefore , the higher frequency of PSDs among domains with a moderate number of domain interactions suggests that PSDs are more likely involved in heritable diseases via mutations that disrupt within-pathway PPIs . Given that PSDs are more susceptible to the heritable diseases , we hypothesized that PSDs could be predictive genomic features for human diseases . Even their modest prediction power could be highly useful if integrated with other disease-associated genomic information . For example , GWASs generally test for associations of more than a million single-nucleotide polymorphisms ( SNPs ) for each disease phenotype , but identify only a few candidates due to highly stringent significance thresholds ( e . g . , p ≤ 10−7 ) . However , GWASs usually detect a large number of candidate genes with moderate associations that have p-values above this stringent threshold ( e . g . 10−7 < p ≤ 10−3 ) . Additional candidate genes , i . e . , those with moderate GWAS significance , may be rescued by meta-analyses with larger sample sizes , but such studies are costly to conduct . We hypothesized that an additional disease-associated feature would enable us to distinguish disease genes from non-disease genes among those with moderate GWAS significance . Therefore , we tested whether disease-associated PSDs could identify disease genes among candidates with moderate GWAS significance derived from two publicly available data sets: CARDIoGRAM [29] , a study of coronary artery disease ( CAD ) ; and the Psychiatric Genomic Consortium ( PGC ) [30] , a study of schizophrenia ( SCZ ) ( Fig 3A ) . To conduct gene-centric analyses , we identified SNPs with moderate GWAS significance that were located within 10 kb upstream or downstream of the gene , resulting in 204 and 1 , 044 genes moderately associated with CAD and SCZ , respectively . We then identified PSDs associated with CAD or SCZ . PSD-pathway relationships were converted into PSD-disease relationships based on significant overlap ( P < 0 . 01 by Fisher’s exact test ) between disease-associated genes and pathway-associated genes . We compiled 212 disease-associated genes for CAD and 233 disease-associated genes for SCZ from OMIM [31] and DO [32] . Based on Fisher’s exact test ( p < 0 . 01 ) , we identified 2 , 664 PSDs for CAD ( S2 Table ) via 97 CAD-associated GOBP pathways ( S3 Table ) , and 2 , 517 PSDs for SCZ ( S4 Table ) via 61 SCZ-associated GOBP pathways ( S5 Table ) . For GOBP pathways , we considered only those that contained at least five member genes . We further selected CAD and SCZ candidate genes with moderate GWAS significance based on the number of disease-associated PSDs in each gene ( S6 and S7 Tables ) . We selected candidate genes with moderate GWAS significance in which at least three disease-associated PSDs occurred ( GWAS∩PSD set ) , resulting in 38 genes for CAD and 157 genes for SCZ . Next , disease predictions made by PSDs were validated using independent disease annotations from two disease-specific databases: 604 CAD-associated genes from CADgene V2 . 0 [33] and 936 SCZ-associated genes from SZdatabase [34] . To further ensure the independence of the validation gene set , we excluded 212 CAD genes and 233 SCZ genes that overlapped with genes that were used to identify disease-associated PSDs , resulting in 466 CAD genes and 767 SCZ genes for the final validation sets . To compare the predictions with GWAS significance only or PSD significance only , we also prepared similar sets that included predictions based on p-values among genes with moderate GWAS significance ( GWAS set ) and on the number of disease-associated PSDs among genes with both moderate and low GWAS significance ( PSD set ) . A clear benefit of using PSDs was observed for CAD , as approximately 30% more CAD genes were identified in the GWAS∩PSD set than in the GWAS set ( Fig 3B ) . An even greater benefit was observed for SCZ ( Fig 3C ) . Interestingly , the PSD set was more predictive for SCZ than the GWAS set . For both CAD and SCZ , the combination of GWASs and PSDs outperformed GWASs only and PSDs only , indicating that GWASs and PSDs contributed largely complementary information about the diseases . Next , we experimentally validated the predictions of a GWAS∩PSD set using a morpholino-based loss-of-function phenotype analysis in zebrafish . Although the majority of human disease genes have zebrafish orthologs [35] , some disease phenotypes , such as those of psychiatric diseases , are not readily classified in zebrafish . Therefore , we tested predictions for CAD genes only . We found 23 zebrafish orthologs for the 38 human candidate CAD genes from the GWAS∩PSD set . After excluding genes that were already known to be involved in CAD or that were highly ranked by GWAS , we selected the following four testable candidate genes in zebrafish for further analysis: tram1 , apod , cypna1 , and slc22a2 . Unfortunately , the zebrafish model for CAD has not been well established . However , we found that 207 human orthologs of zebrafish genes for heart or blood vessel development by GO annotations were significantly associated with CAD genes indicated by OMIM or DO annotations ( p < 1 . 29 × 10−4 , Fisher’s exact test ) or by CADgeneDB annotations ( p < 7 . 46 × 10−3 , Fisher’s exact test ) , indicating significant associations between CAD and heart/vessel development at the pathway level . These results indicate that zebrafish genes validated by abnormal heart or blood vessel phenotypes during embryonic development may have implications for human CAD . To confirm the feasibility of CAD gene validation based on heart/vessel phenotype , atp2a2b , which has been implicated in CAD [36] , was included as a positive control . After microinjection of test gene morpholinos into zebrafish embryos , heart and blood vessel phenotypes were examined using a fluorescent stereomicroscope ( Fig 4A and S2 Fig ) . We found that the majority of embryos with morpholino injections exhibited abnormal heart or blood vessel phenotypes , not only in the CAD-associated atp2a2b group , but also in three of the four candidate gene groups , including the tram1 , cypna1 , and slc22a2 groups ( Fig 4B and 4C ) , strongly implicating the association of these genes with CAD .
The network-based quantitative method of modeling domain-pathway associations presented herein suggested underlying mechanisms of how protein domains associated with specific pathways influence mutational impacts on diseases via perturbations in within-pathway PPIs , and provided a novel genomic feature for interpreting genetic variants to facilitate the discovery of human disease genes . Stratification of coding regions by different susceptibilities to heritable pathogenic variations may improve the assessment of genomic risk for complex human diseases based on exonic variations . For example , if we can identify PSDs for a particular disease as described in this work , more weight may be assigned to the mutations located in the PSDs for the disease than those located in the NSDs in assessing disease risk . Additionally , disease-associated PSDs would be useful predictors for disease gene candidates . The insufficient statistical power of GWASs often omits a large number of SNPs with moderately significant disease associations . Thus , in theory , we may apply the demonstrated procedure of candidate gene selection with moderate significance based on disease-associated PSDs to all GWASs , which may reveal many disease gene candidates that are missed during conventional GWAS analyses . Therefore , PSDs will significantly contribute to the genetic dissection of human diseases . In this study , we present a scoring scheme PS , which quantifies specificity of domain-pathway associations . Although the given quantification strategy was demonstrated in human only , its application to other organisms is conceptually straightforward: ( i ) generate domain profiles for proteins using InterPro databases , ( ii ) construct a co-pathway protein network based on the associations between domain profiles , ( iii ) calculate PS and identify PSDs using gold-standard domain-pathway pairs , ( iv ) infer GOBP pathway terms of proteins or predict proteins with phenotypic effects using the identified PSDs . This network-based scoring scheme to quantify specificity of domain-pathway associations may be a significant addition to our current computational tool box for pathway annotation of domains and proteins . For example , PS can prioritize GOBP terms for an InterPro domain , which may facilitate manual curation for novel entries in the InterPro2GO database . Furthermore , probabilistic models of pathway involvement of proteins could be developed based on PS .
Information regarding domain occurrence for human proteins was downloaded using the BioMart search tool ( http://www . ensembl . org/biomart/martview ) from the InterPro [37] database ( v38 ) . We generated domain profiles , which were represented as an array of Boolean values for each protein with 1 and 0 indicating the presence and absence of a given domain in the protein , respectively . We generated domain profiles for 17 , 013 human protein coding genes using a total of 8 , 362 InterPro domains . A co-pathway protein network was constructed based on the association between domain profiles as described in our previous study [14] and summarized as follows . Most domain profiles are sparse , because most proteins have few domains only . Domain profiles for proteins with more complex domain compositions were considered more informative than those with simpler compositions . To take into account the non-uniformity of information across profiles , we employed mutual information ( MI ) , which considers the entropy ( i . e . complexity ) of profiles . The MI does not require an a priori model , and has high robustness and accuracy for a wide variety of applications . Additionally , the amount of information across individual domains seemed to vary . We observed a power-law distribution of domain occurrence among proteins , from which we hypothesized that rare domains were associated with relatively specific biological processes and prevalent domains contributed to diverse functions . Therefore , we assigned higher weights to rarer domains during the MI calculation , resulting in weighted mutual information ( WMI ) . The weight for each domain was calculated as described in the following definitions . Definition 1 . Domain-specific weight 𝜔j Given n proteins and m domains , the domain-specific weight 𝜔j for each domain j ( 1 ≤ j ≤ m ) is defined as: ωj=∑k=1n∑l=1mckl∑k=1nckj where ckl represents the occurrence value ( 0 or 1 ) , assigned by whether kth protein contains lth domain . Definition 2 . Weighted mutual information Iω Given two proteins X and Y , Iω ( X , Y ) =Hω ( X ) +Hω ( Y ) −Hω ( X , Y ) where Hω ( X ) and Hω ( Y ) represent the weighted entropy of protein X and protein Y , respectively , and can be calculated as follows: Hω ( X ) =−∑t∈{0 , 1}{pω ( X , t ) ∙logpω ( X , t ) } , pω ( X , t ) =∑j∈{j|cXj=t}ωj∑j=1mωj where t represents domain profile value of protein X , which can be {0 , 1} because we adopt Boolean domain profile . Additionally , Hω ( X , Y ) represents the weighted joint entropy between X and Y , and can be calculated as follows: Hω ( X , Y ) =−∑t1t2∈{ ( 00 , 01 , 10 , 11}{pω ( XY , t1t2 ) ∙logpω ( XY , t1t2 ) } , pω ( XY , t1t2 ) =∑j∈{j|cXjist1andcYjist2}ωj∑j=1mωj where t1 and t2 represents domain profile value of protein X and protein Y , respectively . The weight of a protein-protein link or a domain-pathway link was measured by a log likelihood score ( LLS ) , which was based on a Bayesian statistical framework as previously described [15] . Definition 3 . Log likelihood score of a protein-protein link or a domain-pathway link LLS=ln ( P ( L|E ) /P ( ⌐L|E ) P ( L ) /P ( ⌐L ) ) , ifP ( L ) ≠0andP ( ⌐L|E ) ≠0 where P ( L|E ) and P ( ⌐L|E ) represent the frequencies of positive ( L ) and negative ( ⌐L ) gold-standard links observed in the given evidences ( E ) , while P ( L ) and P ( ⌐L ) represent the prior expectations ( i . e . the total frequencies of all positive and negative gold-standard links , respectively ) . In Bayesian words , P ( L ) /P ( ⌐L ) is prior odds and P ( L|E ) /P ( ⌐L|E ) is posterior odds . The posterior odds are the prior odds times the Bayes factor , likelihood . For protein-protein links , we first sorted them by confidence score ( e . g . WMI ) , then computed LLS for each bin of 1000 protein pairs . For the given size of binning , we hardly encountered with P ( L ) = 0 or P ( ⌐L|E ) = 0 . However , if so , we could avoid the problem by taking larger bin size . Protein-protein pairs or domain-pathway pairs with positive LLS values are more likely to be associated with each other for the given evidence than those by random chance . For this study , the positive gold-standard protein-protein links were generated by pairing two proteins annotated for the same GOBP terms [38] and negative gold-standard protein-protein links were generated by pairing two proteins annotated for different GOBP terms . The positive gold-standard domain-pathway links were compiled from the InterPro2GO database and negative gold-standard domain-pathway links were generated by pairing a domain and a pathway that belong to the database but are not associated with each other . We developed a metric , Pathway Specificity ( PS ) , to quantify the specificity of domain-pathway associations , based on the combination of connectivity from a domain to the member proteins of the pathway and domain-specific weights . For the computing PS , we defined a domain-based co-pathway network by taking protein-protein links with only positive LLS . As a first step in the PS calculation , the protein-pathway association ( PPA ) score of each protein for a specific pathway was calculated via summation of LLSs to the protein in the domain-based co-pathway network . Subsequently , we transformed the association score based on the LLS into the probability score . We observed that the sum of LLSs of protein pairs followed a power-law distribution . Thus , we modeled the sum of the LLSs as a Pareto distribution , which is a power-law probability distribution that coincides with social , scientific , geophysical , and many other types of observable phenomena . The p-value of the Pareto distribution is calculated as follows: PPareto ( X>xi ) = ( xminxi ) α where xi is sum of LLS of protein i and xmin is the scale parameter empirically plugged in by the minimum of sum of LLS values and α is the shape parameter that determines the steepness of the slope . As α increases , the p-value of the Pareto distribution is exponentially distributed with intensity α . We wanted to reduce the skewness of sum of LLS distribution by transformation into PPareto , which is subsequently used to compute pathway association of each protein . The number of proteins with sum of LLS is subject to the size of pathways . If a pathway has a small number of member proteins , α tends to be very small . We found that if α < 1 , the skewness of the sum of LLS distribution did not significantly improved . Therefore , we empirically take 1 as the minimum value of α to calculate protein-pathway association score as in Definition 4 . Definition 4 . Protein-pathway association ( PPA ) score for a specific pathway f For a given protein i , the PPA score PPAi ( f ) is defined as follows: PPAi ( f ) =1−PPareto ( X>si ( f ) ) =1− ( smin ( f ) si ( f ) ) α ( f ) {scaleparametersmin ( f ) :minimumvalueofsi ( f ) foraspecificpathwayfshapeparameterα ( f ) =1+n[∑i=1nln ( si ( f ) smin ( f ) ) ]−1 Here , si ( f ) was calculated via summation of the LLSs as follows: si ( f ) =∑k={x|x∈GiandF}llsik , where llsik denotes the LLS of a link between protein i and protein k , Gi indicates a set of all proteins connected to gene i in the network , and F indicates a set of proteins annotated for pathway f . We have assigned probability scores on edges of protein-protein interaction network using Pareto distribution . The si ( f ) for each protein was calculated based on degree of association to each pathway by summation of the assigned probability scores of all links to known proteins for the pathway . Using the PPA score and domain profile matrix , we then defined the domain-pathway association ( DPA ) score as in Definition 5 . Definition 5 . Domain-pathway association ( DPA ) score for a specific pathway f Given a specific domain j , the DPA score DPAj ( f ) is defined as follows: DPAj ( f ) =1|K|∑i∈KPPAi ( f ) ∙cij {K:asetofproteinscontainingdomainj|K|:sizeofthesetKcij:occurrencevalueoftheithproteinandthejthdomain , mentionedinDefinition1PPAi ( f ) :PPAscoreofproteiniforaspecificpathwayf Then , we finally calculate PS as described in Definition 6 . Definition 6 . Pathway Specificity ( PS ) for a domain j and a pathway f PSj ( f ) = ( 1−GIj ) ×DPAj ( f ) , where DPAj ( f ) is the association score of domain j for a specific pathway f , and GIj is the Gini Index of a domain j over all pathways , and is defined as following: GIj=1−∑∀f{DPAj ( f ) ∑∀fDPAj ( f ) }2 GI , which is a common impurity measure for classification-type problems , is maximized when the DPA of a domain for all pathways are equal , and is equal to zero when a domain has a DPA for only one pathway . We calculated PSs for a total of 49 , 636 domain-pathway associations between 5 , 253 InterPro domains and 407 GOBP pathways ( S1 Table ) . Using manually curated associations between InterPro domains and GOBP pathways derived from InterPro2GO [16] as gold standard data , we measured likelihood of true domain-pathway association for given PS scores . We observed strong positive correlations between PS and the likelihood of gold standard domain-pathway association . The significance of the agreements with gold-standard associations was also assessed by Fisher’s exact test . We divided domain-pathway associations into two classes by high significance of agreement with gold-standard associations ( P ≤ 0 . 01 ) , by which 4 , 506 InterPro domains from the top 16 , 000 significant domain-pathway associations were defined as pathway-specific domains ( PSDs ) and the remaining 3 , 856 InterPro domains as non-specific domains ( NSDs ) . We compared the occurrence of disease-associated variants between PSDs and NSDs using pathogenic germline variants compiled from three independent sources: ( i ) SNPs from the GWASdb [17]; ( ii ) OMIM disease gene variants [31 , 39]; and ( iii ) variants from the ClinVar database [19] . We mapped protein domain regions in the human genome using Ensembl-API . We compiled SNPs that were significantly ( p < 10−7 ) associated with nearly 1 , 610 GWAS traits from GWASdb , which mapped them to dbSNP Build 142 and Genome Assembly , GRCh37/hg19 , resulting in 26 , 342 disease-associated SNPs . Only 966 of these SNPs ( ~3 . 6% ) were located in the protein-coding regions , and of these , 569 SNPs were located in InterPro domain regions . For the analysis of cancer germline variants , we compiled 51 germline variants associated with cancer studies from GWASdb , and 20 , 945 somatic variants from breast cancer patients from The Cancer Genome Atlas ( TCGA ) consortium . We also compiled 1 , 779 and 10 , 778 variants for OMIM disease genes from SwissVar ( http://swissvar . expasy . org ) and dbSNP ( http://www . ncbi . nlm . nih . gov/snp , OmimVarLocusIdSNP . bcp file ) , respectively , to generate an OMIMVar set of 11 , 024 OMIM disease-associated variants . We found that 9 , 050 of these variants were located in protein domain regions . ClinVar is another major public archive of relationships among human variants and phenotypes . We obtained 13 , 465 ClinVar variants for the clinical significance term of ‘pathogenic’ , and found that 10 , 680 of them were located in protein domain regions . To generate the null model , we employed variants expected to have a neutral effect , which were derived from the HumVar neutral training set from Polyphen-2 [20] . The HumVar neutral training set was constructed of common human nsSNPs ( minor allele frequency > 1% ) without annotated involvement in disease , which were considered to be non-damaging variants . Three classes of missense disease mutations were designated as described by Sahni et al . [22]: ( i ) quasi-WT that shows no change in wildtype interactions , ( ii ) edgetic that shows loss of some wildtype interactions , ( iii ) quasi-null that shows complete loss of wildtype interactions . We used 40 , 27 , and 32 missense mutations located in PSDs and 24 , 15 , and 36 missense mutations located in NSDs for the edgetic , quasi-null , and quasi-WT classes , respectively . To compare occurrence of neutral or disease-associated variants between PSDs and NSDs , the total number of variants in the entire genomic region for each domain class , i . e . the variation rate ( VR ) , was calculated as follows: VR=#oftestvariantsforthegivendomainregion ( PSDorNSD ) Total#ofnucleotidesforthegivendomainregion ( PSDorNSD ) The background variation rate ( BVR ) for all domain regions , including both PSDs and NSDs , was calculated as follows: BVR=#oftestvariantsforalldomainregions ( PSDsandNSDs ) Total#ofnucleotidesforalldomainregions ( PSDsandNSDs ) VRs for the test variant sets were then normalized to the background variation rate ( BVR ) to calculate the normalized variation rate ( NVR ) as follows: NVR=VRBVR Statistical significance of NVR differences between PSDs and NSDs were evaluated by binomial tests . Wang et al . [25] provided information on 590 interfacing domains ( IFD ) and 135 , 166 domain-domain interactions in a structural level human protein interaction network ( hSIN ) . Among the 590 interfacing domains , 345 domains were PSDs and 245 domains were NSDs . Therefore , there were ~1 . 4-fold more PSDs than NSDs . To evaluate the difference in IFD enrichment between the two domain groups , we measured the ratio of PSDs to NSDs , i . e . the domain ratio , for several groups of IFDs with different numbers of domain interactions ( i . e . domain interaction connectivity ) as follows: DomainRatio=|PSD∩IFD||PSD|/|NSD∩IFD||NSD| We used GWAS data for coronary artery disease ( CAD ) and schizophrenia ( SCZ ) , which were publicly available from the CARDIoGRAM consortium [29] and Psychiatric Genomic Consortium ( PGC ) [30] , respectively . The CARDIoGRAM consortium performed a meta-analysis on 22 GWASs of individuals of European descent imputed by HapMap 2 , which included 22 , 233 cases and 64 , 762 controls . The PGC included a multi-stage schizophrenia GWAS for 36 , 989 cases and 113 , 075 controls . From these GWASs , we found that 3 , 188 ( out of 2 , 420 , 360 ) and 54 , 688 ( out of 9 , 444 , 230 ) SNPs with moderate significance ( 10−7 < p ≤ 10−3 ) were associated with CAD and SCZ , respectively . We assigned each SNP to genes that were located within 10 kb of the gene ( downstream or upstream ) , resulting in the assignment of 3 , 188 SNPs to 204 genes for CAD and 54 , 688 SNPs to 1 , 044 genes for SCZ . These genes were further filtered by the number of PSDs relevant to the diseases . Adult zebrafish were maintained at 28 . 5°C with a 13:11 h light:dark cycle in the Zebrafish Auto System ( pH 7 . 0–7 . 9 , Genomic-Design , Korea ) . Zebrafish embryos were collected after natural breeding and incubated in clean petri dishes with E3 medium ( 297 . 7 mM NaCl , 10 . 7 mM KCl , 26 . 1 mM CaCl2 , and 24 . 1 mM MgCl2 ) containing 1% methylene blue ( Sigma-Aldrich , St . Louis , MO , USA ) at 28 . 5°C . For observation and photography , the embryos were raised ( 24 hours after fertilization ) in the E3 medium containing 0 . 2 mM N-phenylthiourea ( PTU; Sigma-Aldrich Chemistry , cat . # P7629 ) to block melanin formation . Translation-blocking MOs targeting coronary artery disease ( CAD ) candidate genes were designed and synthesized by Gene Tools ( Philomath , OR 97370 , USA ) . Each MO was diluted in distilled water at a concentration of 2 μg/μL and then injected into the yolk of zebrafish embryos at 1–4 cell stages using a gas-based microinjection system ( Genomic-Design , Korea ) . Overall morphology , heart asymmetry , and vascular phenotypes of the Tg ( flk1:EGFP ) zebrafish were observed using a fluorescent stereomicroscope ( SMZ1270 , Nikon , Tokyo , Japan ) . Images were captured using a camera ( DS-Qi2 , Nikon , Tokyo , Japan ) and analyzed using NIS-Elements imaging software ( Nikon , Tokyo , Japan ) . | Protein domains are basic functional units of proteins , yet domain-based pathway annotations for proteins are challenging tasks because many domains are pervasive among diverse pathways . Therefore , we developed a network-based scoring scheme to measure pathway specificity of domains , and then used it to identify pathway-specific domains . Surprisingly , we observed substantially more disease mutations in pathway-specific domains than non-specific domains . We found evidences that mutations of pathway-specific domains tend to perturb pathway integrity via disrupting within-pathway protein-protein interactions . We also demonstrated prediction capacity of pathway-specific domains for complex diseases with experimental validations . Our study demonstrated the usefulness of pathway information for protein domains in interpreting non-random distribution of disease mutations among domains and identification of disease genes and variants . | [
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"coro... | 2019 | Pathway-specific protein domains are predictive for human diseases |
Initial studies of 88 transmission pairs in the Zambia Emory HIV Research Project cohort demonstrated that the number of transmitted HLA-B associated polymorphisms in Gag , but not Nef , was negatively correlated to set point viral load ( VL ) in the newly infected partners . These results suggested that accumulation of CTL escape mutations in Gag might attenuate viral replication and provide a clinical benefit during early stages of infection . Using a novel approach , we have cloned gag sequences isolated from the earliest seroconversion plasma sample from the acutely infected recipient of 149 epidemiologically linked Zambian transmission pairs into a primary isolate , subtype C proviral vector , MJ4 . We determined the replicative capacity ( RC ) of these Gag-MJ4 chimeras by infecting the GXR25 cell line and quantifying virion production in supernatants via a radiolabeled reverse transcriptase assay . We observed a statistically significant positive correlation between RC conferred by the transmitted Gag sequence and set point VL in newly infected individuals ( p = 0 . 02 ) . Furthermore , the RC of Gag-MJ4 chimeras also correlated with the VL of chronically infected donors near the estimated date of infection ( p = 0 . 01 ) , demonstrating that virus replication contributes to VL in both acute and chronic infection . These studies also allowed for the elucidation of novel sites in Gag associated with changes in RC , where rare mutations had the greatest effect on fitness . Although we observed both advantageous and deleterious rare mutations , the latter could point to vulnerable targets in the HIV-1 genome . Importantly , RC correlated significantly ( p = 0 . 029 ) with the rate of CD4+ T cell decline over the first 3 years of infection in a manner that is partially independent of VL , suggesting that the replication capacity of HIV-1 during the earliest stages of infection is a determinant of pathogenesis beyond what might be expected based on set point VL alone .
Despite a diverse HIV-1 quasispecies within chronically infected individuals , a single variant establishes infection in the majority of heterosexual transmission cases , resulting in a severe genetic bottleneck [1]–[3] . A more profound understanding of the interaction between host and viral characteristics and how they shape early pathogenesis and disease progression will be integral for understanding the trajectory and impact of early events after heterosexual transmission . While it is well established that host factors such as HLA-class I alleles can play a major role in determining clinical progression in those individuals recently infected with HIV-1 [4]–[10] the role of transmitted viral characteristics has been understudied due to the lack of suitable cohorts in which virus from both the donor and linked recipient are available . Accordingly , studies using epidemiologically linked heterosexual transmission pairs are essential for understanding how viral evolution , adaptation , and the characteristics of the transmitted variant influence HIV-1 pathogenesis . Previous studies in both heterosexual and homosexual transmission pairs have demonstrated that viral loads ( VL ) in the newly infected partners are correlated with VL in the transmitting partner [11]–[13] . This finding is intriguing as the majority of the linked couples have disparate HLA-I alleles , and would therefore have varying immune responses to the incoming virus . Thus , the characteristics of the virus in the donor quasispecies that impact replication can similarly impact the replication of the newly infecting virus even in the context of what is frequently a distinct immunogenetic environment . Indeed , when host factors known to modulate VL such as the presence of protective or unfavorable HLA-I alleles , gender , age , and HLA-B sharing are taken into account in a multivariable analysis utilizing a general linearized model , the correlation between donor and recipient VL becomes highly significant ( Yue et al . , manuscript in submission ) . It is clear that both humoral [14] and cellular immune responses can drive virus evolution over the course of infection . In the case of the latter , escape mutations arise that abrogate the ability of cytotoxic T lymphocytes ( CTL ) to kill virus-infected cells [15]–[22] . While the selection of CTL escape mutations provides an in vivo advantage for the virus , if a mutation occurs within a functionally constrained region of the genome such as Gag , it could reduce in vitro replicative fitness [17] , [23]–[30] . This phenomenon has been demonstrated for several CTL escape mutations associated with protective alleles such as HLA-B*57 , B*5801 , B*27 , and B*81 [31]–[36] . The ability of protective alleles to target conserved regions of the genome that escape with difficulty , due to the fitness costs incurred by mutations at these epitopes , may partially explain the mechanism of enhanced protection from disease progression in individuals with these alleles [37]–[41] . While evasion from the CTL response may result in such deleterious mutations , the in vivo fitness benefit outweighs that of the replication cost [42] , and the ongoing selection of additional mutations may allow the virus to compensate for these defects [17] , [29] , [32] , [33] , [35] , [43]–[49] . The functional consequence of escape and compensatory mutations upon transmission to an HLA-mismatched individual has not been fully explored . Initial studies in the ZEHRP cohort of 88 transmission pairs demonstrated that the number of transmitted HLA-B associated polymorphisms in Gag , but not Nef , was negatively correlated to set point VL in the newly infected partners , suggesting that an accumulation of escape mutations might attenuate viral replication and provide a clinical benefit during early stages of infection [50] . In addition , in a smaller study of nine newly infected individuals infected by viruses with fitness reducing HLA-B*5703 associated epitope-escape mutations in p24 , a lower set point VL was observed [24] . Previous studies have also shown that elite controllers can harbor viruses with gag-pro sequences that confer reduced in vitro replicative capacities ( RC ) [51]–[53] . In a series of studies , Brockman and colleagues have shown that in vitro RC conferred by gag-pro variants isolated from both subtype B and C chronically infected individuals correlates to VL , demonstrating the role of intrinsic viral characteristics in defining this marker of pathogenesis [43] , [47] , [54] , [55] . However , in these studies the recombination of population-based PCR amplified sequences into the lab adapted NL4 . 3 provirus required the outgrowth of virus in a CEM-CCR5 based cell line , potentially skewing the nature of the virus recovered . In contrast , studies of HIV-1 fitness in acute infection did not yield a statistically significant correlation between RC and VL , potentially due to small sample sizes and the limitations of the methodologies used . The identification of 149 heterosexual epidemiologically linked transmission pairs from a discordant couple cohort in Lusaka , Zambia , provides a unique opportunity to investigate the role that HLA-mediated adaptation of Gag within a chronically infected individual plays in modulating the RC of the transmitted variant . We hypothesize that HLA-mediated adaptation of HIV-1 resulting in Gag variants conferring varying levels of RCs will be a major viral characteristic linking donor and recipient VLs , and that the in vitro RC conferred by the transmitted Gag sequence defines early clinical parameters of HIV-1 pathogenesis . To test this hypothesis and using a novel approach , we cloned gag sequences from the earliest seroconversion plasma sample from 149 newly infected recipients of epidemiologically linked Zambian transmission pairs into the MJ4 proviral backbone [56] . The RC of each Gag-MJ4 chimera was then determined and used to investigate how the RC conferred by the transmitted Gag sequence defines clinical parameters , such as early set point VL and CD4+ T cell decline in the newly infected individuals . These studies allowed us to identify novel residues in Gag that influence RC , and demonstrate a strong correlation between RC and early set point VL , as well as between RC and CD4 decline during the first three years of infection , which was also found to be independent of VL . Thus , the RC of the transmitted virus as defined by its gag gene influences critical aspects of HIV-1 pathogenesis .
All participants in the Zambia Emory HIV Research Project ( ZEHRP ) discordant couples cohort in Lusaka , Zambia were enrolled in human subjects protocols approved by both the University of Zambia Research Ethics Committee and the Emory University Institutional Review Board . Prior to enrollment , individuals received counseling and signed a written informed consent form agreeing to participate . The subjects selected from the cohort were initially HIV-1 serodiscordant partners in cohabiting heterosexual couples with subsequent intracouple ( epidemiologically linked ) HIV-1 transmission [57]–[59] . Epidemiological linkage was defined by phylogenetic analyses of HIV-1 gp41 sequences from both partners [60] . Viral isolates from each partner in the transmission pair were closely related , with median and maximum nucleotide substitution rates of 1 . 5 and 4 . 0% , respectively . In contrast , median nucleotide substitution rate for unlinked HIV-1 C viruses from the Zambian cohort and elsewhere was 8 . 8% [60] . The algorithm used to determine the estimated date of infection ( EDI ) was previously described by Haaland et al . [2] . All patients in this cohort were antiretroviral therapy naïve . Zambian linked recipients were identified 45 . 5 days ( median , IQR = 41 . 5–50 . 5 ) after the estimated date of infection , at which time plasma samples were obtained from both the transmitting partner ( donor ) and the seroconvertor ( recipient ) . The vast majority ( 95% ) of HIV-1 sequences derived from ZEHRP transmission pairs belonged to HIV-1 subtype C with subtypes A , D , G , and J being detected only occasionally [60] . All of the transmission pairs utilized in this study are infected with subtype C HIV-1 . Early set point VL for newly infected individuals was defined as the earliest stable nadir VL value measured between 3 and 9 months post infection and which did not show a significant increase in value within a 3–4 month window . HIV plasma VL was determined at the Emory Center for AIDS Research Virology Core Laboratory using the Amplicor HIV-1 Monitor Test ( version 1 . 5; Roche ) . CD4+ T cell counts were based on T-cell immunophenotyping , with assays done using the FACScount System ( Beckman Coulter Ltd . , London , United Kingdom ) in collaboration with the International AIDS Vaccine Initiative . Genomic DNA was extracted from whole blood or buffy coats ( QIAamp blood kit; Qiagen ) . HLA class I genotyping relied on a combination of PCR-based techniques , involving sequence-specific primers ( Invitrogen ) and sequence-specific oligonucleotide probes ( Innogenetics ) , as described previously [10] . Ambiguities were resolved by direct sequencing of three exons in each gene , using kits ( Abbott Molecular , Inc . ) designed for capillary electrophoresis and the ABI 3130xl DNA Analyzer ( Applied Biosystems ) . Viral RNA was extracted from 140 µL plasma samples using the Qiagen viral RNA extraction kit ( Qiagen ) . Gag-pol population sequences were generated using nested gene specific primers . Combined RT-PCR and first round synthesis was performed using SuperScript III Platinum One Step RT-PCR ( Invitrogen ) and 5 µL viral RNA template . RT-PCR and first round primers include GOF ( forward ) 5′ ATTTGACTAGCGGAGGCTAGAA 3′ and VifOR ( RT-PCR and reverse ) 5′ TTCTACGGAGACTCCATGACCC 3′ . Second round PCR was performed using Expand High Fidelity Enzyme ( Roche ) and 1 µL of the first round PCR product . Nested second round primers include GIF ( forward ) 5′ TTTGACTAGCGGAGGCTAGAAGGA 3′ and VifIR ( reverse ) 5′ TCCTCTAATGGGATGTGTACTTCTGAAC 3′ . Three positive amplicons per individual were pooled and purified via the Qiagen PCR purification kit ( Qiagen ) . Purified products were sequenced by the University of Alabama at Birmingham DNA Sequencing Core . Sequence chromatograms were analyzed using Sequencher 5 . 0 ( Gene Codes Corp . ) , and degenerate bases were denoted using the International Union of Pure and Applied Chemistry codes when minor peaks exceeded at least thirty percent of the major peak height . The percent similarity between donor and recipient population gag sequences was determined by building a neighbor-joining tree using Geneious v5 . 5 . 7 ( Biomatters Ltd . ) . The percent similarity between nucleotide and amino acid alignment was determined based on the output matrix from these neighbor-joining trees . In calculating the percent similarity between amino acid sequences , degenerate bases that resulted in non-synonymous changes and , thus , a mixture of amino acid residues , were translated as an “X” . When one of the amino acids comprising a mixture in the donor was found in the recipient Gag sequence , this was counted as a mismatch , making the average percent similarity reported between donor and recipient gag sequences a maximal estimate of percent mismatch . Viral RNA was extracted from linked recipients at the day of seroconversion time point using the Qiagen viral RNA extraction kit ( Qiagen ) . First round PCR products were generated as was previously described for the gag sequencing of all 149 transmission pairs [50] . Second round PCR was performed to generate gag amplicons for Gag-MJ4 chimera generation using Phusion Hot Start II polymerase ( Fisher ) and 1 µL of the first round PCR product . Nested second round primers include GagInnerF1 ( forward ) 5′ AGGCTAGAAGGAGAGAGATG 3′ and BclIDegRev2 ( reverse ) 5′ AGTATTTGATCATAYTGYYTYACTTTR 3′ , which generate a gag amplicon starting from the initiation codon of gag and extending 142 nucleotides after the gag stop codon and into pro . The 5′ portion of the MJ4 long terminal repeat ( LTR ) was amplified using Phusion Hot Start II polymerase and the MJ4For1b ( forward ) 5′ CGAAATCGGCAAAATCCC 3′ and MJ4Rev ( reverse ) 5′ CCCATCTCTCTCCTTCTAGC 3′ primer set . In order to make the proper insert for cloning , the patient-specific gag and MJ4-LTR sequences were joined using a splice-overlap extension PCR utilizing the MJ4For1b ( forward ) and BclIRev ( reverse ) 5′ TCTATAAGTATTTGATCATACTGTCTT 3′ primer set . Joined splice-overlap-extension PCR products were gel purified using the Qiagen gel extraction kit ( Qiagen ) . Purified Gag-LTR inserts and wild-type MJ4 vector ( NIH AIDS Research and Reference Reagent Program , [56] ) were digested with NgoMIV and BclI restriction enzymes ( NEB ) and ligated overnight at 4°C with T4 DNA ligase ( Roche ) at a 3∶1 insert to vector ratio . JM109 competent cells were transformed with ligation products , plated onto LB/agar plates supplemented with 100 µg/ml ampicillin and grown at 30°C . Gag-MJ4 chimeric DNA was isolated from cultures using the Qiagen miniprep kit ( Qiagen ) . Gag-MJ4 chimeras were sequenced to confirm gag insert fidelity as compared to previously amplified population sequences . Two identical independent clones per linked recipient were chosen for replication assays in order to ensure backbone fidelity during the cloning process . Viral stocks were generated by transfecting 1 . 5 µg purified proviral plasmid DNA into 293T cells ( American Type Culture Collection ) using the Fugene HD transfection reagent ( Roche ) according the manufacturer's protocol . Viral stocks were collected 72 hrs post transfection , clarified by low-speed centrifugation , and frozen at −80°C . The titer of each viral stock was determined by infecting TZM-bl cells ( NIH AIDS Research and Reference Reagent Program ) with 5-fold serial dilutions of virus in a manner previously described [36] , [61] . Both 293T and TZM-bl cell lines were maintained in complete Dulbecco's modified Eagle's medium ( DMEM , Gibco ) supplemented with 10% fetal bovine serum ( HyClone Laboratories ) , 2 mM L-glutamine , and 100 U/ml penicillin G sodium , and 100 µg/ml streptomycin sulfate ( Gibco ) at 37°C and 5% CO2 . In order to assess the RC of Gag-MJ4 chimeras , 5×105 GXR25 cells [62] were infected at an MOI of 0 . 05 , and 100 µl of viral supernatants were collected at 2 day intervals . Briefly , GXR25 cells and virus were incubated with 5 µg/ml polybrene at 37°C for 3 hours , washed 5 times with complete Roswell Park Memorial Institute ( RPMI ) medium ( Gibco ) and plated into 24-well plates . Cells were split 1∶2 to maintain confluency , replaced with an equal volume of fresh media , and viral supernatants were taken at days 2 , 4 , 6 , and 8 as previously described [36] , [61] . Virion production was quantified using a 33P-labeled reverse transcriptase assay . Based on values obtained for days 2–8 , the optimal window for logarithmic growth for all viruses was determined to be between days 2 and 6 , as by day 8 many high replicating viruses had exhausted target cells causing a flattening or decline of the replication curve . Therefore , log10-transformed slopes were calculated based on days 2 , 4 , and 6 for all viruses . Replication scores were generated by dividing the log10-transformed slope of the replication curve for each Gag-MJ4 chimera by the log10-transformed slope of wild-type MJ4 . Two independent Gag-MJ4 chimera clones per acutely infected linked recipient were run in duplicate to confirm cloning fidelity . After both independent clones were confirmed to have identical replicative capacities , one clone was subsequently run in triplicate in two independent experiments in order to generate consistent replicative capacity scores . GXR25 cells were maintained in complete RPMI medium supplemented with 10% fetal bovine serum ( HyClone Laboratories ) , 100 U/ml penicillin G sodium , 100 µg/ml streptomycin sulfate ( Gibco ) , and 10 mM HEPES buffer at 37°C and 5% CO2 . Aliquots of culture supernatants from infected cells were added to an RT-PCR master mix [63] and incubated at 37°C for 2 hours; then the RT-PCR product was blotted onto DE-81 paper , and allowed to dry . Blots were washed 5 times with 1× SSC ( 0 . 15 M NaCl , 0 . 015 M sodium citrate , pH 7 . 0 ) and 3 times with 90% ethanol , allowed to dry , and exposed to a phosphoscreen ( Perkin Elmer ) overnight . Counts were read using a Cyclone PhosphorImager [36] , [61] . HLA-associated polymorphisms were defined as any non-consensus polymorphism that occurred at an amino acid position having known escape mutations adapted to specific HLA-class I alleles as defined by a list of associations generated in a manner similar to that described previously [44] , [64] from 1899 subtype C gag sequences from Zambia and South Africa ( Carlson , et al . , manuscript in preparation ) . Polymorphisms that increased or decreased replicative capacity were defined based on amino acid associations with RC derived from an exploratory pair-wise analysis detailed in the experimental results . To generate a summed polymorphism score , the number of HLA-associated fitness-decreasing polymorphisms was subtracted from the number of HLA-associated fitness-increasing polymorphisms . The relationships between RC and set point VL , donor VL , and average CD4+ counts; RC and the number and quality of HLA-associated polymorphisms; and VL and the number and quality of HLA-associated polymorphisms were analyzed using the Spearman rank correlation . Linear regression analyses were utilized to generate trend lines to facilitate visualization of correlation graphs . Mann-Whitney tests were used to compare the differences in RC between rare and more common polymorphisms . Mann-Whitney tests were used to analyze the difference in median set point VLs between different RC groups ( RC<1 , RC = 1–2 , RC>2 ) . All Spearman correlations , Mann-Whitney tests , and linear regression analyses were performed using Prism GraphPad v5 . 0 ( GraphPad Software , Inc . ) . The Mann-Whitney U test was used to identify statistically significant differences in RC between two groups ( e . g . sequences with or without a given HIV polymorphism ) . Multiple tests were addressed using q-values [65] , which estimates the expected proportion of significant tests that are false positives . To limit the number of tests , we considered only groups containing at least 3 individuals . A subset of volunteers with longitudinal CD4+ T cell counts ( n = 63 ) was analyzed to characterize the relationship between RC and T cell decline . Survival analysis was used to estimate the association between replicative capacity ( RC ) and the drop of CD4+ cell count . The endpoint is defined as the time before CD4+ T cell counts drop below a threshold , e . g . 300 or 350 cells/mm3 . The set point VL is another factor being considered . Kaplan Meier curve and log-rank test were used to compare the survival between the groups with RC<1 and RC>2 . Cox proportional hazard regression was used to assess risk associated with high RC with or without adjustment for the confounding factor of set point VL . The sample size ( n = 66 ) was inadequate for a more complete analysis with additional covariates .
Studying heterosexual transmission of HIV-1 within the context of discordant couples allows for full characterization of viruses from both donor and recipient . From a total of 294 epidemiologically linked transmission pairs identified from 1998–2010 in the Zambia Emory HIV Research Project ( ZEHRP ) discordant couple cohort [66] , we selected 149 individuals that had been enrolled for at least 1 year and had at least 9 months of follow up post serconversion . The median time post estimated date of infection ( EDI ) for all 149 individuals was 45 days . Thus , this represents a unique early infection linked transmission pair cohort . The median log10 VL for donors near the time of transmission and the median log10 set point VL for linked recipients was 5 . 02 ( IQR = 4 . 51–5 . 45 ) and 4 . 39 ( IQR = 3 . 91–4 . 99 ) respectively for all participants included in this study . Figure S1 depicts the phylogenetic clustering of population sequences for the gag gene of the 149 epidemiologically linked transmission pairs and highlights the high degree of sequence similarity between donor and recipient viruses . The majority of population gag sequences isolated from acute/early time points of linked recipients were homogeneous and in most cases were identical to the donor population gag sequence isolated near the EDI . Overall , donor and linked recipient sequences differed in amino acid composition by only 2 . 7% . Mutations were counted when a mixture of nucleotides ( amino acids ) in the donor population sequence resolved to a single residue in the recipient and thus represent maximal values . Of the 149 pairs only 37 recipient sequences had evidence of potential early escape and a majority of these individuals ( 28/37 ) had only a single amino acid change . Therefore , we can conclude that the majority of the sequence polymorphisms present at the seroconversion time point are derived from the chronically infected donor . Additional characteristics of the cohort including set point VL and CD4+ counts are listed in Table 1 . Previous studies investigating the role of Gag viral fitness have employed a recombination approach in which sequences are PCR amplified as a bulk population , allowed to recombine into a gag-deleted NL4-3 , and resulting viruses propagated in permissive cells [43] , [47] , [51] , [53]–[55] . This method has three distinct disadvantages: there is no control over the sites of recombination , it requires the outgrowth of virus which may select for the most fit virus in the population and could also select for sequence changes , and the introduction of subtype C sequences into a lab adapted subtype B proviral backbone may introduce biases due to the interaction of subtype B proteins with subtype C Gag . In order to avoid these limitations and because we are studying individuals recently infected with HIV , where the population is generally homogeneous , we have employed a direct cloning method that allows for the introduction of the entire gag gene into a replication competent , CCR5 tropic , clade C provirus , MJ4 [56] . A splice-overlap-extension PCR was employed to fuse the MJ4-LTR-U5 sequence with the transmitted gag sequence . This ensures that the cis-acting sequences upstream of Gag , which may influence expression levels , are constant throughout all constructs . The resulting chimeras include the entire transmitted gag sequence from the initiation codon to the end of Gag and extend into conserved region of protease by 47 amino acids . For each newly infected individual , at least two independent Gag-MJ4 chimeras were sequence confirmed and assayed for replicative capacity ( RC ) . An analysis of variation of RC between the two independent clones derived from each newly infected linked recipient was 8 . 5% . Testing two independent clones , therefore , ensures that the observed RC is not due to the confounding effect of backbone mutations that might have arisen during the cloning process and provides an estimate of experimentally induced variation . Overall , a low amount of heterogeneity was detected in gag population sequences isolated from linked recipients , with only 28% having one or more mixed bases resulting in amino acid changes in the sequences from which the Gag-MJ4 chimeras were generated . When this was the case , the gag clone with the sequence closest to the donor gag sequence was chosen in order to avoid sampling gags containing de novo escape or reversion . In some cases in which multiple variants appeared to be transmitted , several gag variants were assayed for RC as described in the materials and methods section . In each case , these minor variants were found to have similar or identical RC values ( data not shown ) . In initial replication assays performed in order to test assay precision , wild-type MJ4 exhibited an intra-assay variability of 10 . 4% and an inter-assay variability of 8 . 7% . Figure 1 shows the results of a typical experiment for all 149 Gag-MJ4 chimeras , with wild-type MJ4 depicted in red . The normalized RC values of the chimeras ranged from less than 0 . 01 to greater than 3 . 5 . Some viruses replicated more than 100 times more efficiently than MJ4 , demonstrating that substitution of Gag can have a profound impact on the ability of the virus to replicate in cells . An examination of the RC of transmitted viruses allows us to determine the role of viral replication in defining set point VL in acutely infected individuals before significant viral adaptation to immune pressure of the host has taken place , which might confound the relationship of RC to VL . We observed a statistically significant positive correlation between the replicative capacities of Gag-MJ4 chimeras and set point VLs in newly infected individuals ( Figure 2A; Spearman correlation r = 0 . 17 , p = 0 . 02 ) , a correlation that persists when conditioning on the presence of B*57 in , and the sex of , the linked recipient ( p = 0 . 009 ) . This finding indicates that the RC conferred by the transmitted Gag sequence clearly plays a role in defining early set point VL of newly infected Zambian linked recipients . In several cohorts VL in the transmitting partner and that in the linked seroconvertor have been shown to be correlated [11]–[13] . In order to more fully explore the possible contribution of RC in explaining this phenomenon , we compared VLs of the transmitting partner at the time of transmission to the RC defined by the transmitted Gag sequence . Despite both a higher maximum and wider range of VLs within transmitting partners , we observed a statistically significant positive correlation between RC and the set point VL of the donors , similar to that of their newly infected partners ( Figure 2B; Spearman correlation r = 0 . 18 , p = 0 . 01 ) . This supports the concept that RC , defined by Gag , is a viral characteristic contributing to the positive correlation between donor and recipient VLs that has been previously reported [11]–[13] . Uncovering sites of vulnerability in HIV-1 is a high priority for the informed design of an effective HIV vaccine [21] . Therefore , we examined all 149 Gag sequences and their RC using an exploratory pairwise analysis described previously [43] , [54] , to uncover residues that significantly affect the virus' ability to replicate in vitro . We found 49 residues at 31 unique positions that had a statistically significant effect on RC at p<0 . 05 ( q<0 . 51 ) and 4 residues at 3 unique positions that were significant at p<0 . 002 ( q<0 . 2 ) ( Table S1 ) . In what follows , we will use q<0 . 2 as the significance threshold when individual sites of significance are considered , and q<0 . 51 ( p<0 . 05 ) as the significance threshold when we are testing broad trends , in which we are willing to increase our expected false positive rate as a tradeoff to substantially reduce our expected false negative rate . The locations of all statistically significant polymorphisms ( p<0 . 05 ) , along with their effects on RC as compared to the median RC of all viruses , are plotted linearly on a graphical representation of the Gag protein ( Figure 3A . ) . Residues that dramatically modulate RC were enriched in p17 and p2 ( Fisher's exact test , p<0 . 0001 ) . In addition , roughly two-thirds of the non-consensus residues with p<0 . 05 increase fitness relative to the median RC for the entire population . An expanded data set of 1899 subtype C gag sequences from Zambia and South Africa ( Carlson , Schaefer et . al . , manuscript in preparation ) was utilized to identify residues that affected RC and were also HLA-associated . Within this data set , HLA-associated polymorphisms are classified as being either adapted or non-adapted . An adapted residue is one that is escaped relative to the HLA-allele in question . In contrast , a residue that is non-adapted is the susceptible form and may render the virus vulnerable to immunological targeting by the HLA-allele in question . This new dataset has identified a total of 199 HLA-linked polymorphisms ( q<0 . 2 , p<0 . 0007 ) vs . 59 associations utilized previously from a smaller subset of gag sequences [27] , [50] . Within the 49 residues associated with changes in RC , 7 polymorphisms were found to be adapted to specific HLA class I alleles , clearly demonstrating the impact of the cellular immune response in affecting viral fitness ( Figure 3B , * denotes q<0 . 2 ) . Six polymorphisms were found to be non-adapted to specific HLA class I alleles ( Figure 3C , * denotes q<0 . 2 ) . Since these are non-consensus polymorphisms , it is possible that consensus at these residues is escaped relative to these HLAs , potentially explaining why an adapted consensus residue at this position is the less fit variant . Indeed , 5 consensus residues ( 62K , 451S , 488S , 85L , and 309A ) with p<0 . 05 were found to be adapted to HLA-I alleles , demonstrating that the cellular immune response can drive selection for consensus residues . During our analysis of amino acid polymorphisms linked to changes in RC ( p<0 . 05 ) , we observed a negative correlation between the frequency of polymorphisms and the magnitude of their effect on RC ( Spearman correlation , r = −0 . 89 , p<0 . 0001 ) . Indeed , rare polymorphisms , those occurring in less than 10 of the 149 individuals studied , had significantly greater impact ( both negative and positive ) on RC than polymorphisms that occurred more frequently ( Figure 4A and 4B ) . This finding is especially intriguing in the case of rare deleterious mutations , as these residues may highlight epitopes at which HIV escapes or compensates for fitness defects with great difficulty , similar to those described for elite controllers [52] , and may , therefore , be attractive targets for a cellular-based vaccine . In order to investigate whether there is a cumulative effect of viral escape from cellular immune pressure in Gag on RC , the expanded dataset of HLA-associated polymorphisms generated from an analysis of 1899 gag sequences from Zambia and South Africa ( Carlson , Schaefer et al . , manuscript in preparation ) , described above , was employed . The number of non-consensus polymorphisms located at HLA-associated positions was determined for each MJ4 chimera and then correlated with the RC defined by those Gag sequences . Surprisingly , we found a positive association between the number of HLA-associated polymorphisms and RC ( Fig . 5A; r = 0 . 14 , p = 0 . 05 ) . Although counterintuitive , this is consistent with the fact that not all HLA-associated polymorphisms within a particular Gag sequence will necessarily reduce fitness . We have shown in the previous sections that several non-adapted ( or “susceptible” to HLA pressure ) HLA-associated polymorphisms increase fitness relative to the median of all sequences . Indeed , we observe a highly statistically significant positive correlation between the number of non-adapted HLA-associated polymorphisms and RC ( Spearman correlation , r = 0 . 23 , p = 0 . 003; data not shown ) . Thus , the inclusion of both adapted ( or escaped with respect to specific HLA alleles ) and non-adapted polymorphisms within this expanded HLA-associated dataset may explain the observed positive association between numbers of HLA-associated polymorphisms and RC . Therefore , we hypothesize that it is the balance and interaction of both fitness increasing and fitness decreasing polymorphisms within a particular sequence that ultimately determines the RC of the virus . In order to more accurately determine how the number and quality of HLA-associated polymorphisms affects RC and to correct for the opposing influence of both increasing and decreasing polymorphisms within a particular sequence , a summed polymorphism score was calculated by assigning fitness increasing polymorphisms a score of +1 , fitness decreasing polymorphisms a score of −1 , and neutral polymorphisms a score of 0 . HLA-associated polymorphisms were defined as being positive , negative , or neutral based on the previously described univariate analysis that correlated specific residues within our 149 sequences with changes in RC . In this modified analysis , we observed a highly statistically significant correlation between the summed polymorphism score and RC ( Figure 5B: Spearman rank correlation; r = 0 . 6 , p = <0 . 0001 ) , confirming that the sequence features are approximately independent of each other and suggesting that the offsetting influence of fitness decreasing and increasing polymorphisms is a strong contributor to RC . This finding may explain the observation that , in general , the most-fit viruses are less like the consensus subtype C Gag sequence , consistent with a majority of polymorphisms increasing fitness ( Figure S2 , [55] ) . In a previous report using 88 Zambian linked seroconverters , we reported that increasing numbers of transmitted HLA-B associated polymorphisms within or adjacent to well defined epitopes were associated with lower set point VLs [50] . When we expand this analysis to include all 149 Zambian linked recipients and use the same dataset of HLA-linked polymorphisms used by Goepfert et al . [50] we observe the same correlation ( r = −0 . 15 , p = 0 . 03 , Figure 6A ) . However , when we use the expanded HLA-associated data set ( 199 associations ) to define HLA-associated polymorphisms , we no longer observe a statistically significant negative association between the number of transmitted HLA-associated polymorphisms in Gag and set point VL ( Figure 6B ) . We therefore hypothesized that , as with RC , this correlation between the total number of transmitted HLA-associated polymorphisms in Gag and set point VL in newly infected individuals may be confounded by not taking into account whether polymorphisms increase or decrease fitness . Indeed , using the summed polymorphism score , we observe a highly significant correlation between the summed score of HLA-associated polymorphisms and set point VL ( Figure 6C: r = 0 . 21 , p = 0 . 006 ) . This demonstrates that it is not merely the quantity of HLA-associated polymorphisms present in the transmitted Gag sequence that ultimately defines set point VL , but it is the influence of both fitness increasing and decreasing polymorphisms that contributes to RC and in turn set point VL in newly infected individuals . Though set point VL has been shown to be a relevant marker for disease progression [67] , [68] , CD4+ T cell counts are traditionally used to define those individuals that have progressed to AIDS and are at a higher risk for opportunistic infections [69] , [70] . Therefore , we analyzed a subset of individuals ( n = 66 ) for whom longitudinal CD4+ T cells counts for at least one-year post-infection are available . We observed a statistically significant correlation between the average CD4+ T cell counts and the replicative capacities of Gag-MJ4 chimeras ( Figure 7A , Spearman correlation , r = −0 . 24 , p = 0 . 02 ) . This demonstrates that infection with attenuated viruses may impart some survival benefit to newly infected individuals , at least within the first year of infection . Subsequently , we determined whether individuals infected with poorly replicating viruses exhibit differential pathogenesis over the first three years of infection or whether this early benefit is transient and quickly lost . To answer this question , we studied a subset of the linked recipients ( n = 63 ) for whom CD4+ T cell counts were available at regular three month intervals for greater than one year post-infection . In a Kaplan-Meier survival analysis , in which we defined the endpoint as having a CD4+ T cell count >350 cells/mm3 ( WHO recommendation for initiation of anti-retroviral therapy [71] ) , we observed a statistically significant difference in the number of individuals that maintain CD4+ counts >350 cells/mm3 between those infected with viruses that replicate very poorly ( RC<1 ) and those infected with highly replicating viruses ( RC>2 ) , within the first 3 years of infection ( Figure 7B , Mantel-Cox test p = 0 . 029 ) . This disparity in disease progression was even more pronounced when the endpoint was defined as having CD4+ T cell counts >300 cells/mm3 , demonstrating a median difference of 896 days before falling below the CD4+ count cut off between individuals infected with low and high replicating viruses ( Figure 7C , Mantel-Cox test p = 0 . 014 ) . Using a Cox proportional hazard model , we demonstrate a significantly increased risk of CD4+ T cell counts falling below 350 ( Hazard Ratio ( HR ) 2 . 36; p = 0 . 034 ) or 300 ( HR 3 . 80; p = 0 . 021 ) over the first three years of infection for individuals whose Gag conferred an RC>2 vs . RC<1 . Interestingly , the benefit conferred by low replicating viruses could not be wholly explained by differences in set point VL within this smaller data set . Although there was a trend towards higher VLs between the two most disparate groups , with a 2 . 5 fold difference in median VLs ( Figure 7D ) , we observed no statistically significant differences in median set point VLs between individuals infected with low ( RC<1 ) , medium ( RC = 1–2 ) , and highly ( RC>2 ) replicating viruses . Further , in Cox proportional hazard models that take into account VL , the HR remained high ( 2 . 18 and 3 . 12 respectively ) and p values continued to trend or remain borderline significant ( p = 0 . 093 and 0 . 051 ) ( Table 2 ) , indicating that both VL and RC can independently affect CD4 loss . Moreover , the HR associated with log10 increases in set point VL alone was lower than that for RC alone ( HR = 1 . 75 versus 2 . 62 , and 2 . 09 versus 3 . 80; CD4 <350 and 300 respectively; Table 2 ) . These results suggest that infection with a low replicating virus confers clinical benefit outside of the effect of RC on set point VL , and that the kinetics of viral replication early in infection can ultimately dictate long-term pathogenesis .
Since all of the transmission pairs in this study were infected with subtype C viruses , our approach of precisely cloning gag genes from acutely-infected recipients into a primary isolate ( MJ4 ) provirus has many important advantages over previously employed methods . MJ4 is a CCR5 tropic infectious molecular clone derived from a subtype C clinical isolate from Botswana , providing greater homology to viruses circulating within the Zambian population than other previously used subtype B lab-adapted strains [33] , [47] , [54] , [55] . Additionally , this cloning method for generating Gag-MJ4 chimeric viruses does not rely on recombination based technologies that require the outgrowth of viral quasispecies , which may select for the most fit virus , and in some cases , amino acid changes in the viral stocks that are not present in the individual from which they were derived [32] , [47] , [53]–[55] . The use of a common BclI restriction site located 137 nucleotides after the gag stop codon in MJ4 does result in a chimeric protease , however , this region is 96 . 5% conserved in this cohort and we did not observe a high prevalence of dead or inactive Gag-MJ4 chimeras . The impact of engineering foreign gag sequences into MJ4 on virus replication was highly significant , with many of the chimeras exhibiting RC values greater than a hundred-fold higher than wild-type MJ4 , which in this assay is one of the poorer replicators . This indicates that substitution of Gag can drastically alter the in vitro RC of the virus when all other viral components are constant . Multiple intra-molecular contacts as well as host protein interactions in Gag are necessary for effective intracellular Gag trafficking [72] , [73] , particle formation [74] , budding [75] , [76] , maturation [77] and disassembly [78] . Therefore , immune mediated adaptation of this functionally constrained protein could have clear consequences for viral replication through disruption of these many interactions . It has been well established that the set point VL in those recently infected with HIV-1 is correlated to disease progression and clinical outcome [67] , [68] . Previous data from our group demonstrated that transmission of sequences with increasing numbers of CTL escape mutations in Gag resulted in lower set point VLs in newly infected individuals , a finding that suggested that transmitted HLA-associated polymorphisms in Gag might negatively affect viral replication [50] . We have confirmed this association in the current study after increasing the number of transmission pairs analyzed from 88 to 149 . This result is consistent with studies by Brockman et al . , which have demonstrated a statistically significant link between the RC conferred by gag-pro genes in subtype B and C chronically infected individuals to VL [43] , [47] , [54] , [55] . However , a statistically significant correlation between Gag RC derived from acutely infected individuals and set point VL has not previously been definitively reported in a subtype C cohort . In contrast , in this large group of very early ZEHRP seroconvertors ( with samples drawn a median of 45 days post-EDI ) we observed a clear statistically significant correlation ( p = 0 . 02 ) between the RC conferred by the transmitted gag sequence and the early set point VL in newly infected individuals . This result implies that RC plays a role in defining the overall level of virus replication during the first year of infection . Moreover , in multivariable analyses that take into account the early viral control imposed by the B*57 allele and by gender , the impact of RC on set point VL was found to be independent of these two host factors ( p = 0 . 009 ) . Other factors such as NK cells and restriction factors such as TRIM or APOBEC may potentially affect RC and VL , however little is currently known regarding these potential effects , and future efforts should evaluate the role of such factors . While we observed a statistically significant positive correlation between RC and set point VL , outliers in the data exist that do not fit the trend , and in some cases can be explained by the presence of protective HLA-alleles or by a large number of escape mutations present in the transmitted sequence that are relevant to the HLA background of the newly infected individual . Set point VL is clearly determined by a combination of both host factors , including HLA-alleles , and viral factors such as RC , and this may explain the differences in the absolute correlation for each individual . The RC of Gag-MJ4 chimeras also correlated with VLs near the estimated date of infection in chronically infected donors , consistent with the previously reported observation that donor and recipient VLs are correlated within epidemiologically linked transmission pairs [11]–[13] . The data presented here would suggest that the RC conferred by the transmitted Gag sequence is a contributing viral characteristic of that donor virus responsible for influencing early set point VL in the newly infected partner . In a pair-wise analysis , a large number of residues were associated with changes in fitness ( p<0 . 05 , q<0 . 51 ) , with 4 residues at 3 unique positions at q<0 . 2 . These residues included the polymorphisms 30R and 31I in p17 ( MA ) , and 309S in p24 ( CA ) . However , in an exploratory analysis of those residues associated with changes in RC with a p value<0 . 05 , it was clear that associated polymorphisms were noticeably enriched on a per residue basis in p17 and p2 ( Fisher's exact test , p<0 . 001 ) . The former plays critical roles in intracellular trafficking , and membrane association of Gag [73] , [79] , [80] , while the latter is an important structural element involved in formation of the immature protein shell [81] , [82] and a target of the novel drug Bevirimat during maturational cleavage of the Gag precursor [83] , [84] . Surprisingly , only one third of the associated mutations negatively affected virus replication , while nearly two-thirds of the associations increased fitness . Some of these fitness-increasing mutations represent adapted polymorphisms ( i . e . selected as immune escape ) and in terms of vaccine design it may be important to avoid the inclusion of such epitopes . Polymorphisms positively or negatively affecting replication in the p24 region of Gag were limited to just six residues ( 4 positive , 2 negative ) , in accordance with the conserved nature of this protein . Surprisingly , none of the canonical B*57/B*5801 associated escape mutations within p24 , whose fitness defects have been well documented [24] , [31]–[34] , were found to be significantly associated with decreases in RC in our present study . This may be due to the high prevalence of B*57/B*5801 positive individuals within this cohort ( 25% ) , which could promote viral adaptation to these alleles over time through compensatory mutations [22] . It is also possible that some fitness defects such as those associated with T242N within the TW10 epitope might be missed in the current study , as a previous study has shown that it is cell-type dependent [32] . The most deleterious HLA-associated mutation that we observed was K12E , which reduced RC by almost 10-fold relative to the median RC of the cohort . This polymorphism is found quite rarely in the population ( 3 out of 149 ) , and is statistically associated with HLA-A*74 , an allele found to be highly protective in both this Zambian subtype C cohort as well as others [9] , [85] . The protective effect of A*74 has recently been demonstrated to be independent of HLA-B*57 [86] . The negative in vitro impact of mutations at residue 12 on replication is supported by a longitudinal study of a subset of this seroconvertor cohort ( n = 81 ) , in whom polymorphisms at residue 12 were found to revert at a high rate ( 25%/yr ) , over the first two years of infection ( Schaefer et al . , manuscript in preparation ) . Furthermore , in this same study , escape at position 12 occurred only once and at 24 months post-infection in a total of ten A*74 positive individuals , confirming the high fitness cost associated with CTL-induced escape mutations at this position . We hypothesize that the targeting of this putative epitope , KR9 [86] , may account for part of the protective effect conferred by A*74 and indicates that protective immune responses can target regions of Gag outside of p24 . While the nature of the replication defect in viruses encoding K12E remains to be determined , this residue does lie in the highly basic region at the N-terminus of p17 ( MA ) , which is involved in membrane targeting and membrane association of Gag [80] , [87] , [88] . Rare mutations , such as K12E , which occur in a small subset of the population studied here ( less than 10 individuals of the 149 ) , affected fitness to a statistically greater degree than more common polymorphisms . Rare fitness decreasing mutations are likely unique to specific circumstances such as those where a considerable decrease in RC is warranted in the face of a very effective cellular immune response that is largely abrogated upon mutation . Such mutations have been found to subsequently revert after transmission to individuals lacking the selecting HLA-allele [17] , [42] , [89] and in whom they now confer a fitness deficit for the virus . These sites of rare fitness reducing polymorphisms may emphasize vulnerable epitopes at which HIV-1 escapes from immune pressure with great difficulty . Alternatively , it is possible that , when escape occurs , it is consistently associated with a decrease in RC that cannot be completely compensated . A similar observation was made for rare mutations that greatly increase RC . Global compensatory mutations do exist that can compensate multiple deleterious mutations , such as those within the cyclophilin binding loop [42] , [46] . Some of the rare fitness increasing mutations may be of this type , although those reported previously have generally been quite common in the population . Compensatory mutations can also be secondary site-suppressors of deleterious mutations [90] . Frequently , such mutations are only conditionally beneficial and can be deleterious in a different context , which could explain why some fitness increasing mutations are rare . It is also possible that these mutations do carry some unrecognized in vivo fitness cost that cannot be captured in the in vitro replication system used here . Due to the fact that these mutations are rare , they are difficult to statistically link to HLA alleles or to link to other residues with which they may covary , making the potential fitness defects that these mutations mitigate difficult to elucidate . A key goal of this study was to understand how the cellular immune response might select for mutations in Gag that reduce viral RC , and while identification of specific amino acid polymorphisms that either increase or decrease fitness can be informative , it is equally important to elucidate how the accumulation of specific HLA-associated polymorphisms in Gag affects both RC of the virus and VL in the newly infected person . Previous efforts to correlate the total number of HLA-associated polymorphisms in Gag to RC have yielded inconclusive results [32] , [54] , perhaps because the quality of the polymorphisms in question was not considered . Using an expanded list of HLA-associated polymorphisms ( Carlson , Schaefer et al . , manuscript in preparation ) we report a weak positive correlation between the total number of HLA-associated polymorphisms in Gag and RC . The fact that this correlation was positive is consistent with our observation that a large fraction of the non-consensus HLA-associated polymorphisms increased RC . In particular , in the expanded data set of HLA-associated polymorphisms , we observed that non-adapted residues , which would be predicted to render the virus susceptible to the linked HLA allele , were statistically associated with increased fitness . These findings suggest that CTL escape mutations , which decrease the overall RC of the virus , are being driven to consensus as a result of population level immune pressure . In the absence of immune pressure , the non-escaped ( non-adapted ) residues would be expected to predominate , but if they render the virus susceptible to a large portion of the population , then the consensus residue will be escaped rather than susceptible , despite reducing in vitro fitness . This is consistent with the findings of Kawashima et al . [22] that the frequency of certain HLA-class I alleles within a particular population can influence the fixation of escape mutations in the overall population . Moreover , Wright et al . [55] showed that Gag-NL43 recombinant viruses encoding gag-pro sequences most disparate from the subtype C consensus gag-pro sequence had statistically higher replicative capacities than their more consensus-like counterparts , and this finding has been recapitulated in this current study . Taken together , these data suggest that overall , HLA-mediated adaptation is driving the fixation of consensus residues that are less fit than their susceptible counter-parts . When we account for this ability of HLA-associated polymorphisms to either increase or decrease fitness by assigning a summed polymorphism score , which subtracts the number of fitness decreasing polymorphisms from the number of fitness increasing polymorphisms in a particular sequence , we find a highly statistically significant correlation between RC and the summed polymorphism score ( p<0 . 0001 ) . Although this p-value should be interpreted cautiously , since it reflects the summation of features previously identified to be correlated with RC , the data do suggest that the effect of polymorphisms is cumulative , and that as a Gag sequence accumulates an excess of fitness-reducing polymorphisms , the RC decreases proportionally . Similarly , utilization of a summed polymorphism score improved previously reported correlations between the total number of HLA-associated polymorphisms in Gag and set point VL in newly infected individuals [50] . We observed a highly statistically significantly correlation ( p = 0 . 006 ) between the summed polymorphism score and set point VL in newly infected individuals . Just as this balance of fitness increasing and decreasing polymorphisms impacts RC , it simultaneously influences the set point VL of the newly infected individual . While VL has been demonstrated to influence the rate of disease progression in HIV-1 infected individuals [68] , [69] , it is possible that , during the very earliest stages of infection and before host immune control , the replication rate of the virus may affect the rate of future damage to the immune system . Indeed , we observed a statistically significant negative correlation between RC and average CD4 counts for the first year post infection , suggesting a role for RC in defining this important parameter of pathogenesis at early stages after infection . However , it is possible that this early benefit could be quickly lost due to further adaptation of the virus to the new host's immunogenetic background and further compensation for de novo escape mutations . Consequently , we analyzed individuals with longitudinal CD4 counts out to three-years post infection in order to determine if the observed early benefit was sustained in early chronic stages of infection . Using Kaplan-Meier survival analyses to examine the relative time for individuals infected with viruses encoding gag genes conferring RC values of <1 and >2 to reach CD4 T cell counts of 350 after 3 years of infection , we observed a clear and statistically significant difference . This was even more striking when CD4 counts less than 300 were used as the endpoint . Moreover a Cox proportional hazard model demonstrated a significantly increased risk of CD4 counts falling below both 350 ( HR 2 . 36 ) or 300 ( HR 3 . 80 ) over the first three years of infection for individuals whose gag gene conferred an RC>2 vs . RC<1 . These findings indicate that the RC conferred by the transmitted Gag sequence may have profound and prolonged effects on HIV-1 pathogenesis from acute to early chronic stages of infection . While RC and VL are correlated in the full data set , set point VL does not fully explain the effect of RC on CD4 , because we did not observe any statistically significant differences in set point VL between the two groups ( RC<1 and RC>2 ) for the subset of individuals with CD4+T cell counts ( n = 63 ) . Moreover , in Cox proportional hazard models which take into account VL , the HR remained high ( 2 . 17 and 3 . 11 respectively ) and p values continued to trend or remain borderline significant ( p = 0 . 093 and 0 . 051 ) . This suggests that both VL and RC have independent effects on CD4 decline , however , because this analysis was conducted on a subset of less than half of our initial cohort , additional work is underway to further confirm and extend these results . It seems possible , therefore , that the RC of the transmitted variant may initiate crucial events , early in infection and dissemination , that dictate both acute and later stage pathogenesis regardless of the ability of the immune system to control viral replication down to set point . Infection with highly replicating variants could lead to a more complete depletion of central memory CD4+ T cell pools at this early time that could predispose an individual to more rapid CD4+ T cell loss , irrespective of adequate control of viral replication . This is evidenced in a few individuals infected with highly replicating Gag variants ( RC>2 ) , who go on to control VL to a low set point , but whose CD4+ T cells counts rapidly drop below 300 ( data not shown ) . Additionally , a high level of peak viremia or initial high antigen loads could establish an inflammatory environment that leads to sustained immune activation , which has been implicated as a more reliable marker for disease progression [91] . These possibilities are the focus of ongoing work . In summary , using an in vitro approach to define the impact of polymorphisms in Gag on transmitted virus RC has clearly shown that this property of the virus is a significant contributor to early set point VL in a newly infected individual . More importantly , however , these studies suggest a critical role for RC in defining the trajectory of immune depletion and pathogenesis , beyond simply its impact on VL , and highlight the importance of the very earliest events in virus-host interactions . It also raises the possibility that a vaccine that can attenuate early virus replication would have a positive impact both on vaccinated individuals , as well as non-vaccinated individuals by weakening the transmitted/founder virus and increasing the likelihood of transmission of low replicating variants . | In the majority of HIV-1 cases , a single virus establishes infection . However , mutations in the viral genome accumulate over time in order to avoid recognition by the host immune response . Certain mutations in the main structural protein , Gag , driven by cytotoxic T lymphocytes are detrimental to viral replication , and we showed previously that , upon transmission , viruses with higher numbers of escape mutations in Gag were associated with lower early set point viral loads . We hypothesized that this could be attributed to attenuation of the transmitted virus . Here , we have cloned the gag gene from 149 newly infected individuals from linked transmission pairs into a clade C proviral vector and determined the replicative capacity in vitro . We found that the replicative capacity conferred by the transmitted Gag correlated with set point viral loads in newly infected individuals , as well as with the viral load of the transmitting partner , and we identified previously unrecognized residues associated with increasing and decreasing replicative capacity . Importantly , we demonstrate that transmitted viruses with high replicative capacity cause more rapid CD4+ decline over the first three years , independent of viral load . This suggests that the trajectory of pathogenesis may be affected very early in infection , before adaptive immunity can respond . | [
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] | 2012 | Role of Transmitted Gag CTL Polymorphisms in Defining Replicative Capacity and Early HIV-1 Pathogenesis |
TGFβs act through canonical and non-canonical pathways , and canonical signals are transduced via Smad2 and Smad3 . However , the contribution of canonical vs . non-canonical pathways in cartilage is unknown because the role of Smad2 in chondrogenesis has not been investigated in vivo . Therefore , we analyzed mice in which Smad2 is deleted in cartilage ( Smad2CKO ) , global Smad3-/- mutants , and crosses of these strains . Growth plates at birth from all mutant strains exhibited expanded columnar and hypertrophic zones , linked to increased proliferation in resting chondrocytes . Defects were more severe in Smad2CKO and Smad2CKO;Smad3-/- ( Smad2/3 ) mutant mice than in Smad3-/- mice , demonstrating that Smad2 plays a role in chondrogenesis . Increased levels of Ihh RNA , a key regulator of chondrocyte proliferation and differentiation , were seen in prehypertrophic chondrocytes in the three mutant strains at birth . In accordance , TGFβ treatment decreased Ihh RNA levels in primary chondrocytes from control ( Smad2fx/fx ) mice , but inhibition was impaired in cells from mutants . Consistent with the skeletal phenotype , the impact on TGFβ-mediated inhibition of Ihh RNA expression was more severe in Smad2CKO than in Smad3-/- cells . Putative Smad2/3 binding elements ( SBEs ) were identified in the proximal Ihh promoter . Mutagenesis demonstrated a role for three of them . ChIP analysis suggested that Smad2 and Smad3 have different affinities for these SBEs , and that the repressors SnoN and Ski were differentially recruited by Smad2 and Smad3 , respectively . Furthermore , nuclear localization of the repressor Hdac4 was decreased in growth plates of Smad2CKO and double mutant mice . TGFβ induced association of Hdac4 with Smad2 , but not with Smad3 , on the Ihh promoter . Overall , these studies revealed that Smad2 plays an essential role in the development of the growth plate , that both Smads 2 and 3 inhibit Ihh expression in the neonatal growth plate , and suggested they accomplish this by binding to distinct SBEs , mediating assembly of distinct repressive complexes .
The cartilage growth plate is the primary driver of endochondral bone growth . In the growth plate , resting , columnar , prehypertrophic and hypertrophic chondrocytes are arrayed in discrete zones . Resting chondrocytes , located at the top of the growth plate , are small and relatively quiescent . Upon stimulation by extracellular signals , cells near the bottom of the resting zone transition to columnar chondrocytes , which exhibit a higher rate of proliferation and a flatter morphology . These cells form stacks along the long axis of the developing skeletal element . Columnar cells at the bottom of this zone eventually exit the mitotic phase and become prehypertrophic chondrocytes . Prehypertrophic cells further differentiate into enlarged hypertrophic cells , comprising a zone adjacent to the site of replacement of cartilage by bone . Chondrocyte proliferation and differentiation in the growth plate is tightly regulated by Indian hedgehog ( Ihh ) and parathyroid hormone-related peptide ( Pthrp ) . Ihh , a secreted protein expressed in prehypertrophic chondrocytes , stimulates cell proliferation and differentiation . Its role in proliferation is mediated in part by inducing Pthrp expression in epiphyseal resting chondrocytes . Secreted Pthrp maintains columnar cells in a mitotic state , preventing their transition to the pre-hypertrophic phase , and hence negatively regulating Ihh expression . Once cells escape the zone of influence of Pthrp , they exit the cell cycle , become prehypertrophic , and upregulate Ihh expression , which promotes hypertrophy and matrix mineralization . This feedback loop thus controls the transition of chondrocytes through each zone of the growth plate . Transforming growth factor βs ( TGFβs ) and activins are secreted proteins that are members of the TGFβ superfamily of growth factors . TGFβs and activins bind to distinct receptor complexes , but activate similar signal transduction pathways . Binding of TGFβs or activins to their receptors leads to activation of the kinase activity of the receptor . The activated receptor complexes then transduce signals through multple pathways . These pathways can be broadly divided into Smad-dependent and Smad-independent pathways [1–3] . In the canonical Smad-dependent pathway , activated receptor complexes phosphorylate the receptor-activated Smads ( R-Smads ) , Smad2 and Smad3 . Smads 2 and 3 are transcription factors; once phosphorylated , they form hetero-oligomeric complexes with the transcription factor Smad4 . These complexes enter the nucleus , bind promoters , and regulate target gene expression . In addition , there exist numerous non-canonical Smad-independent pathways for transduction of TGFβ signals , such as MAP kinases , RhoA and mTOR [4–8] . TGFβs play critical roles in growth plate chondrocyte proliferation and differentiation , and in the maintenance of articular chondrocytes [9–17] . There are three TGFβ isoforms in mammals: TGFβ1 , 2 , 3 . Only Tgfb2 mutants exhibit obvious skeletal dysplasia . Tgfb2-/- mice exhibit embryonic lethality , accompanied by skeletal defects that include shortened limbs , axial , and craniofacial defects [18] . The role of the TGFβ signaling pathway in cartilage has been studied most extensively using mice lacking the type II TGFβ receptor TGFβRII . This receptor is required for transduction of the TGFβ pathway by TGFβs 1–3 , but is not used by activin ligands . Tgfbr2;Prrx1Cre mice , in which TGFβRII is ablated in limb bud mesenchyme , exhibit growth plates with decreased proliferation and accelerated onset of hypertrophy , but delayed terminal maturation [19 , 20] . In contrast , conditional deletion of the TGFβRII in committed Col2a1-expressing chondrocytes did not lead to obvious defects in appendicular elements [21] . These findings suggest that TGFβRII transduces TGFβ signals at prechondrogenic stages and/or in perichondrium , but may not have a substantial role in cartilage once a growth plate forms . The extent to which Smad2/3-dependent signaling mediated by TGFβ and activins is required in developing cartilage is unknown . Smad2 , Smad3 and Smad4 are co-expressed throughout the growth plate [12 , 22–24] . Smad2 , 3 and 4 are all present in articular cartilage [12 , 25] . Smad3-/- mice are born with a normal skeletal phenotype , but develop postnatal dwarfism and osteoarthritis-like pathologies in adulthood [12 , 26–28] . The function of Smad2 in cartilage during embryogenesis has not been characterized . Smad2 and Smad3 have some distinct roles in mediating TGFβ/activin signaling . Smad3 can bind DNA directly , whereas Smad2 regulates gene expression by interacting with Smad3 or other transcription factors [29] . Mice lacking Smad2 die before 8 . 5 days of development ( E8 . 5 ) [30] , precluding a genetic analysis of its function in chondrogenesis . It is not known whether Smad2 partially compensates for the loss of Smad3 in the growth plates of Smad3-/- mice . Studies in Smad2-/- vs . Smad3-/- epiblast , epithelial cells and fibroblasts show that Smads 2 and 3 regulate some common and some distinct target genes [31 , 32] . Overexpression of Smad2 or Smad3 can block the spontaneous maturation observed in Smad3-deficient chondrocytes [33] , providing support for the hypothesis that Smad2 and Smad3 may have some overlapping functions in the growth plate . To define the role of Smad2/3-mediated signaling in cartilage , we generated mice with conditional deletion of Smad2 using Col2a1-Cre ( Smad2fx/fx;Col2Cre , hereafter referred to as Smad2CKO ) , mice with Smad3 globally deleted ( Smad3-/- ) , and the corresponding double mutants ( Smad2/3 ) . Loss of Smad2 leads to a growth plate phenotype in neonates that is more severe than that seen in Smad3-/- mice . At the neonatal stage , Smad2/3 ( Smad2CKO;Smad3-/- ) double mutants exhibit more severe defects in the hypertrophic zone than do Smad2CKO or Smad3-/- mice . Defects include elevated levels of proliferation in resting zone chondrocytes at neonatal stages , leading to enlarged columnar and hypertrophic zones . This may be a consequence of depletion of the resting zone due to the accelerated entry of resting zone chondrocytes into the columnar zone . Overall , our results suggest that Smad2 inhibits proliferation of resting zone chondrocytes during embryogenesis , and acts as a negative regulator of Ihh expression , and that Smad2 and Smad3 have some overlapping functions in cartilage . Our results show that both of these Smads are required at neonatal stages for normal chondrocyte proliferation and differentiation in the growth plates .
We generated mice lacking Smad2 and Smad3 in cartilage in order to study the role of canonical Smad2/3-mediated signaling in chondrogenesis . Smad2fx/fx mice were intercrossed with Col2a1Cre mice to generate Smad2fx/fx;Col2a1Cre ( Smad2CKO ) mice . Smad2CKO mice are viable and fertile . The Smad3-/- allele we used has a LacZ cassette and internal ribosomal entry site ( IRES ) inserted in the second exon , leading to a loss-of-function allele [27] . We crossed Smad3+/- mice with Smad2CKO mice to generate Smad2fx/fx;Col2a1Cre;Smad3-/- ( Smad2/3 double mutant ) mice . To confirm efficient Cre-mediated excision , levels of activated C-terminal phosphorylated Smad2 and Smad3 were examined by IHC using an antibody that recognizes both pSmad2 and pSmad3 ( S1 Fig ) . No pSmad2/3 was detected in E16 . 5 or P0 double mutant growth plates , verifying efficient loss of Smads 2 and 3 ( S1D and S1H Fig ) . Consistent with previous studies [34] , at E16 . 5 and P0 , pSmad2/3 is present throughout the resting , columnar , and prehypertrophic zones of the E16 . 5 Smad2fx/fx control growth plate , and this pattern of expression persists at least until birth ( S1A and S1E Fig ) . No obvious differences were noted in the distribution of pSmad3 in Smad2CKO or pSmad2 in Smad3-/- mice compared with control Smad2fx/fx littermates ( S1B , S1C , S1F and S1G Fig ) . These findings confirm previous reports that pSmads 2 and 3 have extensively overlapping distributions in the growth plate [12 , 22–24] . Consistent with previous studies [12 , 26 , 27] , skeletal preparations revealed no apparent skeletal defects in Smad3-/- mice at P0 . Analysis of P0 Smad2CKO mice also revealed no obvious defects ( Fig 1A–1C ) . However , Smad2CKO;Smad3-/- ( Smad2/3 ) double mutant mice exhibited subtle defects in axial and craniofacial elements ( Fig 1A and 1D ) . Lateral views of P0 skulls revealed reduced ossification of the bones of the inner ear in double mutants ( Fig 1E–1H ) . The length of the body ( from top of the skull to the proximal end of the tail ) of double mutants is about 9 . 7% shorter than that of control Smad2fx/fx littermates ( n = 4 , P = 0 . 04 ) ( Figs 1A , 1D and S2A ) . Ventral views revealed that double mutants have slightly smaller skulls , with a shorter nasal to occipital length ( 94% of control ( Smad2fx/fx ) , n = 3 , p = 0 . 05 ) , narrower cranial base ( 93% of control , n = 3 , p = 0 . 04 ) , and reduced ossification of the occipital condyle ( arrow , S2B Fig ) . Dorsal views of the rib cage revealed no obvious differences between Smad2CKO or Smad3-/- mice compared with control Smad2fx/fx littermates ( S2C Fig ) . However , double mutants have a shorter sternum and a bifurcated xiphoid process ( S2C Fig ) . Moreover , localized defects were seen in the vertebral columns of Smad2/3 mutants . Ventral views of the lumbar spine revealed shorter vertebrae and smaller ossified vertebral bodies in double mutants; reduced ossification was not observed in Smad2CKO or Smad3-/- mice ( S2D Fig ) . No differences were evident in the cervical vertebrae in Smad3-/- mice compared to control Smad2fx/fx mice , but the ossification center of the axis ( C2 ) was reduced in Smad2CKO mice and in double mutants ( S2E Fig ) . Cleared skeletal preparations revealed no clear differences in appendicular elements in Smad2CKO , Smad3-/- mice , or Smad2/3 double mutants ( Figs 1I–1L and S2F ) . As discussed above , Smad2/3 double mutants had only subtle skeletal alterations at birth . However , these mice developed progressive postnatal dwarfism , which was not seen in Smad2CKO and Smad3-/- mice ( S3 Fig ) . At 1 month , double mutants were 12 . 5% ( n = 3 , p = 0 . 05 ) shorter than control Smad2fx/fx littermates ( S3C Fig ) . These data indicate that Smad2 and Smad3 have compensatory roles in the regulation of axial skeletal growth after birth . The above analysis revealed no clear impact of loss of Smad2 or Smad3 on appendicular length at P0 . A previous study demonstrated that loss of Smad3 led to appendicular defects beginning in the early postnatal period [12] . However , we used a different Smad3 allele . We therefore performed a histological analysis of P0 appendicular cartilage to investigate whether the Smad3 mutant allele we used [27] exhibits a similar phenotype . We also examined whether loss of Smad2 exerts any effect on growth plate architecture . This analysis revealed that the lengths of the resting zones in Smad2CKO and Smad2/3 double mutants were shorter than in control Smad2fx/fx mice . In contrast , the columnar and hypertrophic zones of Smad2CKO , Smad3-/- , and Smad2/3 double mutant mice were longer than those of control Smad2fx/fx littermates ( Figs 2A and S4 ) . Although both Smad2CKO and Smad3-/- mice exhibited elongated columnar zones , the effect was greater in Smad2CKO mice than in Smad3-/- mice , and the degree of elongation did not differ between Smad2/3 double mutants and Smad2CKO mice , suggesting that Smad2 has a more prominent role than Smad3 in elongation of the columnar zone ( S4 Fig ) . Both Smad2CKO and Smad3-/- mice exhibited elongated hypertrophic zones compared to Smad2fx/fx controls , and there was a significant elongation of the hypertrophic zone in Smad2/3 double mutants compared to either Smad2CKO or Smad3-/- mice . Immunohistochemistry for PCNA was performed to test whether the increases in lengths of the colunmar zones were correlated with increased proliferation . A 2 to 3-fold increase in PCNA-positive cells was seen in the resting zones of Smad2CKO , Smad3-/- and Smad2/3 double mutant mice compared with control Smad2fx/fx littermates; the degree of PCNA staining in the resting zone was similar in Smad2/3 double mutants and Smad2CKO mice ( Fig 2C ) . No differences were detected in the columnar zone ( Fig 2B and 2C ) . TUNNEL assays were performed to evaluate whether differences in cell survival contribute to the longer hypertrophic zones in mutants . No differences were detected ( Fig 2D ) . These results indicate that loss of Smad2 and/or Smad3 promotes the entry of resting chondrocytes into the highly proliferative columnar phase , and that Smad2 appears to have a more prominent role than does Smad3 . The increased lengths of the columnar and hypertrophic zones in mutants is consistent with an increased pool of chondrocytes transiting out of the resting zone and eventually undergoing hypertrophy . These findings suggest that Smad2 and Smad3 function to maintain the pool of resting chondrocytes in a quiescent state in neonatal growth plates . Ihh is expressed in prehypertrophic chondrocytes and is a critical regulator of chondrocyte proliferation and differentiation . Since pSmads 2 and 3 are present in prehypertrophic chondrocytes ( S1 Fig ) , RNA in situ hybridization was performed to assess Ihh RNA levels . The zone of Ihh expression is increased ( Fig 3A ) , and qPCR quantification of RNA isolated from P0 growth plate cartilage showed that the level of Ihh RNA is increased in all three mutant strains compared with control Smad2fx/fx littermates ( S5 Fig ) , suggesting that Smad2 and Smad3 normally inhibit Ihh expression in the growth plate . Smad2/3 double mutants had elevated levels of Ihh RNA and protein compared to both Smad2CKO and Smad3-/- mice , suggesting that both Smad2 and Smad3 contribute to elevated Ihh expression . As shown previously [34] , Ihh protein was found in the control Smad2fx/fx growth plate , with highest levels in the prehypertrophic and hypertrophic zones ( Fig 3B ) . Ihh protein was detected in these regions in Smad2 and Smad3 single and double mutants , but unlike control Smad2fx/fx littermates , was also detected in the resting zones ( Figs 3B , 4C and S5 ) . This suggests that the elevated Ihh RNA level in the prehypertrophic zone leads to increased diffusion of Ihh protein to the resting zone in mutants . Patched1 ( Ptch1 ) is a direct transcriptional target of Ihh signaling . Consistent with elevated levels of Ihh RNA and protein , immunostaining for Ptch1 demonstrated increased levels throughout the growth plates in mutants; this was most evident in the resting zones of Smad2CKO and Smad2/3 double mutant mice ( Figs 3D and S5 ) . Ihh is a downstream target of BMP signaling [35 , 36] , raising the possibility that the increased Ihh RNA expression is a consequence of increased BMP signaling in the growth plates of Smad2CKO , Smad3-/- and Smad2/3 double mutant mice . However , immunohistochemical examination in P0 growth plates revealed no obvious change of pSmad1/5/8 in mutant mice compared with control Smad2fx/fx mice ( Figs 3E and S5 ) . In summary , the elevated level of Ihh RNA in the prehypertrophic zone , and increased domain of Ihh protein localization to the resting zone is correlated with an increase in the level and distribution of Ptch1 . As Ihh promotes chondrocyte proliferation in the growth plate [37] , the increased Ihh level and activity seen in Smad2CKO , Smad3-/- , and double mutants may contribute to the increased rate of proliferation in the resting zone in mutants . The above findings indicate that Smads 2 and 3 act to decrease Ihh RNA levels in the neonatal growth plate . To investigate whether this effect is direct , primary rib chondrocytes were isolated , matured to the prehypertrophic phase by maintenance in chondrogenic differentiation medium , and then treated with TGFβ1 or TGFβ2 . TGFβ1 and TGFβ2 decreased Ihh RNA levels in control Smad2fx/fx chondrocytes ( Fig 4A ) . However , the ability of TGFβ to inhibit Ihh expression was impaired in Smad3-/- mutant chondrocytes , and abolished in Smad2CKO and Smad2/3 double mutant chondrocytes . However , a caveat of these findings is that although levels of Ihh RNA in primary chondrocytes from Smad2CKO and Smad3-/- mice were not reduced compared to control Smad2fx/fx chondrocytes under basal conditions ( no serum and no growth factor addition ) , levels of Ihh RNA were reduced in prehypertrophic chondrocytes from Smad2/3 double mutants under basal conditions ( Fig 4A ) . It is unclear whether this reflects a defect in the ability of Smad2/3 double mutant primary chondrocytes to undergo timely differentiation in vitro . Alternatively , Smads2/3 may play a role in maintaining basal levels of Ihh RNA . Overall however , the in vivo ( Fig 3A ) and in vitro ( Fig 4A ) results indicate that Smad2 and Smad3 are required to inhibit Ihh expression in prehypertrophic chondrocytes in the neonatal growth plate . To test whether Smads 2/3 play a direct role in regulating Ihh promoter activity , a luciferase reporter containing the proximal 742 bp of the mouse Ihh promoter [38] was transfected into ATDC5 chondrocytic cells . After culture in differentiation medium to induce prehypertrophy , the cells were treated with TGFβ1 for 24 hours . To test whether Smad2 and/or Smad3 mediate Ihh inhibition , Smad2 and/or Smad3 levels were knocked down by transfection of verified siRNAs . Reporter assays showed that TGFβ1 inhibits Ihh promoter activity in control chondrocytes by > 50% ( Fig 4B ) . P3TP-Luc activity was used as a positive control for TGFβ activity , and showed robust activation under the same conditions ( Fig 4C ) . Consistent with the analysis in vivo ( Figs 3A and S5 ) , knockdown of Smad2 and/or Smad3 blocked the inhibitory effect of TGFβ on Ihh promoter activity . Activated Smads 2 and 3 bind to Smad binding elements ( SBEs ) that contain ( C ) AGAC motifs [39–42] . ChIP-chip/ChIP-seq studies have confirmed that the SBE is enriched in Smad2/3 binding regions . [43–47] . In silico examination of the 742bp proximal Ihh promoter identified 5 putative SBEs , designated S1 to S5 ( Fig 5A ) . ChIP analysis performed in ATDC5 cells for Smad2 and Smad3 binding showed differential occupation of S1-S3 in the presence of TGFβ; neither Smad2 nor Smad3 associated with S4 or S5 ( Fig 5B ) . In addition , comparative Genomic Analysis using the UCSC Genome Browser ( http://genome . ucsc . edu ) [48] showed that S1 , S2 and S3 are 100% conserved in the mouse , rat , human and dog genomes ( S6 Fig ) . At S1 , TGFβ increased binding of Smad2 but not Smad3 ( Fig 5B ) . In contrast , at S2 , TGFβ increased the association of Smad3 , but had no effect on Smad2 binding . Association of both Smad2 and Smad3 is increased by TGFβ at S3 . These results reveal 3 binding elements for pSmad2/3 within the 742 bp Ihh promoter region , and demonstrate that Smad2 and Smad3 have both common and distinct binding patterns . To examine whether S1-S3 mediate the inhibitory effect of TGFβ on Ihh , six nucleotides covering the conserved SBE regions in S1 , S2 and S3 were replaced with PsiI recognition sites in pIhh742-Luc to generate mutated constructs M1 , M2 and M3 , respectively . Reporter assays revealed no significant inhibitory effect of TGFβ on activity of the M1 and M3 constructs , and the inhibitory effect of TGFβ on activity of the M2 construct was decreased compared with of the control construct ( Fig 5C ) . These results indicated that S1 and S3 exert more inhibitory function than S2 . Similar to the results in Fig 4B showing lower basal activity of the Ihh promoter in Smad2/3 double mutant primary chondrocytes , M1 and M3 exhibited decreased basal activity compared to the control Ihh promoter ( Fig 5C ) , indicating that S1 and S3 also play a role in mediating basal activity of the Ihh promoter . The basis for the lower basal activity of the Ihh promoter is unclear . However , BMP signaling enhances Ihh promoter activity and association of Smad4 with SBEs in mouse teratocarcinoma P19 cells [35] . This raised the possibility that S1-S3 might recruit Smad4 to the Ihh promoter in response to BMPs , and that this recruitment is required for basal Ihh promoter activity . We therefore compared the activities of M1 , M2 and M3 in response to BMP treatment in ATDC5 cells . We observed slightly lower basal levels of activity as in Fig 5C , but found no significant differences in BMP-mediated induction between the control and mutant promoters ( S7 Fig ) . Together , our data indicate that S1 and S3 are important for Smad2 and Smad3-mediated inhibition of Ihh expression . We also find that S1 and S3 are important for maintaining basal levels of Ihh expression , but that this activity is not due to a role for these SBEs in mediating BMP responsiveness in chondrocytes . Smad2 and Smad3 interact with a variety of DNA-binding proteins in different contexts . Hdac4 is expressed in prehypertrophic and hypertrophic zones of the growth plate and represses hypertrophy by binding to and blocking Runx2 activity [49] . Although Runx2 is a potent inducer of Ihh expression [50] , whether or not Hdac4 directly regulates Ihh expression is unknown . Immunostaining confirmed that Hdac4 is present in the lower proliferative , prehypertrophic and hypertrophic zones of control Smad2fx/fx P0 growth plates , and is localized in the nucleus , as reported previously [49] ( Fig 6A ) . The level of Hdac4 protein was greatly diminished in the growth plates of Smad2CKO and double mutant mice ( Figs 6A and S8A ) . The percentage of cells expressing nuclear Hdac4 was significantly decreased at the border of the prehypertrophic and lower columnar zones of Smad2/3 double mutants compared to either Smad2CKO or Smad3-/- mice ( S8A Fig ) , indicating that both Smad2 and Smad3 contribute to decreased Hdac4 nuclear localization . To test whether the effect on Hdac4 expression is transcriptional , RNA levels were examined in growth plate cartilage from control Smad2fx/fx , Smad2CKO , Smad3-/- , and double mutant neonatal mice . No significant differences were observed ( S8B Fig ) . These results suggest that both Smad2 and Smad3 regulate Hdac4 localization and/or stability at the border of the lower columnar and prehypertrophic zones , but Smad2 has a greater impact than Smad3 . Next , we tested whether Smad2 and/or Smad3 associate directly with Hdac4 in chondrocytes . Immunoprecipitation assays revealed weak association between Hdac4 and Smad2 without TGFβ , and this association was increased by TGFβ; no association of Hdac4 with Smad3 was detected ( S8C Fig ) . Because this analysis revealed that Hdac4 associates with Smad2 , we used ChIP to test whether Smad2 and Hdac4 interact on the Ihh promoter . TGFβ increased the level of Hdac4 associated with SBE1 , the site exhibiting the highest level of pSmad2 binding; we did not detect Hdac4 binding to SBE3 , in spite of the fact that Smad2 binds to this site ( Fig 6B ) . These findings suggest that Smad2 regulates Hdac4 protein expression or stabilization , and also recruits Hdac4 to a repressive complex at SBE1 in the Ihh promoter . SnoN is a repressor that can be induced by TGFβ [51 , 52] . It is expressed in prehypertrophic chondrocytes , and inhibits chondrocyte hypertrophy [53] . Immunostaining of P0 growth plates showed that SnoN was expressed and localized within the nucleus in prehypertrophic and hypertrophic zones in control Smad2fx/fx mice ( Fig 6C ) . There was no obvious change in SnoN protein levels in prehypertrophic and hypertrophic chondrocytes in Smad2CKO , Smad3-/- and double mutant mice ( Fig 6C ) . However , there was an increase in SnoN protein levels in the proliferative zones in all mutant strains ( Fig 6C ) . This increase in protein levels was not due to increased levels of SnoN RNA; in fact , SnoN RNA levels were decreased in Smad3-/- and Smad2/3 mutant growth plates ( S8D Fig ) . TGFβ signaling leads to rapid degradation of SnoN in a Smad2/Smad3-dependent manner [54] . Hence the elevated protein levels in mutants in spite of reduced mRNA levels may reflect increased SnoN stablity in the absence of Smads 2 and 3 . Ski proteins can suppress TGFβ and BMP signaling by binding to R-Smads and Smad4 [55 , 56] . Ski was localized to the prehypertrophic and hypertrophic zones in Smad2fx/fx mice ( Fig 6D ) . There was no apparent change in protein levels in prehypertrophic and hypertrophic zones in any mutant strain , but there were increased levels of immunostaining in the proliferative zones in Smad3-/- and Smad2/3 double mutant strains . As is the case for SnoN , this effect appears to be posttranscriptional because loss of Smad3 led to reduced Ski RNA levels in spite of the elevated protein levels ( S8E Fig ) . ChIP was performed to test whether SnoN and Ski might mediate the repressive effects of Smads 2 and 3 on Ihh expression in chondrocytes . This analysis showed that TGFβ increased the association of SnoN and Ski with SBE1 , SBE2 , and SBE3 in the Ihh promoter in ATDC5 cells ( Fig 6E ) . SBE1 exhibited greater association with SnoN than with Ski , whereas more Ski than SnoN bound to SBE2; SnoN and Ski associated with SBE3 at similar levels ( Fig 6E ) . These results parallel the differential binding of Smads 2 and 3 to these sites ( Fig 5B ) , suggesting that SnoN and Ski may interact preferentially with Smad2 and Smad3 , respectively . In addition , siRNA knockdown of SnoN abolished the ability of TGFβ to repress Ihh742-luc reporter activity; siRNA against Ski and Hdac4 significantly reduced the ability of TGFβ to repress Ihh742-luc reporter activity ( S9 Fig ) . Overall , the data suggest that both Smad2 and Smad3 mediate suppression of Ihh transcription by binding to distinct SBEs and associating with different repressors , but that Smad2 has a greater impact on Ihh RNA levels than Smad3 .
Canonical TGFβ and activins transduce signals through Smad2 and Smad3 [57 , 58] . Smad2 and Smad3 interact with different transcriptional regulators on DNA and can bind to distinct sites [32 , 58] . Although Smad2 and Smad3 have similar functions in a number of contexts [32 , 59 , 60] , they exert distinct , and even opposing , effects in others [32 , 61 , 62] . Whether this is the case in cartilage was unknown . Analysis of Smad3-/- mice demonstrated previously that Smad3 suppresses chondrocyte hypertrophy [12] . The finding that overexpression of Smad2 can block the accelerated chondrocyte maturation seen in Smad3-/- chondrocytes suggested that Smad2 and Smad3 exert at least some similar functions in vitro [33] . A limitation of the previous study as well as ours is the use of global Smad3 mutants , raising the possibility that some aspects of the Smad3 mutant growth plate phenotype are due to effects on other cell types , such as the perichondrium . However , direct effects on chondrocytes seem plausible based on in vitro studies [63 , 64] and our results , which document direct effects in Smad3-deficient neonatal chondrocytes . Nonetheless , loss of Smad2 appears to have a greater impact in the non-hypertrophic zone chondrocytes of the growth plate during embryogenesis than does loss of Smad3 . We found that both Smad2 and Smad3 are essential for chondrogenesis in vivo to inhibit proliferation and hypertrophy . Some of the alterations in proliferation , maturation , and gene expression were more pronounced in Smad2CKO;Smad3-/- double mutants than in single mutants , indicating that Smads 2 and 3 exert similar functions in the growth plate . This may be mediated in part by the ability of Smads 2 and 3 to repress Ihh expression . Elevated Ihh RNA levels in mutants were correlated with increased levels of Ihh protein and its direct target Ptch1 in the resting zone . Although altered matrix properties in Smad2/3 mutants may contribute to increased Ihh protein diffusion , it is likely that the elevated Ihh mRNA levels seen in these mutants are also responsible for the elevated Ihh protein levels in the resting zone and elevated Ptch1 levels throughout the growth plate . In accordance , the growth plates of mutants are lengthened at midgestation stages and P0 . This is consistent with previous studies of Ihh function in the growth plate , where depletion of Ihh in prenatal cartilage caused a loss of columnar structure and dwarfism [65] . In spite of the longer growth plates at these stages , double mutants exhibit postnatal dwarfism . This can be explained if the accelerated rate of entry of resting chondrocytes into the proliferative columnar phase leads to premature depletion of the pool of resting chondrocytes and an inability to sustain growth plate elongation at postnatal stages . The precise role of Ihh in the postnatal dwarfism in Smad2/3 double mutants is unclear; postnatal loss of Ihh in cartilage leads to dwarfism [66] . On the other hand , Ihh can promote terminal hypertrophic maturation , which could lead to dwarfism , in cells outside the range of PTHrP [67 , 68] . Additional studies at postnatal stages would be needed to identify the mechanism underlying the postnatal dwarfism phenotype in Smad2/3 mutants . We speculate that direct effects of Smad2 and Smad3 on genes regulating cell cycle progression may contribute . Interestingly , the growth plate phenotype at the neonatal stage in Smad2/3 double mutants is distinct from that seen in mice lacking the type II TGFβ receptor TβRII ( Tgfbr2 ) in cartilage [21] . TβRII is required for responsiveness to all TGFβs . Tgfbr2CKO mice , which were generated using the same Col2a1-Cre allele used here , exhibit defects in formation of intervertebral discs ( IVD ) , but no apparent alterations in chondrocyte differentiation in axial or appendicular elements [21] . Obvious defects in IVD formation were not observed at birth in Smad2/3 double mutants , but there were clear defects in the tibial growth plates . There are several possible explanations for these differences . Loss of Tgfbr2 impacts both canonical Smad2/3 and non-canonical pathways . Hence , the axial defects seen in neonatal Tgfbr2CKO mice but not in neonatal Smad2/3 double mutants could reflect the actions of non-canonical TGFβ pathways . Furthermore , the growth plate defects at neonatal stages seen in Smad2/3 double mutants but not in Tgfbr2CKO mice could reflect a role for Smads 2 and 3 in signaling mediated by ligands other than TGFβs , such as activins [69] . Our analysis suggests that Smads 2 and 3 may regulate Ihh RNA levels by binding to distinct elements in the Ihh promoter . Candidate SBEs can be predicted in the promoter regions of many genes , but these motifs are common , and the majority of them are not occupied by R-Smads when examined using ChIP-chip/ChIP-seq [70] . We found three SBEs within the proximal Ihh promoter that bind Smads 2 and 3 . These sites exhibit differential recruitment of Smads 2 and 3 , and differential association with distinct co-repressors . S1 and S3 in the Ihh promoter mediate more of the repressive activity of TGFβ on Ihh expression than does S2 . A caveat of this study is that the Ihh promoter sequences that regulate Ihh expression in the growth plate have not yet been identified; in vivo mutagenesis studies will be required to identify these . However , the proximal promoter and enhancer region we investigated is the most highly conserved region among 60 mammalian species ( S6 Fig ) , and this region was shown previously to mediate Smad4 effects on Ihh expression [35] as well as the impact of multiple transcription factors on Ihh expression in chondrocytes [38 , 39 , 50] . Our studies revealed that Smad2 and Smad3 associate with transcriptional inhibitors Hdac4 , SnoN and Ski after TGFβ stimulation . Hdac4 inhibits Runx2 activity and Ihh expression [49]; SnoN and Ski repress the transcriptional activation activities of Smad2 and Smad3 [51 , 52]; SnoN and Ski also inhibit Smad1/5/8 mediated transcription activity in ATDC5 cells and W-20-17 osteoblasts [53 , 55] . The studies indicate that Smads 2 and 3 act to repress Ihh promoter activity in vitro , consistent with the elevated Ihh RNA and protein levels seen in Smad2CKO , Smad3-/- and Smad2/3 double mutant mice in vivo; however , they also showed that although chondrocytes isolated from Smad2/3 double mutant chondrocytes are impaired in their ability to decrease Ihh RNA levels in response to TGFβ , they also exhibit decreased basal levels of Ihh mRNA compared with control cells ( Fig 4A ) . The reason for lower basal Ihh RNA levels in Smad2/3 chondrocytes in vitro is unclear . Combined loss of Smad2 and Smad3 may affect the ability of these cells to undergo differentiation in vitro . It is important to bear in mind that the isolated chondrocytes were maintained in the absence of added growth factors , a condition that does not mimic the intact growth plate , in order to study the direct role of TGFβ . Additional studies will be required to establish the role of Smad2 and Smad3 in basal Ihh expression . A low level of association of Smad2 and Smad3 with S1 and S3 may be required to maintain basal activity of the Ihh promoter . Evidence for this comes from the finding that basal levels of Ihh promoter activity are lower in M1 and M3 Ihh promoter constructs . However , it is also possible that these mutations have an impact on the binding of other factors that are required for basal promoter activity . Nonetheless , comparison of the effects of TGFβ vs . no growth factor reveals the importance of S1 and S3 for the inhibitory effects of TGFβ . In summary , our studies reveal a role for Smad2 in the neonatal growth plate , and indicate that both Smad2 and Smad3 maintain the pool of resting chondrocytes in the growth plate . Future studies are warranted to investigate the function of Smad2 directly in postnatal articular cartilage .
Smad2fx/fx mice [31] were intercrossed with the Col2a1-Cre deleter strain [71] to generate Smad2fx/fx;Col2a1Cre ( Smad2CKO ) mice . These mice were intercrossed with Smad3-/- mice [27] to generate Smad2fx/fx;Smad3-/-;Col2a1-Cre ( Smad2/3 double mutant ) mice . Primers for PCR genotyping include sense 5′-TGCTCTGTCCGTTTGCCG -3′ and anti-sense 5′-ACTGTGTCCAGACCAGGC-3′ for detecting the Col2-Cre allele , sense 5′-CCCGGTAAATCTACCCTAG-3′ and anti-sense 5′-TTTCAAAACTATATTTGCCCAAG-3′ for detecting the Smad2-floxed allele , sense 5′-GGATGGTCGGCTGCAGGTGTCC-3′ and anti-sense 5′-TGTTGAAGGCAAACTCACAGAGC-3′ for detecting the Smad3 WT allele , sense 5′-GTTGCAGTGCACGGCAGATACACTTGCTGA-3′and anti-sense 5′-GCCACTGGTGTGGGCCATAATTCAATTCGC-3’ for detecting the Smad3 mutant allele . Embryos and mice were on a mixed C57BL/6J/CD1 background and were maintained in accordance with the NIH Guide for the Care and Use of Laboratory Animals and were handled according to protocols approved by the institution’s subcommittee on animal care ( IACUC ) . This research was approved by the UCLA Animal Research Committee ( ARC ) under protocol 1995-018-71 . Whole mount skeletal preparations were performed as described [72] . For histological analyses , paraffin sections were produced from E16 . 5 , P ( postnatal day ) 0 , 2 weeks , 1 month , and 4 month-old mice . Limb tissues were dissected and fixed in 4% paraformaldehyde in PBS . They were then decalcified with Immunocal ( Decal Chemical Corp . , Tallman , NY , USA ) for 3 days at 4°C , embedded in paraffin , and cut at a thickness of 7 μm . Sections were stained with alcian blue ( Sigma-Aldrich , A5268 ) and nuclear fast red ( Sigma-Aldrich , N8002 ) . Safranin-O staining was performed as described ( Rosenberg , 1971 ) . Heights of proliferative and hypertrophic zones were measured directly from images ( n = 5 ) taken from each of five mice per genotype and significance was evaluated using Student’s t-test . Paraffin sections were deparaffinized and rehydrated by passage through xylene and 100 , 95 , and 70% ethanol . Endogenous peroxidase activity was quenched by incubation for 15 min in 3% hydrogen peroxide . Samples were treated with 1mg/ml hyaluronidase for 30 min at 37° C . Sections were blocked with 5% goat serum , in TBS for 1 h at room temperature and incubated with 1:100 diluted primary antibody at 4° C overnight . Antibodies used were: phospho-Smad2 ( Cell Signaling , Beverly , MA , USA , #3108 ) , PCNA ( Cell Signaling , #13110 ) , Ihh ( Abcam , ab52919 ) , Patched1 ( Novus Biologicals , NB200-118 ) , Collagen X ( Abcam , ab140230 ) , SnoN ( Santa Cruz , sc-9141 ) , Ski ( Santa Cruz , sc-9140 ) and Hdac4 ( Cell Signaling , cs-2072 ) . For colorimetric detection , sections were treated with secondary antibodies conjugated to HRP as per manufacturer’s instructions , and HRP visualized with EnzMet-TM HRP Detection Kit ( Nanoprobes , Yaphank NY , #6001 ) . Nuclei were counter-stained with fast red . For fluorescence detection , sections were treated with secondary antibody ( 1:500 ) labeled with fluorescent dyes ( Cell Signaling , Goat anti-Rabbit Red:R37117 , Goat anti-Rabbit Green: R37116 , Goat anti-Mouse Red: A11032 ) at room temperature for 1 h , and nuclei were counter-stained with DAPI . Alternate sections used for IHC analysis were used for RNA in situ hybridization with 35S-labeled probes for Ihh [38 , 65] . Apoptotic cells were detected by in situ terminal deoxynucleotidyltransferase deoxyuridine triphosphate nick end labeling ( TUNEL ) assay using the In Situ Cell Death Detection Kit ( Sigma , #11684795910 ) following the manufacturer's instructions . All experiments were repeated on sections from at least three embryos of each genotype . All comparisons were between littermates . Primary rib chondrocytes isolated from P0 mice were maintained in αMEM ( Gibco , #12571 ) plus 10% FBS for 3 days in order to mature the cells to the prehypertrophic stage . After 4 hours of cell starvation , recombinant TGFβ1 or TGFβ2 ( R&D Systems , Minneapolis , MN , USA ) was added to the chondrocyte cultures at a concentration of 5 ng/ml followed by incubation in αMEM without FBS for 24 hours . Cells were then fixed in TRIzol . Total RNA was isolated by the phenol-chloroform method and converted to cDNA . The cDNA was amplified and quantified using SYBR Green reagent ( Sigma ) in a Stratagen-TM Mx3005P qPCR System ( Thermo Scientific , USA ) . Primer sequences were Ihh: sense 5’-GACTCATTGCCTCCCAGAACTG-3’ and antisense 5’-CCAGGTAGTAGGGTCACATTGC-3’ , Gapdh sense 5’-ACCAGGTGGTCTCCTCTGACTTCAA-3’ and antisense 5’-TACTCCTTGGAGGCCATGTGGG -3’ . ATDC5 cells were plated at 1 . 5 × 105 cells/well in 24-well plates . After 18 h , the cells were transfected with Lipofectamine . For siRNA transfection , 30 μM siRNA was added per well . Silencer Select Smad2 ( s69492 ) and Smad3 ( s69494 ) siRNAs and non-targeting siRNA ( 1193893 ) were from Thermo Fisher Scientific ( Life Sciences , USA ) . For DNA transfection , 0 . 25 μg of pIhh742-Luc [38] ( Addgene ) , P3TP-Luc ( Addgene ) , and 0 . 025 μg of Renilla plasmids ( Addgene ) were added per well . 24 h post-transfection , medium was replaced with chondrogenic differentiation medium: α-MEM containing 5%FBS , 200 μg/ml ascorbic acid , 60 nm Na2SeO3 , 10 μg/ml transferrin , 1% antibiotic . After 4-days of culture , cells were treated with TGFβ1 at a concentration of 5 ng/ml . Luciferase assays were performed 24 h later . Dual-luciferase reporter assay was performed using the Promaga kit ( E1910 ) in a FLUOstar Omega system ( BMG Labtech , Ortenberg , Germany ) following the manufacturer's instructions . Data are presented as ratios of Luc/Renilla activity from at least three different experiments and each experiment was performed in triplicate for each DNA sample . The sequence information for the 742bp Ihh construct is available in Addgene . It includes the 264bp 5’ untranslated region and 478bp of proximal promoter sequence . ChIP for Smad2 and Smad3 was performed in ATDC5 cells that were matured to prehypertrophy by 4-days of culture in chondrogenic differentiation medium followed by treatment with or without TGFβ1 ( 5ng/ml ) for 4 hours . ChIP was performed using a ChIP kit ( Cell Signaling , #9005S ) according to the manufacturer's instructions . In brief , after cross-linking and cell lysis , chromatin was sheared by sonication to yield DNA fragments in the range of 150 to 900 bp . 90% of the DNA fragments were in the range of 150–200 bp , confirmed by gel electrophoresis . 2% of the diluted cell supernatant was kept as input material to quantify DNA content of the samples . The supernatants were immunoprecipitated overnight at 4°C with antibodies against Smad2 ( Cell Signaling , cs-5339s ) , Smad3 ( Cell Signaling , cs-9523s ) , SnoN ( Santa Cruz , sc-9141 ) , Ski ( Santa Cruz , sc-9140 ) or Hdac4 ( Cell Signaling , cs-2072 ) . For a negative control a rabbit IgG immunoprecipitation was performed in parallel using the same concentration as the ChIP antibody . DNA was isolated using phenol-chloroform followed by quantitative PCR analysis . ChIP-enriched DNA was quantitated using the Stratagen-TM Mx3005P qPCR System ( Thermo Scientific , USA ) with SYBR green PCR master mix ( Sigma ) , using the absolute quantification method , in which ChIP DNA PCRs were run alongside a standard curve of genomic DNA . PCR signals were quantitated by normalization to the total input DNA reaction and the internal intergenic control primer pair ( QIAGEN , #GPM100001C ( - ) 01A ) . At least three independent samples were analyzed . Primer-amplified fragments between 100 and 200 bp were centered on the Smad2/Smad3 consensus binding sites . Primer sequences are as follows: SBE1 , 5’-CTAACCGCGGGTCCCTTC-3’ , and 5’-GCCTCGACTCTGAGCTGC-3’; SBE2 , 5’-CATTTCCCCTCTCACTCGAC-3’ , and 5’-GAAGGGACCCGCGGTTAG-3’; SBE3 , 5’-CTTGCTGCAGGTTCGCTG-3’ , and 5’-CGAGTGAGAGGGGAAATGGA-3’; SBE4 , 5’-GGCATCTCCTGTCCAGGA-3’ , and 5’-CTGCCTGCGATTGTCCTC-3’; SBE5 , 5’-ACACCGTAGGCGGTTGTG-3’ and 5’-TCCTGGACAGGAGATGCC- 3’ . Six nucleotides within SBE1 , SBE2 or SBE3 in a luciferase construct pIhh742-Luc [38] were replaced by restriction endonuclease PsiI recognition sites ( TTATAA ) to generate three mutant constructs M1-Luc , M2-Luc , M3-Luc , respectively , using the QuikChange Site-Directed Mutagenesis Kit ( Agilent Technologies , #200519 ) . The primers for mutagenesis are as follows: M1-Luc , 5’-GCTTTATAACGAGGCGCCGAGGGGGA- 3’ and 5’-TCGTTATAAAGCTGCCCGGCTCGCCG—3’; M2-Luc , 5’-CCGTTATAAGCAGCAGCTCCCGCTCT- 3’ and 5’-TGCTTATAACGGCGCAGCCCGGGGTC—3’; M3-Luc , 5’-TGCTTATAACGCGGGTCCCGAGCCCG—3’ and 5’-GCGTTATAAGCACCCTATCCATGTCC—3’ . Co-IP was performed in ATDC5 cells that were matured to prehypertrophy and treated with TGFβ1 ( 5ng/ml ) or not , as described above . The Pierce-TM Co-Immunoprecipitation Kit ( ThermoFisher , #26149 ) was used according to the manufacturer's instructions . Protein concentrations were determined using the Coomassie Plus Protein Assay kit ( Pierce , Rockford , IL , USA ) . SDS-PAGE was used to separate the protein extracts ( 30 μg ) . After transfer to a polyvinylidene fluoride ( PVDF ) membrane ( NEN Life Science Products , Boston , MA , USA ) , and blocking with 5% milk , the blots were probed with rabbit anti-Hdac4 ( 1 μg/ml ) . After washing , the membrane was incubated with appropriate HRP–conjugated secondary antibody ( Sigma ) for 2 h at room temperature . The immune complexes were detected using ECL substrate ( Pierce , Rockford , IL , USA ) . The blot was repeated using two independent cell preparations . Statistical comparisons were made between the groups using either ANOVA or Student’s t-test as appropriate . P values of <0 . 05 were considered significant and are denoted in each of the figures . | The cartilage growth plate regulates the size and shape of nearly every skeletal element in the body . TGFβs are potent inducers of cartilage formation , but the mechanisms by which they transduce their signals in cartilage during development are poorly understood . Similarly , there is strong evidence that dysregulation of the TGFβ pathway increases the risk for osteoarthritis ( OA ) in humans , but the underlying mechanisms are unknown . TGFβs transduce their signals through a canonical pathway involving Smad2 and Smad3 , and through several non-canonical pathways . However , the roles of canonical vs . noncanonical signaling are unknown in cartilage because the combined roles of Smad2 and Smad3 have not been determined . We generated mice lacking both Smad2 and Smad3 in cartilage in order to determine the role of canonical TGFβ signaling during embryonic development . We determined that Smad2 has a more prominent role than Smad3 in non-hypertrophic chondrocytes in the growth plate , and identified elevated levels of Ihh RNA in neonatal cartilage in Smad2 and Smad3 mutants . These findings may be important because Ihh is a vital regulator of cartilage proliferation and differentiation during cartilage development . More generally , the studies identify how Smad2 and Smad3 can regulate a common target gene through distinct mechanisms . | [
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"con... | 2016 | Smad2 and Smad3 Regulate Chondrocyte Proliferation and Differentiation in the Growth Plate |
Sequence-based protein homology detection has been extensively studied and so far the most sensitive method is based upon comparison of protein sequence profiles , which are derived from multiple sequence alignment ( MSA ) of sequence homologs in a protein family . A sequence profile is usually represented as a position-specific scoring matrix ( PSSM ) or an HMM ( Hidden Markov Model ) and accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection . This paper presents a new homology detection method MRFalign , consisting of three key components: 1 ) a Markov Random Fields ( MRF ) representation of a protein family; 2 ) a scoring function measuring similarity of two MRFs; and 3 ) an efficient ADMM ( Alternating Direction Method of Multipliers ) algorithm aligning two MRFs . Compared to HMM that can only model very short-range residue correlation , MRFs can model long-range residue interaction pattern and thus , encode information for the global 3D structure of a protein family . Consequently , MRF-MRF comparison for remote homology detection shall be much more sensitive than HMM-HMM or PSSM-PSSM comparison . Experiments confirm that MRFalign outperforms several popular HMM or PSSM-based methods in terms of both alignment accuracy and remote homology detection and that MRFalign works particularly well for mainly beta proteins . For example , tested on the benchmark SCOP40 ( 8353 proteins ) for homology detection , PSSM-PSSM and HMM-HMM succeed on 48% and 52% of proteins , respectively , at superfamily level , and on 15% and 27% of proteins , respectively , at fold level . In contrast , MRFalign succeeds on 57 . 3% and 42 . 5% of proteins at superfamily and fold level , respectively . This study implies that long-range residue interaction patterns are very helpful for sequence-based homology detection . The software is available for download at http://raptorx . uchicago . edu/download/ . A summary of this paper appears in the proceedings of the RECOMB 2014 conference , April 2–5 .
Cowen has developed a program SMURFLite for fold recognition based upon the MRF representation of a protein family [17] . Nevertheless , our MRFalign method is significantly different from SMURFLite in a couple of aspects: 1 ) SMURLite builds an MRF based upon multiple structure alignment instead of multiple sequence alignment ( MSA ) . As such , it cannot apply to sequence-based homology detection in the absence of native structures . In contrast , our method builds MRFs purely based upon MSA and thus , applies to sequence-based protein alignment and homology detection; and 2 ) SMURLite can only align a single primary sequence to an MRF , while our method aligns two MRFs to yield higher sensitivity . This difference requires us to develop totally new methods to build MRFs from MSA , measure similarity of two MRFs , and optimize the MRF-MRF alignment potential . Quite a few PSSM-based profile comparison methods for homology detection have been developed , including [11] , [18]–[23] . Some studies such as [20] also combine phylogeny information with PSSM-based profile comparison . Homology detection can also be done without aligning proteins . For example , we can represent a protein sequence or profile as a feature vector and then search for homologs by comparing feature vectors . Early methods such as [24] usually conduct straightforward comparison of feature vectors , but are not very sensitive [25] . Improvement in these alignment-free methods results from the application of discriminative learning approaches such as SVM–Fisher [26] , SVM-pairwise [27] , SVM with the spectrum kernel [28] and SVM with the mismatch kernel [29] . These SVM-based methods are reported to outperform the simple feature comparison methods . Comparing to alignment-based homology detection , alignment-free methods are usually faster but less sensitive .
To train the node alignment potential , we constructed the training and validation data from SCOP70 . The sequence identity of all the training and validation protein pairs is uniformly distributed between 20% and 70% . Further , two proteins in any pair are similar at superfamily or fold level . In total we use a set of 1400 protein pairs as the training and validation data , which covers 458 SCOP folds [30] . Five-fold cross validation is used to choose the hyper-parameter in our machine learning model . In particular , every time we choose 1000 out of the 1400 protein pairs as the training data and the remaining 400 pairs as the validation data such that there is no fold-level redundancy between the training and validation data . A training or validation protein has less than 400 residues and contains less than 10% of residues without 3D coordinates . The reference alignment for a protein pair is generated by a structure alignment tool DeepAlign [31] . Each reference alignment has fewer than 50 gap positions in the middle and the number of terminal gaps is less than 20% of the alignment length . The data used to test alignment accuracy has no fold-level overlap with the training and validation data . In particular , we use the following three datasets to test the alignment accuracy , which are subsets of the test data used in [4] to benchmark protein modeling methods . Set3 . 6K: a set of 3617 non-redundant protein pairs . Two proteins in a pair share <40% sequence identity and have small length difference . By “non-redundant” we mean that in any two protein pairs , there are at least two proteins ( one from each pair ) sharing less than 25% sequence identity . Set2 . 6K: a set of 2633 non-redundant protein pairs . Two proteins in a pair share <25% sequence identity and have length difference larger than 30% . This set is mainly used to test the performance of one method in handling with domain boundary . Set60K: a very large set of 60929 protein pairs , in most of which two proteins share less than 40% sequence identity . Meanwhile , 846 , 40902 , and 19181 pairs are similar at the SCOP family , superfamily and fold level , respectively , and 151 , 2691 and 2218 pairs consist of only all-beta proteins , respectively . We use the following benchmarks to test remote homology detection success rate . We run PSI-BLAST with 5 iterations to detect sequence homologs and generate MSAs for the first three datasets . The MSA files for the three SCOP benchmarks are downloaded from the HHpred website ( ftp://toolkit . genzentrum . lmu . de/pub/ ) . Pseudocounts are used in building sequence profiles . Real secondary structure information is not used since this paper focuses on sequence-based homology detection . To evaluate alignment accuracy , we compare our method , denoted as MRFalign , with sequence-HMM alignment method HMMER [12] and HMM-HMM alignment method HHalign [13] . HHMER is run with a default E-value threshold ( 10 . 0 ) . HHalign is run with the option “-mact 0 . 1” . To evaluate the performance of homology detection , we compare MRFalign , with FFAS [11] ( PSSM-PSSM comparison ) , hmmscan ( sequence-HMM comparison ) and HHsearch and HHblits [33] ( HMM-HMM comparison ) . HHsearch and hmmscan use HHalign and HMMER , respectively , for protein alignment . Three performance metrics are used including reference-dependent alignment precision , alignment recall and homology detection success rate . Alignment precision is defined as the fraction of aligned positions that are correctly aligned . Alignment recall is the fraction of alignable residues that are correctly aligned . Reference alignments are used to judge if one residue is correctly aligned or alignable . To reduce bias , we use three very different structure alignment tools to generate reference alignments , including TM-align [34] , Matt [35] , and DeepAlign [31] . As shown in Tables 1 and 2 , our method MRFalign exceeds all the others regardless of the reference alignments on both dataset Set3 . 6K and Set2 . 6K . MRFalign outperforms HHalign by ∼10% on both datasets , and HHMER by ∼23% and ∼24% , respectively . If 4-position off the exact match is allowed in calculating alignment recall , MRFalign outperforms HHalign by ∼11% on both datasets , and HHMER by ∼25% and ∼33% , respectively . On the very large set Set60K , as shown in Table 3 , our method outperforms the other two in each SCOP classification regardless of the reference alignments used . MRFalign is only slightly better than HHalign at the family level , which is not surprising since it is easy to align two closely-related proteins . At the superfamily level , our method outperforms HHalign and HMMER by ∼6% and ∼18% , respectively . At the fold level , our method outperforms HHalign and HHMER by ∼7% and ∼14% , respectively . As shown in Tables 4 and 5 , our method MRFalign exceeds all the others regardless of the reference alignments on both data sets Set3 . 6K and Set2 . 6K . MRFalign outperforms HHalign by ∼8% and ∼5% , respectively , and HMMER by ∼15% and ∼13% , respectively . If 4-position off the exact match is allowed in calculating alignment precision , MRFalign outperforms HHalign by ∼8% and ∼9% , and HMMER by ∼14% and ∼18% on Set3 . 6K and Set2 . 6K , respectively . On the very large set Set60K , as shown in Table 6 , our method outperforms the other two in each SCOP classification regardless of the reference alignments used . At the family level , our method outperforms HHalign and HMMER by ∼3% and ∼4% , respectively . At the superfamily level , our method outperforms HHalign and HMMER by ∼4% and ∼5% , respectively . At the fold level , our method outperforms HHalign and HHMER by ∼5% and ∼8% , respectively . To evaluate homology detection rate , we employ three benchmarks SCOP20 , SCOP40 and SCOP80 introduced in [32] . For each protein sequence in one benchmark , we treat it as a query , align it to all the other proteins in the same benchmark and then examine if those with the best alignment scores are similar to the query or not . We also conducted homology detection experiments using hmmscan , FFAS , HHsearch and HHblits with default options . The success rate is measured at the superfamily and fold levels , respectively . When evaluating the success rate at the superfamily ( fold ) level , we exclude those proteins similar to the query at least at the family ( superfamily ) level . For each query protein , we examine the top 1- , 5- and 10-ranked proteins , respectively . As shown in Table 7 , tested on SCOP20 , SCOP40 and SCOP80 at the superfamily level , our method MRFalign succeeds on ∼6% , ∼4% and ∼4% more query proteins than HHsearch , respectively , when only the first-ranked proteins are considered . As shown in Table 8 , at the fold level , MRFalign succeeds on ∼11% , ∼11% and ∼12% more proteins than HHsearch , respectively , when only the first-ranked proteins are evaluated . At the superfamily level , SCOP20 is more challenging than the other two benchmarks because it contains fewer proteins similar at this level . Nevertheless , at the fold level , SCOP80 is slightly more challenging than the other two benchmarks maybe because it contains many more irrelevant proteins and thus , the chance of ranking false positives at top is higher . Similar to alignment accuracy , our method for homology detection also has a larger advantage on the beta proteins . In particular , as shown in Table 9 , tested on SCOP20 , SCOP40 and SCOP80 at the superfamily level , MRFalign succeeds on ∼7% , ∼5% and ∼7% more proteins than HHsearch , respectively , when only the first-ranked proteins are evaluated . As shown in Table 10 , at the fold level , MRFalign succeeds on ∼13% , ∼16% and ∼17% more proteins than HHsearch , respectively , when only the first-ranked proteins are evaluated . Note that in this experiment , only the query proteins are mainly-beta proteins , the subject proteins can be of any types . If we restrict the subject proteins to only beta proteins , the success rate increases further due to the reduction of false positives . To evaluate the contribution of our edge alignment potential , we calculate the alignment recall improvement resulting from using edge alignment potential on two benchmarks Set3 . 6K and Set2 . 6K . As shown in Table 11 , our edge alignment potential can improve alignment recall by 3 . 4% and 3 . 7% , respectively . When mutual information is used , we can further improve alignment recall by 1 . 1% and 1 . 9% on these two sets , respectively . Mutual information is mainly useful for proteins with many sequence homologs since it is close to 0 when there are few sequence homologs . As shown in Table 11 , if only those proteins with at least 256 non-redundant sequence homologs are considered , the improvement resulting from mutual information is ∼3% . Figure 1 shows the running time of MRFalign with respect to protein length . As a control , we also show the running time of the Viterbi algorithm , which is used by our ADMM algorithm to generate alignment at each iteration . As shown in this figure , MRFalign is no more than 10 times slower than the Viterbi algorithm . To speed up homology detection , we first use the Viterbi algorithm to perform an initial search without considering edge alignment potential and keep only top 200 proteins , which are then subject to realignment and rerank by our MRFalign method . Therefore , although MRFalign may be very slow compared to the Viterbi algorithm , empirically we can do homology search only slightly slower than the Viterbi algorithm . We conducted two experiments to show that our MRFalign is not overtrained . In the first experiment , we used 36 CASP10 hard targets as the test data . Our training set was built before CASP10 started , so there is no redundancy between the CASP10 hard targets and our training data . Using MRFalign and HHpred , respectively , we search each of these 36 test targets against PDB25 to find the best match . Since PDB25 does not contain proteins very similar to many of the test targets , we built a 3D model using MODELLER from the alignment between a test target and its best match and then measure the quality of the model . As shown in Figure 2 , MRFalign can yield much better 3D models than HHsearch for most of the targets . This implies that our method can generalize well to the test data not similar to the training data . In the second experiment , we divide the proteins in SCOP40 into three subsets according their similarity with all the training data . We measure the similarity of one test protein with all the training data by its best BLAST E-value . We used two values 1e-2 and 1e-35 as the E-value cutoff so that the three subsets have roughly the same size . As shown in Table 12 , the advantage of our method in remote homology detection over HHpred is roughly same across the three subsets . Since HHpred is an unsupervised algorithm , this implies that the performance of our method is not correlated to the test-training similarity . Therefore , it is unlikely that our method is overfit by the training data .
This paper has presented a new method for sequence-based protein homology detection that compares two protein sequences or families through alignment of two Markov Random Fields ( MRFs ) , which model the multiple sequence alignment ( MSA ) of a protein family using an undirected general graph in a probabilistic way . The MRF representation is better than the extensively-used PSSM and HMM representations in that the former can capture long-range residue interaction pattern , which reflects the overall 3D structure of a protein family . As such , MRF comparison is much more sensitive than HMM comparison in detecting remote homologs . This is validated by our large-scale experimental tests showing that MRF-MRF comparison can greatly improve alignment accuracy and remote homology detection over currently popular sequence-HMM , PSSM-PSSM , and HMM-HMM comparison methods . Our method also has a larger advantage over the others on mainly-beta proteins . We build our MRF model of a protein family based upon multiple sequence alignment ( MSA ) in the absence of native structures . The accuracy of the MRF model depends on the accuracy of an MSA . Currently we rely on the MSA generated by PSI-BLAST . In the future , we may explore better alignment methods for MSA building or even utilize solved structures of one or two protein sequences to improve MSA . The accuracy of the MRF model parameter usually increases with respect to the number of non-redundant sequence homologs in the MSA . Along with more and more protein sequences are generated by a variety of sequencing projects , we shall be able to build accurate MRFs for more and more protein families and thus , detect their homologous relationship more accurately . An accurate scoring function is essential to MRF-MRF comparison . Many different methods can be used to measure node and edge similarity of two MRFs , just like many different scoring functions can be used to measure the similarity of two PSSMs or HMMs . This paper presents only one of them . In the future we may explore more possibilities . It is computationally intractable to find the best alignment between two MRFs when edge similarity is taken into consideration . This paper presents an ADMM algorithm that can efficiently solve the MRF-MRF alignment problem to suboptimal . However , this algorithm currently is about 10 times slower than the Viterbi algorithm for PSSM-PSSM alignment . Further tuning of this ADMM algorithm is needed for very large-scale homology detection .
Given a protein primary sequence , we run PSI-BLAST [36] with 5 iterations and E-value cutoff 0 . 001 to find its sequence homologs . PSI-BLAST also generates an MSA of the sequence homologs . Let be a finite discrete random variable representing the amino acid at column i in the MSA , taking values from 1 to 21 , corresponding to 20 amino acids and gap . Then we can use a multivariate random variable , where N is the number of columns , to model the MSA . We use an MRF to define the probability distribution of X . MRF is an undirected graph that can be used to model a set of correlated random variables . As shown in Fig . 3 , an MRF node represents one column in the MSA and an edge represents the correlation between two columns i and k when . We ignore very short-range correlation ( i . e . , ) since it is not very informative . The MRF consists of two types of functions: and , where is an amino acid preference function for node i and is a pairwise amino acid preference function for edge ( i , k ) that reflects interaction between two nodes . Then , the probability of observing a particular protein sequence X can be calculated as follows . ( 1 ) where Z is the normalization factor . We use two kinds of information in MRFs for their alignment . One is the occurring probability of 20 amino acids and gap at each node ( i . e . , each column in MSA ) , which can also be interpreted as the marginal probability at each node . The other is the correlation between two nodes , which can be interpreted as interaction strength of two MSA columns and calculated by several different ways . For example , we can use a contact prediction program such as PSICOV [37] and PhyCMAP [38] for this purpose . PSICOV assumes that is a Gaussian distribution function and calculates the correlation between two columns by inverse covariance matrix . PhyCMAP takes sequence information ( including mutual information ) as input and predicts the probability of two residues forming a contact , which can be used to indicate the interaction strength of two columns . However , it takes time to run these programs , in current implementation we calculate the mutual information ( MI ) and its power series of two columns as interaction strength . That is , we use MI , MI2 , … , MI11 to quantify all the pairwise interaction strength where MI is the mutual information matrix . The MI power series are much more informative than the MI alone , as tested in our contact prediction program PhyCMAP . Our scoring function for MRF-MRF alignment is a linear combination of node alignment potential and edge alignment potential with equal weight . Let T and S denote two MRFs for the two proteins under consideration . There are three possible alignment states M , and where M represents two nodes being aligned , denotes an insertion in T ( i . e . , one node in T is not aligned ) , and denotes an insertion in S ( i . e . , one node in S is not aligned ) . As shown in Fig . 4 , each alignment can be represented as a path in an alignment matrix , in which each vertex can be exactly determined by its position in the matrix and its state . For example , the first vertex in the path can be written as ( 0 , 0 , dummy ) , the 2nd vertex as and the 3rd vertex as . Therefore , we can write an alignment as a set of triples , each of which has a form like where represents the position and u the state . As mentioned before , an alignment can be represented as a path in the alignment matrix , which encodes an exponential number of paths . We can use a set of binary variables to indicate which path is chosen , where and are the lengths of the two MSAs , is an entry in the alignment matrix and u is the associated state . is equal to 1 if the alignment path passes with state u . Therefore , the problem of finding the best alignment between two MRFs can be formulated as the following quadratic optimization problem . ( P1 ) where and are node and edge alignment potentials as described in previous section . Meanwhile , is equal to 0 if either u or v is not a match state . L is the alignment length and is used to make the accumulative node and edge potential have similar scale . Note that L is unknown and we will describe how to determine it later in this section . Finally , the solution of P1 shall be subject to the constraint that all those with value 1 shall form a valid alignment path . This constraint shall also be enforced to all the optimization problems described in this section . It is computationally intractable to find the optimal solution of P1 . Below we present an ADMM ( Alternating Direction Method of Multipliers ) method that can efficiently solve this problem to suboptimal . See [42] for a tutorial of the ADMM method . To use ADMM , we rewrite P1 as follows by making a copy of z to y , but without changing the solution space . ( P2 ) Problem P2 can be augmented by adding a term to penalize the difference between z and y . ( P3 ) P3 is equivalent to P2 and P1 , but converges faster due to the penalty term . Here is a hyper-parameter influencing the convergence rate of the algorithm . Some heuristics algorithms were proposed for choosing at each iteration , such as [43] , [44] . Empirically , setting to a constant ( = 0 . 5 ) enables our algorithm to converge within 10 iterations for most protein pairs . Adding the constraint using a Lagrange multiplier to Eq . ( 7 ) , we have the following Lagrangian dual problem: ( P4 ) It is easy to prove that P3 is upper bounded by P4 . Now we will solve P4 and use its solution to approximate P3 and thus , P1 . Since both z and y are binary variables , the last term in ( P4 ) can be expanded as follows . ( 5 ) For a fixed , we can split P4 into the following two sub-problems . ( SP1 ) where ( SP2 ) where The sub-problem SP1 optimizes the objective function with respect to y while fixing z , and the sub-problem SP2 optimizes the objective function with respect to z while fixing y . SP1 and SP2 do not contain any quadratic term , so they can be efficiently solved using the classical dynamic programming algorithm for sequence or HMM-HMM alignment . In summary , we solve P4 using the following procedure . Initialize z by aligning the two MRFs without the edge alignment potential , which can be done by dynamic programming . Accordingly , initialize L as the length of the initial alignment . Solve ( SP1 ) first and then ( SP2 ) using dynamic programming , each generating a feasible alignment . If the algorithm converges , i . e . , the difference between z and y is very small or zero , stop here . Otherwise , we update the alignment length L as the length of the alignment just generated and the Lagrange multiplier using subgradient descent as in Eq . ( 6 ) , and then go back to Step 2 ) . ( 6 ) Due to the quadratic penalty term in P3 this ADMM algorithm usually converges much faster and also yields better solutions than without this term . Empirically , it converges within 10 iterations for most protein pairs . See [42] for the convergence proof of a general ADMM algorithm . A summary of this paper appears in the proceedings of the RECOMB 2014 conference , April 2–5 [45] . | Sequence-based protein homology detection has been extensively studied , but it remains very challenging for remote homologs with divergent sequences . So far the most sensitive methods employ HMM-HMM comparison , which models a protein family using HMM ( Hidden Markov Model ) and then detects homologs using HMM-HMM alignment . HMM cannot model long-range residue interaction patterns and thus , carries very little information regarding the global 3D structure of a protein family . As such , HMM comparison is not sensitive enough for distantly-related homologs . In this paper , we present an MRF-MRF comparison method for homology detection . In particular , we model a protein family using Markov Random Fields ( MRF ) and then detect homologs by MRF-MRF alignment . Compared to HMM , MRFs are able to model long-range residue interaction pattern and thus , contains information for the overall 3D structure of a protein family . Consequently , MRF-MRF comparison is much more sensitive than HMM-HMM comparison . To implement MRF-MRF comparison , we have developed a new scoring function to measure the similarity of two MRFs and also an efficient ADMM algorithm to optimize the scoring function . Experiments confirm that MRF-MRF comparison indeed outperforms HMM-HMM comparison in terms of both alignment accuracy and remote homology detection , especially for mainly beta proteins . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"biology",
"computational",
"biology"
] | 2014 | MRFalign: Protein Homology Detection through Alignment of Markov Random Fields |
Chronic activation of the immune system in HIV infection is one of the strongest predictors of morbidity and mortality . As such , approaches that reduce immune activation have received considerable interest . Previously , we demonstrated that administration of a type I interferon receptor antagonist ( IFN-1ant ) during acute SIV infection of rhesus macaques results in increased virus replication and accelerated disease progression . Here , we administered a long half-life PASylated IFN-1ant to ART-treated and ART-naïve macaques during chronic SIV infection and measured expression of interferon stimulated genes ( ISG ) by RNA sequencing , plasma viremia , plasma cytokines , T cell activation and exhaustion as well as cell-associated virus in CD4 T cell subsets sorted from peripheral blood and lymph nodes . Our study shows that IFN-1ant administration in both ART-suppressed and ART-untreated chronically SIV-infected animals successfully results in reduction of IFN-I-mediated inflammation as defined by reduced expression of ISGs but had no effect on plasma levels of IL-1β , IL-1ra , IL-6 and IL-8 . Unlike in acute SIV infection , we observed no significant increase in plasma viremia up to 25 weeks after IFN-1ant administration or up to 15 weeks after ART interruption . Likewise , cell-associated virus measured by SIV gag DNA copies was similar between IFN-1ant and placebo groups . In addition , evaluation of T cell activation and exhaustion by surface expression of CD38 , HLA-DR , Ki67 , LAG-3 , PD-1 and TIGIT , as well as transcriptome analysis showed no effect of IFN-I blockade . Thus , our data show that blocking IFN-I signaling during chronic SIV infection suppresses IFN-I-related inflammatory pathways without increasing virus replication , and thus may constitute a safe therapeutic intervention in chronic HIV infection .
Persistent inflammation during chronic HIV infection is a central contributing factor to immune exhaustion , CD4 T cell depletion and progression to AIDS [1–3] . Previous studies aimed at understanding the nature of this immune dysfunction have revealed a key role for type I interferons ( IFN-I ) . IFN-I has been shown to suppress HIV infection in vitro [4] and SIV infection in rhesus macaques in vivo [5] . Studies in non-human primates have demonstrated a link between type I IFN responses and pathogenic SIV infection [6–8] . While IFN-I signaling resulting from SIV infection waned during the transition from acute to chronic non-pathogenic infection in SIV natural hosts African green monkeys and sooty mangabeys , a persistent response was associated with pathogenic infection and progression to AIDS in experimental SIV infection of rhesus and pigtail macaques . In humans , plasma levels of IFN-I have been shown to correlate directly with plasma HIV RNA and inversely with CD4 T cell count [9] . Moreover , administration of IFN-I to HIV-infected persons resulted in lower CD4 T cell counts [10] and increased CD8 T cell activation [11] . These findings attributed , at least in part , the severity of infection and exacerbation of the disease to type I IFN signaling and raised considerable interest in the potential therapeutic benefits of blocking IFN-I during infection . Associations between IFN-I and chronic viral infections have led to numerous studies where IFN-I signaling was manipulated [12] . Blockade of IFN-I signaling with anti-type I IFN receptor ( IFNAR ) antibody in murine LCMV infection resulted in reduced immune activation and improved viral clearance [13 , 14] . Recently , two independent studies showed that administration of anti-IFNAR antibodies to ART-suppressed , HIV-infected humanized mice resulted in reduced immune activation and lowered reservoir size [15 , 16] . The efficacy of IFN-blockade in the mouse models has provided a rationale for testing in the SIV-infected non-human primates . In our prior study , we found that blocking IFN-I during acute SIV infection in rhesus macaques resulted in reduced expression of antiviral genes , increased size of the SIV reservoir and accelerated CD4 T cell depletion and progression to AIDS [5] . Quite reasonably , this adverse outcome of IFN-I blockade during acute infection raised major concerns for the safety of IFN-I blockade during chronic HIV infection . As treatment with antiviral drugs reduces but does not completely normalize inflammation and IFN-I signaling [2 , 17 , 18] , it is important to assess the effects of manipulation of IFN-signaling during chronic infection under ART . Thus , our primary objective in the present study was to test the effect of IFN-blockade on both inflammatory status and the control of viral replication during ART-treated and untreated chronic SIV infection in monkeys , and thus to establish the safety profile of this experimental therapy for clinical use in ART-treated HIV infection .
Eight weeks after rectal SIVMAC251 challenge , 25 rhesus macaques initiated daily ART while 10 remained untreated ( Fig 1 ) . To block type I IFN signaling , infected animals were treated with a PASylated type I interferon receptor antagonist ( IFN-1ant ) [5] which was obtained by fusion of the native IFN-1 antagonist with a 600 residue conformationally disordered chain of Pro/Ala/Ser amino acids [19] , leading to a significantly prolonged plasma half-life of 19 . 4 ± 2 . 6 h in rhesus macaques and a 70-fold enhanced area under the curve compared with the unmodified IFN-1 antagonist ( S1 Fig ) , while maintaining high receptor binding activity with KD = 451 ± 3 pM . Antagonist or saline placebo were administered either twice or three times per week from weeks 16 to 24 post-infection , with 3x weekly doses resulting in higher plasma concentrations compared to 2x weekly injections . Among the animals that did not receive ART , 4 placebo ( group 1 ) and 6 IFN-1ant-treated animals ( group 2 ) received 3x weekly injections from week 16 to week 24 and had no significant difference in plasma virus load ( VL ) before administration of the antagonist ( S2 Fig ) . In the ART-treated groups , 9 placebo-treated animals ( group 4 ) and 11 IFN-1ant-treated animals ( group 4 ) received twice weekly injections while a set of 5 animals with a higher initial median plasma virus load ( group 5 ) received 3 times weekly IFN-1ant . Despite significantly higher plasma VL from week 4 to 5 post-infection in the 3 times weekly IFN-1ant group , Plasma VL at start of ART ( week 8 ) and at start of antagonist administration ( week 16 ) showed no statistical difference between the groups . We first assessed the longitudinal effects of IFN-I antagonist administration on the expression levels of ISGs at several timepoints: Baseline , post-infection/pre-ART ( week 6 ) , pre-IFN-1ant ( week 14 ) , during IFN-1ant ( weeks 17 , 18 , 19 , 22 ) and post-IFN-1ant ( week 26 ) . For this purpose , we chose a set of 19 ISGs previously demonstrated to be reduced by IFN-1ant treatment during acute SIV infection [5] . As measured by mRNA-seq , the expression levels of several ISGs were increased as expected in all groups following SIV infection ( Fig 2A ) . In the absence of ART , animals treated with IFN-1ant also showed reduction of ISG expression levels compared to placebo animals; but these remained at higher levels in all ART-untreated animals compared to ART-treated animals . In the animals that received ART , there was a significant reduction of ISG expression by week 14 , after 6 weeks of ART administration ( P ≤ 0 . 0001; S3 Fig ) . With levels already markedly reduced by ART , administration of the antagonist to these animals from week 16 to week 24 resulted in further reduction of the ISG expression by week 18 and 19 in the 2x weekly treatment group , but were modest in the 3x weekly group ( Fig 2A–2C ) . Quantification of the overall effect of IFN-1ant treatment on ISG expression by comparison of the pre-blockade and post-blockade ( wk14 to wk19 post-infection ( p . i . ) absolute expression levels of the ISG set showed consistent reduction after IFN-1ant treatment in both the 2x weekly ART-treated and ART-untreated animals ( Fig 2B ) . The overall expression of the ISG set showed a 1 . 9-fold reduction in ART-untreated IFN-1ant animals as compared to placebo ( P ≤ 0 . 001; Fig 2C ) . Among ART-treated animals , 2x weekly administration of the IFN-1ant resulted in a 1 . 24-fold lower expression of these ISGs ( P = 0 . 03 ) whereas a 3x per week regimen did not result in significant changes ( Fig 2C ) . Overall , the observations of lowered ISG expression after administration of IFN-1ant in the 2x weekly ART group and in the ART-naïve group , demonstrated the pharmacological efficacy of the PASylated IFN-1ant during chronic ART-treated and ART-naïve SIV infection , building on our prior results in which antagonist efficacy was achieved in acute SIV infection . In order to assess the extent of the antagonist-mediated suppression , plasma levels of the cytokines IL-1β , IL-1ra , IL-6 and IL-8 were measured before and after antagonist administration . Despite a significant reduction in ISG expression levels , antagonist treatment in ART-untreated and ART-treated SIV-infected macaques had no effect on circulating levels of these cytokines ( Fig 3 ) , suggesting a selective suppression of inflammatory pathways . Collectively , these data demonstrate that IFN-1ant suppressed type I IFN associated inflammatory pathways . Importantly , ART status and viremia impacted on the extent to which antagonism of type I IFN signaling affected expression levels of ISGs . In the absence of ART , ISG expression was reduced but remained higher than when ART alone was given . By itself , ART significantly reduced ISG expression levels and further reduction upon antagonist treatment was more apparent with low viremia . To assess the risk that blocking type I IFN signaling could pose for the control of SIV replication , we measured plasma VL during and after antagonist treatment . In the absence of ART , there was no significant difference between the plasma VL of animals receiving antagonist or placebo from antagonist administration at week 16 p . i . up to week 50 post-infection ( Fig 4A ) . In the ART-treated arm , all groups had measurable plasma VL upon initiation of the antagonist or placebo at week 16 ( median plasma VL ranging from 190 to 950 RNA copies/ml ) and showed intermittent rebound in plasma VL up to week 49 ( Fig 4B ) . However , from antagonist administration at week 16 until 34 weeks later ( i . e . week 50 p . i ) , pairwise tests of VL or area under the curve showed no significant difference between IFN-1ant-treated animals compared to the placebo group . Regression models adjusting for week 8 ( ART start ) and week 14 ( pre-IFN-1ant ) plasma VL also showed no significant effect of the antagonist . Of note , elevated median plasma VL observed from week 20 to 42 in the animals treated 3x per week with antagonist are likely due to the higher VL in these animals prior to initiation of the antagonist treatment . Although statistical significance was not reached , median plasma VL of animals treated 3x per week were consistently higher compared to other ART-treated groups from weeks 10–15 ( before antagonist; Fig 4B ) . To confirm that high plasma VL were unlikely to occur when administering the same IFN-1ant dose regimen to animals having more suppressed viremia , we extended ART treatment and delayed start of the IFN-1ant administration to week 35 instead of 16 in a separate group of animals . In these animals , the virus was further suppressed by week 35 and administration of 3x-weekly antagonist with follow-up for an additional 33 weeks ( week 68 p . i ) was not associated with plasma VL increases ( Fig 4C ) . Thus , administration of PASylated IFN-1ant to chronically SIV-infected macaques did not significantly impact plasma VL and appeared to be safe both in ART-treated and untreated infection . Individual plasma VL curves from baseline to week 50 p . i . for all groups are presented in S4 Fig . Given that type I IFN are also important for the control of other viral infections such as CMV , we measured plasma CMV load before and after antagonist administration and observed no increase in both the proportion of CMV+ animals or the plasma virus loads of animals that were CMV infected prior to antagonist administration ( S1 Table ) . In addition , administration of the antagonist had no effect on blood chemistry ( S2 Table ) which is routinely used to gauge major bodily functions . Together , these data reinforce our findings that treatment of SIV-infected macaques with PASylated antagonist , during untreated or ART-suppressed chronic infection , was well tolerated and did not induce overt immunodeficiency . We next set out to address whether administration of PASylated type I IFN antagonist impacts the size of the SIV reservoir . Virus DNA was measured in CD4 T cell subsets sorted from PBMC and LN ( according to gating strategies shown in S5 Fig ) . In the PBMC , CD4 T cells were sorted into total CCR5+ , central memory ( CM ) and effector memory ( EM ) cells . While some EM cells express CCR5 [20] , all CCR5+ cells were gated prior to EM in order to measure virus DNA in the total fraction of cells expressing CCR5 given its role SIV infection . As expected , ART initiation at week 8 significantly reduced the amount of SIV gag DNA copies measured in all cell subsets in all animals in each experimental group ( Fig 5A ) . By week 24 , treatment with 3x weekly IFN-1ant in ART-treated animals marginally reduced SIV gag DNA copies in CCR5+ T cells only ( P = 0 . 02 ) . However , administration of 2x or 3x weekly IFN-1ant did not show significant effects on CM , EM or total SIV gag copies . In the absence of ART , SIV gag DNA copies remained high from week 8 through 24 in PB cell subsets , with no difference between antagonist and placebo groups . In the LN , CD4 T cells were sorted into CM , EM , germinal center follicular helper ( GC Tfh ) and non-germinal center Tfh ( non-GC Tfh ) subsets . After ART initiation , the amount of SIV gag DNA copies continuously decreased up to week 50 p . i . in all LN subsets in all animals , with no effect of the antagonist treatment given from week 16 to week 24 ( Fig 5B ) . In the absence of ART , the cell-associated SIV gag levels remained high across all timepoints and unaffected by IFN-1ant administration in all LN subsets . While cell-associated virus measures are commonly reported using the same numerical denominator , adjusting for the actual frequencies of each subset within a sample gives a more accurate insight into the contribution of each cell subset to the total cell-associated virus DNA level . After adjusting for cell subsets frequencies in our samples , the distribution of SIV gag DNA copies across the various PB and LN subsets remained similar between IFN-1ant and placebo treated animals ( Fig 5C and 5D ) . In the PBMC , infected cells belonged almost entirely to the central memory CD4 T cell type ( 94–100% in ART-treated and 86–91% in ART-untreated ) . In the LN , infected cells were in majority CD4 CM in ART-treated ( up to 71% ) and GC Tfh in ART-untreated ( up to 55% ) animals . We previously observed that type I IFN blockade during untreated , acute SIV infection results in increased reservoir size and accelerated progression to AIDS [5] , and raised legitimate concerns regarding safety . In the current data , blocking type I IFN signaling during chronic SIV infection did not increase the size of the SIV virus reservoir irrespective of ART treatment status , even in animals in which residual SIV viremia persisted during ART . Even though type I IFN antagonist treatment did not significantly affect cell-associated or plasma virus loads , we nevertheless explored its potential effect on modulating T cell function during SIV infection . T cell exhaustion is associated with persistent antigenic stimulation; many reports have previously highlighted that chronic type I IFN signaling during viral infections results in CD8 T cell exhaustion [21–23] and have shown that blocking IFN-I signaling restores T cell function in LCMV-infected mice [13 , 14 , 24] . Therefore , we assessed T cell activation and exhaustion by measuring frequencies of CD8 memory T cells expressing CD38 , HLA-DR , Ki67 , LAG-3 , PD-1 and TIGIT before and after antagonist administration . None of these markers of T cell activation and exhaustion were affected by antagonist treatment in either ART-untreated or ART-treated SIV infection ( Fig 6A and 6B ) . From our RNA-seq data , comparison of the expression levels of T cell activation and exhaustion genes ( S6 Fig ) as well as GSEA of T cell activation and exhaustion pathways ( S3 Table ) showed no significant effect of antagonist treatment . Furthermore , administration of the antagonist did not affect CD4:CD8 ratio and frequencies of CCR5+ CD4 T cells in the PBMC ( Fig 6C ) . Therefore , blocking type I IFN signaling during ART-treated or untreated chronic SIV infection showed no significant impact on T cell activation or exhaustion . Finally , we explored whether antagonist administration under ART would influence recrudescence of viremia upon ART interruption . Within a week after ART stop , there was a resurgence of viremia that reached pre-ART levels by week 3 and remained high up to 14 weeks after ART interruption in all groups ( Fig 7 ) . Comparison of individual antagonist groups to placebo showed that after adjustment for VL at ART initiation ( week 8 ) there was no significant effect of the antagonist on the mean log10 plasma VL set point defined as 6 consecutive weeks starting 4 weeks after ART cessation . Thus , despite significantly reducing expression of ISGs , blocking type I IFN signaling in ART-treated chronic SIV infection did not result in increased plasma VL compared to placebo even after ART interruption .
Our findings are primarily relevant to the implementation of IFN-I blockade strategies in clinical HIV studies . Type I IFNs are important mediators of antiviral immunity but their permanent engagement in chronic HIV infection also contributes to a persistent inflammatory state that promotes pathology . As is being advocated for inflammatory diseases such as systemic lupus erythematous or systemic sclerosis [12] , therapeutic blockade of IFN-I signaling could reduce inflammation and improve control of HIV infection . However , adverse outcomes observed in acute SIV infection [5] raised major safety concerns for the potential use of similar approaches during the chronic stage . Here , we assessed the effect of blocking IFN-I signaling during ART-treated and ART-untreated chronic SIV infection . Our principal findings were: ( 1 ) administration of an IFN-I receptor antagonist with prolonged half-life to ART-treated and ART-untreated SIV-infected rhesus macaques showed a therapeutic benefit in terms of lowering inflammation in part as observed by amelioration of ISG expression despite unaltered levels of measured pro-inflammatory cytokines; and ( 2 ) in contrast to observations made during acute SIV infection [5] , blockade of IFN-I signaling during chronic SIV infection did not lead to loss of control of viral replication . In this regard , our study shows that in chronic SIV infection , even in situations with residual viral replication , blocking type I IFN signaling did not lead to loss of control of the infection; and supports the rationale that the use of an IFN-I antagonist during chronic HIV infection is safe . The difference in outcome of IFN-I signaling blockade between acute [5] and chronic SIV infection is most likely due to the timing of IFN-I signaling for control of the infection . Significant increase in plasma VL and SIV reservoir size upon blockade of IFN-I signaling in the acute phase implies a critical role for IFN-I at the onset of infection . In contrast , our data show that once persistent infection has been established , IFN-I signaling plays a less prominent role in the control of virus replication . Recently , administration of exogenous IFN-I along with ART to chronically SIV infected animals was shown to increase ISGs expression with no effect on virus control [25] . Similar differences in the role of IFN-I between acute and chronic viral infection have been reported in LCMV studies . For instance , addition of exogenous IFN-I increases control of the infection in the acute stage but does not decrease virus titers in the chronic stage [22 , 26] . While such findings support the notion that the importance of IFN-I signaling for viral control changes over the course of the infection , the underlying reasons remain unclear . It remains uncertain what clinical benefits would emerge as a result of blocking IFN-I signaling during chronic HIV infection . In ART-treated HIV infection , IFN-I signatures remain elevated despite effective HIV suppression by cART [27] . Targeting IFN-I could further suppress residual inflammation and rescue T cells from exhaustion . Despite reduced expression of some of the most prominent antiviral ISGs downstream of IFNAR such as Mx1 and OAS2 [28] , blocking IFN-I signaling in our study had no measurable impact on T cell activation or exhaustion . Of note , the higher antagonist dose ( 3x weekly ) in our study was less efficacious in reducing ISG expression , which may be due to partial agonist activity of the antagonist to induce minimal ISG expression , which has been reported at high concentrations in vitro [29] . In addition , because the 3x weekly ART group had a generally higher VL compared to animals that received twice weekly IFN-1ant ( Fig 4 ) , there is likely a threshold where any measurable effect of the antagonist added to the effect of ART is influenced by the viremia at the time antagonist treatment is initiated . Our findings possibly reinforces the importance of timing on the outcome of IFN-I signaling blockade as administration of IFN-I antagonist [5] and more recently anti-IFN antibody treatment [30] in SIV infected macaques was shown to reduce T cell activation . The observations in our study also differ from observations made in prior studies on IFN-I blockade in chronically HIV-infected humanized mice which showed reduced expression of ISGs along with enhanced viral suppression and reduced T cell activation [15 , 16] . There are notable differences in experimental design that could account for this discrepancy . With a maximum of 4 weeks antibody treatment in the mice as opposed to 8 weeks of a long half-life IFN-I antagonist in our study , it is possible that both timing and duration could influence the outcome of blocking IFN-I signaling . Blockade for a limited time may rescue immune responses but viral clearance mechanisms may require a contribution from the IFN-I signaling pathway . Another possibility may be related to the animal models used . While the studies in humanized mice indicate that a reduction of cellular makers of inflammation was associated with improved control of virus replication , our data in non-human primates suggest that a lengthy blockade of IFN-I signaling reduces ISG expression but has no effect on other inflammatory pathways or T cell activation and thus may not affect control of virus replication . The important point , however , is that blockade of IFN-I signaling in chronically SIV-infected non-human primates did not lead to an increase in SIV replication as is the case in acute SIV infection . Dissection of experimental variables such as timing , duration , dose and possibly the evaluation of combination interventions in future studies will delineate optimal conditions for the therapeutic blockade of IFN-I signaling . A recent study in LCMV demonstrated that virus control and T cell exhaustion are mediated by different type I IFNs despite their use of the same receptor [24] and HIV studies on administration of various IFN-I subtypes to humanized mice revealed differences in their ability to suppress the virus [31 , 32] . Thus , an intriguing hypothesis is that by manipulating specific type I IFNs and their subtypes ( i . e . IFNα vs . IFNβ ) in HIV/SIV infection , it may be possible to decouple the various biological activities of the IFN-system and selectively target deleterious activities while maintaining beneficial ones . Administration of antiretrovirals in our study successfully suppressed SIV but most animals had virus loads above the limit of detection even after 40 weeks of ART . Consequently , the antagonist was administered to animals with detectable plasma VL . The presence of this residual viremia allowed evaluation of the safety aspect of IFN blockade in chronic SIV infection , which was a concern in light of previous findings that blockade during acute infection accelerated mortality . While our study concluded that IFN-I blockade in chronic SIV infection did not impair control of the infection , it remains to be seen whether the pre-intervention plasma virus load influences the outcome of IFN-I blockade . The use of , for example , a macrophage-tropic virus to assess how IFN-blockade influences infection of myeloid cells will help gain a full understanding of the clinical implications of blocking IFN-I in chronically HIV-infected persons . Despite reductions in risk of death with ART , high rates of serious non-AIDS events associated with inflammation [3] continue to reduce quality and expectancy of life in HIV-infected people [33 , 34] . Thus , therapeutic blockade of IFN-I signaling which plays a key role in the persistence of inflammation , even during suppressive ART , has the potential to safely improve clinical outcome in HIV-infected persons .
40 Mamu A01- B08- B17- adult Indian origin Macaca mulatta were challenged by two intrarectal inoculations within five days of 1ml SIVMAC251 ( 1ml of 1:25 dilution , stock 8 . 91 x 108 SIV RNA copies ml-1 ) . At week 8 post-infection , 30 animals were started on antiretrovirals while 10 animals were left untreated . The ART regimen was subcutaneous injections of 20mg/kg/day Tenofovir and 30mg/kg/day Emtricitabine ( Gilead ) , as well as orally administered drugs including 100mg BID Raltegravir ( Merck ) , 800mg BID Darunavir ( Janssen Pharmaceuticals ) and 100mg BID Ritonavir ( Abbvie ) , all given mixed with food . To investigate the effect of blocking IFN-I signaling , infected animals received i . m . injection of 3 . 5mg per injection given 2 or 3 times per week with a type I interferon receptor antagonist ( IFN-1ant ) used previously [5] whose plasma half-life was significantly increased by PASylation [19] as assessed in healthy macaques . PAS-IFN-1ant was produced by fermentation in E . coli according to a published procedure [19] where a human IFN-α2b carrying the amino acid substitutions R120E , E159K , S160R and R162K ( in the mature protein ) was equipped with a structurally disordered N-terminal PAS#1 sequence of 600 residues and secreted into the bacterial periplasm to facilitate formation of the structural disulfide bonds . Purification was achieved by substractive anion exchange chromatography and ammonium sulfate precipitation followed by a cation exchange and a finishing anion exchange chromatography , resulting in a homogenous protein preparation with ≤5 . 5 IU endotoxin per mg protein . Based on plasma concentrations measured after in vivo administration of the antagonist to healthy macaques , the chosen regimen of 2 and 3 times per week should result in minimum plasma concentrations of 5 nM and 10–20 nM respectively ( S1 Fig ) . Detailed experimental set-up and IFN-1ant treatment is presented in Fig 1 . Animal use and all study procedures ( protocol VRC-13-453 , renewed once as VRC-16-678 ) were approved by the Vaccine Research Center ( VRC ) Animal Care and Use Committee ( ACUC ) , meeting National Institutes of Health standards; and in accordance with the American Association for Accreditation of Laboratory Animal Care guidelines , all federal , state , and local regulations , and in compliance with The Guide for the Care and use of Laboratory Animals . All animals , Indian origin rhesus macaques ( Macaca mulatta ) were socially housed at the National Institutes of Health with oversight from facility behavioral management staff . Primary enclosures consisted of stainless steel primate caging provided by a commercial vendor . Animal body weights and cage dimensions were regularly monitored . Overall dimensions of primary enclosures ( floor area and height ) met the specifications of The Guide for the Care and Use of Laboratory Animals , and the Animal Welfare Regulations ( AWR's ) . All primary enclosures were sanitized every 14 days at a minimum , in compliance with AWRs . Secondary enclosures ( room level ) met specifications . All animals were housed under controlled conditions of humidity , temperature and light ( 12-hour light/12-hour dark cycles ) . Animals were fed commercial monkey chow , twice daily , with supplemental fruit or other produce at least three times per week . Filtered water was available ad libitum . Animals were observed at least twice daily by trained personnel , including behavioral assessments . Environmental enrichment included provision of species appropriate manipulatives , and foraging opportunities , as well music and video watching opportunities multiple times per week . No adverse events have been associated with study interventions . For procedures requiring chemical immobilization and sedation , different anesthetics were used at the discretion of the attending veterinarian according to the IACUC approved protocol . Prior to immunization , drug treatments or blood draws , anesthetics included Ketamine Hydrochloride 5 . 0–25 . 0 mg/kg IM with xylazine 0 . 5–1 . 0 mg/kg . For technical procedures , Buprenorphine Hydrochloride 0 . 015 mg/kg was administered . For euthanasia according to endpoints specified in the IACUC approved protocol , animals were initially sedated with ketamine ( 10–25 mg/kg , IM ) followed by lethal overdose of sodium pentobarbital to effect . Plasma was separated from EDTA blood by centrifugation and PBMCs were isolated by density centrifugation using Ficoll-Paque Plus ( GE Healthcare ) and Leucosep Centrifuge Tubes ( Grenier Bio-One ) . Lymph nodes ( LN ) were collected into RPMI 1640 ( Gibco ) supplemented with 10% fetal bovine serum ( Gibco ) and 1% Penicillin-Streptomycin-Glutamine ( Gibco ) and cell suspensions were passed through a 70μm filter to remove debris . For CD4 T cell subsets sort , PBMC were stained with fluorochrome-labelled mAbs anti-CD28-CY5PE , anti-CCR5-PE , anti-CD3-CY7APC , anti-CD4-BV605 , anti-CD8-Pacific blue ( BD Biosciences ) and anti-CD95-BV785 ( in house conjugated , BD Biosciences ) . LN cells were stained with anti-CD28-CY5PE , anti-CD3-CY7APC , anti-CD4-BV605 ( BD Biosciences ) , anti-CD8-BV570 , anti-CXCR5-PE ( eBioscience ) and anti-CD95-BV785 ( in house conjugated , BD Biosciences ) . PBMC and LN CD4 subsets of interest were sorted and lyzed in proteinase K ( 100ug mL-1 , Sigma Aldrich ) for SIV gag qPCR . For assessment of T cell activation and exhaustion , cryopreserved PBMC were thawed and stained with fluorochrome-labelled mAbs anti-CD38-FITC ( Stem Cell ) , anti-Ki67-CY7PE , anti-CD28-CY5PE , anti-CD3-CY7APC ( BD Biosciences ) , anti-HLA-DR-TRPE ( Life Technologies ) , anti-PD-1-BV711 , anti-CD95-BV785 ( Biolegend ) , anti-TIGIT-APC ( ThermoFisher ) and anti-LAG3-PE ( R&D ) . All samples were stained with Aqua LIVE/DEAD Fixable Dead Cell Stain . Plasma SIVgag RNA was assayed as described previously [35] . For cell associated virus , SIVgag and rhesus albumin DNA were simultaneously quantified in cell lysates by qPCR using plasmid standards for absolute quantification of gag and albumin copy numbers with the following primers and probes used at final concentrations of 625nM and 250nM , respectively: SIV-Gag-F GTCTGCGTCATpTGGTGCATTC SIV-Gag-R CACTAGkTGTCTCTGCACTATpTGTTTTG SIV-Gag-P CTTCpTCAGTkTGTTTCACTTTCTCTTCTGCG Rh-Alb-F TGCATGAGAAAACGCCAGTAA Rh-Alb-R ATGGTCGCCTGTTCACCAA Rh-Alb-P AGAAAGTCACCAAATGCTGCACGGAATC Plasma concentrations of IL-1β , IL-1ra , IL-6 and IL-8 were measured by bioplex assay using a premixed non-human primate kit from Millipore according to the manufacturer recommendations . DNA was isolated from plasma using the QIAamp DNA Blood Mini Kit ( QIAgen 51106 ) according to manufacturer’s instructions . Isolated DNA was then analyzed using an Applied Biosystems QuantStudio Real-Time PCR system ( 12K Flex ) with Cytomegalovirus ( CMV ) specific primers ( Forward: ATC CGC GTT CCA ATG CA , Reverse: CGG AGG AGC ACC ATA GAA GGT ) and a TaqMan Probe ( 6FAM CCT TCC CGG CTA TGG MGBNFQ ) . Each sample was run in triplicate for 40 cycles along with positive controls . Copy number was calculated by comparison to a standard curve and the viral load was reported as CMV copies/mL of plasma . RNA was extracted from cryopreserved PBMCs using RNAzolRT ( Molecular Research Center ) according to the manufacturers’ instructions . Purified RNA was used for transcriptome analysis . Briefly , polyadenylated transcripts were purified on oligo-dT magnetic beads , fragmented , reverse transcribed using random hexamers and incorporated into barcoded cDNA libraries based on the Illumina TruSeq platform . Libraries validated by microelectrophoresis were sequenced on an Illumina HiSeq 4000 in 100-base single-read reactions . RNA-seq analysis was conducted at the Yerkes Nonhuman Primate Genomics Core Laboratory . Estimates of gene-wise and isoform-wise expression levels for individual genes were performed using DESeq2 [36] . The RNA-seq data were submitted to the Gene Expression Omnibus repository at the National Center for Biotechnology Information database ( GSE112148 ) . To identify pathways differentially modulated by IFN-1ant , Gene Set Enrichment Analysis was performed as follows . For each contrast , transcripts were ranked by differential expression using the Signal2Noise metric . GSEA was performed using the desktop module available from the Broad Institute ( www . broadinstitute . org/gsea/ ) . GSEA was performed on the ranked transcript lists using 1 , 000 phenotype permutations , and random seeding . Gene sets used included the MSigDB ( http://software . broadinstitute . org/gsea/msigdb/collections . jsp ) H ( hallmark ) , C5 ( GO ) , C2 ( curated ) , C7 ( immunologic ) gene sets ( [37] ) , and additional custom gene sets . | Innate and adaptive immune activation is one of the strongest predictors of HIV disease progression to AIDS and non-AIDS morbidity and mortality . Type I interferon ( IFN-I ) signaling is a major driver of such activation even in ART-treated people . Therefore , manipulation of IFN-I signaling has been proposed as a therapeutic approach to improve HIV disease outcome . However , we previously reported that blockade of IFN-I signaling in rhesus macaques during acute SIV infection results in increased SIV reservoir size , accelerated CD4 T cell depletion and faster progression to AIDS . Our present study addresses the more clinically relevant effects of IFN-I signaling blockade in chronic SIV infection during treatment with antiretroviral therapy . We found that administration of a novel , long half-life , PASylated IFN-I receptor antagonist suppressed IFN-I-related inflammatory pathways but , in contrast to the case of acute SIV infection , did not result in loss of control of SIV replication . Thus , our findings provide a rationale for the safe and effective use of such interventions to reduce inflammation in HIV-infected people during ART . | [
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"virus... | 2018 | Type I IFN signaling blockade by a PASylated antagonist during chronic SIV infection suppresses specific inflammatory pathways but does not alter T cell activation or virus replication |
P-glycoprotein ( Pgp ) and multidrug resistance-associated proteins ( MRPs ) are ATP-dependent transporters involved in efflux of toxins and xenobiotics from cells . When overexpressed , these transporters can mediate multidrug resistance ( MDR ) in mammalian cells , and changes in Pgp expression and sequence are associated with drug resistance in helminths . In addition to the role they play in drug efflux , MDR transporters are essential components of normal cellular physiology , and targeting them may prove a useful strategy for development of new therapeutics or of compounds that enhance the efficacy of current anthelmintics . We previously showed that expression of Schistosoma mansoni MDR transporters increases in response to praziquantel ( PZQ ) , the current drug of choice against schistosomiasis , and that reduced PZQ sensitivity correlates with higher levels of these parasite transporters . We have also shown that PZQ inhibits transport by SMDR2 , a Pgp orthologue from S . mansoni , and that PZQ is a likely substrate of SMDR2 . Here , we examine the physiological roles of SMDR2 and SmMRP1 ( the S . mansoni orthologue of MRP1 ) in S . mansoni adults , using RNAi to knock down expression , and pharmacological agents to inhibit transporter function . We find that both types of treatments disrupt parasite egg deposition by worms in culture . Furthermore , administration of different MDR inhibitors to S . mansoni-infected mice results in a reduction in egg burden in host liver . These schistosome MDR transporters therefore appear to play essential roles in parasite egg production , and can be targeted genetically and pharmacologically . Since eggs are responsible for the major pathophysiological consequences of schistosomiasis , and since they are also the agents for transmission of the disease , these results suggest a potential strategy for reducing disease pathology and spread .
Schistosomiasis is a major endemic disease that affects hundreds of millions worldwide , causes nearly 300 , 000 deaths annually , and has an estimated human health burden on a par with malaria or tuberculosis [1]–[3] . The causative agents of schistosomiasis are parasitic flatworms of the genus Schistosoma . Adult schistosomes reside in the vasculature of the host , where they take up nutrients and deposit eggs which evoke a host immunopathological response that is responsible for the development of the pathophysiological effects of chronic schistosomiasis . Like other organisms , schistosomes must eliminate toxic metabolites and xenobiotics , and , as parasites , must in addition deal with potentially toxic compounds generated by the host [4] . Multidrug resistance ( MDR ) proteins are cellular efflux transporters with broad substrate specificities that likely play essential roles in this process , as well as in other significant aspects of parasite physiology . Several of these transporters are members of the ATP binding cassette ( ABC ) superfamily of proteins , including P-glycoprotein ( Pgp ) , multidrug resistance-associated proteins ( MRPs ) , breast cancer resistance protein ( BCRP ) , and others [5] , [6] . Their major role in normal cellular physiology is to remove or exclude xenobiotics and metabolic toxins , but they are also involved in a wide array of physiological functions [7]–[9] , including regulation of cell death [10] and immune function [11] . As their name suggests , MDR transporters also mediate multidrug resistance , a phenomenon in which cells that develop resistance to a particular drug also show unexpected cross-resistance to several structurally unrelated compounds . Though MDR transporter-mediated multidrug resistance was described originally in mammalian cells [12] , MDR transporter expression levels and allele frequencies are also altered in anthelmintic-resistant populations of helminths , including schistosomes [13]–[22] . The role these transporters might be playing in helminth and other parasite drug resistance has recently been reviewed [23]–[27] . Praziquantel ( PZQ ) is the current drug of choice against schistosomiasis . It is highly effective against all schistosome species , and shows minimal adverse effects [28]–[30] . However , schistosomes show stage- and sex-dependent differences in susceptibility to PZQ [31]–[33] , and the mode of action of the drug remains unresolved three decades following its introduction [34] , [35] . Though currently there is little compelling evidence that PZQ resistance constitutes a major problem in the field , several reports of worm isolates exhibiting reduced PZQ susceptibility following drug pressure have appeared in the literature , and could be harbingers of the emergence of more widespread resistance [36]–[38] . Recent studies on changes in gene expression in response to PZQ may provide clues to the mode of action of the drug and to possible molecular mechanisms underlying development of resistance [39] , [40] . ABC transporter cDNAs that have been characterized in schistosomes include SMDR2 [41] , a S . mansoni orthologue of Pgp , and SmMRP1 [42] , a S . mansoni orthologue of MRP1 . SMDR2 RNA is expressed at higher levels in female parasites than in males [21] , [41] , while males express higher SmMRP1 RNA levels than females [42] . Notably , S . mansoni adults upregulate expression of both of these transporters in response to PZQ [21] , [42] . Furthermore , higher basal levels of both SMDR2 and SmMRP1 correlate with reduced PZQ susceptibility [21] , [42] , and PZQ inhibits , and is also a likely substrate of , SMDR2 [43] . Based on these findings , we have hypothesized that schistosome MDR transporters may be modulating the responsiveness of parasites to PZQ [44] . We also predict that schistosome multidrug transporters play critical roles in worm physiology , development , and perhaps in modifying host responses . In this report , we use genetic and pharmacological approaches to examine the effects on schistosomes of interference with normal MDR transporter function . We find that knockdown of SMDR2 or SmMRP1 expression in adult worms , or exposure of parasites to pharmacological inhibitors of these transporters , disrupts egg production in S . mansoni cultured ex vivo . Furthermore , administration of any of four structurally diverse Pgp inhibitors to schistosome–infected mice results in a reduced egg burden in the livers of those infected mice . Schistosome eggs are associated with the majority of morbidity in chronic schistosomiasis , and are the agents of disease transmission . Our findings indicate that MDR transporters may be essential components of pathways involved in schistosome reproduction , and may serve as highly “drugable” targets for new antischistosomals that decrease egg-dependent pathology and could serve to reduce disease transmission .
We used electroporation of SMDR2 and SmMRP1 siRNAs to knock down expression of the multidrug resistance proteins SMDR2 and SmMRP1 in adult worms . As shown in Fig . 1 , electroporation of adult parasites with siRNA targeted against either sequence results in substantial reduction of the relative expression level of that gene , both at the RNA and protein levels . Levels of RNA expression for both genes in pooled adult schistosomes are reduced by 50–70% compared to controls . Addition of SmMRP1 siRNA to the SMDR2 siRNA does not appear to affect RNA levels of SMDR2 , nor does addition of SMDR2 siRNA appear to additionally decrease levels of SmMRP1 RNA . Protein expression , as measured by immunoblotting with anti-Pgp and anti-MRP1 antibodies , is also reduced . Adult schistosomes perfused from the murine host and maintained in vitro will continue to produce eggs , though only those deposited during the first 48 h following perfusion from the host appear to be viable [45] . We compared the cumulative number of eggs produced by worms over a 2–3-day span following electroporation with siRNA against SMDR2 or SmMRP1 ( or both ) . We also counted eggs produced by control worms electroporated with luciferase siRNA or with no treatment . As shown in Fig . 2 , knockdown of either MDR transporter gene ( or both ) resulted in a significant reduction in cumulative egg production compared to controls . As shown above , knockdown of MDR transporter expression in adult S . mansoni results in decreased parasite egg production . Previous work described in a patent [46] showed that exposure of worms to verapamil , a mammalian L-type voltage-gated Ca2+ ( Cav ) channel blocker and also an inhibitor of SMDR2 [43] and mammalian Pgp [47] , [48] , reduces egg production . We have confirmed these results for verapamil , finding no eggs whatsoever following incubation of adults in 10 µM verapamil for 2 days . Based on these results , we examined other structurally diverse Pgp and MRP1 inhibitors for their effects on S . mansoni egg production . Drugs tested included: the immunosuppressant cyclosporin A ( CSA ) , which is also an inhibitor of mammalian Pgp; R ( + ) -verapamil ( dexverapamil ) , an enantiomer of verapamil which is significantly less active than the S ( − ) enantiomer against Cav channels , but which retains potent and selective competitive inhibitory activity against Pgp [49]; C-4 , a curcumin derivative that is a cell-permeable , reversible Pgp inhibitor [50]; tariquidar ( aka XR9576 ) , a third-generation , selective and highly potent Pgp inhibitor [51]–[53] ( which also appears to be a substrate of BCRP at low concentrations and an inhibitor of BCRP at >100 nM concentrations [54] ) ; and MK 571 , a potent inhibitor of MRP1 [55] . As shown in Fig . 3 , exposure of adult worms to any of these compounds ex vivo resulted in a dramatic , dose-dependent reduction in cumulative parasite egg production over two ( tariquidar , MK 571 ) or five ( CSA , dexverapamil , C-4 ) days in culture . Specifically , exposure of worms to CSA ( Fig . 3A ) results in a ∼75% decrease in egg production at concentrations of 1 µM–22 . 5 µM . Worms exposed to C-4 ( Fig . 3B ) show a 62% decrease at 10 µM and a 92% decrease at 25 µM concentrations , while dexverapamil ( Fig . 3C ) produces a ∼65% decrease at 1–2 µM . Exposure of parasites to tariquidar at concentrations ≥12 . 5 µM results in no eggs being deposited whatsoever ( Fig . 3D ) , and in apparent worm lethality ( absence of movement or response to stimuli ) after 72 h exposure ( data not shown ) . The MRP1 inhibitor MK 571 also disrupts egg production , with no eggs deposited following exposure to 50 µM MK 571 ( Fig . 3E ) . Disruption of egg production also occurs when females cultured alone are exposed to Pgp inhibitors ( Fig . 3F ) . Drug-treated females could not be rescued by addition of untreated males; egg production was still inhibited ( data not shown ) , indicating that the process being targeted is likely autonomous to the female worms or the eggs themselves . Drug treatment , exemplified by dexverapamil , appears to affect the morphology of female reproductive organs ( Fig . S1 ) . Those eggs that were deposited by worms treated with MDR inhibitors such as tariquidar were often morphologically abnormal ( Fig . 4 ) , appearing malformed , necrotic , and sometimes disintegrated or in the process of fragmentation . However , other than C-4 , the Pgp inhibitors do not appear to be acting on the eggs themselves . Thus , eggs isolated from infected mouse livers and subsequently exposed to CSA , dexverapamil , or tariquidar hatch normally ( data not shown ) . Eggs exposed to C-4 do not appear to hatch . To test whether MDR inhibitors would also disrupt egg production by parasites within the murine host , we administered three intraperitoneal doses ( 100 µl volume each ) of either CSA ( 60 mg/kg ) , C-4 ( 50 mg/kg ) , dexverapamil ( 60 mg/kg ) , or tariquidar ( 15 mg/kg ) to S . mansoni-infected mice at 5–6 weeks post-infection . Livers of infected mice treated with any of the four Pgp inhibitors showed significantly reduced egg burden compared to the vehicle-injected control ( Fig . 5A ) . Egg burden was reduced approximately 80% following administration of C-4 , 65% following administration of dexverapamil , 55% following administration of tariquidar , and 50% following administration of CSA . These changes were reflected in significant reductions in the number of liver granulomas found in drug-treated and control mice , except in the case of CSA , which showed no difference from control ( Fig . 5B ) . The largest reduction in granuloma number per cm2 ( 45% ) was found for dexverapamil . We also observed a significant reduction in granuloma size when infected mice were treated with any of the four Pgp inhibitors ( Table 1 ) . To determine whether the effects of these drugs on parasite egg production persist outside of the host , we perfused adult worms from C-4- , dexverapamil- , and CSA-treated mice and measured subsequent egg production during culture ex vivo . These cultured adult worms do not show a significant decrease in egg production , except for those parasites perfused from mice that had been treated with CSA ( Fig . 5C ) .
In this report , we used genetic and pharmacological approaches to disrupt normal MDR transporter function in S . mansoni . Strikingly , both approaches produced quite similar phenotypes . Knockdown in adult schistosomes of SMDR2 , SmMRP1 , or both resulted in a marked reduction in parasite egg production ex vivo , as did exposure of adult worms to the different MDR inhibitors . Notably , schistosomes residing within the murine host were also apparently susceptible to disruption of MDR function . S . mansoni-infected mice treated with any of four different Pgp inhibitors , including the potent third-generation inhibitor tariquidar , showed significant reductions in parasite egg burden in their livers . These results point to an essential role for ABC-type MDR transporters in schistosome reproduction . Previous studies by us and others have investigated the involvement of these transporters in PZQ action and susceptibility . For example , we showed that PZQ interacts directly with the S . mansoni Pgp orthologue SMDR2 , acting to both inhibit substrate transport , and as a likely substrate itself [43] . Furthermore , both SMDR2 and SmMRP1 are upregulated in response to PZQ and higher expression of these transporters is associated with reduced PZQ susceptibility [21] , [42] . Here , however , we show that SMDR2 and SmMRP1 additionally appear to play important roles in schistosome reproductive physiology . Though the MDR inhibitors we used in these experiments are structurally diverse and have wholly different molecular targets and modes of action , one characteristic they share is that they all inhibit mammalian Pgp or MRP1 . CSA has previously been shown to have schistosomicidal activity at higher concentrations , most potently during the early course of infection [56] . This activity appears to be independent of the drug's immunosuppressive properties [56] , [57] , and the precise mode of the drug's antischistosomal action remains largely undefined [58] . CSA has also been shown to “sterilize” worms when administered every day over an eight-day period to S . mansoni-infected mice ( days 28–35 post-infection ) , essentially eliminating liver egg burden [59] , a result comparable to ours . CSA also enhances the pulmonary granuloma response in egg injection assays [60] , which appears to be consistent with the lack of reduction we observe in the number of liver granulomas in CSA-treated infected mice ( Fig . 5B ) . Interestingly , CSA was the only drug treatment in infected mice that appeared to have significant lasting effects on schistosome egg production after parasites had been removed from the CSA-treated host ( Fig . 5C ) , perhaps indicating a long-lived or irreversible effect on reproductive physiology . A second drug we used , dexverapamil , is an enantiomer of verapamil that is far less active against L-type Cav channels than the active enantiomer , but which retains potent inhibitory activity against mammalian Pgp . It too significantly disrupted egg production . Interestingly , a racemic mixture of verapamil was previously claimed in a patent to reduce egg production in S . mansoni [46] , and we have confirmed that finding . The reduction in egg production following exposure of worms to dexverapamil , along with our results showing that verapamil is a potent inhibitor of SMDR2 [43] , point to inhibition of S . mansoni Pgp , and not disruption of Cav channel function , as a likely mode of action . C-4 is a derivative of curcumin that reverses the MDR phenotype and that reversibly inhibits mammalian Pgp transport of rhodamine [50] . Interestingly , curcumin , which also reverses MDR [61]–[63] , has been shown to have antischistosomal activity at high ( 50–100 µM ) concentrations and to reduce parasite egg production ex vivo at lower ( 5–10 µM ) concentrations [64] . Tariquidar is one of the third-generation Pgp inhibitors developed specifically for high potency and selectivity against Pgp , and it completely eliminates S . mansoni egg production ex vivo at concentrations ≥12 . 5 µM . Finally , MK 571 , an MRP1 inhibitor , also disrupts egg production . Pairing of male and female worms is required for normal development and maturation of female schistosomes ( reviewed in [65] ) . Thus , it is possible that the MDR inhibitors primarily affect male worms , and indirectly affect egg production in females . However , all of the Pgp inhibitors we tested decrease egg production in female worms cultured in the absence of males ( Fig . 3F ) , and treated females are not “rescued” by addition of untreated males to the culture . Thus , inhibition of egg production does not appear to be due to effects of the drugs on male worms , and pairing is not required for those effects to appear . All four of the Pgp inhibitors we tested ex vivo reduce liver egg burden in S . mansoni-infected mice . Use of other drug concentrations or routes of administration may enhance this effect and reduce pathology more dramatically . However , the fact that both genetic ( RNAi ) and pharmacological interference with normal MDR transporter function in schistosomes affects egg production suggests a common mode of action underlying this outcome , and that proper functioning of the parasite reproductive system may be dependent on MDR transporter activity . Furthermore , since excretion of eggs is essential for parasite transmission , and since host responses to egg deposition represent the major source of pathology in chronic schistosomiasis , disruption of egg production by interference with MDR transporter function could signal a vulnerability for exploitation in development of new antischistosomal therapeutics that exploit a multifaceted approach to reduce morbidity and the spread of the disease [66] . Furthermore , since higher levels of schistosome MDR transporters are associated with reduced PZQ susceptibility [21] , [42] , it will be interesting to determine whether knockdown or inhibition of these transporters potentiates the antischistosomal activity of PZQ .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the U . S . National Institutes of Health . Animal handling and experimental procedures were undertaken in compliance with the University of Pennsylvania's Institutional Animal Care and Use Committee ( IACUC ) guidelines ( Animal Welfare Assurance Number: A3079-01; IACUC protocol number 802105 ) . C-4 [ ( E ) -4-Chloro-N- ( 3- ( 3- ( 4-hydroxy-3-methoxyphenyl ) acryloyl ) phenyl ) benzamide] was from EMD Biosciences and cyclosporin A was from Enzo Life Sciences . R ( + ) -verapamil HCl ( dexverapamil ) and MK 571 were from Sigma-Aldrich . Tariquidar was from MedKoo Biosciences . The mouse monoclonal antibodies against Pgp ( C219 ) and MRP1 ( ab3371 ) were from Abcam . The anti-rabbit tubulin antibody was from Santa Cruz Biotechnology ( H-235 ) . Suppliers of molecular biology reagents are designated within the text . Female Swiss Webster mice infected with S . mansoni ( NMRI strain ) obtained from the NIAID Schistosomiasis Resource Center at the Biomedical Research Institute in Rockville , MD were perfused 6–7 weeks post-infection , as described [67] . Perfused worms were maintained in RPMI ( Invitrogen ) plus 10% FBS ( Sigma ) , 1% penicillin/streptomycin , and 0 . 012% Timentin ( Plantmedia ) at 37°C and 5% CO2 . Knockdown of RNAs encoding SMDR2 ( NCBI Acc . # L26287 ) or SmMRP1 ( NCBI Acc . #GU967672 ) was as described [68] , [69] . Briefly , following an overnight incubation in RPMI , adult worms ( 5 males plus 5 females ) were placed in a 0 . 4 cm electroporation cuvette ( USA Scientific Plastics ) containing 50 µl siPORT ( Ambion ) and 3 µg SMDR2 siRNA ( IDT ) , SmMRP1 siRNA ( IDT ) , or luciferase siRNA ( Ambion ) . For electroporation , a 20 ms square wave pulse of 125 volts was applied . The siRNAs were designed using IDT SciTools RNAi Design and the target sequences used in the studies were: SmMRP1 siRNA , 5′- GACCAATCAGCTAACCATAAATTTGTT- 3′ , which maps to bp 3834–3860 of the SmMRP1 coding region RNA; and SMDR2 siRNA , 5′-TCGATCAAACCAACCAATCTCCTGTTT- 3′ , which maps to bp 2332-2358 of the SMDR2 coding region RNA . The luciferase siRNA used for our control shows no significant similarity to any sequences from the S . mansoni gene database . Following electroporation , worms were incubated en masse in RPMI medium for 2 days . They were then sorted into 2–3 males/female pairs per well in a 12-well plate , in which they were maintained for an additional 48 to 72h , and subsequently removed from the medium , quick-frozen in liquid nitrogen , and stored at −80°C until further use . The number of eggs deposited in each well by these worms over this 48 to 72 h period was counted ( see below ) . Total RNA was extracted as described [42] , using either RNAqueous-4-PCR ( Ambion ) or NucleoSpin RNA XS ( Macherey-Nagel ) , and subsequently treated with Turbo-DNAase ( Ambion ) or rDNAase ( Macherey-Nagel ) according to the manufacturer's instructions . For protein extractions , worms were homogenized in cell disruption buffer ( Ambion Paris Kit ) with a cocktail of protease inhibitors ( Sigma ) at 4°C and incubated for 15 min on ice . Lysates were centrifuged at 13 , 000 rpm for 10 min at 4°C and the supernatant collected was used immediately or stored at −20°C . Total protein concentrations were measured using a Bradford assay ( Fermentas ) with BSA ( Sigma ) as a standard . Real-time RT-PCR was used to measure RNAi knockdown . It was performed using the Brilliant II SYBR green qRT-PCR Master kit ( Stratagene ) on an Applied Biosystems 3500 according to the manufacturer's recommendations . Primers used for the amplification of SMDR2 , SmMRP1 and 18S ribosomal RNA have been described previously [21] , [42] . Data were analyzed using the 2−ΔΔCt method [70] to determine the relative expression ratio between target ( SmMRP1 , SMDR2 ) and reference genes ( 18S RNA ) . Knockdown was also measured at the protein level by immunoblotting . Protein samples ( 25 µg ) were electrophoresed on Bis-Tris gels in MOPS running buffer ( Invitrogen ) , blotted , and probed with anti-Pgp , anti-MRP , or anti-β-tubulin antibodies , as described [21] , [42] . Drugs were dissolved in dimethyl sulfoxide or ethanol for stock solutions , which were subsequently diluted 1:1 into Cremophor-EL ( Sigma ) , and finally to an appropriate concentration in culture media . For in vitro treatments , 2 or 3 adult worm pairs were incubated in our standard media with different concentrations of drug ( or carrier for controls ) for two days . For treatments of S . mansoni-infected mice , drugs dissolved in 1:1 DMSO/Cremophore-EL ( or carrier alone for controls ) were diluted to 100 or 200 µl in PBS and administered intraperitoneally to mice beginning at 5–6 weeks post infection with approximately 200 cercariae . Each infected mouse was treated once per day on three alternate days with R ( + ) -verapamil ( dexverapamil ) HCl ( 60 mg/kg ) , C-4 ( 50 mg/kg ) , tariquidar ( 15 mg/kg ) or cyclosporin A ( 60 mg/kg ) . Worms subjected to different treatments were placed in individual wells of a multiwell plate , with 2–4 worm pairs ( male + female ) per well , and maintained in our standard worm culture medium at 37°C and 5% CO2 . As reported by others [45] , [71] , adult worms perfused from mice will produce eggs while cultured ex vivo . At various times ( typically 2d or 5d ) , we counted the cumulative number of eggs produced from treated and control worms , using a dissecting microscope . The number of eggs per control females typically ranged from 20 to 100 , and varied within that range between different batches of perfused worms . For that reason , for all experiments except those in Fig . 3F , worm counts within each experiment were normalized to the mean value for the control worms for that experiment . The state of pairing of males and females was dynamic over the course of the incubation; paired worms would often separate , and these separated worms would often become paired again , In addition to obtaining egg counts , abnormal morphology of eggs was also noted and photographed . Adult schistosomes were fixed , stained with hydrochloric carmine ( Sigma ) , and examined on a Leica SP5 two-photon confocal microscope , as described [72] , [73] . Approximately 24 h following drug treatment , and while simultaneously collecting adult schistosomes , livers from drug-treated and control mice were isolated and weighed . A 0 . 25–0 . 5 g portion from the equivalent lobe of liver from different treatment conditions was dissected and incubated in 4% KOH for 16 to 24 h at 37°C as described [67] . The suspensions were examined for S . mansoni eggs , which were sampled and counted under a dissecting microscope multiple times for each mouse , and the number of eggs per gram of liver calculated . In some experiments , egg numbers were also corrected for the number of females perfused from each mouse , though that value did not vary significantly between the different treatments . In order to correct for variation between experiments , average egg counts per gram of liver within each experiment were normalized , with the mean control value set as 1 . Remaining liver tissue was formalin-fixed , paraffin-embedded , and stained with haematoxylin and eosin . Granulomas within a set area of the sections were counted and the number of granulomas per cm2 calculated . To calculate granuloma area , the diameter of those granulomas surrounding a single egg from each section were measured using QCapture Pro software . Granulomas were assumed to be a spherical shape [74] and sizes calculated from the different fields of the histopathological sections . Data are expressed as mean ± SEM , and were tested for statistical significance using either ANOVA or unpaired t-tests , as noted in the figure legends . | Schistosomes are parasitic flatworms that are the causative agents of schistosomiasis , a major tropical disease . As adults , schistosomes reside within the host vasculature , taking up nutrients , evading host defenses , and expelling wastes and toxins . Multidrug resistance transporters are involved in removal of toxins and foreign compounds , including drugs , from cells . These transporters have broad selectivity , and when upregulated or mutated , can confer resistance to a wide spectrum of drugs against mammalian tumor cells . They are also associated with drug resistance in various parasites , including helminths . In this report , we have used knockdown of expression of these proteins and pharmacological inhibition of their transport function to dissect their physiological role in the schistosome life cycle . We find that either reducing transporter expression or pharmacologically inhibiting transporter function leads to disruption of egg production by adult worms . Eggs deposited within the host are the major cause of disease pathology , and eggs excreted by the host are the means of continuation of the life cycle and transmission of the disease . The capability to interfere with schistosome egg production could have major implications for development of new treatment strategies . | [
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] | 2011 | Genetic Knockdown and Pharmacological Inhibition of Parasite Multidrug Resistance Transporters Disrupts Egg Production in Schistosoma mansoni |
Caloric/dietary restriction ( CR/DR ) can promote longevity and protect against age-associated disease across species . The molecular mechanisms coordinating food intake with health-promoting metabolism are thus of significant medical interest . We report that conserved Caenorhabditis elegans microRNA-80 ( mir-80 ) is a major regulator of the DR state . mir-80 deletion confers system-wide healthy aging , including maintained cardiac-like and skeletal muscle-like function at advanced age , reduced accumulation of lipofuscin , and extended lifespan , coincident with induction of physiological features of DR . mir-80 expression is generally high under ad lib feeding and low under food limitation , with most striking food-sensitive expression changes in posterior intestine . The acetyltransferase transcription co-factor cbp-1 and interacting transcription factors daf-16/FOXO and heat shock factor-1 hsf-1 are essential for mir-80 ( Δ ) benefits . Candidate miR-80 target sequences within the cbp-1 transcript may confer food-dependent regulation . Under food limitation , lowered miR-80 levels directly or indirectly increase CBP-1 protein levels to engage metabolic loops that promote DR .
The promotion of healthy aging is a goal of modern medicine , and simple interventions that protect against age-associated decline and disease are the dream of many in the general population . Genetics , environment , and stochastic factors all make substantial and complex contributions to healthspan . Single gene mutations that affect conserved pathways in model organisms can extend life and slow age-associated decline [1] , [2] . Environmental factors such as diet can also have a profound effect on the quality of aging . For example , dietary restriction ( DR ) , limitation of calorie intake with maintained vitamin and mineral support , can extend lifespan and protect against diseases of age across many species [3] . Elaboration of molecular mechanisms that control DR in simple animal models may thus inform on strategies to activate health-promoting metabolism to help address clinical challenges associated with aging . In the nematode Caenorhabditis elegans , food limitation that results in lifespan extension can be introduced via several protocols [4] , [5] , [6] , [7] , [8] , although the specific genetic requirements for longevity benefits of different DR regimens are not fully overlapping . For example , the transcription factor DAF-16/FOXO is dispensable for longevity induced in the feeding-impaired eat-2 mutant , whereas with a DR protocol in which bacterial food is diluted on plates , DAF-16/FOXO is essential for lifespan extension [4] , [9] . Such observations most likely reflect highly complex regulatory loops that control the precise metabolic state . microRNAs ( miRNAs ) can be metabolic regulators [10] . miRNAs are small , ∼22 nt non-coding RNAs that can bind to transcripts via partial sequence complementarity to down-regulate translation of those target mRNAs . Many miRNAs are conserved over their lengths or in the critical 5′ seed region , defining families across species [11] , [12] , [13] . Although the co-evolution of miRNAs and their targets is a complex process [14] , some miRNA/target pairings have been molecularly and functionally conserved . For example , discovery of LET-7 miRNA regulation of target RAS in C . elegans [15] inspired anti-oncogenic therapies for mammalian lung tumors [16] . We took advantage of the powerful reagents for miRNA study in C . elegans [17] , [18] and our previous characterization of a DR fluorimetric signature of endogenous gut fluorescence in these transparent nematodes ( derived from lipofuscin+advanced glycation end products [19] ) to identify miRNAs that might regulate DR . Here we report bantam-homolog miR-80 as a food-regulated miRNA that normally represses DR when food is abundant . Transcription factors DAF-16 , HSF-1 , and CBP-1 are required for mir-80 ( Δ ) benefits . Of these , the cbp-1 transcript includes sequences that might be directly targeted by miR-80 to coordinate this circuit . Our data suggest an approach to metabolic activation of DR even under ad lib feeding that could inspire strategies for treating obesity , limiting age-associated disease , and promoting healthy aging .
Our previous studies revealed that age pigment levels ( lipofuscin+advanced glycation end products ) inversely correlate with locomotory healthspan—low age pigment levels late in life are typical of animals that age gracefully and maintain strong locomotory vigor , whereas high age pigment levels are typical of same-chronological age animals that age poorly and appear decrepit [19] . Thus , to identify C . elegans miRNAs that might impact healthy aging , we screened available C . elegans mir deletion strains [20] for differences in autofluorescent age pigment levels in old animals . We found that mir-80 ( nDf53 ) [hereafter referred to as mir-80 ( Δ ) ] , exhibits low age pigment fluorescence levels late in life compared to wild type ( WT ) animals ( Fig . 1A , ∼58% lower , p<0 . 0005 ) . The low age pigment phenotype of mir-80 ( Δ ) is rescued by a transgene array harboring a mir-80 ( + ) gene , confirming that the low age pigment phenotype is conferred by mir-80 deletion itself . Thus , late in adult life ( ∼2/3 through the WT lifespan ) , mir-80 ( Δ ) mutants exhibit low age pigment accumulation typical of healthy aging animals . To test if mir-80 ( Δ ) mutants exhibited additional healthspan phenotypes , we next measured two indicators of maintained muscle integrity and function late into adult life–pharyngeal pumping rates and swimming vigor . Pharyngeal pumping is the mechanism by which food is pulled into the gut using specialized cardiac-like muscles . Pharyngeal pumping rates decline markedly with age , such that after the first week of life , feeding capacity is greatly diminished [21] , a functional decline that tracks with physical changes in muscle integrity [22] , [23] , [24] . We find that pumping rates are significantly higher in mir-80 ( Δ ) late in life ( day 11 ) as compared to WT ( 44% increase ) , a phenotype reversed by a mir-80 ( + ) transgene ( Fig . 1B , right graph; p<0 . 005 ) . Importantly , 5 day old WT and mir-80 ( Δ ) ( i . e . , young adult; Fig . 1B , left graph ) have similar pumping rates . Thus , mir-80 ( Δ ) mutants are not simply hyper-activated for pumping , but rather maintain pumping function better late into life . We conclude that mir-80 ( Δ ) exerts a positive effect on the quality of cardiac-like muscle aging . As occurs with human skeletal muscle sarcopenia ( the debilitating progressive loss of muscle mass and strength that accompanies aging across species ) , C . elegans body wall muscle deteriorates with age , featuring sarcomere loss [22] , [24] . Physical decline is correlated with loss of locomotion vigor . We compared late-age swimming ( body bend frequency ) in WT and mir-80 ( Δ ) to show that mir-80 ( Δ ) mutants are significantly more vigorous swimmers in late adulthood ( Fig . 1C right panel; 69% increase at day 11 , p<0 . 0001 ) . Early in adult life WT and mir-80 ( Δ ) swim similarly ( Fig . 1C , left panel ) . We conclude that mir-80 ( Δ ) delays locomotory aging without altering young adult swimming behavior itself . Given that mir-80 ( Δ ) mutants exhibit several features of extended healthspan , we examined the longevity phenotype . We find that mir-80 ( Δ ) mutants exhibit both mean and maximum healthspan extension , subject to mir-80 ( + ) transgene rescue ( Fig . 1D , p<0 . 0001 , individual lifespan data in Fig . S1; average age increase at 75% mortality over all lifespan studies in this paper ( 13 ) was 24 . 1%+/−4 . 7% ) . Thus , deletion of mir-80 confers longevity . In summary , mir-80 ( Δ ) confers multiple features of extended adult healthspan late in life: lowered intestinal age pigment accumulation , maintained pharyngeal pumping capacity , increased swimming vigor , and lifespan extension . Because mir-80 ( Δ ) does not exhibit notable defects in development ( [20] , and our observations ) , it appears that mir-80 has a predominant and focused impact on aging of the adult . To identify genes required for mir-80 ( Δ ) -regulated DR , we used RNAi to knockdown genes previously implicated in DR lifespan benefits , hypothesizing that genes required for mir-80 ( Δ ) DR should be needed for the Exmax shift and low age pigment levels typical of multiple DR states . Of the 18 genes we screened , we found that RNAi knockdown of transcription factors daf-16/FOXO , heat shock transcription factor hsf-1 , and CREB binding protein homolog cbp-1 modulated both the Exmax shift and low age pigment levels of mir-80 ( Δ ) ( Tables S1 , S2 , Figs . 4A , B; 5A , B; 6A , B ) . Interestingly , of the three transcription factors required for mir-80 ( Δ ) healthspan , cbp-1 is the only one for which the transcript is predicted to include potential miR-80 miRNA target sequences ( Fig . 6D ) . One candidate miR-80 binding site is present in the cbp-1 5′UTR , and another is present within exon 8 . To test whether direct CBP-1 regulation might be a mechanism by which miR-80 controls metabolic state , we constructed translational reporters in which the cbp-1 promoter drives expression of a GFP that includes either no candidate miR-80 binding sites ( NBS ) or both the 5′UTR and the exon 8 candidate binding sites ( 5+8BS ) ( Fig . S7A ) . We compared GFP expression levels of these constructs in ad lib fed animals +/− mir-80 , with a focus on the posterior gut region in which mir-80 regulation is most dramatic . We find that the NBS construct is not regulated by miR-80 ( Δ ) ( Fig . S7B , left panel ) ; whereas the 5+8BS construct is expressed at a higher level in the absence of mir-80 ( Fig . S7B , right panel ) . Although rigorous testing in native context will be required to validate cpb-1 as a direct miR-80 target , our data suggest binding sites in the cbp-1 transcript may contribute to cbp-1 inhibition by miR-80 when levels are high in food . If mir-80 represses cbp-1 translation , then we would expect higher levels of CBP-1 protein in mir-80 ( Δ ) animals . We measured CBP-1 protein levels using anti-CBP antibodies against human CREBBP for WT and mir-80 ( Δ ) mutants ( day 7 ) . We find that CBP-1 protein levels are significantly increased in mir-80 ( Δ ) mutants compared to WT ( p<0 . 05 , Fig . 6E ) . Thus , in whole animal context , mir-80 ( Δ ) is associated with increased CBP-1 protein . Our data are consistent with a model in which in the presence of food , cbp-1 is translationally repressed by binding of miR-80 to target sites within the cbp-1 transcript ( Fig . 7 ) . When food is lacking , miR-80 levels drop , translational repression of cbp-1 is relieved , and CBP-1+DAF-16+HSF-1-mediated transcriptional changes induce DR within the animal . Interestingly , the human CREBBP transcript might be targeted by miR-80 family members or another miRNA homologous to the exon 8 site ( Fig . S7C ) , suggesting miRNAs could exert a conserved role in DR metabolic regulation that might be harnessed in the future to promote healthy metabolism with anti-aging applications .
mir-80 is an abundant , widely-expressed miRNA , and thus might be involved in global regulation of metabolism coordinated across tissues . Indeed , multiple mir-80 reporters indicate broad cellular expression and regulation by E . coli food availability . However , not all tissues/cells reflect similar magnitudes of regulation , with the largest fold food-induced change in expression in posterior intestine ( Fig . 3D ) . The dramatic gut regulation raises the possibility that intestinal cells , well-positioned to monitor nutrient uptake , might play the most critical role in metabolic sensing and control . We speculate that miR-80 level changes in intestinal cells might initiate body-wide signaling via gut secretion of insulins and other hormones , analogous to human gastrointestinal tract and adipose tissue hormonal signaling to hypothalamus [39] . Because mir-80 ( Δ ) induces skn-1::GFP expression in the ASI neurons ( previously suggested to be similar to hypothalamic neurons [7] ) but mir-80 is not expressed in ASI neurons ( Fig . S4A , B ) , relief of miR-80 repression under food limitation could act upstream of ASI skn-1 induction via a gut-to-neuron signaling relationship . The requirement for daf-16 , hsf-1 , and cbp-1 in mir-80 ( Δ ) DR is interesting in multiple regards . First , DAF-16/FOXO and HSF-1 can each individually bind to CBP-1 in nematodes and mammals ( C . elegans DAF-16 and CBP-1; mammalian FOXO3A and CBP [40]; mammalian HSF-1 and CBP1 [41] ) , underscoring their capacity to co-regulate transcription . Second , previous work identified C . elegans daf-16 and hsf-1 as required for the CBP-1-dependent bDR lifespan extension [37] . In the bDR study , cbp-1 ( RNAi ) blocked expression of DAF-16 and HSF target genes sod-1 and sip-1 , respectively , rather than blocking transcriptional induction of daf-16 and hsf-1 that accompanies bDR . These data suggest that the CBP-1 cofactor couples and modifies transcriptional outputs of DAF-16- and HSF-1-dependent longevity pathways under bDR conditions , a model likely to apply for mir-80 ( Δ ) -induced DR . Although our study focused on DR genes that have most dramatic impact on the age pigment DR signature , we emphasize that our data support that additional genes contribute in a complex network to regulate age pigment phenotypes in mir-80 ( Δ ) . For example , knockdown of either AMPK subunit encoded by the C . elegans genome , aak-1 or aak-2 , can alter age pigment levels ( Tables S1 and S2 ) , but not Exmax shift , suggesting separate regulation of lipofuscin content and levels . We thus anticipate that our data just touches the surface of a large interrelated network of metabolic genes and processes that are regulated by miR-80 . We fully expect that miR-80 regulates dietary restriction by binding to multiple target transcripts . An interesting candidate target , however , is the cbp-1 gene itself , which we have shown to be critical for mir-80 ( Δ ) -induced DR benefits . The potential cbp-1 target sites for miR-80 binding are unusual , being situated in the 5′ UTR and within a highly conserved exon . Interestingly , the 5′ UTR sequences in cbp-1 are perfectly conserved in C . brenneri , C . briggsae , and C . remanei ( though not in C . japonica ) and the exon 8 site is somewhat conserved among all ( Fig . S7D ) . Exon targeting by miRNAs is common in plants [42] and has been demonstrated for mammalian transcription factors Nanog , Oct4 , and Sox2 [43] , [44] , fly DICER [45] , and is now predicted in many additional genes after algorithm refinements that consider coding sequences [45] , [46] . Ideally , we could test direct miR-80 targeting in vivo by manipulation of a cbp-1 transgene , +/− candidate miR-80 binding sites . Technical challenges , including the long length of the cbp-1 gene/cDNA , as well as an apparent exquisite sensitivity of CBP-1 activity levels for health and viability [47] , [48] , precluded direct study . Our studies of expression of a GFP transgene flanked by the 5′ UTR and the exon 8 sites from cbp-1 supported that miR-80 can down-regulate artificial construct expression in posterior gut . Although not definitive proof of direct targeting , these data , together with our findings that CBP-1 protein levels are elevated in DR ( Fig . 6E; DR induction of CBP-1 also reported in [32] , [37] ) and miR-80 levels drop in DR ( Fig . 3 , Fig . S3 , S5 ) are consistent with a model in which miR-80 mediates DR regulation by directly effecting CBP-1 levels ( Fig . 7 ) . Even if miR-80 effects are indirect , it is clear that cbp-1 is critical for mir-80 ( Δ ) -induced age pigment and lifespan changes . Given that cbp-1 plays a role in dietary restriction associated with growth in axenic medium , growth on diluted bacteria , and eat-2 feeding impairment [37] and intersects with the insulin pathway for lifespan extension [37] , and that we have noted mir-80 expression regulation under bacterial dilution and dietary deprivation , and a partial engagement of the insulin signaling pathway in mir-80 ( Δ ) -induced longevity ( Fig . 4D ) , the miR-80/CBP-1 regulatory loop may constitute a core mechanism by which diverse and intersecting metabolic pathways are coordinately regulated to respond to nutrient availability .
A detailed list of strains is included as Table S3 . We grew C . elegans under standard conditions [55] at 20°C unless otherwise indicated . The food sources we used were E . coli strain OP50-1 or HT115 for RNAi feeding experiments ( Caenorhabditis Genetics Center , University of Minnesota , Twin Cities , MN , USA ) . To generate synchronized cultures , we bleached gravid adults and starved L1 progeny . The wild-type strain was var . Bristol N2 [55] . The mir-80 ( nDf53 ) allele breakpoints are 5′- tgctttcgatgtctatactctc -3′ and 5′-tctggcgaacgaaatgagt-3′ , encompassing part of the promoter region , the entire precursor sequence and ∼300 bp downstream . We genotyped mir-80 ( Δ ) by PCR using primer pairs mir80Out-F ( 5′- ttcgtcgccatcaacacacg-3′ ) +mir80Out-R ( 5′- gagcgcggatagatatacagtcag-3′ ) that flank the deletion and mir80Flank-F ( 5′- caacaacgatgtgaatgctcgtc-3′ ) +mir80Flank-R ( 5′- ctcgcacacggacggactgcc-3′ ) that bind internal to nDf53 . We worked with a 6× outcrossed line . The mir-80 deletion mutant does not exhibit gross developmental phenotypes ( [20]; our observations ) . Developmental timing , L1 nuclei numbers , early adult locomotion , pumping rates , defecation rates , amphid neuron dye filling , and dauer entry/exit behaviors are within wild type ranges in mir-80 ( Δ ) , supporting that mir-80 does not contribute an essential role in development and basic function . Thus , mir-80 deletion primarily impacts adult maintenance and DR phenotypes . For the Pmir-80L:mCherry transcriptional reporter , we amplified the mir-80 promoter using primers 5′-cgagatgagaagtaagaagagtgg-3′ and 5′-tccgtgtgcgagagagtgagcgag-3′ and cloned into the Pmec4::mCherry plasmid vector at the start codon of mCherry from [56] using the In-fusion cloning kit ( Clontech Inc ) . The resulting plasmid was injected at 50 ng/ul into wild type animals along with a rol-6 co-injection marker ( 100 ng/ul ) to generate extrachromosomal transgenic lines ZB3039-ZB3043 . We grew age-synchronized animals ( see above ) under standard conditions ( 20°C , OP50-1 ) and scanned animals ( n≥50 per strain ) for age pigment accumulation ( Day 4 , Day 9 , Day 11 ) using a Fluorolog 3 spectroflorimeter as in Gerstbrien et al . [19] . All graphs represent mean data from at least 3 independent trials . For Exmax determination at Day 4 , we used Datamax software ( Horiba Scientific ) to identify the peak excitation value . The peak for tryptophan fluorescence was also analyzed to normalize scores , as TRP levels do not change markedly with age . We synchronized strains by alkaline bleaching [57] and placed synchronized L1 larvae ( Day 1 ) on NGM plates seeded with OP50-1 bacteria . On Day 4 or day 7 , we moved approximately half the animals to plates containing OP50-1 with 50 uM FUdR . We used the other half for total RNA extraction using TRIZOL as described below . ∼1 . 5 ug of total RNA was used for cDNA synthesis using the Invitrogen SuperScript III cDNA synthesis kit and OligoDT primers to synthesize cDNA from all poly-adenylated RNA . We used 100 ng of cDNA to measure gene expression levels using the standard curve approach . Standard curves were generated from wild type cDNA by utilizing multiple dilutions of cDNA ( 1000 , 100 , 10 , 1 , 0 . 1 , 0 . 01 ng ) and probing for expression levels of the house-keeping gene , actin ( act-1 ) . Primers used were act1RT-F ( 5′- ttactctttcaccaccaccgctga-3′ ) and act1RT-R ( 5′- tcgtttccgacggtgatgacttgt -3′ ) for act-1 , ama1RT-F ( 5′- cctacgatgtatcgaggcaaa-3′ ) and ama1RT-F ( 5′- cctccctccggtgtaataatg-3′ ) for ama-1 , hsf1RT-F ( 5′-tagtaatggcagagatgcgtgcga-3′ ) and hsf1RT-R ( 5′- tggctgcatgacagagacgagaaa-3′ ) for hsf-1 and hsp16 . 2RT-F ( 5′- atggaacgccaatttgctccagtc-3′ ) and hsp16 . 2RT-R ( 5′- tccttggattgatagcgtacgacc-3′ ) for hsp-16 . 2 . We plotted Ct values obtained from amplification for target DR genes against this standard curve to determine transcript levels . We synchronized strains ( refer Fig . S7A , B , and Table S3 ) by alkaline bleaching [57] and placed synchronized L1 larvae ( Day 1 ) on NGM plates seeded with OP50-1 bacteria . On Day 4 , 100 mCherry ( + ) animals were picked and GFP fluorescence was measured in the spectrofluorimeter at 488 nm excitation and 511 nm emission . We measured fluorescence using ImageJ with a region-of-interest ( ROI ) that included the entire length of the body ( using the line tool ) and then plotted a histogram of the mean intensity along the length of the line . We synchronized strains by alkaline bleaching [57] and placed synchronized L1 larvae ( Day 1 ) on NGM plates seeded with OP50-1 bacteria . On Day 7 , 250 animals were placed in 50 ul of RIPA buffer ( 20 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 1 mM Na2EDTA , 1 mM EGTA , 1% NP-40 , 1% Sodium deoxycholate , 2 . 5 mM beta-glycerophosphate , 1 mM Na3VO4 ) +Protein sample buffer and heated at 95°C for 15 mins . 25 ul of samples was loaded onto a MiniProtean TGX gradient gel ( 4–20% , Bio-Rad ) and transferred onto PVDF membrane following separation . Membrane was blocked using 5% non-fat milk in PBST buffer for 1 hour . Membrane was then incubated with CBP-1 and TUB-1 antibodies ( Santa Cruz Biotechnology ) at 1∶500 and 1∶4000 dilutions respectively in 2% non-fat milk overnight at 4°C . Protein bands were detected using the ECL reagent ( Invitrogen ) using horseradish peroxidase conjugated secondary antibodies ( Jackson ImmunoResearch Labs ) at 1∶10 , 000 dilutions . Band intensities were calculated using ImageJ [58] . | Dietary restriction , limitation of calorie intake with maintained vitamin and mineral support , can extend lifespan and protect against diseases of age across many species . Elaboration of molecular mechanisms that control dietary restriction in simple animal models may therefore inform on strategies to activate health-promoting metabolism to help address clinical challenges associated with aging and age-associated disease . We characterize a single Caenorhabditis elegans microRNA gene that keeps dietary restriction programs off when food is abundant . A mir-80 deletion exhibits beneficial features of dietary restriction regardless of food availability , including extended maintenance of mobility and cardiac-like muscle function later into life as well as lifespan extension . We identify three key longevity genes that are required for these benefits . We hypothesize that miR-80 is a core regulator by which diverse and intersecting metabolic pathways are coordinately regulated to respond to nutrient availability . | [
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In a pioneering cross-sectional study among Bolivian immigrants in the city of São Paulo , Brazil , the epidemiological profile , clinical manifestations and morbidity of Chagas disease were described . The feasibility of the management of Chagas disease at primary healthcare clinics using a biomedical and psychosocial interdisciplinary approach was also tested . Previously , a Trypanosoma cruzi ( T . cruzi ) infection rate of 4 . 4% among 633 immigrants was reported . The samples were screened using two commercial enzyme-linked immunoassay ( ELISA ) tests generated with epimastigote antigens , and those with discrepant or seropositive results were analyzed by confirmatory tests: indirect immunofluorescence ( IFI ) , TESA-blot and a commercial recombinant ELISA . PCR and blood cultures were performed in seropositive patients . The majority of the 28 seropositive patients were women , of whom 88 . 89% were of child-bearing age . The predominant clinical forms of Chagas disease were the indeterminate and atypical cardiac forms . Less than 50% received the recommended antiparasitic treatment of benznidazole . An interdisciplinary team was centered on primary healthcare physicians who applied guidelines for the management of patients . Infectologists , cardiologists , pediatricians and other specialists acted as reference professionals . Confirmatory serology and molecular biology tests , as well as echocardiography , Holter and other tests , were performed for the assessment of affected organs in secondary healthcare centers . The published high performance of two commercial ELISA tests was not confirmed . An interdisciplinary approach including antiparasitic treatment is feasible at the primary healthcare level for the management of Chagas disease in Bolivian immigrants . The itinerant feature of immigration was associated with a lack of adherence to antiparasitic treatment and was considered a main challenge for the clinical management of this population . This approach is recommended for management of the infected population in endemic and nonendemic areas , although different strategies are needed depending on the severity of the disease and the structure of the healthcare system .
Chagas disease , which is caused by the protozoan flagellate parasite T . cruzi [1] , affects approximately 6 million Latin American inhabitants of Mexico , Central and South America . It causes approximately 12 , 500 deaths annually , and 41 , 200 new cases are estimated each year [1–3] . Migration from disease-endemic areas to nonendemic areas within countries or between countries and continents , especially in the 20th century , has led to the urbanization and globalization of Chagas disease . More than 15 million people from disease-endemic areas now live in nonendemic areas [4] . Approximately 2 . 9% ( 0 . 7–4 . 9% ) [4 , 5 , 6] of immigrants were determined to be infected by T . cruzi in 15 European countries , excluding Spain [4] . Maternofetal transmission was estimated to occur in 0 to 3 of 4 , 000 newborns in nine European countries in 2009 [6] . The prevalence of infection in pregnant women varied from 4 . 7–17 . 7% and was higher in Bolivians [7] . In the USA , estimations have indicated that more than 300 , 000 individuals are infected [4] . In addition , approximately 3 , 600 infected individuals in Japan [8] and >3 , 000 in Australia [4] have been estimated . As shown in Table 1 , infection in immigrants varied from 0 . 62 to 10 . 3% [9–27] according to the age , to the center ( blood banks , primary healthcare clinics , antenatal , maternity or specialized clinics ) , and the rate of infection was higher in Bolivian immigrants , ranging from 10 . 2 to 34 . 1% [9–25] . To date , the majority of centers have reported a mild chronic Chagas disease ( indeterminate form or mild cardiac forms ) in approximately 2/3 of patients and less commonly the digestive or cardiodigestive form , as shown in Table 2 [10–14 , 16–21 , 28] . However , centers in Spain and the USA have also registered chronic Chagas disease patients with severe cardiopathy [21 , 26] . In the 20th century , the endemic area in Bolivia encompassed approximately 80% of the country , and the prevalence of infection was estimated to be 28 . 8% in 1988 [2 , 3] . This value varies from 4 . 9% to 51 . 0% among the general population according to the different districts [29] , and from 17 . 3 to 70 . 5% among pregnant women , influencing the rates of maternofetal transmission [9] . Since 1990 , intergovernmental initiatives of Latin American countries in South and Central America for the control and elimination of Triatoma infestans ( T . infestans ) and for the interruption of blood bank transmission have resulted in important changes in other countries of South and Central America . In Bolivia , the prevalence of infection in the general population [30] and in blood donors has decreased [31] similarly to the prevalence of infection in young children [32] . Vectorial transmission was interrupted in some districts ( Departments of La Paz and Oruro ) . A recent survey addressing congenital infection in Bolivia showed a prevalence of congenital infection ranging from 2–4% , in contrast to a previous value of 5% [33] . Estimates from PAHO-2006 [2] and WHO-2015 [1] indicated T . cruzi infection prevalences of 6 . 8% and 6 . 1% , respectively , in the general Bolivian population and 8 . 0% and 2 . 3% , respectively , in blood donors . In parallel with Chagas disease nonendemic areas outside Latin America , the risk of T . cruzi transmission in the blood and its derivatives in nonendemic urban areas of Brazil is now very low: 0 . 21% of blood donors were infected [2] , and vector-transmitted infection was detected in only 0 . 01% of children under 5 years of age from 11/2001 to 05/2008 [34] . Transmission by T . infestans has been under control in São Paulo State since the 70s [35 , 36 , 37] and in other endemic areas of this country since 2006 [2 , 38] . Taking into consideration the asymptomatic or oligosymptomatic condition of the majority of T . cruzi-infected patients and the lack of knowledge about Chagas disease , healthcare workers were not aware of Chagas disease , and the epidemiological background of the patient was not often investigated . Furthermore , serological tests were not performed for the early diagnosis of acute maternofetal transmission , infected blood donation , infected donors or recipients in organ transplantation or even Chagas disease reactivation in immunosuppressed hosts [5 , 39] . The influx of Bolivian immigrants into Brazil started more than sixty years ago , initially through a cultural exchange program [40] . The Bolivian immigrant profile has changed since 1980 and more dramatically in recent years; it is characterized by people who work in sewing workshops ( textile manufacturing ) in the city of São Paulo under poor labor conditions ( lack of contract security and labor rights , long working hours and low wages ) [40 , 41] . According to estimates published in the media , more than 300 , 000 documented and undocumented Bolivian immigrants live in the São Paulo Metropolitan Area , mostly in the city of São Paulo . Only one prospective study on the prevalence of infection in Bolivian immigrants has been conducted in Brazil [27] , and no studies were published on the access of the Bolivian population to local health systems for Chagas disease care . The arrival of a large number of Bolivian immigrants in São Paulo State introduced new scenarios regarding the epidemiology of Chagas disease and new questions related to disease control in urban centers where transmission interruption had already been consolidated . The present study is part of a larger research project being conducted in São Paulo City that also describes the prevalence of infection in this population ( 27 ) , their access to primary healthcare and to the reference centers for Chagas disease in the city of São Paulo and knowledge about Chagas disease of the target population . We described a benign profile of chronic Chagas disease in Bolivian immigrants and noticed the itinerant immigration as a main characteristic of this population . We were unable to confirm the high performance of two commercial ELISA tests , one employed as a screening test and the other as a confirmatory test . Moreover , we reported the feasibility of this interdisciplinary approach centered on primary healthcare , the Brazilian Family Health program for management and antiparasitic treatment . After reviewing the data on the prevalence of T . cruzi infection and morbidity in Bolivian immigrants in nonendemic areas , the aims of the present study were to describe the performance of screening and confirmatory serological tests and the clinical and epidemiological profiles of infected patients among Bolivian immigrants in the city of São Paulo . Additionally , we aimed to assess the feasibility of the management of Chagas disease at the primary healthcare level ( Family Health Program of the Brazilian Ministry of Health ) using a biomedical and psychosocial interdisciplinary approach and to test guidelines specifically prepared for the management of chronic Chagas disease at the primary healthcare level .
The study was approved by the Ethics Committees of the “Hospital das Clínicas da Faculdade de Medicina” of the University of São Paulo and the School of Medical Sciences of “Santa Casa de Misericórdia” of São Paulo . All patients or the legal guardians of those under 18 years old signed an informed consent form to participate in the research . The community health agents and the clerical healthcare team worked actively to recruit seropositive patients from March 2014 to September 2015 and also to deliver the results of all patients serological tests for Chagas disease from February 2014 to October 2014 . The training of the health personnel team composed of physicians , nurses , laboratory workers and community agents was performed in five different phases: 1 ) initial meeting for training on ecoepidemiology , parasitology , pathology , diagnosis , treatment , follow-up , prevention and control of Chagas disease; 2 ) training of primary healthcare physicians on the management of Chagas disease by infectologists; 3 ) continued dialogue and discussion about the feasibility of the proposed approach as well as the reasons for the lack of adherence of patients to medical care and antiparasitic treatment; 4 ) continued supervision of the antiparasitic treatment and evolution of Chagas disease patients; 5 ) consolidation of the role of primary healthcare physicians to disseminate training for the management of Chagas disease in the same unit . They were able to prepare new physicians for the management of non-Bolivian patients who were infected with T . cruzi . A pediatrician and a cardiologist who were accessible five days a week also provided support to the primary care team . Referral to a gastroenterologist and a specialized cardiology center to monitor digestive or cardiac function was possible during the project . Routine laboratory tests and tests for specific assessments of affected organs were also available during the follow-up period . Serological exams to confirm T . cruzi infection and tests to monitor parasitemia were performed in the Laboratory of Immunology and Laboratory of Parasitology of Hospital das Clínicas da Faculdade de Medicina , University of São Paulo , Brazil . The interdisciplinary team was composed of primary healthcare physicians , infectologists , cardiologists , psychologists and epidemiologists from the Instituto of Tropical Medicine of São Paulo of the University of São Paulo and the Center for Epidemiologic Surveillance of São Paulo; researchers of health laws , public health and socio-anthropology; and biologists of zoonosis control from the “Superintendência de Controle de Endemias” in São Paulo State . Infected patients who were screened during the seroprevalence survey were referred to the Family Health Program physician . They were evaluated based on their clinical background and physical examination to search for signs of myocardiopathy , esophageal emptying disorders , megaesophagus or megacolon . Subsequently , they were subjected to a conventional electrocardiogram and thoracic and esophageal X ray . A double contrast barium enema was recommended in the case of constipation that lasted longer than one week . Patients were further classified as proposed by the Brazilian consensus [45] with: a ) indeterminate form—without signs or symptoms and a normal ECG and thoracic and esophageal X rays; b ) cardiac form—abnormalities found in Chagas disease patients [46]: right bundle-branch block , left anterior fascicular block , ST-T wave changes , electric inactive areas , abnormal Q waves or low QRS voltage , complex ventricular arrhythmias ( polymorphic ventricular arrhythmias , couplet , non-sustained or sustained ventricular tachycardia ) , second degree or third degree ( complete ) atrioventricular block , junctional rhythm , atrioventricular dissociation and atrial fibrillation . An echocardiogram and 24-hour Holter were recommended and performed at a secondary healthcare level if clinical symptoms/signs or ECG abnormalities were detected; c ) and digestive form if image data confirmed the presence of esophageal-emptying disturbances or megaesophagus or megacolon . Volunteers with negative serological results were invited to participate in a meeting with the interdisciplinary team to be informed about the results of the screening tests and about Chagas disease . The blood culture assay was performed as previously described by Luz et al . , 1994 [50] . Six culture tubes were examined after 30 , 60 and 90 days of culture . The results were expressed as positive if at least one tube was positive and negative if all were negative . To perform the statistical analysis , version 20 . 2 of the SPSS® software was used . Descriptive statistics and 95% confidence intervals for proportions were calculated , whenever appropriate . The main outcomes included the following: a ) sensitivity of serological screening tests and comparison among perfomances of serological confirmatory tests in seropositive or discrepant results; b ) distribution of infected patients according to clinical forms; c ) distribution of infected patients according to the departments where they lived in Bolivia; d ) proportion of treated patients and reason for non-adherence; e ) frequency of adverse events related to the treatment; f ) feasibility of this interdisciplinary approach at the primary healthcare level in the National Public Health System of Brazil for the management of Chagas disease; and g ) usefulness of guidelines specifically prepared for this approach .
To analyze the performance of different serologies , data from twenty eight seropositive samples analyzed by two screenings and three confirmatory tests are presented in Table 3 . The recombinant ELISA test using trypo and epimastigote antigens with a confirmatory value revealed a lower sensitivity ( 96 . 4% ) than the other confirmatory tests: the TESA-blot and IF . Sera from eighteen patients presented discordant results by the screening tests ELISA 1 and 2 , and seven of them were from children <2 years old . All the serological tests showed a similar frequency of negative results , excluding ELISA 2 , which presented 18 seropositive results and IF , which revealed one seropositive result without confirmation by at least two confirmatory tests ( Table 3 ) . Thus , 605 samples were considered negative ( 587 samples were negative by ELISA 1 and 2 , and 18 samples had discrepant results but negative results in at least two confirmatory tests ) . Qualitative PCR did not reveal T . cruzi kDNA over the detection limit in any of the 18 serum samples . A second sample available from seven volunteers with discrepant results revealed concordant serum-negative results with the two screening ELISA 1 and 2 tests and the three confirmatory tests and in whole blood by PCR . Blood cultures were positive in 23 . 04% ( 95% CI: 0 . 2–46 . 0 ) of patients with seropositive results; qualitative/quantitative PCR was positive in 30 . 8% ( 95% CI: 5 . 7–55 . 9 ) , and either blood culture or PCR was positive in 46 . 2% ( 95% CI: 19 . 1–73 . 3 ) of them . Women of child-bearing ( 10–49 years of age ) age represented 88 . 89% of the total number of seropositive women . The main Bolivian Departments where the patients were born are shown in Table 4 . From seropositive patients , 21 . 4% had mothers who were seropositive to T . cruzi antigens , but most of them lived in endemic areas . Triplets of 4 . 5 years old from a seropositive mother lived in the Department of Santa Cruz for 3 . 5 years . Thus , maternofetal transmission could be possible , but vector-transmitted infection could not be excluded . Among the 18 patients who attended the first medical consultation at the primary healthcare level , 72 . 2% were women , 77 . 7% had an electrocardiogram , 66 . 7% had a thoracic X-ray and 72 . 2% had an esophageal X-ray with contrast . Only one patient was referred to a secondary healthcare level to perform an echocardiogram . The data showed that 71 . 42% had an indeterminate form , and 28 . 57% had electrocardiographic abnormalities that are not commonly reported in Chagas disease ( disturbances in ventricular repolarization and sinus arrhythmia ) . No involvement of the digestive system was observed . Only 44 . 4% of the patients who attended the first medical consultation received antiparasitic treatment with benznidazole 5 mg/kg/day up to 300 mg/day for 60 days ( N = 8 ) . Women of child-bearing age represented 87 . 2% of treated patients . Follow-up period after the treatment varied from 2–15 months . The 9th patient moved to another area of the city and did not return after 15 days of treatment . Concerning adverse events , dysgeusia was referred by 37 . 5% and gastrointestinal disturbances by 12 . 5% . One patient whose therapy was interrupted on day 56 due to ageusia fully recovered her healthy status 14 days later . Serology remained positive in two treated patients one year after the treatment , and one of two patients who were PCR-positive in the pretreatment period became negative . Four of the five male patients and five of the 13 women did not receive benznidazole . The main reasons for medical appointment non-attendance or antiparasitic treatment refusal are shown in Table 5 . As shown in Table 5 , itinerant immigration from Bolivia-São Paulo-Bolivia was identified as the main reason for the lack of adherence to medical follow-up and antiparasitic treatment .
A large number of Bolivian immigrants arrive in the city of São Paulo , mainly because of the availability of temporary work , causing changes in the epidemiological profile of Chagas disease in a nonendemic area within an endemic country . A prevalence of T . cruzi infection of 4 . 4% has been found in this Bolivian population [27] , thus introducing new public health challenges regarding the control of the disease through blood derivatives , organ transplants or maternofetal transmission . The risk of chronic disease reactivation in immunosuppressed patients must also be considered in this new context . Considering the young age of the Bolivian immigrants and the high proportion of child-bearing age women , serological tests to diagnose this infection in women of child-bearing age and/or who are pregnant should be implemented . In our study , both screening tests with epimastigote antigens , selected by their high performance for the diagnosis of Chagas disease , have shown high sensitivity ( 100% ) in comparison to other screening tests employed in Europe [53 , 54] . However , ELISA 2 provided false-positive results at a rate of approximately 3% . The TESA-blot had the best performance in terms of sensitivity ( 100% ) and specificity . It is also able to distinguish between antibodies against T . cruzi and against Leishmania sp . [48] . Nevertheless , this test is expensive and might not be as accessible as other commercial tests ( TESA-blot production was recently interrupted by Bio-Mérieux ) . IF had the same sensitivity but provided one false-positive result in relationship to the other tests . ELISA 3 was also employed as a confirmatory test in the search for an accessible commercial test with high sensitivity and specificity due to the inclusion of both trypomastigote and epimastigote recombinant antigens . However , this ELISA test showed a low sensitivity ( 96 . 4% ) and is not recommended as a screening test . In our study , the best screening test was ELISA 1 , and the performance of ELISA 2 and 3 was lower than expected [48] . The rate of PCR positivity ( 30 . 8% ) observed herein was lower than that previously described ( 41 . 6% ) in a similar small sample of Bolivian infected patients [55] . This finding could be related to the presence of the TcV molecular type , for which the RT PCR primers used in this work were less sensitive . This TcV type was commonly reported in the districts of Bolivia in which the patients lived [56 , 57] . Moreover , PCR inhibition was excluded through the use of positive controls . Regarding the child-bearing age women ( 10–49 years ) analyzed in the present study , the described prevalence of 6 . 1% [27] was higher than that described by Munhoz et al . [13] but lower than those registered in Europe ( Tables 1 and 2 ) [10 , 11 , 12 , 14 , 17 , 23 , 24] . Moreover , in recent and previous publications , a higher prevalence was registered for general Bolivian immigrants in European countries [5 , 12 , Table 1] compared with São Paulo [27] . The patients evaluated in the present study migrated recently from Bolivia ( 0–5 years ) [27] , and most of them were young adults that lived in the urban area of La Paz . Thus , they might be less exposed to infection because the prevalence of T . cruzi infection in that area was lower than in other Bolivian Departments . Another explanation for the lower prevalence found in our study could be related to the screening centers . In fact , our work was prospectively performed in a primary healthcare center for the general population , which was perhaps more representative of the general immigrant population than antenatal clinics , maternity hospitals or Tropical Medicine centers . As reflected in Table 2 , both in our work and in the majority of reports in the literature , most of the patients presented a benign form of the disease in the chronic phase ( indeterminate form ) . Mild electrocardiographic atypical disturbances were reported in 28 . 57% of cases , none of which had typical or severe cardiopathy . Cardiac involvement was reported in 9 . 0–19 . 8% of some centers outside the endemic area [14 , 18 , 19 , 21] , sometimes with severe cardiopathy [21] , as shown in Table 2 . However , the electrocardiographic disturbances were not commonly described for comparison to those found in the present analysis . Regarding the management of chronic Chagas disease , access to diagnosis and treatment facilities for Chagas disease/infection is proposed to occur at different healthcare levels . In our work , diagnosis using screening tests , detection of affected organs , definition of chronic clinical forms and antiparasitic treatment for mild cases took place at the first primary healthcare center . Support of some specialties ( infectologists , cardiologists and pediatricians ) as well as the entire interdisciplinary team was accessible at this level , as previously recommended [58] . For the management of chronic cardiac and non-cardiac forms , primary healthcare physicians were trained regarding the correct interpretation of electrocardiographic abnormalities attributed to Chagas disease or to other cardiopathies . Mild chronic Chagas disease in young Bolivian immigrants , predominantly child-bearing age women , was easily managed in primary healthcare centers by a multidisciplinary team . This experience focusing on primary healthcare physicians of the Brazilian Family Health Program had been highly successful . Similar experiences in other contexts have been reported by “Médecins Sans Frontières ( MSF ) /Doctors Without Borders” in Latin American countries [59] . At the secondary healthcare level , we performed parasitological , molecular and confirmatory serological tests for positive and discrepant samples and more complex complementary exams for functional organ evaluations . Reference centers for severe adverse effects of antiparasitic treatment and specialized support for severe forms of the disease were also available . However , no patients were referred due to the absence of these effects or to such severe clinical manifestations . At the third healthcare level , further interventions for more severe cardiopathy or complicated digestive forms ( pacemakers , transplants or untreatable cardiac heart failure; cardiac arrhythmia; or surgery for megaesophagus or megacolon ) were planned but were not registered . In summary , we validated the feasibility of the management of chronic Chagas disease by primary healthcare physicians of the Family Health Program of the National Health System . We suggest that this Program could be responsible for the management of non-severe chronic Chagas disease and its antiparasitic treatment . All necessary support for more severe cases could be provided by specialized centers and reference laboratories . One important limitation affecting our study was the lack of patient adherence to the treatment , which had the effect of further reducing the sample size . The main associated factor was the itinerant immigration via the Bolivia-São Paulo State-Bolivia route , which remains one of the greatest challenges for future approaches . We hope that a direct link established with the “Programa Nacional de Chagas , Unidad de Epidemiologia , Ministerio del Salud , Bolivia” could implement mutual cooperative actions for the management and antiparasitic treatment of these immigrants . Moreover , as fear of job loss is one cause of lack of adherence , we suggest a more flexible schedule for medical appointments . A similar effect has been observed due to the absence of legal documents , which has been the focus of our project on access to healthcare . Our recommendation was to capacitate primary health services to orient the immigrant regarding the documentary regularization for their permanence in the country . The sustainability of the project was promoted through the training of the health personnel to act as multipliers . Guidelines are now accessible “on- and off-line” [46] and shown to be useful to healthcare physicians for Chagas disease management at the primary care level . The mobile app is also accessible to help physicians through continuous education in epidemiological and clinical aspects , diagnosis and antiparasitic treatment of Chagas disease . Considering the emergence of new epidemiological scenarios introduced through the movement of the immigrant-infected population and the results of the present study in terms of the management of chronic Chagas disease , new challenges in the organization of Brazilian health services for primary and specialized healthcare are: a ) to train primary healthcare physicians in the management of chronic Chagas disease for approximately one million infected Brazilian people as well as for the infected immigrant population; b ) to clearly indicate specialized reference centers for Chagas disease care; and c ) to achieve the approval of specific clinical and therapeutic protocols by the Health Ministry to implement diagnostic and antiparasitic treatment strategies throughout the country . Finally , the training of health professionals and undergraduate students through the implementation of education in the nuclear curriculum must be continuously stimulated . | Chagas disease affects approximately 6 million Latin American people . It is considered a neglected tropical disease since it mainly affects vulnerable , poverty-stricken people . Public health policies and investments in research on new treatment and control instruments have not been prioritized . In fact , disease urbanization occurred in Latin America in the 80s , and an estimated 15 million people moved from disease-endemic areas to nonendemic areas . Estimations have indicated that 2 . 9% of immigrants were infected by T . cruzi in 15 European countries , and more than 300 , 000 infected immigrants resided in the USA . In São Paulo , the estimated number of Bolivian immigrants exceeds 300 , 000 . This study revealed the presence of mild clinical manifestations in predominantly young infected individuals , including reproductive-age women . For the first time , the feasibility of managing chronic Chagas disease at primary healthcare level centers with a biomedical and psychosocial interdisciplinary approach has been reported in the National Public Health System of Brazil . Additionally , the utilized commercial tests did not confirm the previously expected performance for diagnosis of infection . Finally , the itinerant characteristics of the Bolivian immigrant population was reported as a main factor underlying the lack of adherence to antiparasitic treatment and a main challenge for complete clinical management . | [
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"disease... | 2017 | Interdisciplinary approach at the primary healthcare level for Bolivian immigrants with Chagas disease in the city of São Paulo |
Plasmodium salivary sporozoites are the infectious form of the malaria parasite and are dormant inside salivary glands of Anopheles mosquitoes . During dormancy , protein translation is inhibited by the kinase UIS1 that phosphorylates serine 59 in the eukaryotic initiation factor 2α ( eIF2α ) . De-phosphorylation of eIF2α-P is required for the transformation of sporozoites into the liver stage . In mammalian cells , the de-phosphorylation of eIF2α-P is mediated by the protein phosphatase 1 ( PP1 ) . Using a series of genetically knockout parasites we showed that in malaria sporozoites , contrary to mammalian cells , the eIF2α-P phosphatase is a member of the PP2C/PPM phosphatase family termed UIS2 . We found that eIF2α was highly phosphorylated in uis2 conditional knockout sporozoites . These mutant sporozoites maintained the crescent shape after delivery into mammalian host and lost their infectivity . Both uis1 and uis2 were highly transcribed in the salivary gland sporozoites but uis2 expression was inhibited by the Pumilio protein Puf2 . The repression of uis2 expression was alleviated when sporozoites developed into liver stage . While most eukaryotic phosphatases interact transiently with their substrates , UIS2 stably bound to phosphorylated eIF2α , raising the possibility that high-throughput searches may identify chemicals that disrupt this interaction and prevent malaria infection .
Malaria is a mosquito-borne infectious disease of humans and other animals caused by parasitic protozoans of the genus Plasmodium . In 2013 , there were 198 million cases of malaria and 584 , 000 fatalities ( WHO world malaria report 2014 ) , underscoring its role as a major pathogen . Sporozoites are the infectious and quiescent forms of the malaria parasite residing in the salivary glands of Anopheles mosquitoes . Malaria transmission begins with the injection of salivary sporozoites ( Ssp ) into the skin of a vertebrate host by infected mosquitoes . The parasites enter the blood circulation and rapidly invade hepatocytes where the crescent-shaped sporozoite progressively transforms into a spherical liver stage ( or exo-erythrocytic stage , EEF ) . Many genes required for the Ssp transformation into liver stages are transcribed in the Ssp [1–3] . However , translation is repressed by phosphorylation of eIF2α by eIK2 kinase , also named UIS1 ( UIS , Upregulated in Infective Sporozoites ) [4 , 5] . If the eIK2 kinase uis1 is knocked out , the Ssp transcripts are translated prematurely while sporozoites are still inside the salivary glands of mosquitoes [4] . During the normal parasite cycle , liver-stage transcripts are only translated when Ssp enter hepatocytes and eIF2α-P is de-phosphorylated [6] . Thus , Ssp quiescence is regulated by phosphorylation and de-phosphorylation of eIF2α . Parasites rapidly multiply inside hepatocytes and generate thousands of merozoites that enter the blood and infect host erythrocytes where they grow , multiply , and transform into schizonts that contain additional infective merozoites . Following entry of merozoites into erythrocytes , a phosphatase must de-phosphorylate eIF2α-P to permit the completion of the parasite’s cycle [7] . Treatment of Ssp with salubrinal , a specific inhibitor of eIF2α phosphatase [8] , markedly increases eIF2α phosphorylation in the parasite and inhibits their transformation into liver stages [4 , 6] . The central role of eIF2α-P phosphatases in the Plasmodium life cycle is highlighted by the observation that the parasites are not viable if they bear the phosphomimetic mutation Ser59Asp in eIF2α that cannot be de-phosphorylated [7] . Nevertheless , no eIF2α-P phosphatase has been identified in Ssp . In mammalian cells , the de-phosphorylation of eIF2α-P is mediated by the PP1 phosphatase whose activity requires the co-factor GADD34 ( Growth Arrest and DNA Damage-Inducible Protein ) or its homologue CReP [9] . The substrate specificity of PP1 and its localization are regulated by association with these co-factors [10] . Nevertheless , GADD34/CReP is absent in Plasmodium and the molecular mechanism of eIF2α-P de-phosphorylation in the parasite is still unknown . Here we show that the knockout of pp1 in P . berghei sporozoites did not affect Ssp quiescence or the levels of eIF2α phosphorylation . These findings excluded a central role of PP1 in the transformation of Ssp into liver stages . We provide evidence that the eIF2α-P phosphatase in Plasmodium is a unique serine/threonine phosphatase belonging to the PP2C/PPM family and termed UIS2 [11] . We also show that expression of this phosphatase is regulated at the protein level to support proper parasite development .
Plasmodium pp1 transcription takes place during the erythrocytic cycle ( Fig 1A ) , which occurs predominantly in gametocytes . To test whether PP1 phosphorylates eIF-2α , we attempted to knock out pp1 in P . berghei erythrocytic schizonts ( S1 Fig and S1 Table ) but two attempts failed , supporting previous findings demonstrating that this gene is essential for blood stage development [12 , 13] . In fact , we showed that high levels of pp1 mRNA was present in the erythrocytic cycle ( Fig 1A ) . We obtained instead a P . berghei pp1 conditional knockout ( cKO ) using the FlpL/FRT site-specific recombination system ( S2 Fig ) [14] . In these Pbpp1 cKO parasites , pp1 locus was intact in the blood stages ( S2 and S3 Figs ) and pp1 genomic locus was disrupted only when the parasites were developing into sporozoites in mosquito ( S3B Fig ) [14] . The pp1 mRNA level was significantly decreased in these mutants ( Fig 1B ) and PP1 protein was not detected in Pbpp1 cKO Ssp ( Fig 1C ) . The pp1 cKO parasites completed the life cycle in Anopheles mosquitoes and produced similar numbers of midgut and salivary gland sporozoites as the wild type ( wt ) ( S4 Fig ) . This is consistent with the low level of Pbpp1 mRNA in the mosquito vector ( Fig 1A ) . The mutant sporozoites then developed into liver stages and produced hepatic merozoites in vitro as shown by IFA ( Fig 1D ) . PP1 was disrupted in 96 . 3% ( SD +/- 4 . 6% ) of the hepatic schizonts ( S5 Fig ) . Quantitative PCR analysis showed that the liver stage development of Pbpp1 cKO Ssp in HepG2 cells and in mice was indistinguishable from the wild type TRAP/FlpL ( - ) Ssp ( S6 Fig ) . However , the pp1 cKO merozoites exiting the hepatocytes did not infect the mouse blood ( S7 Fig ) . Thus , these findings corroborate the essential role of pp1 in the parasite´s erythrocytic cycle . In addition , the levels of phosphorylation of eIF2α in wt and pp1 cKO sporozoites were indistinguishable by immunoblot ( Fig 1E ) . In sum , PP1 is not the enzyme that de-phosphorylates eIF2α-P , which is required for liver stage transformation of Ssp . Plasmodium sporozoites up-regulate a unique subset of genes in the mosquito salivary glands collectively termed UIS [5 , 17] . UIS1 ( eIK2 ) is an eIF2α kinase that controls the latency of Ssp [4] and UIS2 is the only phosphatase among the 30 uis genes [5] . Thus , we reasoned that UIS2 could be a candidate to de-phosphorylate eIF2α-P when Ssp transform into liver stages . To test this possibility , we first performed pull-down assays to determine whether UIS2 interacts with eIF2α-P using extracts from P . berghei blood stage parasites . To inhibit endogenous phosphatases in the extracts , we added either salubrinal [Sal , a selective inhibitor of eIF2α phosphatase [4 , 8]] , or guanabenz acetate [GA , a selective inhibitor of PP1 [18]] . The lysates were then incubated with immobilized GST-PfeIF2α . The beads were extensively washed and the bound parasite proteins were detected by immunoblot analysis . We found that endogenous UIS2 was pulled-down by GST-eIF2α from the lysates containing Sal but not from those lysates containing GA ( Fig 2A top 2 panels and S8 Fig ) . The pull down of UIS2 was associated with the phosphorylation of GST-PfeIF2α ( Fig 2A bottom 2 panels ) , indicating that UIS2 only bound phosphorylated PfeIF2α . Further evidence for the specificity of UIS2/eIF2α-P interaction was obtained by generating mutants of PfeIF2α in which the regulatory ser59 was substituted either with alanine or with aspartic acid that mimics a phosphorylated serine . The PfeIF2αS59D bound UIS2 but PfeIF2αS59A did not interact with UIS2 ( Fig 2B ) . Plasmodium UIS2 contains a predicted metallo-phosphatase domain enclosed by large N and C-terminal domains ( Fig 2C ) . To determine which domain binds PfeIF2α-P , we expressed the recombinant domains of UIS2 in E . coli . We show in Fig 2D that only the UIS2 N-terminus stably bound to eIF2α-P but not to non-phosphorylated PfeIF2α ( S9 Fig ) . The association between UIS2 and eIF2α-P under physiological conditions was documented in extracts of P . berghei erythrocytic parasites by co-immunoprecipitation with anti-eIF2α-P or anti-UIS2 antibodies followed by immunoblot analysis ( Fig 2E ) . Next , we tested whether the UIS2 phophatase domain dephosphorylated eIF2α-P . Indeed , we found that the recombinant GST-PbUIS2PD was able to dephosphorylate eIF2α-P in vitro ( Fig 3A ) . The activity of the phosphatase was inhibited by EDTA and Cd2+ ( Fig 3B ) , which are inhibitors of the PP2C/PPM [19 , 20] , but was unaffected by okadaic acid ( Fig 3B ) , which inhibits PP1 ( IC50 = 15–20 nM ) and PP2A ( IC50 = 0 . 1 nM ) . The phosphatase displayed a strong preference for Mn2+ over Mg2+ ( Fig 3B ) . Thus , PbUIS2 activity is similar to that of the PP2C/PPM family of phosphatases [21] . We then investigated the expression pattern and levels of the phosphatase uis2 during the P . berghei life cycle ( Fig 4A ) . Transcripts were detected during the liver , erythrocytic and mosquito stages . Notably , uis2 mRNA level increased substantially when midgut sporozoites entered the salivary glands of Anopheles . This is consistent with the results of subtractive cDNA hybridization between P . berghei Ssp and midgut sporozoites [5] and with the comparative microarray analysis of P . yoelii midgut sporozoites and Ssp [17 , 22] . The presence of the UIS2 protein in the Ssp was then detected by immunoblot ( Fig 4B ) . The uis2 transcript is also present in the blood stages ( Fig 4A ) . The presence of UIS2 in the P . berghei blood stage parasites was detected by pull down and co-IP assays ( Fig 2 ) . To further investigate the uis2 function , we first tried to knock out the gene in P . berghei blood stages where uis2 mRNA levels are very low ( Fig 4A ) ; however , several attempts failed ( S10 Fig ) . This result indicated that uis2 is essential for the development of the Plasmodium erythrocytic cycle . Next , we utilized the yeast FlpL/FRT site-specific recombination system to generate the cKO of the uis2 gene ( S1 Text and S11A Fig ) [14] . In the uis2 cKO construct , the 3’UTR of TRAP together with DHFR flanked by 2 FRT sites were inserted after the stop codon of uis2 . We then demonstrated the correct integration of the uis2 cKO clone in the uis2 genome locus ( S11B Fig ) . The FlpL recombinase was under the control of the TRAP gene promoter that is only transcribed when the parasite reaches the mosquito midgut . Since there was no expression of FlpL recombinase in the blood stages , the uis2 cassette was not affected . When uis2 cKO reached the mosquito midgut , the uis2 locus was disrupted ( S11C Fig ) . The disruption of uis2 expression was confirmed by immunoblot with specific antibodies ( Fig 4B ) . The uis2 cKO parasite developed normally in the mosquito vector . The number of Ssp in uis2 cKO and wt parasites was very similar ( S12 Fig ) . Next , we compared the phosphorylation levels of eIF2α in Ssp obtained from uis2 cKO and from wt parasites by immunoblot using specific antibodies [7 , 15 , 16 , 23] . We found that phosphorylation of eIF2α was greatly enhanced in the absence of the uis2 phosphatase ( Fig 4B ) and the mutant sporozoites invaded HepG2 cells as effectively as wt ( Fig 4C ) . Nevertheless , the development of the uis2 cKO Ssp in HepG2 cells was profoundly inhibited two days post-infection: the mutants maintained the sporozoite shape while the wt parasites rounded up and developed into spherical liver stages ( Fig 4D ) . In addition , the number of exo-erythrocytic forms ( EEFs ) and the P . berghei 18S rRNA of the uis2 cKO parasites were profoundly decreased in the liver stages as compared to the wt parasites ( Fig 4E and 4F ) . When the uis2 cKO sporozoites were injected into mice either by mosquito bite or by intravenous injection , only 4 out of 11 mice were infected as detected by Giemsa staining of blood smears . In addition , the pre-patent day was delayed for 3 days in the 4 mice infected with the uis2 cKO sporozoites as compared to wt ( Table 1 ) . We then examined the genotype of the blood and liver stage parasites originated by uis2 cKO sporozoites . The parasites were uis2 ( + ) and expressed the UIS2 antigen as the wt ( S13 Fig ) , indicating that the uis2 locus had not been completely excised by the recombinase FlpL . The incomplete excision in the FlpL/FRT-mediated conditional mutagenesis system has been previously reported [14] . The overall conclusion of these experiments is that uis2 is essential for liver stage development . As mentioned above , when midgut sporozoites invade the salivary gland of mosquitoes uis1 and uis2 are up-regulated [5] . Yet , the uis1 kinase is dominant , inhibits protein translation , and maintains the salivary gland sporozoites in a latent state . The eIF2α-P is dephosphorylated when sporozoites are injected into the mammalian host and transform into liver stages [4 , 6] . We found that the uis2 mRNA level was decreased in the liver stages as compared to the sporozoite stage ( Fig 5A ) , which was consistent with the results of previous transcriptome analysis [24] . Nevertheless , uis2 protein level increased in the liver stages as compared to the sporozoite stages ( Fig 5B ) . These results suggest that uis2 translation may be repressed in the sporozoite stage and the translational repression may be alleviated in host liver stages . The up-regulation of UIS2 protein level is associated with the eIF2α-P dephosphorylation in host liver [4] . The 3’UTR of uis2 mRNA contains several putative NREs ( Nanos response elements ) . In Drosophila and C . elegans , Pumilio protein binds to NREs on hunchback mRNA to inhibit its translation [25] . In Plasmodium there are two Pumilio proteins ( Puf1 and Puf2 ) [26 , 27] . We show in Fig 5C that puf1 is predominantly transcribed in gametocytes and puf2 in sporozoites . We then used gel shift assays to document the binding of the Pumilio protein Puf2 to the uis2 mRNA ( Fig 5D ) . Since uis2 mRNA level was down-regulated in puf2 ( - ) sporozoties ( Fig 5A ) but the UIS2 protein level was up-regulated ( Fig 5B ) , these results suggest that Puf2 protein may inhibit uis2 translation in sporozoites . The mRNA and protein levels of UIS2 from the puf2 ( - ) sporozoites and wt liver stages were very similar . Taken together , our findings provide the mechanistic explanation to the previous reports showing that puf2 ( - ) sporozoites round up and start transforming into liver stages in the mosquito salivary glands [23 , 28 , 29] . The potential translational repression of uis2 in Ssp is consistent with the normal development of Pbuis2 cKO parasites in the mosquito salivary glands where uis2 is highly transcribed ( Figs 4A and S12 ) . As expected , the alleviation of uis2 repression during liver stages corroborates the defective development of Pbuis2 cKO parasites during liver stage where uis2 is significantly translated . Although both uis 1 and uis2 are highly transcribed in Ssp , only uis1 is dominant . Thus , it is the binding of the Pumilio protein Puf2 to the uis2 mRNAs that likely inhibits the phosphatase translation . Once translated , US2 binds to eIF2α-P but not to eIF2α . The results imply that small molecules that disrupt the essential UIS2-eIF2α-P interaction will likely interrupt establishment of parasites in hepatocytes and possibly reveal new leads to combat malaria infection .
We show here that UIS2 is the phosphatase that controls the development of the dormant Ssp into the liver stages . Its activity is enhanced by Mn2+ , inhibited by Cd2+ , but is not affected by okadaic acid , a powerful inhibitor of PP1 and PP2A phosphatases . Thus , UIS2 belongs to the PP2C/PPM family of phosphatases . The human genome encodes ~ 500 protein kinases , ~2/3 of which are serine/threonine kinases , and approximately 40 serine/threonine phosphatases [30 , 31] . In Plasmodium , there are ~ 80 protein kinases and ~30 protein phosphatases [13 , 32 , 33] . These disparate numbers raise the question of how few phosphatases recognize specifically the very large number of phosphorylated proteins . In the case of the PP1 mammalian phosphatase , enzyme specificity and localization are regulated by a large number of multiple co-factors . However , this is not the case for Plasmodium UIS2 that encompasses catalytic and regulatory domains within the same polypeptide chain [21] . The N-terminal domain of UIS2 interacts stably with the eIF2α-P substrate placing it in close proximity to the catalytic site . We showed that eIF2α-P interacted with endogenous UIS2 from lysates of blood stage parasites . We did not use lysates from sporozoites because of the repression of uis2 expression by Puf2 . The interaction of UIS2/ eIF2α-P is in sharp contrast to other phosphatases whose interaction with the substrate is unstable and is enhanced by co-factors . In the blood stage , the mRNA level of uis2 is very low but UIS2 is translated and is essential for the parasite’s blood stage development . However , the function of uis2 in the blood stage is unknown . In mammalian cells , PP1 acts in conjunction with the regulatory subunit GADD34 or CReP to de-phosphorylate eIF2α-P . These co-factors are absent in Plasmodium . In yeast , the N-terminal extension on eIF2α contains a PP1-binding motif ( KKVAF ) that enables eIF2α to target PP1 to dephosphorylate eIF2α-P [10 , 34] . PP1-binding motif is also absent in Plasmodium eIF2α . As shown here , the parasite utilizes instead the PP2C/PPM phosphatase UIS2 to regulate the phosphorylation level of eIF2α . Perhaps additional proteins have evolved to control eIF2α dephosphorylation in organisms that do not contain recognizable homologs of GADD34/CReP or PP1-binding motif ( KKVAF ) in eIF2α , such as in Plasmodium , S . pombe , and Aspergillus . Small molecules have been useful to distinguish enzyme activities in different organisms . For example , the small molecule GA directly binds to PP1 and selectively inhibits the stress-induced dephosphorylation of eIF2α-P in mammalian cells and in Toxoplasma [18 , 35] . Nevertheless , GA does not inhibit eIF2α-P de-phosphorylation in Plasmodium , supporting our conclusion that PP1 is not the eIF2α-P phosphatase in Plasmodium . Instead , Sal is a selective inhibitor of dephosphorylation of eIF2α-P in Plasmodium sporozoites [4] . Also shown here , levels of eIF2α-P increased substantially in the Sal treated erythrocytic stages of the parasite . However , in mammalian cells Sal inhibits GADD34/PP1 complex that is responsible for eIF2α dephosphorylation [8] , but the molecular mechanism is unknown [36] . There is no ortholog of GADD34 , and UIS2 is the eIF2α-P phosphatase . Therefore , the explanation of Sal effect in Plasmodium is unknown . Our findings indicate that Plasmodium has developed a different strategy to de-phosphorylate eIF2α . Thus , there is a distinct possibility that UIS2 inhibitors will have no side effects on human cells . We have previously reported that uis1 encoded the eIF2α kinase eIK2 [4] and here we show that uis2 encodes the eIF2α phosphatase . It is to be expected that uis2 ( - ) and uis1 ( - ) Ssp have contrasting phenotypes . The uis2 ( - ) Ssp maintained sporozoite shape 48 hours post-invasion into host hepatocytes ( Fig 4D ) . This is in contrast to the uis1 ( - ) Ssp , which prematurely transform into spherical liver stages in mosquito salivary glands [4 , 23 , 28 , 29] . UIS2 is highly transcribed and translationally repressed in Ssp , but the repression is alleviated when the dormant Ssp transform into liver stages ( Figs 4A , 5A and 5B ) . The translation of uis2 is tightly controlled by the Pumilio protein Puf2 . In Drosophila and C . elegans Pumilio proteins bind to specific nucleotide motifs at the 3´ UTRs sequences of hunchback mRNAs to inhibit their translation [25] . In Plasmodium there are two Pumilio proteins ( Puf1 and Puf2 ) [26] . Puf1 and Puf2 are transcribed predominantly in gametocytes and sporozoites , respectively [3 , 25 , 26] . The function of Plasmodium Puf1 is unknown and Puf2 is essential to maintain the infectivity of malaria Ssp [23 , 28 , 29] . Puf2 likely represses translation of uis2 mRNA in the mosquito Ssp . In puf2 ( - ) parasites , uis2 mRNA is translated prematurely and the parasites progressively transform into liver stages while they reside in the salivary glands [23 , 28 , 29] . When wt Ssp parasites are injected into the mammalian host , the translational repression of uis2 is probably alleviated , eIF2α-P is dephosphorylated [4] , and liver stage messages are decoded ( Fig 6 ) . In sum , the conditional knockouts of pp1 and uis2 in Ssp together with the biochemical evidence demonstrating that UIS2 binds to and de-phosphorylates eIF2α-P reveal that UIS2 , and not PP1 , is the eIF2α phosphatase in Plasmodium Ssp . These findings raise the possibility of using high-throughput screenings of small molecules to disrupt UIS2-eIF2α-P interaction , which is essential for the parasite’s survival , and perhaps lead to the discovery of new drugs to interrupt the parasite’s development in hepatocytes .
All animal work has been conducted according to Institutional Animal Care and Use Committee ( IACUC ) Laboratory Animal Protocol: 140102 . Pbuis2 ( PBANKA_132800 ) and Pbpp1 ( PBANKA_102830 ) mRNA levels were analyzed by real-time RT-PCR using cDNA prepared from blood , liver and mosquito-stage parasites of P . berghei . Axenic liver stages of the parasite were prepared by incubation of P . berghei salivary gland sporozoites at 37°C in DMEM plus 10% FBS for 6 hours as described previously [37 , 38] . Total RNAs were extracted using TRIzol reagent and treated with DNase . The absence of genomic DNA contamination was confirmed by PCR amplification on same-treated RNA samples that lacked reverse transcription . The specificity of amplification for each PCR product was confirmed by dissociation curve analysis . Real-time PCR was performed using iQ SYBR Green Supermix ( Bio-Rad Laboratories ) , according to the manufacturer’s instructions . The 1ml reaction mix contained 1X iQ SYBR Green Supermix , 300 nM forward/reverse primers , and cDNA reverse-transcribed from 2 μg RNAs . The temperature profile included 95°C for 10 min , 35 cycles of denaturation at 95°C for 15 sec , and annealing/extension at 60°C for 1 min . Transcript expression was normalized to the expression of the control gene , arginyl-tRNA synthetase ( PbArgRS , PB000094 . 03 . 0 ) . The normalized expression was calculated as following: relative amount of Pbuis2 cDNA / relative amount of PbArgRS cDNA . Gene-specific primers were PbArgRS ( sense 5’- ttggtgattggggaacac-3’ , antisense 5’- cttgatataaaagggtcaaac-3’ ) ; Pbuis2 ( sense 5’- actgaaaatgaacatgccttacta -3’ , antisense 5’- catatgggtgagcttcttcctt-3’ ) ; Pbpp1 ( sense 5’-cccgaaaaggaaataaatgg-3’ , antisense 5’-ttggagccgaaaataaagtaac-3’ ) . A fragment of P . berghei UIS2 ( from 413 to 618 amino acids ) fused at the C-terminal with GST was expressed in E . coli and was used to immunize mice . Western blot of P . berghei sporozoites lysates with the mouse antibody ( 1:1 , 000 dilution ) revealed a 160 kDa band ( endogenous PbUIS2 is 156 kDa ) . The identity of Puf2 proteins from P . berghei and P . yoelii is 95% . The Puf2 antiserum was raised in mice by immunization with the GST-fused P . yoelii Puf2 recombinant protein . The Puf2 antibody ( 1:1 , 000 dilution ) recognizes a band of about 60 kDa band in western blots of P . berghei or P . yoelii sporozoites lysates . The anti-total eIF2α and anti-eIF2a-P sera were generated in William J . Sullivan Jr . ’s lab [15 , 16] . The Anti-eIF2a-P sera ( 1:500 dilution ) specifically recognize Plasmodium eIF2a-P [4 , 7 , 23] . Anti-total eIF2α sera ( 1:1 , 000 dilution ) recognizes both phosphorylated and non-phosphorylated forms of eIF2α from Toxoplasma [15] and Plasmodium [4 , 7 , 23] . The eIF2α-P phosphatase activity was measured by two methods: ( 1 ) His-tagged PfeIF2α was incubated with GST-PfPK4 kinase domain and [γ-32P] ATP for 1 hour as described [7] and purified by Ni-NTA affinity chromatography . The [γ-32P] labeled PfeIF2α-P was then used as the phosphatase substrate as following: One μg GST or GSTPbUIS2PD was added into 100 μg [γ-32P] labeled PfeIF2α-P in the assay buffer ( 20mM Tris-HCl , pH7 . 0; 50 mM NaCl ) with 5 mM MnCl2 . After 1 hour incubation at 37°C , the total proteins were separated by SDS-PAGE and subjected to autoradiography and coomassie blue staining . ( 2 ) Malachite green colorimetric assay: GSTPbUIS2PD in assay buffer ( 50mM Tris-HCl , pH7 . 0 ) was pre-incubated for 30 min in the absence or presence of MgCl2 , MnCl2 , CdCl2 , or okadaic acid . PfeIF2α-P was added and incubated for an additional 30 min . Then , 2 volumes of malachite green reagent ( 0 . 15% malachite green , 1% ammonium molybdate and 12 . 5% concentrated HCl v/v ) were added before reading the absorbance at 630 nm . P . falciparum eIF2α coding sequence was codon-optimized and expressed with a GST fusion tag in E . coli [7] . The GST-PfeIF2a was immobilized and purified on glutathione affinity column . The lysates of P . berghei blood stage parasites were incubated with immobilized GST-PfeIF2α in the presence or absence of eIF2α phosphatase specific inhibitor Salubrinal ( Sal , 50 μM ) , or PP1 inhibitor Guanabenz acetate ( GA , 70 μM ) for 2 hours . After 3 times wash with high-salt NETN buffer ( 300 mM NaCl , 20 mM Tris-HCl , pH 8 . 0 , 0 . 5 mM EDTA . 0 . 5% ( v/v ) Nonidet P-40 ) , the GST-PfeIF2α and its binding proteins were eluted with 10 mM reduced glutathione/ 50 mM Tris-HCL pH 8 . 0 . The eluates were analyzed by immunoblots using antisera specific to PP1 , UIS2 , phosphorylated eIF2α , and total eIF2α . Ten thousand P . berghei sporozoites were added to confluent HepG2 . After 60 min incubation at 37°C , the HepG2 cells were fixed with 4% paraformaldehyde and blocked with 3% BSA in PBS . To detect the parasites outside the HepG2 cells , the slide was stained with anti-CSP 3D11 Ab [39] followed by Alexa Fluor 594 goat anti-mouse IgG ( Molecular Probes ) . Then , to detect both sporozoites outside and inside hepatocytes [40] , the HepG2 cell membranes were permeabilized with 100% chilled methanol . The cells were again blocked and stained with anti-CSP 3D11 Ab followed by Alexa Fluor 488 goat anti-mouse IgG as secondary Ab . Sporozoites were counted under a fluorescent microscope . GST-tagged PbPuf2 was expressed in E . coli and purified by affinity chromatography . The biotinylated uis2-RNA was synthesized using HiScribe T7 High Yield RNA Synthesis Kit ( New England Biolabs ) . The Puf2 protein was incubated with 5 nM biotinylated uis2-RNA in 1X REMSA buffer containing 5% glycerol . Reactions were resolved on a native 6% polyacrylamide gel in 0 . 5X TBE and transferred to a nylon membrane . Band shifts were detected using Thermo scientific LightShift Chemiluminescent RNA EMSA kit . | Malaria is transmitted to humans by female mosquitoes as they take a blood meal . Plasmodium sporozoites are the infectious and quiescent forms of malaria parasites , which reside in the salivary glands of mosquitoes . Global protein synthesis is inhibited in sporozoites through phosphorylation of the translational factor eIF2α . However , the development of the parasites in the host liver requires de-phosphorylation of eIF2α-P . We find that a unique Plasmodium phosphatase termed UIS2 de-phosphorylates eIF2α-P in malaria . The eIF2α is highly phosphorylated in the uis2 mutant sporozoites . The uis2 mutant parasites did not change their morphology after delivery into the host and could not properly infect the host . We also showed that UIS2 expression was inhibited by the Pumilio protein Puf2 . However , this repression was relieved when sporozoites developed into liver stage . In sum , our findings revealed a new mechanism that evolved to control eIF2α dephosphorylation and suggest that identification of UIS2 inhibitors may be useful in anti-malaria therapy . | [
"Abstract",
"Introduction",
"Results",
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"Materials",
"and",
"Methods"
] | [] | 2016 | UIS2: A Unique Phosphatase Required for the Development of Plasmodium Liver Stages |
Dengue has emerged as the most important vector-borne viral disease in tropical areas . Evaluations of the burden and severity of dengue disease have been hindered by the frequent lack of laboratory confirmation and strong selection bias toward more severe cases . A multinational , prospective clinical study was carried out in South-East Asia ( SEA ) and Latin America ( LA ) , to ascertain the proportion of inapparent dengue infections in households of febrile dengue cases , and to compare clinical data and biological markers from subjects with various dengue disease patterns . Dengue infection was laboratory-confirmed during the acute phase , by virus isolation and detection of the genome . The four participating reference laboratories used standardized methods . Among 215 febrile dengue subjects—114 in SEA and 101 in LA—28 ( 13 . 0% ) were diagnosed with severe dengue ( from SEA only ) using the WHO definition . Household investigations were carried out for 177 febrile subjects . Among household members at the time of the first home visit , 39 acute dengue infections were detected of which 29 were inapparent . A further 62 dengue cases were classified at early convalescent phase . Therefore , 101 dengue infections were found among the 408 household members . Adding these together with the 177 Dengue Index Cases , the overall proportion of dengue infections among the study participants was estimated at 47 . 5% ( 278/585; 95% CI 43 . 5–51 . 6 ) . Lymphocyte counts and detection of the NS1 antigen differed significantly between inapparent and symptomatic dengue subjects; among inapparent cases lymphocyte counts were normal and only 20% were positive for NS1 antigen . Primary dengue infection and a specific dengue virus serotype were not associated with symptomatic dengue infection . Household investigation demonstrated a high proportion of household members positive for dengue infection , including a number of inapparent cases , the frequency of which was higher in SEA than in LA .
Dengue is the most important mosquito-borne viral disease of humans . The disease is now endemic in more than 100 countries and threatens more than 2 . 5 billion people . It currently affects about 50 to 100 million people each year [1] . Dengue viruses ( DENV ) are enveloped , single-stranded positive-sense RNA viruses ( family Flaviviridae , genus Flavivirus ) . There are four types of DENV: DENV-1 , DENV-2 , DENV-3 and DENV-4 . Dengue virus infection induces life-long protective immunity to the homologous serotype , but confers only partial and transient protection against subsequent infections with any of the other three serotypes [2] . The disease spectrum ranges from inapparent infection or mild dengue fever [3] , probably the most common form , to a potentially severe form of dengue characterized by plasma leakage and hemorrhage , known as severe dengue . Uncommonly , severe dengue may manifest as hepatitis , encephalopathy or rhabdomyolysis [2] , [4]–[7] . About 500 , 000 people are estimated to have severe dengue and about 25 , 000 , mostly children , die from it each year [8] . The underlying causes determining the outcome of DENV infection remain unknown . Although previous exposure , viral strain and human host genetic polymorphisms also influence the clinical outcome of DENV infection , we still know little about the complex interplay between host and pathogen in the pathogenesis of dengue [9]–[12] . Inapparent infections have largely been detected retrospectively through serology . The uses of genome detection or virus isolation have enabled detection of inapparent infections in cluster studies designed to detect natural infections in the community [13] , [14] . The present study was designed to identify symptomatic and inapparent dengue-infected subjects in genetically-related individuals living in the same household , in line with the main aim of the DENFRAME project which is to explore the influence of human genetic variants and their functional roles in the pathogenesis of dengue disease in humans . We based the identification of dengue-infected subjects upon virological techniques , namely virus isolation and detection of the genome . We also took this opportunity to evaluate prospectively a commercial NS1 capture assay [15] , [16] that could potentially be implemented in laboratories for the diagnosis of acute dengue [17]–[19] .
A multinational , prospective study was conducted in South-East Asia ( Cambodia and Vietnam ) and Latin America ( Brazil and French Guiana ) . We used virological techniques to identify dengue patients diagnosed at the acute phase of disease among the patients presenting with dengue-like illness . We then performed a household investigation , comparing clinical data and biological markers from subjects with a broad range of dengue disease patterns , including inapparent dengue cases that are rarely captured in clinical studies . This clinical study's aims were: ( i ) to estimate the proportion of inapparent dengue infections among members of the households of laboratory-confirmed symptomatic dengue cases , ( ii ) to calculate the proportion of dengue-infected subjects at the time of the household investigation , and ( iii ) to compare clinical and biological data from inapparent and symptomatic dengue-infected subjects . Five institutions were involved in this study during the recruitment period: Instituto Evandro Chagas ( IEC ) in Belém ( Pará state , Brazil ) , Institut Pasteur du Cambodge ( IPC ) in Phnom Penh ( Cambodia ) , Institut Pasteur de la Guyane ( IPG ) in Cayenne ( French Guiana ) and Institut Pasteur de Ho Chi Minh Ville ( IPHCM ) in Vietnam were responsible for the recruitment of patients and virological analyses; the Institut Pasteur ( IP ) in Paris ( France ) designed the study and was responsible for central monitoring and data analysis . As shown in the two maps ( Figure 1 ) , volunteers were recruited at four clinical sites: Vinh Thuan District Hospital ( Vietnam ) , Kampong Cham Referral Hospital ( Cambodia ) , the IPG in Cayenne ( French Guiana ) and public outpatient and emergency rooms managed by the Belém Health Secretariat in the districts of Guamá , Marco , Marambaia and Sacramenta , and the outpatient unit of the IEC ( Brazil ) . The virology laboratories of the four institutions responsible for recruitment are all National Reference Centers ( NRC ) for Arboviruses ( IEC is also a WHO collaborative center ) . These laboratories carried out virological , NS1 antigen ( Platelia Dengue NS1 Antigen , Bio-Rad , Marnes La Coquette , France ) , and serological techniques . We recruited subjects with acute dengue-like illness at the study sites . These subjects were identified by the treating physicians and were included if they satisfied the following criteria: ( i ) aged over 24 months; ( ii ) oral temperature >38°C and onset of symptoms within the last 72 h; and ( iii ) presenting with at least one clinical manifestation suggestive of dengue-like illness: severe headache , retro-orbital pain , myalgia , joint pain , rash or any bleeding symptom . Furthermore , for inclusion in the second step of the study , the subject had to come from a familial household containing more than two people during the seven days preceding illness . We first identified the dengue-infected subjects ( referred to in this study as Dengue Index Cases or DIC ) and non-dengue-infected subjects ( defined as Non-Dengue Cases - NDC ) on the basis of virological results from an acute sample ( see below ) . We then recruited individuals from the households of the DIC . We thus constituted three groups of participants: 1 ) DIC , 2 ) household members ( HHM ) , and 3 ) NDC not related to the DIC . For all groups ( DIC , HHM and NDC ) , we applied the same exclusion criteria: women who were pregnant or breastfeeding , individuals with a focal source of infection ( e . g . otitis media , pneumonia , meningitis ) , patients presenting with a known chronic illness , and patients with malaria . Moreover , to ensure the feasibility of this study , each study site was asked to target a convenient sample of 50 households and to recruit subjects from July 2006 to June 2007 in line with the approval granted by the Institutional Review Board and the timing of the dengue season at each site . Participants were examined during sequential visits , as shown in the study design charts ( Figure 2 ) . At each visit , data were collected with a standardized questionnaire . Severe dengue cases were classified , according to WHO recommendations on the basis of the clinical data . Biological data were also recorded at the sequential visits [2] . Blood samples were collected during the visits and were rapidly processed by the laboratories of each of the recruiting sites , for dengue diagnosis and biological testing . Blood sample volume was adapted for children weighing less than 20 kg . Paired blood samples were collected for subjects presenting dengue-like illness to allow classification as DIC or NDC: during the acute phase ( Visit 1 ) and during the convalescence phase ( Visit 4: 15 to 21 days after the onset of fever ) . Blood samples were taken from hospitalized DIC within 24 hours of defervescence ( Visit 3 ) . HHM were visited at home for blood collection within 24 to 72 hours of DIC identification ( Home Visit 1 ) . For practical and logistical reasons this delay of up to 72 hours was unavoidable . HHM were supplied with a monitoring diary card and a thermometer , to enable them to follow their temperature over a 7-day period . For HHM with a positive diagnosis of dengue or with an onset of fever during the seven days of monitoring , a second visit with blood collection for dengue diagnosis was organized ( Home Visit 2 ) . Blood analyses included virological and serological dengue diagnosis , complete blood count , transaminases and bilirubin levels . Finally , the data were coded and entered into the computer via a secure website specifically developed with the PHP/MySQL system . All serum samples collected at Visit 1 or at Home Visit 1 or Home Visit 2 were tested: ( i ) for acute dengue diagnosis , defined as positive virus isolation on mosquito cells [20] and/or positive viral RNA detection by reverse transcriptase-polymerase chain reaction ( RT-PCR ) [21] , and ( ii ) for the diagnosis of early convalescent dengue cases based on a standardized DENV IgM capture enzyme-linked immunosorbent assay ( MAC-ELISA ) [22] , and DENV IgG detection by indirect ELISA ( in-house protocol developed by each NRC for Arboviruses ) . NS1 antigen detection was also performed . Only subjects with febrile dengue infection diagnosis were classified as DIC . Subjects in the early stage of dengue convalescence at Visit 1 ( i . e . positive NS1 antigen detection with concomitant DENV IgM detection , or isolated DENV IgM detection with no positive viral tests ) were not classified as DIC; we did not perform a household investigation for them . For the classification of dengue-infected HHM at Home Visit 1 , we included both HHM with an acute ( febrile or inapparent ) dengue infection diagnosis and HHM with isolated DENV IgM detection , presumably related to an infection preceding that of the DIC ( i . e in the early convalescence phase ) . During the 7-day period of home monitoring , several new febrile cases of dengue-infected HHM were also confirmed through Home Visit 2 . We were unable to use the DENV IgM/IgG ratio to distinguish between primary and secondary dengue infections , due to a lack of standardization of DENV IgG tests among laboratories [23] . We therefore established two groups of dengue-infected participants , based on the presence or absence of DENV IgG during the acute phase of the disease . In this study , we considered the presence of DENV IgG in the acute phase of the study to be suggestive of previous dengue infection . All sera were also checked for DENV IgM and IgG at Visit 4 . Finally , if all these dengue tests were negative , participants were classified as NDC . The study was approved by the Institutional Review Board of the Institut Pasteur and by the ethics committees of each of the countries concerned . It was conducted in accordance with the Declaration of Helsinki , and the participants or the parents of minors participating in the study gave written informed consent before inclusion . The clinical protocol , the questionnaires , the standard operating procedures and informed consent forms were adapted and translated for each clinical site . All the documentation was accessible through a dedicated website with a specific login access ( www . denframe . org ) . The centralized electronic database was based at the Institut Pasteur in Paris and registered with the Commission Nationale de l'Informatique et des Libertés ( CNIL ) in France . We present here the data from all four study sites in Latin America and South-East Asia . DIC are described according to region , disease severity , DENV type , age group and IgG status . We estimated the proportion of inapparent dengue infections among HHM , and we calculated the proportions of dengue-infected subjects among household subjects , in total and according to the IgG status at the time of household investigation . We compared clinical data and biological markers between inapparent dengue-infected subjects , symptomatic dengue-infected subjects , and non-dengue-infected participants at the time of the household investigation . We created binary variables to evaluate the potential effect of DENV infection on biological markers ( hematocrit , platelets , neutrophils , lymphocytes , monocytes , ASAT , ALAT , bilirubin ) . For lymphocytes and neutrophils , we used a threshold of 2×109/l . We used chi-squared or Fisher's exact tests to compare categorical variables between symptomatic cases , inapparent dengue-infected cases and non-dengue-infected subjects among HHM . Univariate and multivariable logistic regression models were used to assess the effect of covariates on the odds ratios ( OR ) of symptomatic dengue-infected cases , inapparent dengue-infected cases , and non-dengue-infected subjects among HHM . For the multivariable logistic regression models including data from household members , we used two-stage hierarchical regression models taking into account the family household structure [24] . Potential confounders with a P value of less than 0 . 20 in univariate analysis were retained for the final multivariable analyses . STATA version 10 . 0 ( Stata Corp . , College Station , TX , USA ) and a significance level of 5% were used for all statistical analyses .
We screened 473 febrile subjects for dengue infection . Thirty ( 6 . 3% ) had at least one criterion for non inclusion in the study at presentation; the remaining 443 ( 93 . 7% ) were included in the study . We identified 215 ( 48 . 5% ) of these 443 subjects as DIC , 21 ( 4 . 7% ) as dengue convalescent cases , 187 ( 42 . 2% ) as NDC , and 20 ( 4 . 5% ) could not be classified because some biological markers were lacking . Recruitment levels during the study period were very low in French Guiana ( 9 DIC and 24 NDC ) , whereas there had been a large number of dengue cases during the rainy season of the previous year [25] . For the 215 subjects classified as DIC , 149 ( 69 . 3% ) were positive by genome detection and viral isolation , 43 ( 20 . 0% ) were positive by genome detection only , 15 ( 7 . 0% ) were positive by viral isolation only , and a very few subjects ( n = 8 , 3 . 7% ) were ultimately classified as DIC by the virologists , based on positive NS1 detection , clinical data and serological results ( negative IgM at Visit 1 followed by seroconversion IgM at convalescent phase ) . The proportions of subjects classified as either NDC or DIC differed between Latin America and South-East Asia: 69 . 5% ( 130/187 ) of the total NDC in the study , and 47 . 0% ( 101/215 ) of the DIC , were recruited in Latin America whereas 30 . 5% ( 57/187 ) of the NDC and 53 . 0% ( 114/215 ) of the DIC were recruited in South-East Asia ( P<10−4 ) ( Figure 3A ) . In other words , in Latin America , in two thirds of subjects presenting with dengue-like illness , the cause was not related to dengue infection . Given the inclusion criteria , the dengue-like illness symptoms were not different between NDC and DIC ( data not shown ) . However , all biological variables , including counts of platelets , lymphocytes and neutrophils , were significantly lower , whereas hematocrit and liver enzyme levels were higher in the DIC group than in the NDC group ( data not shown ) . Table 1 shows the distribution of DIC by region and according to IgG status at Visit 1 as a function of DENV type and age group . The proportions of severe dengue and dengue fever cases with DENV IgG ( suggestive of previous DENV infection ) and without DENV IgG in the acute phase were similar ( Table 1 ) : 15 ( 55 . 6% ) severe dengue cases tested negative for DENV IgG and 12 ( 44 . 4% ) tested positive for DENV IgG , versus 49 ( 31 . 8% ) and 105 ( 68 . 2% ) of the subjects with non severe disease , respectively ( P = 0 . 017 ) . DENV-1 , -2 and -3 were found with similar frequencies in South-East Asia , whereas DENV-3 predominated in Latin America . Fifteen of the severe dengue cases reported in South-East Asia were infected with DENV-2 ( 53 . 6%; 15/28 ) . Interestingly , seven severe dengue cases positive for DENV-2 virus and negative for DENV IgG in the acute phase but with subsequent DENV IgM and IgG seroconversion were identified . This serological pattern suggests that these patients had primary DENV infection . Two DIC in Vietnam were reported with co-detection of multiple DENV strains by RT-PCR: DENV-2/DENV-1 and DENV-4/DENV-2 respectively; the viral cultures were negative for both subjects . Only the first virus detected was considered for further statistical analysis ( DENV-2 and DENV-4 , respectively ) . According to the WHO criteria , twenty-eight ( 13 . 0% ) subjects were classified as severe dengue ( based on severe plasma leakage and/or severe hemorrhages and/or severe organ impairment ) . All these cases were from clinical sites in South-East Asia ( 25 in Vietnam and 3 in Cambodia , as presented in Table S1 ) . At visit 1 , presentation with the following combination of features was significantly associated with the occurrence of severe dengue in this population: being male , over the age of seven years , with no retro-orbital pain but with bleeding , low monocyte count , normal liver enzyme levels and DENV-2 type infection . For 163 ( 75 . 8% ) DIC , data were available for all the biological markers at visits 1 and 4 ( Figure 3A ) . All these markers had returned to normal levels by visit 4 , and all participants , including the 28 severe dengue cases displayed clinical recovery from dengue disease ( data not shown ) . Agreement for household investigations was obtained from 177 ( 82 . 3% ) DIC , corresponding to a total of 651 household members . We compared the distribution of the covariates ( as listed in Table S1 ) between the 38 DIC with no familial investigation and the 177 DIC who underwent familial investigation; no significant differences were found in the distribution of the covariates between these two groups ( data not shown ) . All 28 patients with severe dengue infection underwent household investigation . In total , 141 ( 21 . 7% ) of the 651 household members refused to participate in the study . We therefore screened 510 participants , 497 ( 97 . 5% ) of whom were eligible for the study . All but one of these 497 household members were genetically related to the DIC . Eighty-four were not classifiable due to the lack of some biological results . Full assessment of DENV infection was carried out according to the study protocol for the remaining 413 of these subjects ( Figure 3B ) during Home Visit 1 . At the time of the household investigation ( Home Visit 1 ) , 39 subjects were identified as being in the acute phase of dengue infection: 29 ( 74 . 4% ) cases were inapparent and 10 ( 25 . 6% ) had symptomatic dengue infection . An additional 62 subjects were classified as being in the early phase of convalescence from dengue infection . The remaining 312 subjects were considered as non-dengue-infected at the time of Home Visit 1 ( Figure 3B ) ; however , five of them developed some clinical symptoms of dengue fever and were laboratory-confirmed as having acute dengue infection during the 7-day home monitoring . We excluded them ( n = 5 ) from the remaining analysis ( n = 312 subjects with 7-day home monitoring ) that thus included 307 subjects ( Figure 3B ) . It should be noted that a second home visit and blood sampling was not possible , for ethical and logistical reasons , for HHM without any clinical symptoms after the 7-day home monitoring . Hence , among the 307 remaining subjects , some may have had an inapparent dengue infection after Home Visit 1 . Therefore , we considered that at least 101 ( 39 acute or 62 early convalescent ) dengue infections were found amongst 408 HHM ( 24 . 8%; 95% confidence interval ( CI ) : 20 . 6–28 . 9 ) at the time of Home Visit 1 ( Figure 3B ) . Thus , adding together the 177 DIC and the 101 DENV-infected HHM , the overall proportion for dengue among the study participants was estimated at 47 . 5% ( 278/585; 95% CI: 43 . 5–51 . 6 ) ( Figure 3B ) . We have also estimated these proportions according to the IgG status ( Table 2 ) at the time of Home Visit 1 ( excluding the 5 subjects with known symptomatic infection – 3 were IgG positive and 2 were IgG negative ) . Among the 585 subjects , 6 had missing IgG data . Among 425 subjects with positive IgG , the estimated proportion of dengue-infected subjects was 43 . 8% ( 186/425; 95% CI: 39 . 0–48 . 5 ) and , among the 154 with negative IgG , this estimated proportion was 57 . 1% ( 88/154; 95% CI: 49 . 3–65 . 0 ) . In 101 ( 57 . 1% ) households , there was only one dengue-infected case . For the 76 ( 42 . 9% ) households with at least two dengue-infected cases , DENV type had been determined for all subjects in 29 households . Nine ( 31 . 0% ) households were found to have two different DENV types circulating during the same time period: DENV-1 & DENV-3 ( n = 2 in Brazil , n = 4 in Cambodia ) , DENV-1 & DENV-2 ( n = 1 in Vietnam ) , and DENV-2 & DENV-3 ( n = 2 in Vietnam ) . Hematologic and hepatic biological markers observed among non-dengue-infected cases ( n = 307 ) , inapparent dengue-infected cases ( n = 29 ) , and symptomatic dengue-infected subjects ( n = 192 ) are described in Table S2 . Tables 3 & 4 show comparisons between non-dengue-infected and inapparent dengue-infected cases , and symptomatic and inapparent dengue-infected subjects , respectively , among the household subjects . Table S3 presents the main characteristics of subjects with acute dengue infection compared to non-dengue-infected subjects among the household subjects . In the comparisons between non-dengue-infected and inapparent dengue-infected subjects , taking into account potential confounders , only neutrophil and monocyte levels differed significantly whereas presence of IgG at Visit 1 was almost significant with the non-dengue-infected group . The comparison between symptomatic and inapparent dengue-infected subjects ( Table 4 ) showed significant difference between groups for lymphocyte counts and positive NS1 antigen detection . In this analysis , no significant difference was found for DENV types identified or IgG detection during the acute phase .
Several previous epidemiological studies have focused on school-based surveillance aiming at improving dengue-vector control measures [3] , [14] , studying the dynamics of patterns of dengue transmission [26]–[28] or describing a model that takes into account the role of human movement in the transmission dynamics of vector-borne pathogens [29] . Earlier cluster investigation methods were designed as an alternative approach to the commonly used prospective cohort study method for investigating the natural history of dengue virus infection in South-East Asia and Latin America [13] , [30] . Although different study designs have demonstrated the feasibility of identification of inapparent dengue cases , it remains difficult to recruit these subjects . We designed our study to include family household investigation in order to identify a group of inapparent dengue-infected subjects and to compare them with symptomatic dengue-infected and non-dengue-infected subjects living in the same family household . The study design was based on family household recruitment specifically in order to collect data and biological samples , and to study secondarily the host susceptibility to dengue infection and disease . Unlike studies based on cohorts from hospital referrals , this multi-country study captured dengue cases ranging from inapparent infections , through mild disease to severe dengue fever , using definitions of clinical cases and diagnostic methodology standardized across the four sites . The period of inclusion , from July 2006 to June 2007 , spanned the dengue season at each site , although incidence of dengue was low that year in French Guiana . The main objective of this study was to identify dengue infections and particularly inapparent infections among dengue patients' household family members in South-East Asia and Latin America . Based on our data , we estimated the proportion to be about 45% among those participating in the household study . Most of the dengue cases studied had symptomatic infections , covering the spectrum of disease from dengue fever to severe dengue cases . We also identified inapparent infections in the population . We observed dengue-infected subjects classified as DIC and some of their HHM without acute dengue infection but with a positive IgM detection , suggesting an early convalescent phase after dengue infection with no clinical symptoms . In this study we identified 29 inapparent dengue infections but we believe this number underestimates the proportion of inapparent dengue cases because we were not able to take blood samples from non-symptomatic subjects at Home Visit 2 . We postulated that dengue is transmitted to members of the DIC's family household during the period of the index subject's infection , and thus designed our study to detect inapparent dengue infections with a home visit organized shortly after identification of DIC . Obviously , we cannot confirm whether the index subject's DIC was always the source of infection in other family members , but we can postulate that a non-hospitalized DIC who remains at home during acute illness represents a potential source of DENV transmission to Aedes . According to our study design , clustering of cases within a household could be the result of a single or very few infected mosquitoes biting different household members during a short period of time , perhaps within a single gonotrophic cycle as previously suggested [14] , [31] . This is also consistent with a previous observation that over periods from 1 to 3 days , dengue cases were clustered within short distances , i . e . , within a household [32] . No mosquito captures were , however , conducted in our study to identify DENV-positive Aedes mosquitoes . DENV sequencing would help resolve the extent of localized transmission . We characterized subjects with acute dengue infection using virus isolation and detection of the genome . We also used NS1 antigen detection , a more recently recognised diagnostic tool . As for many tropical infectious diseases , there is an urgent need for validated diagnostic tools for dengue . In parallel with the virological techniques , we evaluated detection of the NS1 antigen with the Platelia Dengue NS1 Ag test . In this study , this test was found to have good sensitivity ( 83 . 6%; 95% CI: 78 . 5–88 . 6 ) and specificity ( 98 . 9%; 95% CI: 96 . 6–99 . 9 ) in both Asia and Latin America , as reported in previous studies [17] , [33] , [34] . A recent multi-country study observed unequal sensitivity between geographical regions that remains unexplained , suggesting further assessments are needed [35] . The use of viral detection antigen is particularly useful during the first five days of illness with NS1 assays that are significantly more sensitive for primary than secondary dengue [18] , [34] , [36] . However , NS1 antigen could be detected in only 20% of inapparent DENV-infection . This finding suggests that NS1 antigen may have a role in dengue disease pathogenesis and also indicates that this test cannot be relied upon for detection of inapparent dengue infection . By comparing HHM not infected with dengue with those presenting with inapparent dengue infection , we showed that neutrophil and monocyte counts were early indirect biological markers of dengue infection , whereas platelet counts and the frequency of IgG detection at the first visit did not differ between the two groups ( Table 3 ) . A comparison of inapparent dengue-infected HHM with symptomatic dengue-infected subjects showed that lymphocyte counts and detection of the NS1 antigen differed significantly between these two groups ( Table 4 ) . Moreover , the NS1 antigen was detected during the acute phase in most of the dengue cases tested , and the sensitivity of this test was even higher in severe dengue cases ( 26/28 , Table S1 ) , possibly reflecting higher viral loads . These findings may indirectly reflect the progression of the immune response to DENV , leading in some cases to severe acute lymphopenia and a lack of virological control , with high rates of NS1 antigen circulation in the blood that may be correlated with high-level or prolonged viremia [7] , [36] . Severe dengue cases were also more likely to be male , to have lower monocyte counts or normal liver enzyme levels , and to be infected with DENV-2 , although quantitative RT-PCR did no permit study of the magnitude of the viremia . We showed that half of the severe dengue cases had not previously been infected with DENV , as confirmed by the occurrence of DENV IgG seroconversion during convalescent phase [7] . In all dengue-infected subjects , including inapparent , we observed a decrease in neutrophil and monocyte counts . On one hand , it may suggest a direct effect of dengue illness on hematopoiesis , although such an effect is in conflict with data reported elsewhere in the literature [37] . On the other hand , DENV is detected in peripheral monocytes during acute disease , and the infection of monocytes leads to cytokine production , suggesting that virus-monocyte interactions are relevant to pathogenesis [38]–[40] . Moreover , DENV can induce apoptosis in monocytes , and this may lead to decreases in the number of these cells in severe dengue cases [41] . In this study we only observed severe dengue cases in South-East Asia . Disease severity and pathogenesis remain largely unexplained and certainly related to complex interactions of several factors , including virus strain , immune response to previous dengue infection and host genetic background . The introduction of the Asian 1 DENV-2 genotype into the Americas in the 1980s led to the emergence of severe dengue cases on this continent . Following this introduction a new genotype emerged , named Asian/American DENV-2 genotype [42]–[44] . During the study period , this Asian/American genotype was circulating in French Guiana ( Philippe Dussart , personal data ) and probably in the north of Brazil , however DENV-2 did not cause an outbreak and we did not report any severe dengue case among Brazilian subjects . Two constraints of the study design deserve mention . All methods ( biological markers , virological testing , NS1 antigen detection and IgM serology ) were standardized across the four reference laboratories , with the exception of the IgG ELISA . As a consequence , we were unable to calculate the IgM/IgG ratio [45] , [46] . However , as the intention was to include dengue cases during the acute phase of infection , this ratio was not a crucial endpoint for the study . Another constraint of this study was that we did not include infants and children below 24 months of age in the DENFRAME project . However , several previous reports already provide insight into the epidemiology of dengue in this specific population [47]–[50] . These findings confirm the complexity of dengue disease in humans and the need to strengthen multidisciplinary research efforts to improve our understanding not only of virus transmission but also host responses to DENV in various human populations . It will therefore be interesting , based on clinical data and biological samples collected in this study , to further evaluate the host susceptibility to dengue infection and disease using family-based association analyses . Moreover , we think that technological transfer of standardized diagnostic methods in laboratories based in tropical countries is essential if we are to estimate disease burden and to optimize vector control interventions . Together with improvements in clinical care for dengue patients and better understanding of dengue pathogenesis , the development of a preventive vaccine and antiviral drugs would complete the arsenal of weapons for combating dengue worldwide . | Dengue is the most important mosquito-borne viral disease in humans . This disease is now endemic in more than 100 countries and threatens more than 2 . 5 billion people living in tropical countries . It currently affects about 50 to 100 million people each year . It causes a wide range of symptoms , from an inapparent to mild dengue fever , to severe forms , including dengue hemorrhagic fever . Currently no specific vaccine or antiviral drugs are available . We carried out a prospective clinical study in South-East Asia and Latin America , of virologically confirmed dengue-infected patients attending the hospital , and members of their households . Among 215 febrile dengue subjects , 177 agreed to household investigation . Based on our data , we estimated the proportion of dengue-infected household members to be about 45% . At the time of the home visit , almost three quarters of ( 29/39 ) presented an inapparent dengue infection . The proportion of inapparent dengue infection was higher in South-East Asia than in Latin America . These findings confirm the complexity of dengue disease in humans and the need to strengthen multidisciplinary research efforts to improve our understanding of virus transmission and host responses to dengue virus in various human populations . | [
"Abstract",
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] | 2012 | Clinical and Virological Study of Dengue Cases and the Members of Their Households: The Multinational DENFRAME Project |
Homologous recombination ( HR ) is critical for the repair of double strand breaks and broken replication forks . Although HR is mostly error free , inherent or environmental conditions that either suppress or induce HR cause genomic instability . Despite its importance in carcinogenesis , due to limitations in our ability to detect HR in vivo , little is known about HR in mammalian tissues . Here , we describe a mouse model in which a direct repeat HR substrate is targeted to the ubiquitously expressed Rosa26 locus . In the Rosa26 Direct Repeat-GFP ( RaDR-GFP ) mice , HR between two truncated EGFP expression cassettes can yield a fluorescent signal . In-house image analysis software provides a rapid method for quantifying recombination events within intact tissues , and the frequency of recombinant cells can be evaluated by flow cytometry . A comparison among 11 tissues shows that the frequency of recombinant cells varies by more than two orders of magnitude among tissues , wherein HR in the brain is the lowest . Additionally , de novo recombination events accumulate with age in the colon , showing that this mouse model can be used to study the impact of chronic exposures on genomic stability . Exposure to N-methyl-N-nitrosourea , an alkylating agent similar to the cancer chemotherapeutic temozolomide , shows that the colon , liver and pancreas are susceptible to DNA damage-induced HR . Finally , histological analysis of the underlying cell types reveals that pancreatic acinar cells and liver hepatocytes undergo HR and also that HR can be specifically detected in colonic somatic stem cells . Taken together , the RaDR-GFP mouse model provides new understanding of how tissue and age impact susceptibility to HR , and enables future studies of genetic , environmental and physiological factors that modulate HR in mammals .
DNA is constantly subjected to endogenous and environmental DNA damaging agents that can lead to toxicity , mutations , and ultimately disease [1] . Maintaining genomic stability in the face of the thousands of DNA lesions that are formed in each cell every day poses a major challenge , especially in the case of double strand breaks ( DSBs ) , which are acutely toxic and can lead to the loss of millions of base pairs if a portion of a chromosome is lost [1] , [2] . The two major pathways used by cells to repair DSBs are non-homologous end-joining ( NHEJ ) , which directly rejoins DNA ends , and homologous recombination ( HR ) , which requires a homologous duplex for DSB repair [3]–[8] . The correct balance of NHEJ and HR is essential for preventing genomic instability [4] , [9] . If there is a deficiency in HR ( e . g . , loss of function of BRCA2 ) , cells can suffer misrepair of DSBs , resulting in cytotoxicity and translocations that promote cancer and aging [10]–[12] . Ironically , despite the fact that HR is essential , too much HR can also be detrimental , since HR carries the risk of misalignments that cause insertions , deletions , as well as loss of heterozygosity ( LOH ) [13] , [14] . It is likely that HR events contribute to sequence changes in virtually all cancers , since loss of function of almost all tumor suppressor genes requires LOH , and many , if not most , LOH events are caused by HR [14]–[16] . Further , sequence changes generated by HR have been found in multiple cancers [17]–[22] , and many conditions that promote HR also promote cancer ( as a few examples , exposure to UV light [23] , [24] , exposure to benzo[a]pyrene [25] , [26] and mutations in BLM [27] and Ku70/80 [28] , [29] ) . Dozens of genes are either directly involved in HR or modulate HR activity [6] , [30] . An essential early step in HR is the resection of double strand ends to create a 3′ single stranded overhang [31] , [32] . Subsequently , BRCA2 helps to load RAD51 onto the single stranded DNA to form a nucleoprotein filament that is capable of homology searching [33]–[37] . Strand invasion leads to formation of a D-loop that is then either resolved by synthesis-dependent strand annealing , which is not associated with crossovers , or by second-end capture and formation of a double Holliday junction , which may or may not be associated with a crossover [5] , [30] , [38]–[40] . Although crossovers during HR are relatively rare [4] , [41] , HR-associated crossovers have been shown to cause LOH [14]–[16] , [20] . In addition to its important role in the repair of two-ended double strand breaks , HR is essential for repair of one-ended double strand breaks that arise as a consequence of replication fork breakdown [5] , [30] , [42] . In HR deficient cells , such broken ends cannot be faithfully repaired via reinsertion into the sister chromatid , leading to an increase in misrepair via joining to an inappropriate end [4] , [9] , [30] . Despite HR's critical role in maintaining genomic stability , little or nothing is known about HR activity in most tissues in vivo , due to the lack of effective tools for studying HR in mammals . Using mouse models that harbor sequences amenable to studies of HR , key insights about HR in vivo have been gleaned for certain cells types and tissues . In pioneering work by the Schiestl laboratory , pun mice , which carry a natural duplication wherein a change in pigmentation indicates an HR event , have been used to study the impact of genes and exposures on HR [43] , [44] . Additionally , mice engineered to be heterozygous at the Aprt locus have been used to show that LOH is often driven by HR in vivo [45] , [46] . More recently , our laboratory set out to create mouse models in which HR can be detected via direct repeat HR reporters . Studies in S . cerevisiae first demonstrated that direct repeat substrates are useful for studying HR [47]–[49] . Briefly , two expression cassettes for a selectable marker are integrated into the genome adjacent to each other . Each expression cassette lacks sequences that are essential for expression . If the expression cassettes misalign and undergo homologous recombination , sequence information can be transferred from one cassette to the other , which can reconstitute full-length sequence to enable expression of the selectable marker ( e . g . , Figure 1A; black bars indicate deleted sequences ) . Studies exploiting direct repeat HR substrates in mammalian cells have given rise to fundamental information about the mechanism of HR as well as the impact of sequence orientation , distance between repeats , and exposures on HR [50]–[53] . The Nickoloff laboratory incorporated a site for the homing endonuclease I-SceI , which creates a double strand break that induces HR . Controlling the position of the double strand break gave rise to additional insights into the underlying mechanisms of HR [54] , [55] . More recently , the Jasin laboratory designed HR substrates wherein a site-specific double strand break induces HR events that can be detected by expression of EGFP [56] , and these assays have been used extensively to reveal the genetic underpinnings of HR [4] . We later created a plasmid-based fluorescence recombination assay which was used for studies of the impact of inflammatory chemicals on HR [57] . To move from in vitro studies to in vivo studies , we subsequently used elements of the plasmid assay to create a fluorescence-based direct repeat HR substrate in mice . The fluorescent yellow direct repeat ( FYDR ) mice carry a direct repeat substrate wherein HR can lead to the reconstitution of the full-length coding sequence of the enhanced yellow fluorescent protein ( EYFP ) gene [58] , [59] . The FYDR mice are the first genetically engineered animal model that specifically detects HR , and the FYDR HR substrate intentionally does not include a site for artificial introduction of a double strand break ( e . g . , via I-SceI ) , since our primary objective is to enable studies of environmental , genetic and physiological factors that modulate HR . The use of fluorescence has proved to be an effective approach for detecting HR in the FYDR mice in vivo [58] , [60]–[63] . As expected [50] , spontaneous recombination at the HR substrate is rare ( the frequency of recombinant cells is ∼1/105 ) [58] , [59] . Nevertheless , the frequency of recombinant cells can be quantified by flow cytometry , and a fluorescent readout makes it possible to identify the cell types that have undergone HR within intact tissue via histological analysis . Furthermore , independent recombination events ( as opposed to frequency of cells harboring recombinant DNA ) are detectable as fluorescent foci in freshly excised intact tissue by imaging whole organs [60] , [61] . To learn more about the factors that impact the frequency of recombinant cells , we also developed a 3D imaging platform for intact tissue , which made it possible to determine how many recombinant cells result from de novo recombination events versus cell division [64] . These studies showed that both de novo recombination and clonal expansion drive the accumulation of recombinant cells with age [61] , [64] . Taken together , studies using the FYDR mice show that fluorescence detection of HR in vivo provides valuable insights into genetic , environmental and physiological factors that modulate HR [58]–[60] , [62] , [63] . Importantly , however , only a limited number of tissues can be studied in the FYDR mice as a consequence of poor expression in some tissues ( presumably due to the random locus integration following pronuclear injection ) [58] , [65] . We therefore set out to generate a recombination reporter mouse with broad reporter expression . In order to create a mouse model in which HR can be studied in virtually any cell type , we created targeting vectors to enable integration of a direct repeat recombination reporter into the Rosa26 locus [66] . Here we describe the Rosa26 Direct Repeat-Green Fluorescent Protein ( RaDR-GFP ) mice , which harbor two uniquely truncated EGFP expression cassettes in tandem . HR at the direct repeat can reconstitute full-length EGFP coding sequence , giving rise to fluorescence ( Figure 1A ) . Using this system , we were able to quantify HR in all tissues tested using flow cytometry . Furthermore , we show that several tissues are susceptible to DNA damage-induced HR , and using a novel automated image analysis program for analysis of fluorescence within intact tissue , we show that HR events accumulate in the somatic stem cells of the colon . The RaDR-GFP mice therefore open doors to studies of exposure-induced HR and make it possible to perform an integrated analysis of how cell type , tissue type and age impact HR in vivo . Together with the development of quantitative approaches for assessing HR , the RaDR-GFP mice enable studies of how genetic and environmental factors modulate susceptibility to HR events in cancer-relevant tissues .
To study recombination in vivo , we previously created a direct repeat substrate in which two EGFP expression cassettes are positioned in tandem ( Figure 1A ) [66] . Essential sequences were deleted from each of the EGFP cassettes to create Δ5egfp , which lacks 15 bp at the 5′ end , and Δ3egfp , which lacks 81 bp at the 3′ end . Recombination between the non-functional expression cassettes can reconstitute full-length coding sequence , which can then be expressed under the CMV enhancer/chicken beta-actin promoter [CAG] ( Figure 1B ) [66] , [67] . The promoter , intron , and polyadenylation signal sequences are the same as for the established FYDR mouse model [58] . In the FYDR model , expression levels were high in some tissues ( such as pancreas ) , but there was almost no expression in other tissues ( such as the colon ) , presumably as a consequence of gene silencing associated with the locus of integration . To enable broad expression , we targeted the HR reporter to the Rosa26 locus , which was originally identified for its nearly ubiquitous expression [68] . Using a Rosa26 targeting construct ( a kind gift from Dr . P . Soriano ) [68] , we previously created a targeting vector that includes a short arm ( SA ) , a positive selection marker ( NeoR ) , a direct repeat HR substrate , a long arm ( LA ) , and a negative selection cassette ( diphtheria toxin fragment A; DTA ) ( Figure 1C ) [66] . The construct design strategy is shown in Figure S1 . While our prior studies were focused on HR in ES cells in vitro , here we set out to create a knock-in mouse . The targeting construct was electroporated into mouse 129S4/SvJae ( 129 background ) ES cells . Out of 100 colonies , we identified seven candidates using primers designed to yield a 1 . 16 kb product from wild type DNA and a 1 . 24 kb product from the targeted allele ( Figure 1C–D ) . Five out of seven candidates harbored the diagnostic 8 . 2 and a 2 . 3 kb HindIII fragments when analyzed by Southern blot ( Figure 1C and 1E ) . Ten to fourteen 129 ES cells were injected into 3 . 5-day-old C57BL/6 blastocysts , and the resultant chimeric males were bred with 129 females to establish the RaDR-GFP mouse line . While the 129 background was maintained , the transgene was also backcrossed into the C57BL/6 background for 10 generations . The transgene follows Mendelian inheritance with 49 . 5% of offspring of heterozygous/wild type parents inheriting the transgene ( n = 99 ) . To initiate studies of HR in the RaDR-GFP mice , we first analyzed primary ear fibroblasts . Cells were harvested , expanded in culture , and examined by flow cytometry . Gates defining ‘green fluorescent’ and ‘autofluorescent’ cells were drawn conservatively to prevent autofluorescent from being identified as fluorescent , while capturing the majority of the EGFP expressing cells ( Figure 2B ) . To formally determine whether or not green fluorescent cells had indeed undergone HR , we isolated fluorescent cells to learn if they harbor full-length EGFP coding sequence . We previously designed PCR primers that specifically amplify Δ3egfp , Δ5egfp , or full-length EGFP ( Figure 2A and Table S1 ) . Here , we developed methods to analyze cells for the presence or absence of each cassette using cDNA as a template , rather than genomic DNA as previously described [66] . Our rationale for this approach was that by exploiting the multiple copies of cassette sequences present in mRNA , we would be able to query the presence and absence of cassettes in single cells in future experiments . As a first step , primers were used to analyze cDNA from control ES cell lines that had previously been targeted with each cassette individually , as well as ES cells that harbor both Δ3egfp and Δ5egfp [66] . Conditions were optimized so that both Δ3egfp and Δ5egfp are detectable in a single PCR reaction so that each cassette serves as a positive control for the other . Results show specific detection of each cassette in isolation and together , and full length sequence is only observed in the positive control EGFP expressing cells , as expected ( Figure 2C , first five panels ) . To create the RaDR-GFP mice , we created new early passage clones of ES cells targeted with the recombination substrate . PCR analysis of RaDR-GFP cells that carry the unrecombined substrate reveals both the Δ3egfp and Δ5egfp cassettes , but not the full length EGFP , as expected ( Figure 2C , panel six ) . Having created RaDR-GFP mice that carry the Rosa26 targeted HR substrate ( Figure 1C–E ) , we next set out to determine whether or not fluorescent cells from these animals indeed harbor the full length EGFP sequence , as anticipated following HR . Fluorescent and autofluorescent control cells were isolated from a single cell suspension of disaggregated RaDR-GFP pancreatic cells using FACS ( Figure 2B ) . Primers that flank the coding sequence were optimized for nested PCR ( Table S2 ) , and cDNA was analyzed either by direct PCR or nested PCR , as indicated . Analysis of autofluorescent RaDR-GFP pancreatic cells revealed the presence of Δ3egfp and Δ5egfp , whereas full-length EGFP sequence was not detected ( Figure 2D ) . In contrast , full-length EGFP was readily detected in samples of green fluorescent RaDR-GFP pancreatic cells ( Figure 2D ) . The Δ3egfp and Δ5egfp cassettes were also detected ( Figure 2D ) , which is consistent with their potential retention following HR ( Figure 1A ) . The RaDR-GFP HR substrate is designed so that over a dozen base pairs need to be restored to give rise to a functional full-length EGFP coding sequence [66] . As restoration of a significant number of nucleotides requires HR for alignment and transfer of sequence information , these data show that fluorescence is an indicator of homologous recombination at the RaDR-GFP substrate . Ultimately , this mouse model can be used to study the underlying molecular changes that caused sequences to be restored to full length . Gene conversions without a crossover can be identified by the presence of one of the two original cassettes , along with full-length sequence . In contrast , replication fork repair or gene conversion with crossover will result in a triplication wherein both of the original cassettes are present along with the full-length sequence ( Figure 3 ) . We had previously performed this type of analysis on ES cells that had been clonally expanded in vitro [66] . Here , we set out to develop methods that would enable studies of HR in vivo . Because clonally expanding single cells from mouse tissues is difficult , we set out to develop methods that would enable analysis of single fluorescent cells isolated from mouse tissues using FACS . Initial data indicate that single cell analysis can indeed be used to identify cells with each of the three major recombination classes ( Figure S2B ) . Previous studies of FYDR positive control mice ( which express EYFP from the same promoter and locus as the HR reporter ) show that there is little or no expression of EYFP in many tissues ( presumably due to silencing ) , which greatly limits the utility of the FYDR model [65] . While we anticipated that targeting the EGFP direct repeat reporter to a site with ubiquitous expression would overcome this barrier to studies of HR , prior studies of expression at the Rosa26 locus had been done using the Rosa26 promoter [68] , whereas the CAG promoter drives the RaDR-GFP transgene . To address the formal possibility that EGFP expression from the RaDR-GFP reporter might not be ubiquitous , we assessed the extent of expression of EGFP from a positive control mouse in which EGFP is expressed specifically from the CAG promoter at the Rosa26 locus ( see Materials and Methods for details ) . Analysis of tissues from the FYDR positive controls showed high expression of EYFP in the pancreas , and low expression in the liver and the colon ( Figure 4A , upper row ) , which is similar to the low expression previously observed in the kidney and lung [65] . In contrast , expression of EGFP in the Rosa26 positive control mice was very strong in all three tissues ( Figure 4A , bottom row ) . By using the same imaging parameters , these data also show that fluorescence from EGFP is significantly brighter than that of EYFP . Analysis by flow cytometry similarly shows that EGFP fluorescence is high not only in pancreas , liver and colon ( Figure 4B ) , but also in eight additional tissues ( Table 1 ) . The nearly ubiquitous expression of EGFP in the positive control mice suggests that fluorescent recombinant cells in the RaDR-GFP mice would be detectable in most mouse tissues . Furthermore , the positive control mice are essential for comparisons of HR frequency among tissues , since the frequency of GFP positive cells in the positive control mice provides the required baseline for comparing HR frequencies among tissues in the RaDR-GFP mouse model . To explore the feasibility of studying HR in multiple tissues ( including tissues that had previously been inaccessible to HR analysis ) , 11 tissues from RaDR-GFP mice were disaggregated and analyzed by flow cytometry , first by gating for live cells , and subsequently by gating for fluorescent cells . Remarkably , fluorescent recombinant cells were present in all tissues ( Figure 4C ) . Recombinant cells were relatively frequent in the pancreas ( similar to the FYDR mice ) and in the spleen . Recombinant cells were also observed at a significant frequency in the kidney , heart , liver , mammary gland , and colon of the RaDR-GFP mice ( all of which had previously been inaccessible for studies of HR within mammalian tissues in vivo ) . In contrast , very few fluorescent cells were detected in stomach or brain tissue ( Figure 4C ) . The observation that ∼90% of cells from brain tissue of the Rosa26 positive control mice are fluorescent ( Table 1 ) indicates that fluorescent recombinant cells can be detected . These results together therefore show that there are very few recombinant cells in the brain ( note that the detection of rare fluorescent cells is limited to ∼1/106 ) . One possible explanation for the low frequency of EGFP positive cells in the brain is the short time period during which HR is active in the developing brain [69] , where it plays a critical role in neurogenesis and cancer suppression [70] . It is possible that relatively few recombinant cells accumulate in the RaDR mouse brain compared to other tissues due to the short time during which HR is highly active . Although further studies are needed for a more in depth understanding of HR among tissues , taken together , these studies show for the first time that spontaneous HR is pervasive in adult mammalian tissues . Our previous studies , as well as results presented here , show that recombinant cells can be detected in situ within intact pancreata of FYDR mice as fluorescent foci ( Figure 5A ) ( see [60] , [61] ) . Importantly , since recombination is a rare event and pancreatic cells do not migrate significantly , independent recombination events can be identified as isolated fluorescent foci . Analysis of recombination events provides greater sensitivity compared to the frequency of recombinant cells as a means for detecting genetic and environmental factors that modulate HR [65] . To explore the efficacy of RaDR-GFP mice for studies of HR events within intact tissue , pancreatic tissue from a RaDR-GFP mouse was stained with DAPI and imaged using fluorescence microscopy at low magnification ( ×1 ) . Fluorescent foci are readily apparent in the RaDR-GFP pancreatic tissue ( Figure 5B ) . Tissue from 11 RaDR-GFP mice was compressed to 0 . 5 mm and imaged for manual quantification of foci . Using this approach , we observed that the median frequency of spontaneous recombination events is ∼140/cm2 . In addition , unlike the FYDR mice , recombinant foci are also readily detected in both the intact liver and the intact colon ( Figure 5C ) . Differences in the frequency of foci among tissues reflect both the frequency of HR events as well as the optical properties of each tissue . Therefore , it is difficult to discern tissue-specific differences in HR using this approach ( note that flow cytometry of disaggregated tissues overcomes this limitation ) . Importantly , however , for studies of factors that modulate HR in a specific tissue , analysis of HR events in situ provides a powerful approach both in terms of increased sensitivity [65] and in terms of learning about HR in specific cell types ( see below ) . Although HR events are rare , it is nonetheless possible to identify fluorescent foci within frozen 5 µm sections using epifluorescence microscopy . After imaging , sections can be stained with hematoxylin and eosin ( H&E ) to reveal tissue architecture . Image overlays for pancreatic fluorescent foci reveal that for both FYDR and RaDR-GFP , recombination is detected in pancreatic acinar cells ( Figure 5A and 5C , right ) . These observations are consistent with studies of FYDR mice in which analysis of >100 pancreatic foci revealed only acinar cells [61] . In the case of liver and colon , overlay of fluorescent images with H&E images reveals fluorescent hepatocytes in the liver , and fluorescent epithelial cells in the colon ( Figure 5C ) . Pancreatic acinar cells , liver hepatocytes and colonic epithelial cells all give rise to tumors in their respective tissues , raising the possibility that the RaDR-GFP mice can be used to study the etiology of cancer ( see Discussion ) . Somatic stem cells are of particular interest in cancer research . In the colon , there are only one or a few somatic stem cells at the base of each colonic crypt . Somatic stem cells are defined as being cells that have the ability to give rise to the epithelial layer in that crypt [71]–[73] . Therefore , a single HR event in a colonic somatic stem cell can lead to “crypt conversion” wherein all of the epithelial cells of its crypt share the same genetic change ( Figure 5D ) . Since transit cells are short lived , lasting only a few days before the epithelial layer of the crypt is replaced [73] , mutations in transit cells are less likely to contribute to cancer compared to mutations in colonic somatic stem cells , which can persist throughout the lifetime of the animal [73] . Analysis of thin sections via epifluorescence microscopy revealed a cross section of a colonic crypt in which it appears that all of the central epithelial cells are fluorescent ( Figure 5C , bottom right ) , suggesting that a stem cell from this crypt replaced the crypt epithelial cell layer with fluorescent daughter cells ( crypt boundaries can be identified by a ring of epithelial cells with higher staining intensity; Figure 5C ) . To learn more about the possibility of crypt conversion , colonic tissue was processed to gently remove crypts . Intact wholly fluorescent crypts were readily identified among disaggregated crypts from RaDR-GFP mice ( e . g . , Figure 5E ) , which is consistent with replacement of crypt epithelial cells by a single somatic stem cell that had undergone HR at the RaDR-GFP substrate . Taken together , the RaDR-GFP mice enable studies of HR in a cell type that is highly relevant to colon cancer . Aging is a critical risk factor for almost all cancers . To learn about the potential for recombinant cells to accumulate with age in the colon , we imaged and analyzed colonic tissue from young ( 3–4 months old ) and old ( 9–10 months old ) animals . Foci were counted by eye in a blinded fashion , and results indicated that there was no significant difference in the frequency of recombinant cell foci between the young and old animals ( Figure 6D , left ) . Foci in colonic tissue appear both as a consequence of transit cell recombination and somatic stem cell recombination . Given that transit cells are only present for a few days , unless the rate of recombination changes for young and old animals , one would not anticipate an observable increase in the frequency of transit cell foci . In contrast , as described above , somatic stem cells can persist for years [73] , which raises the possibility that fluorescent foci that result from recombination events in stem cells would accumulate and be detectable by the presence of whole crypt conversion . In order to favor detection of HR events in somatic stem cells , we therefore set out to create an image analysis program that differentiates large foci ( more likely to be due to whole crypt conversion ) from small foci ( more likely to be the result of HR in transit cells ) . We created a foci counting program that favors detection of large foci by using automated quantification techniques that exploit both intensity and morphological features . Classification was enabled using support vector machines . We simulated the data using a noise model , which includes the homogenous noise of the sample as well as the detection noise , to analyze the performance of our algorithms . To avoid false positives , only large foci with a consistent morphology and intensity were counted , and small foci or irregularly shaped foci were excluded ( Figure 6A ) . Although this approach has a potentially high false negative frequency , it is more important to avoid false positives than false negatives . Analysis of the lumen of large samples of colonic tissue shows the clear appearance of bright foci ( Figure 6B ) . Using the automated analysis software , large foci were marked with a dark cross if considered to be positive ( Figure 6C ) . Direct comparison of Figure 6B and Figure 6C shows that the majority of the large foci are identified by the program . We validated this approach by comparing the automated counting results to manual counts . A more detailed description of this software will be published separately . Using our image analysis software , we reanalyzed the colonic tissue from young and old mice . Remarkably , there is a highly significant ( p<0 . 01 ) increase in the frequency of larger foci with age ( Figure 6D , right ) . Since the largest foci result from clonal expansion of somatic stem cells , these results indicate that recombination events indeed accumulate in colonic somatic stem cells . It is noteworthy that inclusion of foci from transit cells is anticipated to lead to smaller foci that mask detection of changes in the more rare larger foci , as indicated in Figure 6D ( left ) such that inclusion of false positives damps the signal from the somatic stem cells . Taken together , these results provide some of the first insights into the relative susceptibility of transit cells and somatic stem cells to recombination with age , and open doors to future studies of conditions that modulate the risk of recombination in cells that have the potential to give rise to cancer . Alkylating agents are carcinogenic , used for cancer chemotherapy , and have been shown to be recombinogenic in mice [74] , [75] . We were therefore interested in the extent to which RaDR-GFP tissues would be susceptible to exposure-induced HR . Here , we focused on methylnitrosourea ( MNU ) , a model SN1 alkylating agent similar to temozolomide , which is used in cancer chemotherapy [74] . In parallel ongoing studies of FYDR mice , we tested multiple exposure conditions for efficacy in inducing HR , and we found that the combination of MNU and thyroid hormone ( T3 ) , which impacts pancreas physiology , was the strongest inducer of HR among the conditions that we tested . We therefore asked whether or not the RaDR-GFP model is sensitive to exposure-induced HR by treating animals with combined MNU/T3 ( see Materials and Methods ) . In addition to pancreas , we also evaluated colon and liver ( Figure 7A ) . For all three tissues , MNU/T3 was a strong inducer of HR . For the pancreas , the increase in the frequency of de novo recombination events was most dramatic ( Figure 7B ) , making it infeasible to quantify recombinant foci manually . Automated image analysis using a modified version of our foci analysis program ( optimized for the pancreas ) enables quantification of small/faint foci that are difficult to quantify by eye ( Figure 7C ) . Furthermore , the automated foci counting program enables future studies of foci characterization based on size and other morphological characteristics . Automated quantification of foci in RaDR-GFP mouse pancreata shows that , on average , exposure to MNU/T3 leads to ∼400 new recombination events per cm2 ( Figure 7D ) . In addition to the pancreas , exposure-induced HR was also observed in the liver and colon of RaDR-GFP mice ( Figure 7D ) . Taken together , these results demonstrate the efficacy of RaDR-GFP mice for studies of exposure-induced HR in multiple tissues .
Although HR is essential [76] , [77] , its activity must be carefully controlled in order to maintain genomic integrity [30] , [70] . Inherent defects that either suppress or induce HR are known to be tumorigenic [11] and exposures that induce HR are often carcinogenic [22] , [44] . Despite its importance , progress in our understanding of the role of HR in mammals has been hampered by the lack of effective tools for studying HR in many mammalian tissues . Here , we describe the RaDR-GFP mice , which harbor an integrated direct repeat that causes cells to fluoresce following HR . By targeting the reporter to the Rosa26 locus , expression of the transgene is nearly ubiquitous , thus enabling studies of HR in nearly all major organs , including liver , colon , spleen , heart , lung , kidney , stomach , thymus , brain , breast , and pancreas , many of which have been hitherto inaccessible for analysis . HR events at the RaDR-GFP substrate can occur via several different mechanisms . Prior studies of ES cells show that most recombinant fluorescent RaDR-GFP cells have undergone gene conversion without crossovers [66] , which are thought to result primarily from the synthesis dependent strand annealing pathway ( see [5] , which includes animations for HR pathways ) . DSB-induced crossovers between sister chromatids can also be detected by the RaDR-GFP substrate . Importantly , one of the critical roles of HR is to repair one-ended DSBs at broken replication forks , and these events can readily be detected using the RaDR-GFP substrate ( Figure 3 ) . One challenge when using the direct repeat approach for studies of HR is that these canonical HR events can be overshadowed by single strand annealing ( SSA ) , a subpathway of HR that is the most frequent spontaneous event at a direct repeat [5] , [66] . Specifically , when a DSB is formed between repeats , the ends are resected to reveal 3′ overhangs that can readily anneal to one another . As we are primarily interested in conditions that stimulate problems during replication , we designed the RaDR-GFP substrate so that SSA is not detected ( Figure 3 shows that SSA gives rise to an expression cassette that harbors both of the original deletions ) . This approach enables studies of spontaneous and exposure-induced HR events that are less frequent at a direct repeat , yet biologically important , such as replication fork repair . Taken together , both DSBs and broken replication forks can lead to fluorescence in the RaDR-GFP model , thus providing a window into how mammalian cells respond to a broad range of conditions that impact genomic stability by either suppressing or inducing HR in vivo . To learn about spontaneous HR in vivo , we quantified recombinant fluorescent cells in 11 different tissues and found that recombinant cells are present in all tissues studied . The frequency of recombinant cells is highly variable among tissues , ranging from very low in the brain and stomach , to very frequent in the pancreas and spleen . The observation that recombinant cells are relatively frequent in the pancreas suggests that HR is highly active in this organ ( which is consistent with the studies of aging; see below ) . Interestingly , mutations in BRCA2 , which plays a key role in initiating HR , are known to increase the risk of pancreatic cancer [30] , [78] . Thus , for the pancreas , there is a correlation between HR activity and the potential for a defect in HR to contribute to cancer [79] . For some other tissues , the frequency of HR is either unexpectedly high , or unexpectedly low . In the case of the heart , which has a relatively low proliferative index , there are a surprisingly high number of recombinant cells . One possibility is that progenitor cells that gave rise to cardiac tissue underwent HR , leading to the appearance of recombinant fluorescent cells in the adult tissue . One way to differentiate HR during development versus in the adult animal is to monitor tissue during aging to see if HR is active in adult animals . In contrast to cardiac tissue , the stomach had an unexpectedly low frequency of recombinant cells . It is noteworthy that not all of the cells in the disaggregated stomach tissue from the positive control mice were fluorescent ( ∼75% were positive by flow cytometry ) . This means that for some cell types , HR will not give rise to fluorescence . Although beyond the scope of this particular study , knowledge about HR in specific cell types can be achieved through a comparison of EGFP expression in RaDR-GFP mice ( yielding information about HR ) and EGFP expression in the positive control mice ( yielding the baseline frequency of cells in which HR can be detected ) . As the RaDR-GFP mice age , the frequency of recombinant somatic stem cells increases in the colon . Being able to monitor the burden of recombinant cells is valuable for long-term studies of conditions that impact HR . The burden of cells harboring sequence changes is critical to cancer development since an increase in the frequency of cells harboring a tumorigenic mutation leads to an increased risk of subsequent tumor-promoting mutations . Interestingly , exposure to MNU/T3 induced hundreds of recombination events in the RaDR-GFP mice . In essence , the burden of recombinant cells in young DNA damage-exposed mice is similar to aged mice , calling attention to the burden of mutant cells as a commonality for these two key risk factors for cancer . Being able to monitor HR over time and in response to exposures shows that RaDR-GFP mouse model can be used for studies of long-term exposures and physiological factors that impact the burden of recombinant cells , thus providing insights into fundamental processes that promote cancer . A key advantage of fluorescence as a marker for HR is that it is possible to reveal the underlying cell types that have undergone HR . Using a fluorescent overlay on H&E images , we observed fluorescent recombinant pancreatic acinar cells , liver hepatocytes and colonic epithelial cells . Knowledge about genomic stability in all three of these cell types is relevant to cancer . Although most pancreatic carcinomas are thought to originate from ductal cells [80] , mutation of Kras in acinar cells can lead to neoplasia of the ductal phenotype [81] , and furthermore there is evidence that acinar cells can undergo acinar to ductal transdifferentiation [82] . HR is also detectable in hepatocytes , which are precursors to hepatocellular carcinomas . Additionally , being able to study genetic change in vivo in the liver has broad implications , since liver genotoxicity is a major barrier in drug development [83]–[85] . In the colon , we observed HR in colonic epithelial cells . Most epithelial cells are rapidly sloughed off , making these cells unlikely targets for initiating mutations for cancer . In contrast , colonic somatic stem cells persist for years [72] , [73] . Our observation that there are crypts in which all cells appear to be fluorescent is consistent with an HR event in a somatic stem cell or early daughter cell of that crypt . Interestingly , methods have previously been developed for visualizing cells that have lost Dlb-1 gene function in colon crypts [86] . In Dlb-1 heterozygous mice , LOH can lead to a positive crypt by any of several different mechanisms ( e . g . , point mutations , frameshifts , deletion , HR , etc . ) . An advantage of the RaDR-GFP substrate is that it is designed to specifically detect HR . To learn about exposure-driven HR , we elected to exploit an alkylating agent that provides insights into the biology of cancer chemotherapeutics . The model agent MNU is an SN1 type methylating agent that generates methylated bases such as 3-methyladenine , 7-methylguanine and O6-methylguanine [74] . Several methylating agents creating these lesions are currently used in cancer chemotherapy including temozolomide , which is used to treat metastatic melanoma and malignant gliomas [87] . Importantly , HR activity contributes to resistance to methylating agents used in the clinic [87] . Furthermore , HR induced in healthy tissues during treatment with chemotherapeutic alkylating agents may be linked to therapy-induced secondary cancers [88] . Because of the broad reporter expression and sensitivity to methylation-induced HR , the RaDR-GFP mice offer a new approach for probing the extent to which treatments impact genomic stability both within the tumor and within healthy tissues , which is relevant to the risk of secondary cancers . In addition to FYDR and RaDR-GFP mice , several other mouse models that harness fluorescence as a marker for HR have been developed , including the HPRTdupGFP , which is currently in development in the Noda laboratory and promises to offer its own advantages . In addition , the Jasin laboratory extended their studies of DSB-induced HR in vitro to an animal model . The DR-EGFP mice harbor a recombination reporter that carries sequences for site-specific cleavage by I-SceI , and thus enable studies of DSB-induced HR in cells cultured from that mouse [89] . Using this model , it has been shown that a deficiency in Brca1 leads to reduced HR in cultured cells , and that DSB-induced HR can be studied in various cell types in vitro using cells derived from disaggregated tissues of the DR-EGFP mouse . While the use of a homing endonuclease greatly increases the frequency of HR , making it easier to quantify , the endonuclease needs to be introduced in vitro , which is not compatible with studies of HR in vivo . Furthermore , the DR-EGFP reporter is integrated into the Pim-1 locus . In the absence of a positive control , it is not possible to assess the relative frequency of HR among tissues , since a low frequency of fluorescent cells may be due to either a lower rate of HR or suppressed expression of EGFP . In contrast , for the RaDR-GFP mice , it is possible to compare HR among tissues since the number of cells that potentially express EGFP can be deduced using a complementary positive control mouse line with the identical locus and promoter . Unlike the DR-EGFP studies of HR in cells that have been isolated from mice , the mice and the methods described here enable analysis of HR in cells within their normal tissue context in vivo , which enables studies of more complex physiological processes , including cancer development and chronic exposures . Many mouse models have been developed for studies of point mutations/small deletions in vivo ( Pig-a , MutaMouse , Big Blue , Plasmid lac-z , cII , Gpt-Δ [90]–[95] . For each of these mouse models , as well as for the RaDR-GFP mice , susceptibility to sequence changes is being monitored at a specific locus . Although vulnerability to sequence changes is anticipated to be locus dependent , these models nevertheless provide useful tools for assessing the impact of genetic and environmental factors that impinge on genomic stability . Unlike the transgenic models that are used to study point mutations , the RaDR-GFP model exploits fluorescence . The median frequency of fluorescent cells in RaDR-GFP tissues is approximately ∼2/105 , whereas the frequency of point mutations is much more rare ( ∼1/108 per base pair ) [1] . Consequently , strategies that exploit fluorescence to detect cells that have undergone a specific point mutation within intact tissue have not yet been described . Success in studies of point mutagenesis has been achieved by isolating DNA from mouse tissues , packaging the DNA into phage particles , and subsequently detecting mutation events via phenotypic change in E . coli [91]–[95] . This process is laborious , expensive , slow , and significant expertise is required in order to obtain reliable data , which together severely limit the utility of these models . In contrast , analysis of recombinant cells within intact RaDR-GFP tissue requires minimal expertise , can be performed with standard fluorescent microscopy , and requires much less time ( e . g . , processing one RaDR-GFP tissue takes minutes , as opposed the many days that are required for analysis of point mutations ) . Nevertheless , as the underlying factors that modulate point mutagenesis are very different from those that drive HR , methods that enable studies of point mutations and HR are highly complementary . Intensive research in the past decade has given rise to sophisticated models for the molecular basis of HR , and has revealed that imbalanced HR contributes to genomic instability and cancer [75] , [96] , [97] . Here , we describe a novel mouse model that enables studies of HR in at least 11 different tissues . Here we show that HR is pervasive among mammalian tissues , that the frequency of HR is tissue-dependent , and that recombination events accumulate with age . The RaDR-GFP mice open doors to a wide range of studies . Knowledge about the extent to which HR is normally active in different tissue types is relevant to our understanding of how defects in HR lead to cancer in certain tissues . By crossing with genetically engineered mice , it is now possible to establish how specific genes impact HR throughout mammalian tissues , and furthermore how HR capacity impinges on cancer development . For example , the HR capacity of tumors that are anticipated to be HR deficient ( e . g . , those that arise in a Brca2+/− mouse model ) can potentially be formally tested in vivo using the RaDR-GFP model . In terms of exposures , HR can be monitored over time , which makes this model compatible with studies of long-term environmental conditions that are relevant to human cancer risk . Furthermore , this model can serve as a tool in the development of cancer chemotherapeutics by providing a window into tissue specific effects . In particular , the risk of secondary cancers can be reduced by developing approaches that induce HR and associated genotoxicity in the tumor , while suppressing sequence rearrangements in healthy tissues . Additionally , in terms of cancer treatment , the RaDR-GFP mice make it possible to assess the efficacy of pharmaceutical agents that are designed to either suppress or induce HR in a tumor-specific fashion . Taken together , we have demonstrated how key processes , including tissue context , aging and exposure to a DNA damaging agent , impact the risk of HR in vivo . By creating new avenues for studies of HR in multiple tissues , the work described here enables future studies of genetic , environmental , and clinical conditions that impact genomic stability in mammals .
Plasmid construction was described previously [66] . Briefly , truncated EGFP coding sequences ( Δ5egfp lacking 15 bases at the 5′ end and Δ3egfp lacking 81 bases at the 3′ end ) were amplified by PCR from plasmid pCX-EGFP , using primers that each insert unique sequences . PCR products were cloned in a tandem orientation ( Δ5egfp followed by Δ3egfp ) into the pCX-NNX backbone to form the direct repeat HR substrate , yielding plasmid pCX-NNX-ΔGF . The HR substrate was then cloned into pBigT-TpA , released together with the neomycin resistance gene and cloned into pRosa26PA [68] ( a kind gift from Dr . P . Soriano , Mount Sinai School of Medicine ) to yield the targeting plasmid pRosa26-ΔGF ( Figure S1 ) . All animals were housed and handled in Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) -accredited facilities with diets , experimental methods , and housing as specifically approved by the Institutional Animal Care and Use Committee . The MIT CAC ( IACUC ) specifically approved the studies as well as the housing and handling of these animals . The pRosa26-ΔGF targeting plasmid ( Figure S1 ) was linearized by digestion with XhoI ( New England Biolabs ) and electroporated into mouse 129 embryonic stem ( ES ) cells . Clones were selected for resistance to G418 by growing in selective media ( 40% DMEM + glucose , 40% EmbryoMax DMEM , 1% β- mercaptoethanol , 15% FBS , penicillin , streptomycin , glutamine , nonessential amino acids , LIF , G418 ) and screened for correct targeting by PCR and Southern blot . Cells from clones with correct targeting were injected into the blastocoel of 3 . 5-day-old C57BL/6 blastocysts , which were implanted into pseudopregnant female mice . All ES cell manipulations and transgenic mouse development were performed by the ES Cell and Transgenics Facility at the Swanson Biotechnology Center of the Koch Institute for Integrative Cancer Research at MIT . All procedures involving mice were approved by the Massachusetts Institute of Technology Committee on Animal Care and in accordance with the National Institutes of Health guidelines for the humane care of animals . To identify clones with correct targeting of the RaDR-GFP substrate , we used a forward primer annealing 5′ to the targeted locus and a reverse primer landing in the neomycin resistance gene within the construct , yielding a 1 . 24 kb PCR product ( Figure 1C ) . In the absence of insertion , the forward primer yields a 1 . 16 kb PCR product with a reverse primer landing within the Rosa26 locus ( Figure 1C ) . All primer sequences and exact PCR amplification conditions can be found in Tables S1 , S2 , S3 . PCR detection of the Δ5egfp , Δ3egfp , and full-length EGFP sequences was performed as described previously [66] . Embryonic stem ( ES ) cells ( 104–106 ) or RaDR-GFP mouse pancreatic cells ( ∼1000 ) were lysed with 1 ml TRIzol ( Life Technologies ) and either stored at −80°C or processed immediately . Total RNA was extracted and column purified using the RNeasy Mini Kit ( Qiagen ) . Briefly , TRIzol-lysed cells were mixed with 200 µl chloroform and centrifuged at 12 , 000 g for 15 min at 4°C . The aqueous phase was mixed with 500 µl ice-cold isopropanol and applied to an RNeasy column . The column was washed based on the manufacturer's protocol and RNA was eluted with 30 µl RNase-free water . Total RNA ( 500–2000 ng ) was converted to cDNA with the SuperScript III First-Strand Synthesis System for RT-PCR ( Life Technologies ) with both random hexamers and oligo ( dT ) . The volume was brought to 10 µl with RNase-free water and incubated at 65°C for 5 min before placing on ice for at least 1 min . Reverse transcriptase master mix was added and the reaction was incubated at 25°C for 10 min , 50°C for 50 min and 85°C for 5 min . Finally , E . coli RNase H ( 1 µl ) was added and the reaction was incubated at 37°C for 20 min to remove RNA-cDNA duplexes before proceeding with PCR . PCR detection of full-length EGFP sequences was performed with primers A FL FOR and C FL REV using Platinum Taq DNA Polymerase ( Life Technologies ) . 5 µl 10× diluted cDNA was used as the template in the presence of 0 . 2 µM primers and enzyme mix according to the manufacturer's instructions . cDNA was denatured at 94°C for 3 min , and then incubated for 40 cycles at 94°C for 45 s , 56°C for 45 s and 72°C for 1 . 5 min . Reactions were then incubated at 72°C for 5 min and placed on ice . In order to detect Δ5egfp and Δ3egfp , two primer sets were used in a single reaction . Primers E D5 FOR2 and F D5 INT REV were used to detect Δ5egfp , and primers G D3 INT FOR and H D3 REV2 were used to detect Δ3egfp . Each reaction contained 0 . 2 µM primers . PCR reactions were incubated at 94°C for 3 min , and then at 94°C for 45 s , 55°C for 30 s and 72°C for 1 min 10 s for 40 cycles . Samples were incubated at 72°C for a final 5 min and placed on ice . External PCR primers were designed to anneal upstream and downstream of the EGFP coding sequence . Primers ( 0 . 2 µM ) BPEF3 and NEST Rev were added to Platinum Taq DNA polymerase mix with 5 µl 10× diluted cDNA following the manufacturer's protocol . Reactions were incubated at 94°C for 3 min , and then for 40 cycles at 94°C for 45 s , 58°C for 30 s and 72°C for 1 min 10 s . Reactions were ended with incubation at 72°C for 5 min and then placed on ice . PCR products were purified using the MinElute PCR Purification Kit ( Qiagen ) and eluted with the same volumes of EB buffer . Purified PCR products ( 5 µl ) were used for subsequent full length EGFP PCR as described above . PCR products were analyzed by 1 . 5% agarose gel electrophoresis . Single cells from RaDR mouse spleen were sorted by FACS into 5 µl lysis buffer ( 400 ng/µl proteinase K and 17 µM SDS in nuclease-free water ) . As a control , a single colony of RaDR-GFP ES cells was also added to lysis buffer . Cell lysates were freeze-thawed once at −80°C , and added to a total volume of 50 µl Platinum Taq DNA Polymerase ( Life Technologies ) mix with 0 . 2 µM primers BPEF3 and NEST Rev ( Table S2 ) . External PCR was performed as described above . External PCR products ( 2–5 µl ) were then used for internal PCR as described above . The EGFP probing sequence was 32P-labeled by random priming ( NEBlot , New England Biolabs ) . Genomic DNA was isolated from candidate clones and digested with HindIII ( New England Biolabs ) . DNA fragments were resolved by electrophoresis and transferred to a nylon membrane ( Hybond-XL , GE Healthcare ) . The blot was incubated at 65°C in ExpressHyb ( BD Biosciences/Clontech ) with the 32P-labeled EGFP probe . The probed blot was visualized on a Storm 840 PhosphorImager ( Molecular Dynamics ) . B6 . Cg-Gt ( ROSA ) 26Sortm6 ( CAG-ZsGreen1 ) Hze/J mice ( Jackson Laboratory ) carry the green fluorescent protein gene ZsGreen1 at the Rosa26 locus driven by the CAG promoter , with an upstream STOP codon flanked by loxP sites and a downstream WPRE mRNA stabilizer . These mice were crossed with B6 . C-Tg ( CMV-cre ) 1Cgn/J mice ( Jackson Laboratory ) that carry the Cre recombinase gene driven by the CMV promoter , resulting in the deletion of loxP-flanked sequences in all tissues including the germline . Mice positive for both transgenes were then backcrossed to C57BL/6J . The resulting Cre negative progeny expressing ZsGreen1 under the CAG promoter at the Rosa26 locus were used to determine the reporter expression profile . Mice were in the C57BL/6 background , and were bred in house . All animals were housed in pathogen free barrier facilities and treated humanely with regard for alleviation of suffering . Tissues were kept in 0 . 01% trypsin inhibitor ( Sigma ) on ice for up to 16 hours before analysis . Tissues were minced with scalpel blades or with a gentleMACS tissue dissociator ( Miltenyi Biotec ) and digested with 2 mg/ml collagenase V ( Sigma ) in HBSS ( Invitrogen ) at 37°C for 45 min . After digestion , the cell suspension was triturated and filtered through a 70 µm cell strainer ( BD Biosciences ) into an equal volume of DMEM with 20% FBS on ice . Cells were pelleted at 1500 rpm for 10 minutes , resuspended in OptiMEM ( Invitrogen ) and passed through a 35 µm cell strainer ( BD Biosciences ) before flow cytometry . Cells were analyzed with a FACScan flow cytometer ( BD Biosciences ) or sorted with a MoFlo cell sorter ( Cytomation ) . Live cells were gated using forward and side scatter and then examined for fluorescence ( excitation 488 nm , emission 580/30 nm ) . For RNA extraction from spleen cells , 1000 EGFP positive or 1000 non-EGFP positive cells were sorted into 200 µl TRIzol using a MoFlo ( Cytomation ) or a FACSAria ( BD Biosciences ) cell sorter . TRIzol volumes were then made up to 1 ml and cells were stored at −80°C until RNA extraction . Whole organs were processed for imaging by compressing between coverslips to a thickness of 0 . 5 mm . The colon was cut lengthwise to expose the lumen . Tissues were imaged with a Nikon 80i microscope ( ×1 objective ) in the FITC channel using a fixed exposure time . Serial images scanning the entire tissue surface were captured using an automated stage . Images were automatically compiled using NIS Elements software ( Nikon ) or Adobe Photoshop ( Adobe Systems ) . Brightness and contrast of all images were adjusted identically in Adobe Photoshop . Fluorescent foci were either counted manually in a blinded fashion or with an in-house program written in MatLab ( MathWorks ) . Tissue surface area was determined using ImageJ ( NIH ) by manually tracing the tissue outlines . Frozen sections ( 5 µm ) were imaged with a ×60 objective in the FITC channel , stained with hematoxylin and eosin , and imaged again under visible light . Images were then overlaid manually . For each estimate of the average number of foci per cm2 , the entire organ was evaluated in order to suppress the impact of variations in foci number in different regions of each organ . Colonic crypts were isolated according to [98] , with some modifications . Briefly , tissue samples were washed with HBSS to remove any fecal material . Dissected samples ( 0 . 5 to 1 cm2 ) were treated with 1 mM EDTA , 0 . 05 mM dithiothreitol ( Sigma ) at 37°C . After incubation for 30 min , tissue samples were gently shaken in the EDTA/DTT solution by inverting the tubes to release epithelial cells . This process was repeated twice . Crypts were stained with 1 µg/ml Hoechst 33342 ( Invitrogen ) and imaged with an Axio Observer Z1 microscope ( Zeiss ) at ×10 in the brightfield , FITC , and DAPI channels . Crypt images were captured using Axiovision Rel . 4 . 8 software ( Zeiss ) and compiled with Image J 1 . 46r ( NIH ) . Images were preprocessed using median filtering , and intensity shoots identified with an extended maxima transform [99] were treated as foci candidates . Candidates were segmented using a local thresholding-based algorithm where the threshold for each focus was adaptively selected by modeling the focus as a two-dimensional Gaussian distribution . Based on intensity and morphological features extracted by preprocessing and segmentation , foci candidates were classified into true foci and noise , and foci were further classified into large bright foci and small irregular foci using a support vector machine ( SVM ) with a radial basis function ( RBF ) kernel . The SVM was trained on annotations from an experienced biologist over multiple images . Five- to seven-week-old heterozygous RaDR-GFP mice ( C57BL/6 background ) were used . DNA damage was elicited by combined treatment with N-methyl-N-nitrosourea ( MNU , Sigma ) and thyroid hormone ( T3 , Sigma ) . Details will be published separately . Briefly , T3 was administered in the diet ( prepared by TestDiet ) at 4 ppm according to [100] . MNU was administered at 25 mg/kg as an intraperitoneal injection at the time of peak cell proliferation in the pancreas induced by T3 . Control mice were fed an identical diet without T3 , and received control PBS injections . Feeding of T3 continued for 2 days after MNU injection . 3 . 5 weeks after MNU injection , mice were humanely euthanized and organs were harvested for the RaDR-GFP assay . Recombinant cell frequencies and foci frequencies do not follow a normal distribution and were therefore compared using a two-tailed Mann–Whitney test . A p value of less than 0 . 05 was considered to be statistically significant . | Cancer is a disease of the genome , caused by accumulated genetic changes , such as point mutations and large-scale sequence rearrangements . Homologous recombination ( HR ) is a critical DNA repair pathway . While generally accurate , HR between misaligned sequences or between homologous chromosomes can lead to insertions , deletions , and loss of heterozygosity , all of which are known to promote cancer . Indeed , most cancers harbor sequence changes caused by HR , and genetic and environmental conditions that induce or suppress HR are often carcinogenic . To enable studies of HR in vivo , we created the Rosa26 Direct Repeat-Green Fluorescent Protein ( RaDR-GFP ) mice that carry an integrated transgenic recombination reporter targeted to the ubiquitously expressed Rosa26 locus . Being able to detect recombinant cells by fluorescence reveals that the frequency of recombination is highly variable among tissues . Furthermore , new recombination events accumulate over time , which contributes to our understanding of why our risk for cancer increases with age . This mouse model provides new understanding of this important DNA repair pathway in vivo , and also enables future studies of genetic , environmental and physiological factors that impact the risk of HR-induced sequence rearrangements in vivo . | [
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"molecular",... | 2014 | Rosa26-GFP Direct Repeat (RaDR-GFP) Mice Reveal Tissue- and Age-Dependence of Homologous Recombination in Mammals In Vivo |
The Leishmania OligoC-TesT and NASBA-Oligochromatography ( OC ) were recently developed for simplified and standardised molecular detection of Leishmania parasites in clinical specimens . We here present the phase II evaluation of both tests for diagnosis of visceral leishmaniasis ( VL ) , cutaneous leishmaniasis ( CL ) and post kala-azar dermal leishmaniasis ( PKDL ) in Sudan . The diagnostic accuracy of the tests was evaluated on 90 confirmed and 90 suspected VL cases , 7 confirmed and 8 suspected CL cases , 2 confirmed PKDL cases and 50 healthy endemic controls from Gedarif state and Khartoum state in Sudan . The OligoC-TesT as well as the NASBA-OC showed a sensitivity of 96 . 8% ( 95% CI: 83 . 8%–99 . 4% ) on lymph node aspirates and of 96 . 2% ( 95% CI: 89 . 4%–98 . 7% ) on blood from the confirmed VL cases . The sensitivity on bone marrow was 96 . 9% ( 95% CI: 89 . 3%–99 . 1% ) and 95 . 3% ( 95% CI: 87 . 1%–98 . 4% ) for the OligoC-TesT and NASBA-OC , respectively . All confirmed CL and PKDL cases were positive with both tests . On the suspected VL cases , we observed a positive OligoC-TesT and NASBA-OC result in 37 . 1% ( 95% CI: 23 . 2%–53 . 7% ) and 34 . 3% ( 95% CI: 20 . 8%–50 . 9% ) on lymph , in 72 . 7% ( 95% CI: 55 . 8%–84 . 9% ) and 63 . 6% ( 95% CI: 46 . 6%–77 . 8% ) on bone marrow and in 76 . 9% ( 95% CI: 49 . 7%–91 . 8% ) and 69 . 2% ( 95% CI: 42 . 4%–87 . 3% ) on blood . Seven out of 8 CL suspected cases were positive with both tests . The specificity on the healthy endemic controls was 90% ( 95% CI: 78 . 6%–95 . 7% ) for the OligoC-TesT and 100% ( 95% CI: 92 . 9%–100 . 0% ) for the NASBA-OC test . Both tests showed high sensitivity on lymph , blood and tissue scrapings for diagnosis of VL , CL and PKDL in Sudan , but the specificity for clinical VL was significantly higher with NASBA-OC .
The leishmaniases are a group of vector-borne diseases caused by parasites of the genus Leishmania . The parasites are transmitted by phlebotomine sand flies and can cause , depending on the infecting species , three main clinical manifestations of leishmaniasis: visceral leishmaniasis ( VL ) , post kala-azar dermal leishmaniasis ( PKDL ) and cutaneous leishmaniasis ( CL ) including the mucocutaneous form [1] . VL is the most severe form in which the internal organs are invaded and tends to be 100% fatal if not appropriately treated . While CL and MCL are clinical manifestations as a result from replication of the parasite in the dermis and naso-oropharyngeal mucosa respectively , PKDL is a skin disorder seen in a number of treated VL patients . VL , PKDL and CL are endemic in several parts of Sudan and especially VL represents a major health problem in this country [2] . Although serological tests such as the direct agglutination test ( DAT ) [3] , [4] and the rK39 dipstick test [5]–[7] have become the mainstay in VL diagnosis [8] , parasite detection by microscopic analysis of aspirates from the lymph nodes , bone marrow or spleen is still used in some endemic regions . The diagnostic standard for CL and PKDL is microscopic analysis of tissue biopsies or scrapings . However , microscopy is hampered by its low and variable sensitivity and the need for rather invasive sampling in the case of VL . Sensitivity may be increased by prior in vitro cultivation of the parasite , but this technique is cumbersome and time consuming . Serological tests are of high value to support clinical diagnosis of VL but they are less useful in patients co-infected with HIV [9] and antibodies remain detectable for years after successful treatment [10] , [11] . Antibody detection with the rapid rK39 dipstick test is not yet implemented in Sudan due to the reported low diagnostic performance of the test in this region [12]–[14] . Molecular diagnostics such as the polymerase chain reaction ( PCR ) and nucleic acid sequence based assay ( NASBA ) offer attractive alternatives to conventional parasite detection as they are generally highly sensitive and specific . PCR detects the parasite's DNA through in vitro thermocyclic conditions [15] , while NASBA amplifies the RNA by an isothermal reaction [16] . However , broad application of these powerful techniques in diagnosis and control of leishmaniasis is hindered by their complexity and lack of standardised test formats . Recently , the Leishmania OligoC-TesT and NASBA-Oligochromatography ( OC ) were introduced as promising PCR and NASBA formats for simplified and standardised molecular detection of Leishmania parasites [17] , [18] . The tests are based on amplification of a short sequence within the Leishmania 18S ribosomal DNA ( PCR ) or RNA ( NASBA ) , followed by simple and rapid detection of amplified products in dipstick format . Both tests are available as self-containing kits including all components needed except for the Taq polymerase enzyme in the OligoC-TesT and the Nuclisense Basic Kit in the NASBA-OC due to licensing issues . The tests showed high sensitivity and specificity on experimentally prepared specimens and on clinical specimens from a limited number of confirmed cases and healthy controls ( phase I ) [17] , [18] and satisfactory repeatability and reproducibility in a multicenter evaluation study [19] . In this phase II study , we evaluated the two tests in Sudan on confirmed and suspected VL , CL and PKDL patients and on healthy endemic controls .
VL and PKDL suspects and endemic healthy controls were recruited in Gedarif State and CL suspects in Khartoum State between October 2008 and January 2009 . VL and PKDL suspects were recruited in the health centers of villages within the endemic areas while the endemic healthy controls were volunteers from the same villages but not visiting the health centers . CL suspects were recruited at the Dermatology Hospital in Khartoum . Confirmed leishmaniasis cases were given appropriate treatment in the same health center or hospital as they were diagnosed . A participant was included in the study if 2 years or older and written consent was obtained from the individual or his/her guardian . Specimen collection was performed by the Faculty of Medicine of Khartoum University . Ethical clearance for the study was obtained from the ethical committees of the Federal Ministry of Health Committee in Sudan and the University of Antwerp in Belgium . A participant was classified as ( i ) confirmed VL case if there was clinical suspicion for VL , DAT on serum was positive ( titer ≥1∶3200 ) and parasites were observed in lymph or bone marrow aspirates by microscopic analysis; ( ii ) suspected VL case with positive DAT if there was clinical suspicion for VL , DAT on serum was positive , no parasites were observed in lymph or bone marrow aspirates and no previous history of VL was reported; ( iii ) suspected VL case with negative DAT if there was clinical suspicion for VL , DAT on serum was negative , no parasites were observed in lymph or bone marrow aspirates and no previous history of VL was reported; ( iv ) endemic healthy control if there was no clinical suspicion for VL , no previous history of VL and DAT on serum was negative; ( v ) confirmed CL or PDKL case if there was clinical suspicion for CL or PKDL and parasites were observed in lesion or skin scrapings by microscopic analysis; and ( vi ) suspected CL case if there was clinical suspicion for CL and no parasites were observed in lesion or skin scrapings . Clinical suspicion for VL was defined as a history of fever for two weeks or more and splenomegaly or lymphadenopathy and for CL and PKDL the presence of skin lesions or nodules . The reference tests were performed by experienced laboratory technicians immediately after specimen taking at the collection sites as described in the WHO manual on visceral leishmaniasis [20] . Two hundred µl anti-coagulated blood ( confirmed and suspected VL cases and healthy endemic controls ) , and/or inguinal lymph node aspirate , and/or bone marrow aspirate ( confirmed and suspected VL cases ) or lesion or skin scrapings ( confirmed and suspected CL and PKDL cases ) was mixed with 200 µl of AngeroNA buffer ( Mallinckrodt Baker , USA ) . This buffer allows specimen storage without loss of DNA and RNA quality . The same specimens were used for the reference tests and for the index tests . Specimens were shipped at 4°C from the collection site to Soba University hospital laboratory of Khartoum University and stored at 4°C for a maximum of two weeks . Nucleic acids of the specimens were extracted according to the method described by Boom et al . [21] . Elution was done in 50 µl of Tris-EDTA ( TE ) buffer and stored at −20°C until analysis . The extracts were tested with the Leishmania OligoC-TesT and NASBA-OC between October 2008 and February 2009 as described by Deborggraeve et al . [17] and Mugasa et al . [18] . In brief , with the OligoC-TesT DNA of the parasite is amplified by PCR and subsequently 40 µl of denatured PCR product is mixed with 40 µl of migration buffer preheated at 55°C for at least 20 minutes followed by dipping the Oligo-strip into the solution . The NASBA-OC amplifies an RNA sequence of the parasite by NASBA reaction after which 4 µl of the amplified product is mixed with 76 µl of migration buffer preheated at 55°C and the Oligo-strip is dipped into the solution . Test results are read after 10 minutes for both tests . Executors of the index tests were trained during a one-week workshop held in June 2007 at Makerere University , Kampala , Uganda . No external quality control confirming the reference test or index test results could be performed during the study . Executors of the index tests were not blinded to the results of the reference tests . The sensitivity and specificity of the Leishmania OligoC-TesT and NASBA-OC were calculated from data entered into contingency tables . Differences in sensitivity and specificity between the two tests and differences in test results of specimen types were estimated by the Mc Nemar test . Concordances between the two tests were determined using the kappa index . All calculations were estimated at a 95% confidence interval ( 95% CI ) .
In the study the following participants were recruited: 90 confirmed and 67 suspected VL cases with positive DAT , 23 suspected VL cases with negative DAT , 7 confirmed and 8 suspected CL cases , 2 confirmed PKDL cases and 50 healthy endemic controls .
Molecular diagnostics are attractive alternatives to conventional parasite detection as they combine sensitivity with specificity . Recently , the Leishmania OligoC-TesT and NASBA-OC were developed as standardised formats for molecular detection of Leishmania parasites [17] , [18] . Here we present the phase II evaluation of the two tests on confirmed and suspected VL , CL and PKDL cases and on health endemic controls from Sudan . Although the study was set out as a phase II diagnostic evaluation , it has some limitations . A weak point is the lack of external quality control on a subset of specimens carried out by another reference laboratory . Furthermore , the executors of the index tests were not blinded to the participant classification and thus the results of the reference tests . Although we are convinced that these limitations did not influence the study results , these shortcomings should be avoided in future phase II and III evaluation trials . In addition , response to treatment was not included in the standard references as we were not able to follow up the patients after treatment . Lastly , the number of participants in some subgroups is too low to make major conclusions . The Leishmania OligoC-TesT as well as the NASBA-OC showed a high sensitivity ( >95% ) on lymph , blood and bone marrow from the confirmed VL patients . The observation that analysing lymph or blood yielded similar sensitivity as bone marrow is very promising towards less invasive diagnosis . Indeed this means that the OligoC-TesT and NASBA-OC on lymph or blood could indicate the infection status of VL suspected cases . Similar findings were reported in a study on the phase III evaluation of conventional PCR for VL diagnosis in Nepal [23] . Although the number of confirmed CL and PKDL cases in our study was limited , all were positive with both tests indicating their potential for accurate detection of the parasites in skin tissues . In addition , the high sensitivity is confirmed by the observation that the tests were able to detect Leishmania RNA or DNA in the blood or lesion scrapings of to the majority of the suspected VL and CL cases . Several cases in the suspected patient with positive DAT group might be true leishmaniasis cases given the suboptimal sensitivity of the conventional parasite detection tests . In 2008 , Deborggraeve et al . reported a sensitivity of conventional PCR on probable VL cases ( defined as clinical suspicion of VL and positive DAT test but negative in conventional parasite detection ) of 67 . 6% on blood and 71 . 8% on bone marrow [23] . The sensitivities of the index tests on the lymph node aspirates of the suspected VL cases is surprisingly low ( 34–37% ) . This might indicate that the parasite load in blood is higher than in lymph . The fact that this is not observed in the confirmed VL cases is probably due to the general higher parasite load in this patient group because of the low sensitivity of the reference test . PCR positivity in the suspected VL group with negative DAT was not significantly lower than with positive DAT . This confirms the low correlation in DAT and PCR status in these groups as observed earlier [23] . While antibody levels in the blood are a marker for the host response , the PCR/NASBA status of the blood is a marker for the presence of parasites and might therefore be complementary . PCR and NASBA can offer an added value compared to immunodiagnosis in HIV co-infected VL patients . On the 50 healthy endemic controls , the OligoC-TesT showed a specificity of 90% while this was 100% for the NASBA-OC . The positive OligoC-TesT results might be due to asymptomatic infections , which are known to be common in VL endemic regions . The inclusion of these asymptomatic carriers in the control group could be explained by the low concordance between negative DAT status and PCR outcome on blood from endemic control persons as described by Deborggraeve et al . [23] and Bhattarai et al . [24] . However , while NASBA-OC showed equal sensitivity on the confirmed and suspected VL cases , the test did not detect these assumed asymptomatic infections in the endemic control group . This discrepancy can be explained in two ways . Firstly , although not indicated by the negative controls taken along in specimen analysis , contamination of the PCR can never be fully excluded . Secondly , the OligoC-TesT might be slightly more sensitive than the NASBA-OC and thus pick up asymptomatic infections which might have very low parasite loads in the blood . The observed equal sensitivity for both tests on the confirmed and suspected VL cases might be biased by the fact that these cases are individuals presenting syndromes and thus probably have higher parasite loads than healthy parasite carriers . Hence , NASBA-OC might provide a better marker for active disease than the OligoC-TesT , as NASBA-OC does not detect asymptomatic infections while more than 95% of the active VL cases are still positive with the test . Furthermore , both tests might be useful as a test of cure after treatment of VL . As cure does not always equals parasite clearance , NASBA-OC might be more suitable than the OligoC-TesT . However , specific evaluation studies are needed to confirm this hypothesis . One should keep in mind that the PCR-OC and NASBA-OC are not yet an option for routine diagnosis at the primary care level as they require basic molecular biology lab facilities . VL typically affects populations in rural areas where health centers most often suffer from infrastructural limitations and thus only apply less sophisticated diagnostic methods . Yet , they can be valuable tools in leishmaniasis diagnosis at reference hospitals with basic molecular biology lab facilities . In addition , the evaluated tools can offer an added value in disease surveillance and epidemiological studies in which specimens are analysed at central reference laboratories . In conclusion , the Leishmania OligoC-TesT and NASBA-OC showed high sensitivity on lymph and blood of VL patients and on scrapings from CL and PKDL patients from Sudan . A significant higher specificity for active VL with the NASBA-OC than with the OligoC-TesT was observed . Both tests are not yet an option for routine diagnosis of leishmaniasis at the primary care level due to their infrastructural requirements but they might be powerful diagnostic tests in disease surveillance programmes and in monitoring intervention studies . | The leishmaniases are a group of vector-borne diseases caused by protozoan parasites of the genus Leishmania . The parasites are transmitted by phlebotomine sand flies and can cause , depending on the infecting species , three clinical manifestations of leishmaniasis: visceral leishmaniasis ( VL ) , post kala-azar dermal leishmaniasis ( PKDL ) and cutaneous leishmaniasis ( CL ) including the mucocutaneous form . VL , PKDL as well as CL are endemic in several parts of Sudan , and VL especially represents a major health problem in this country . Molecular tests such as the polymerase chain reaction ( PCR ) or nucleic acid sequence based assay ( NASBA ) are powerful techniques for accurate detection of the parasite in clinical specimens , but broad use is hampered by their complexity and lack of standardisation . Recently , the Leishmania OligoC-TesT and NASBA-Oligochromatography were developed as simplified and standardised PCR and NASBA formats . In this study , both tests were phase II evaluated for diagnosis of VL , PKDL and CL in Sudan . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"infectious",
"diseases/protozoal",
"infections"
] | 2010 | Diagnostic Accuracy of the Leishmania OligoC-TesT and NASBA-Oligochromatography for Diagnosis of Leishmaniasis in Sudan |
Tuberculosis ( TB ) is an escalating global health problem and improved vaccines against TB are urgently needed . HLA-E restricted responses may be of interest for vaccine development since HLA-E displays very limited polymorphism ( only 2 coding variants exist ) , and is not down-regulated by HIV-infection . The peptides from Mycobacterium tuberculosis ( Mtb ) potentially presented by HLA-E molecules , however , are unknown . Here we describe human T-cell responses to Mtb-derived peptides containing predicted HLA-E binding motifs and binding-affinity for HLA-E . We observed CD8+ T-cell proliferation to the majority of the 69 peptides tested in Mtb responsive adults as well as in BCG-vaccinated infants . CD8+ T-cells were cytotoxic against target-cells transfected with HLA-E only in the presence of specific peptide . These T cells were also able to lyse M . bovis BCG infected , but not control monocytes , suggesting recognition of antigens during mycobacterial infection . In addition , peptide induced CD8+ T-cells also displayed regulatory activity , since they inhibited T-cell proliferation . This regulatory activity was cell contact-dependent , and at least partly dependent on membrane-bound TGF-β . Our results significantly increase our understanding of the human immune response to Mtb by identification of CD8+ T-cell responses to novel HLA-E binding peptides of Mtb , which have cytotoxic as well as immunoregulatory activity .
Tuberculosis ( TB ) remains a major health burden with one-third of the world population infected with Mycobacterium tuberculosis ( Mtb ) and 1 . 7 million deaths annually [1] . The currently available vaccine , M . bovis Bacillus Calmette Guerin ( BCG ) , induces variable protection; although it protects well against severe childhood forms of TB , its ability to protect against pulmonary TB regardless of age is highly variable and at best partly [2] , [3] urging the search for better vaccines [4] . The HIV pandemic has increased TB disease incidence in recent years , and vaccines which are effective and safe in HIV-infected individuals are urgently needed . After decades of neglect , the search for new vaccines against TB has only recently been reinitiated , but thus far few if any vaccines have been able to induce protection superior to that of BCG in animal models [4] . Optimal vaccine development requires insight into the mechanisms of protective host immunity . The immune response elicited upon Mtb infection is complex and incompletely understood . One of the most studied effector molecules of anti-Mtb immunity is IFNγ , which is produced predominantly by CD4+ T- and NK-cells . Effector immunity mediated by CD8+ T-cells has been less well characterized , and work reported thus far has focused almost exclusively on CD8+ T-cell responses restricted by classical HLA class Ia or CD1 a , b , c molecules [5] . The human HLA class I family comprises both classical ( class Ia ) and non-classical ( class Ib ) members . The former ( i . e . HLA-A , -B , -C ) molecules are highly polymorphic and comprise of 506 , 872 and 274 different protein variants respectively , with large variations in potential bound peptides . HLA class Ib genes , instead , display limited polymorphism: there are 3 , 4 and 10 protein variants described for HLA-E , -F and -G , respectively [6] . This minor variation between class Ib alleles is remarkable and suggests an evolutionary distinct role for class Ia and class Ib molecules . Non-classical HLA class I molecules including HLA-E are able to function as antigen presenting molecules for CD8+ T-cells , and can present both self and foreign ( pathogen derived ) antigens [7]–[9] . HLA-E presented self antigens include the signal sequences of classical HLA class I molecules and sequences from TCR-Vβ chains , the latter of which are recognized by CD8+ T-cells with cytotoxic ( CTL ) activity [10] . There is only one single amino acid difference between the different HLA-E proteins; this coding variation is located at position 107 ( arginine to glycine ) on the loop between β-strands in the α2 domain of the heavy chain , outside the peptide binding groove [11] . The lack of allelic variation in the peptide binding groove limits the number of possible peptides that can bind to HLA-E , and it is highly likely that similar peptides can be presented by both HLA-E variants [11] . The frequency of both variants , called HLA-ER ( E*0101 ) and HLA-EG ( E*0103 ) is equal amongst different populations suggesting balanced selection in divers populations [12] . Whether HLA-ER and HLA-EG can display functional differences has not been studied in detail [11] , [12] , but it has been demonstrated that HLA-EG homozygous cells express higher levels of HLA-E and had higher peptide binding affinity , although this was tested in rather artificial models only [11] . Recognition of peptides presented by HLA-E may result in CD8+ effector T-cell activation [7]–[9] . For example , HLA-E can present antigens derived from pathogens including Mtb [13] , cytomegalovirus [7] and Salmonella typhi [9] . The nature of the Mtb antigen ( s ) recognized by the only two reported human HLA-E restricted CD8+ T-cell clones , which produced IFNγ after co-incubation with Mtb-infected dendritic cells , remains unknown [13] . In addition to its limited genetic polymorphism , HLA-E offers another potential advantage in relation to vaccination in the context of HIV , a highly prevalent co-infection in TB . In Southern Africa alone approximately 70% of TB-patients are also HIV-infected [14] . HIV infection down-regulates expression of HLA-A and -B molecules through its Nef proteins , thus decreasing antigen presentation capacities [15] . In contrast , HLA-E is resistant to HIV-nef-mediated down-regulation due to a single amino acid substitution in the HLA-E cytoplasmic tail [16] . Persistent expression of HLA-E during HIV infection renders the HIV infected cells resistant to NK-mediated lysis [16] . Thus , while HIV might affect antigen presenting cells of the myeloid lineage like monocytes and macrophages [17] and inhibit antigen presentation by class Ia molecules , HLA-E dependent antigen presentation is likely to be less affected by HIV co-infection . Thus , targeting Mtb specific HLA-E restricted immunity by vaccination may be a novel and advantageous approach for several reasons . Furthermore , if BCG vaccination would already be able to prime HLA-E restricted T-cell responses , HLA-E peptide based vaccines might be able to boost BCG induced responses . To date , most studies on HLA-E have focused on NK cells , which can recognize and kill target cells via cognate HLA-E/CD94-NKG2A interactions . As described above , a limited number of studies has shown that direct recognition of pathogen-derived antigen presented by HLA-E can occur via the T-cell receptor ( TCR ) [18] , [19] . Recognition of specific peptides presented by HLA-E was found to result in CTL activity directed towards the peptide presenting cell [18] , [19] . The molecular interactions between the TCR and HLA-E have not been studied extensively , and particularly , HLA-E peptide binding motifs have been determined only in relation to interactions of HLA-E with CD94/NKG2 receptors [20] . The peptide anchor residues critical for peptide binding to HLA-E might be conserved in both cases , but the residues at position 5 and 8 , which are critically involved in the interaction with NKG2 [20] may be more variable for interactions with the TCR . However , in a study analyzing recognition of CMV UL40 derived peptide by a single TCR , peptide position 8 seemed critical for discrimination between self and non-self [21] . Crystallography revealed an interaction that mimicked the typical TCR-MHC class Ia complexes [21] . T-cells restricted to the mouse equivalent of HLA-E , Qa-1 , are positively selected in the thymus , demonstrating specific recognition of Qa-1-peptide complexes in early T-cell development [22] . Thymic selection of Qa-1 restricted T-cells furthermore suggests positive selection of such T-cells in vivo as relevant immune players . Interestingly , detailed analysis of Qa-1 restricted CD8 T-cells in mice revealed suppressive capacities of these cells [23] . Suppression was specifically directed towards T-cells with intermediate avidity independent of the antigen , thus downregulating both self and non-self specific T-cells [24] . To start unraveling antigen specific human HLA-E restricted T-cell responses in the context of TB , we have used bioinformatics , HLA-E peptide-binding assays and functional characterization of the responding T-cells , in order to identify and characterize potential CD8+ T-cells that recognize Mtb peptides in the context of HLA-E .
In terms of its bound peptidome , HLA-E remains a largely uncharacterized MHC class I molecule . This prompted us to use bioinformatics and in vitro peptide/HLA-E binding affinity assays to identify possible T-cell epitopes presented by this molecule . Given the limitation of available resources , a highly guided approach was used which combined the use of legacy peptides , including known HLA-E binders ( i . e . MHC class I leader sequences ) , the use of partial binding data [20] and iterated use of discriminant analysis ( DA ) and diversity analysis to suggest peptides for testing ( Table 1 ) [25] . DA often outperforms other approaches analyzing MHC-peptide binding because the method results in classification by class , from multiple data sources , regardless of the affinity measure used to identify binders [25] . Likewise , a peptide eluted from cell-surface MHC must obviously be able to bind , with reasonable affinity . Any peptide found , by overlapping peptide scanning , to be a T-cell epitope must , again , have bound to MHC prior to T cell recognition . Thus , all of these conceptually distinct ways of classifying peptides will yield equivalent definitions of binders versus non-binders . This allows a DA-based approach to rationalize a wide array of data . Other methods may weigh contributions from high-affinity peptides more than lower-affinity peptides , whereas DA will always weigh contributions equally according to class and will therefore generate sets of peptides with a greater probability of being active [25] . This prediction was complemented by diversity analysis to identify a set of peptide sequences which combined high potential binders with high diversity in terms of position-dependent physical properties as encoded using composite scores [26] , [27] . Combination of all bioinformatics methods resulted in selection of 69 potential HLA-E binding peptides from the total Mtb H37Rv genome for detailed binding and immunological assays ( Table 1 ) . Peptides were synthesized and were tested for binding to recombinant human HLA-E*0103 in a competition assay , using a fluorescently labeled natural ligand [28] as the standard HLA-E binding peptide , and the predicted HLA-E peptides ( n = 69 ) as competitors . Binding affinity ( IC50<50 µM ) was observed for 36 out of 68 peptides tested ( 53% ) , 18 peptides had a relatively high binding affinity for HLA-E ( IC50<5 µM ) ( Table 1 ) . As found for other HLA molecules as well [29] , actual affinities determined in this biochemical cell free binding assay did not fully correlate with epitope prediction scores derived from bioinformatics ( Table 1 ) . Therefore we decided to test all 69 peptides for recognition by T-cells . All predicted HLA-E binding peptides were tested for their capacity to induce CD8+ T-cell proliferation as measured by CFSE dilution . Proliferating CD3+CD8+CD56− T-cells were gated , thus excluding NK and CD4+ T-cells . We first assessed proliferation in PBMCs from healthy adult volunteers from the Netherlands who produced IFNγ ( n = 10 ) or lacked IFNγ ( n = 10 ) in response to Mtb-derived PPD . In all cases , unstimulated PBMC failed to proliferate whereas positive control mitogen ( PHA ) stimulated cells proliferated strongly ( Figure 1 ) . Proliferative responses were defined as >10% proliferating CD3+CD8+CD56− T-cells based on CFSE dilution ( see M&M section for details ) . Each of the 10 PPD-responding individuals tested recognized one or more of the 69 peptides ( Figure 2A ) . There was significant inter-individual heterogeneity in the peptide repertoire recognized by the donors , and peptides that induced proliferation in one donor did not necessarily induce similar responses in other donors ( Figure 1 , 2A ) . Most donors recognized multiple predicted HLA-E binding epitopes ( range 1–27 , median 5 ) . Conversely , 55/69 peptides ( 79% ) were recognized by at least one PPD responder . Nine peptides were recognized by 3 independent adult donors and 2 peptides ( #14 and 62 ) were even recognized by 4 PPD responding adult donors . Peptide sequence analysis did not reveal a structural motif associated with frequent recognition ( data not shown ) . Compared to PPD responders , PPD nonresponsive donors showed much lower , though not undetectable frequencies of peptide responses ( Figure 2A , 2C; p = 0 . 028 ) . Five out of 10 non-PPD-responders did not recognize any of the peptides tested , whereas the other 5 donors recognized up to 6 peptides ( overall range 0–6 , median 1 ) . One peptide ( #13 ) which was recognized by 3 non-responder donors , revealed a broad presence in many mycobacterial strains , including M . marinum , M . ulcerans , M . smegmatis and M . leprae but likely also others , suggesting that T-cell proliferation may have resulted from cross-reactivity with ubiquitously present non-tuberculous mycobacterial species . Finally , there was no clear correlation between the number of peptides recognized and the levels of IFNγ produced following PPD stimulation in a 6-day lymphocyte stimulation test ( data not shown ) . Since some of the PPD non-responder donors responded to some of the test peptides , we decided to test cells derived from umbilical cord bloods ( UCB ) considering these to be immunologically most naïve , even though it has been demonstrated that also fetal exposure to mycobacteria can occur in utero [30] . UCBs recognized very few of the Mtb peptides with putative HLA-E binding Mtb peptides; three UCBs recognized none of the 69 peptides , one UCB recognized a single peptide and the remaining UCB recognized 2 individual peptides ( Figure 2A & 2C ) . Taken together , these results suggest that the majority of responses against putative HLA-E binding Mtb peptides in adults most likely are the result of mycobacterial exposure or infection . We next studied responses to a selection of the predicted HLA-E binding Mtb epitopes in a second , independent cohort of 10-week old South African infants that were routinely vaccinated with BCG at birth ( n = 12 ) . Proliferative responses in PBMC against predicted HLA-E binding peptides were observed in 6 out of 12 infants tested ( Figure 2B ) . All 6 infants recognized 2 or more peptides . Six infants did not recognize any of the 10 peptides tested , despite detectable PPD responses . Infant #8 recognized all peptides tested but to a variable extent ( range 23–53% of CD8+ T-cells ) . In line with the observations above the high affinity HLA-E binding peptides #62 and #48 were recognized with high efficiency . By contrast , peptide 61 was recognized by 2 BCG vaccinees , but not by any of the PPD responsive Dutch donors , suggesting possible differences in the induction of peptide specific responses between BCG vaccination and natural ( NTM ) exposure , or different ethnic groups , or both . In this setting , it was not possible to include control infants from the same geographic region since all infants routinely receive BCG within 24 hours after birth . Taken together , the results indicate that CD8+ T-cells recognizing Mtb peptides containing a predicted HLA-E binding motif are detected preferentially and with appreciable frequencies in individuals responsive to PPD as well as in infants vaccinated with BCG . Of interest also is that the observed variation in peptide recognition patterns points to a relatively large array of HLA-E presented epitopes . CD8+ T-cell proliferation against the predicted HLA-E binding Mtb peptides in polyclonal PBMC cultures might also ( partially ) have resulted from presentation by classical HLA class I molecules , due to possibly similar peptide binding motifs . To demonstrate that peptides presented by HLA-E can result in T-cell proliferation , K562 cells lacking endogenous HLA molecules but expressing only HLA-E were loaded with peptide and co-cultured with purified CD8+ T-cells . In this system no soluble peptide was present , thus also excluding T-T cell presentation [31] . CD8+ T-cell proliferation was observed in response to the selected peptides only when the target cells expressed HLA-E and had been peptide loaded . No such responses could be detected in the absence of HLA-E ( Figure 2D ) . These data demonstrate that Mtb peptides presented by HLA-E can induce CD8+ T-cell proliferation . To investigate the functional and phenotypical characteristics of the putative HLA-E binding peptide responding T-cells in more detail , we examined responses in 3 donors ( 2 , 4 , 6 ) against 4 peptides ( 54 , 55 , 62 , 68 ) using peptide specific T-cell lines . Peptides and donors were selected based on the following criteria: 1 . donors recognized multiple peptides; 2 . there was clear peptide dependent T cell proliferation in that specific donor; 3 . peptides were not recognized by PPD non-responders and 4 . sufficient PBMCs available for detailed analysis . The 3 donors were fully typed for all HLA class I alleles , and prediction algorithms were run using HLA types of the donors and the selected peptides using the syfpeithi database ( www . syfpeithi . de ) . The probability of peptide binding to HLA class Ia molecules was low for most donor-peptide combinations , further supporting that the observed CD8+ T-cell reactivity was mostly the result of HLA-E/TCR interactions . This is also supported by the above finding that K562 cells presenting Mtb peptides via HLA-E but not HLA class Ia can induce CD8+ T-cell proliferation . T-cell lines were generated by peptide stimulation and mostly displayed a CD3+CD8+CD56−CD16−TCRαβ+ phenotype , with a minority of CD56+ cells , as well as a subset expressing CD94 ( Figure 3A ) . NKG2 family receptors were not abundantly expressed; of two lines tested neither expressed the activating NKG2C receptor . In contrast one line expressed the inhibitory NKG2A receptor on about 10% of its cells . Both lines however expressed NKG2D ( Figure 3A , B ) . The majority of cells expressed “cell mediated cytotoxicity” markers: granzyme B , granulysin and low levels of perforin ( Figure 3 ) . Interestingly , several T-cell lines also displayed a phenotype partially compatible with regulatory T-cells , although not all classical markers were expressed: T-cells expressed high levels of CD25 and LAG-3 in the absence of CD127 , but lacked FoxP3 , GITR or CTLA4 ( Figure 3A ) . All 4 T-cell lines had a remarkably similar phenotype , with the exception of IFNγ production , which was produced by only 2 out of 4 lines upon activation ( Figure 3B ) . Next , to determine the cytotoxic potential of the peptide generated T-cells in the exclusive context of HLA-E , we used K562 cells expressing HLA-E in the absence of any other HLA class I molecules . Untransfected K562 cells and HLA-E/HLA-B7 signal sequence transfected K562 cells were used as controls . Cells were loaded with peptide , labeled with 51Chromium and co-cultured with peptide induced effector T-cells . HLA-E expression on target cells was verified before each experiment . Co-expression of the natural HLA-E ligand , HLA-B7 signal sequence , induced HLA-E expression on the cell surface , detectable with both the HLA-E specific monoclonal antibody 3D12 as well as the pan HLA class I antibody W6/32 , that also recognizes HLA-E ( Figure 4A ) . Similarly , peptide loading of HLA-E transduced cells also induced equal surface expression detectable by both antibodies ( Figure 4A ) . The peptide 55 specific T-cell line specifically lysed HLA-E expressing target cells loaded with peptide 55 but not peptide 68 . Control cells expressing HLA-E containing the HLA-B7 signal sequence were not recognized by these T-cells , thus confirming peptide specific recognition in the context of HLA-E ( Figure 4B , left plot ) . The results were further supported by the observation that a T-cell line generated against peptide 62 specifically lysed K562 cells loaded with peptide 62 only in the presence of HLA-E ( Figure 4B , right plot ) . Cytotoxicity was observed in all 4 T-cell lines and was consistently specific for the specific cognate Mtb peptide used to generate the T-cell line , and strictly required HLA-E expression . Taken together , these results demonstrate that T-cell lines generated against predicted HLA-E binding peptides are capable of recognizing and subsequently lysing target cells only when cognate peptide is presented by HLA-E , but not when irrelevant HLA-E binding peptides are presented . Recognition of peptide loaded targets , however , does not demonstrate direct recognition of antigens on mycobacterium infected cells . To investigate this , HLA-A , B , C fully mismatched monocytes were infected with live M . bovis BCG , labeled with 51Cr and co-cultured with peptide specific T-cell lines . All 4 T-cell lines tested were able to lyse BCG infected , but not uninfected monocytes ( Figure 4C ) , although the level of killing varied between different T cell lines . A control CD8+ cytotoxic T cell clone specific for the male HY antigen did not lyse BCG infected monocytes ( Figure 4C ) , whereas it potently recognized the male HY antigen presented by HLA-A2 ( data not shown ) . Importantly , these results demonstrate that T-cells generated against single HLA-E binding Mtb peptides can recognize live mycobacterium infected monocytes . As mentioned above , the HLA-E/peptide specific T-cells also expressed several markers that have been associated with human CD8+ Tregs , notably CD25 and LAG-3 in the absence of CD127 [32] , although they did not express FoxP3 , GITR and CTLA4 ( Figure 3 ) . Interestingly , mouse T-cells restricted to the murine equivalent of HLA-E , Qa-1 , display potent Treg activity [23] . For these reasons we decided to test whether T-cell lines specific for predicted HLA-E binding Mtb peptides also had immunosuppressive activity . T-cells were co-cultured with a well-characterized Th1 responder clone , Rp15 1-1 [32] , [33] , which recognizes Mtb hsp65 p3–13 peptide when presented by HLA-DR3 . Using this previously reported , well-standardized read-out system [32] , all 4 lines tested were found to suppress proliferation of the responder clone in a dose dependent fashion ( Figure 5A ) . To exclude the possibility that the reduced proliferation of the responder clone was the consequence of responder cell lysis , we performed a series of additional experiments . First , HLA-E expression was analyzed on the responder T-cell clone at various time points after peptide-APC induced activation . We did not observe any HLA-E expression on the responder T-cells ( data not shown ) , thus excluding the possibility that HLA-E peptide recognition resulted in direct responder cell killing . However , since alternative mechanisms might be responsible , we labeled the Rp15-1-1 responder clone and an equal number of cells of an irrelevant T-cell clone ( added to control for input cell numbers ) pulsed with low and high doses CFSE , respectively , and added both cells into the co-culture suppression assay . After 16 hours , a time point prior to division of responder cells , but sufficient for cytolysis to occur ( typically detectable after 4–5 hours ) fluorescent intensities of responder and irrelevant control T-cell clones were not altered by the addition of HLA-E/peptide induced Tregs , irrespective of the presence of the cognate peptide of the responder clone . Nevertheless , the addition of Tregs exerted strong suppression of [3H] TdR incorporation after 72 hrs . These control experiments indicate that HLA-E/peptide induced Tregs inhibited proliferation of , but did not lyse , responder T-cells ( Figure 5B ) . As shown above , the HLA-E binding Mtb peptide specific T-cell lines had both cytotoxic and regulatory activity . Dual functionality has been observed in polyclonal lines [32] , but it remains unclear whether these functions are exerted by the same or different T-cell subpopulations . To investigate single vs dual functionality , single cell derived T-cell lines or ‘clones’ were derived from limiting dilution of a polyclonal T cell line . Three out of the 5 T-cell clones obtained after expansion had potent regulatory activity ( Figure 5C ) , whereas the other 2 had no such activity . These findings imply that only a subset of HLA-E/peptide induced T cells has regulatory properties . The results also exclude that the experimental protocols used to generate T-cell lines skewed towards expansion of cells with regulatory activity . Next , 3 of the 5 T-cell clones were tested for cytotoxic activity towards peptide loaded HLA-E+ target cells . One clone ( 4G10 ) which lacked regulatory activity displayed potent cytotoxic activity , whereas 2 other clones ( 3E11 , 3C1 ) which had potent regulatory activity had only moderate cytolytic activity ( Figure 5D ) . This demonstrates that cytotoxic and regulatory activity can be , but are not necessarily mediated by the same HLA-E/peptide induced cells ( Figure 5C and 5D ) . Since suppression was not mediated by cellular cytotoxicity , alternative possibilities were explored . First , supernatants were generated by stimulating all 4 T-cell lines with cognate and control peptides in the presence and absence of HLA-E expressing APCs , and by stimulation with αCD3/28 ( to allow maximal cytokine production ) . Supernatants were collected at various time points , and then transferred to stimulated responder T-cells . None of the added supernatants was able to inhibit the responder T-cell clone , whereas the physical presence of the regulatory T-cells was able to do so ( data not shown ) , indicating that cell-cell contact is required for suppression . Several molecules have been described that can mediate cell-cell contact dependent suppression , including CTLA4 [34] , GITR [35] and , more recently , membrane bound TGFβ1 [36] , [37] . Since our regulatory T-cell-lines did not express significant levels of CTLA4 or GITR ( Figure 3A ) , these molecules were unlikely to be involved , such that we decided to examine whether membrane bound TGFβ ( mTGFβ ) might be involved . The function of active TGFβ can be inhibited by addition of latency associated peptide ( LAP ) , which reverts active TGFβ to a latent , inactive form [38] . Addition of recombinant LAP to co-culture assays indeed resulted in reversal of suppression , strongly implicating a functional involvement of TGFβ in inhibition of T-cell proliferation ( Figure 6A ) . Addition of LAP to the indicator clone only resulted in an increased proliferation of maximally 20% ( data not shown ) , similar to previous studies [36] . However the increased proliferation observed upon addition of LAP to co-cultures of Tregs and indicator clones resulted in an increase of proliferation over 40% supporting a specific reversal of suppression . Since suppression was contact dependent and acidified supernatants did not contain any TGFβ1 as measured by ELISA ( data not shown ) , we assumed that TGFβ was membrane bound . Cell surface staining using a TGFβ1 specific monoclonal antibody indeed revealed the presence of mTGFβ at the surface of activated T-cell lines ( Figure 6B ) . Taken together , these data demonstrate that mTGFβ is expressed on HLA-E restricted , Mtb peptide reactive T-cell lines , which have regulatory activity , and that its functional inhibition abrogates , in part , the suppression by these T-cells . Thus , Mtb derived peptides presented by human HLA-E are recognized by CD8+ T-cell populations that can exert both cytotoxic and immunoregulatory functions , the latter involving membrane bound TGFβ .
We describe to the best of our knowledge for the first time , Mycobacterium tuberculosis-derived peptides that can be presented by HLA-E molecules and are recognized by human CD8+ T-cells . These observations significantly extend our knowledge of the repertoire of epitopes and human antigen presentation pathways in mycobacterium specific host immunity , complementing current knowledge on the well established classical HLA class Ia , class II and CD1 group 1 presentation molecules and presentable ( peptide and non-peptide ) ligands [39] . Human CD8+ T-cells from PPD-responsive adults and BCG-vaccinated infants recognized newly identified Mtb peptides with predicted HLA-E binding motifs , resulting in T cell proliferation . The responding cells had cytotoxic activity , and lysed target cells in a peptide-specific and HLA-E dependent fashion , strongly suggesting peptide/HLA-E cognate recognition via the TCR . Moreover , these epitopes likely are also recognized during infection since live mycobacterium infected monocytes were lysed by HLA-E/peptide stimulated effector T cells . In addition to their cytolytic capacity , HLA-E/peptide reactive T-cell lines also had strong immunoregulatory properties , since they inhibited proliferation of unrelated responder T-cells . This suppression was dose-dependent , required cell-cell contact and was mediated , at least in part , by membrane bound TGFβ1 ( mTGFβ1 ) . Thus , the human CD8+ T-cell lines described here have dual functionality , in that they are able to lyse antigen loaded target cells , which may be linked to protective effector mechanisms in controlling intracellular infection , and to exert immunoregulatory activities . T cell lines or ‘clones’ derived from limiting dilution assays demonstrated that cytotoxic and regulatory activity can be , but are not necessarily mediated by the same HLA-E/peptide induced cells . It has previously been described that antigens from Salmonella typhi [9] and cytomegalovirus [7] can be presented by human HLA-E molecules . In the case of Mtb , 2 HLA-E restricted T-cell clones have been reported , yet the precise peptides from Mtb that were recognized ( which were present in Mtb-DC-conditioned medium ) remain unknown . It is also unknown to what extent these T-cell clones might represent the global response against Mtb in humans [13] . In general , the number of potential HLA-E peptide epitopes identified in the literature and the nature of T-cells recognizing them has been very limited . Interestingly , HLA-E is enriched in the Mtb phagosome compared to regular HLA class I molecules ( HLA-A2 ) , suggesting that HLA-E may have unique functions in presenting phagosomal antigens , which is particularly relevant to Mtb since it resides in immature phagosomes [40] . To the best of our knowledge , the current study describes the first large scale analysis of pathogen derived , putative HLA-E binding peptides , revealing a significant contribution of HLA-E dependent peptide presentation in pathogen recognition . Moreover , our study represents the first genome wide pathogen screening based on bioinformatic algorithms , predicting peptides with a potential HLA-E binding motif . In our experiments , epitope prediction scores , peptide binding affinity measurements , T-cell recognition and functional profiling were used to guide peptide selection . Interestingly , some of the peptides studied were not capable of competing with the high affinity natural ligand in the peptide/HLA-E competition assay , regardless of being efficiently recognized by T-cells . Thus , for prediction of HLA-E binding peptides , some caution seems warranted when selection is based solely on HLA-E binding affinity in biochemical , cell free binding assays . In addition , the results show , in line with literature on peptides bound by HLA-class Ia and II molecules , that high affinity peptide binding does not necessarily translate into high efficiency T-cell recognition . This may be due to the lack of processing , induction of tolerance or alternative binding registers not involved in TCR engagement . Although the subsequent rounds of peptide prediction ( initially motif scores were used , followed by discriminant analysis and finally diversity ranking ) and binding aimed to improve the prediction algorithms , this might not have been unequivocally successful: although numbers are sometimes small , the data suggest that peptides derived from all 3 prediction rounds bind equally well and are recognized by T-cells to a similar extent . Thus the motif was not further improved and dedicated studies should be performed for motif optimization . The abundant recognition of Mtb peptides with an HLA-E binding motif , points towards a contribution of non-classical HLA molecules to pathogen specific immunity in general and perhaps more particular for mycobacteria in view of Mtb's phagosomal localization [40] . We hypothesize that functional effector T-cells induced by HLA-E binding Mtb peptides contribute to pathogen clearance in vivo . Peptides containing a HLA-E binding motif were also recognized by infants following BCG vaccination , suggesting that antigen presentation of mycobacterial peptides in vivo can result in activation of CD8+ T-cell immunity in the context of HLA class Ib molecules . This is further supported by the observed lysis of M . bovis BCG infected monocytes by the HLA-E/peptide stimulated T-cell lines , suggesting specific recognition of mycobacterium infected target cells by HLA-E restricted T- cells . Since HLA-E is a highly conserved molecule , one might have expected more uniform recognition patterns of synthetic peptides derived from Mtb between different individuals . However , as shown in the present study , peptide recognition is rather diverse and none of the peptides is recognized by all responsive or vaccinated donors . Thus , the repertoire of HLA-E binding peptides from Mtb -and perhaps also other antigens- may be larger than initially anticipated . Nevertheless , a small number of peptides ( e . g . #62 ) was recognized by over 30% of the PPD responsive donors in 2 quite different cohorts . It remains also possible that a proportion of the response could have been due to proliferation in response to classical HLA class Ia family members that can bind the same peptides . However , in the donors tested the motifs of the recognized peptides did not conform to any of the classical HLA-A and B allele specific peptide binding motifs . Some peptides were also recognized by donors who did not have detectable T-cell responses to PPD in vitro . We do not have detailed information about these ( anonymous bloodbank ) donors and can therefore not exclude that some donors may have encountered mycobacteria in the past [41] . The fact that certain peptides in these donors were more able to induce CD8+ T cell responses compared to PPD might be due to their preprocessed nature , facilitating high efficiency antigen presentation and/or to their higher molarity , since proteins and peptides all were tested at 10 µg/ml concentrations , regardless of their molecular mass . Umbilical cord blood samples , however , displayed lower levels of proliferation , suggesting that sensitization to ( environmental ) ( myco ) bacteria had caused proliferation in non-responders . Of note , some of the peptides recognized by PPD non-responder donors are not unique to Mtb , but are also present in environmental mycobacteria and other bacteria , such that the observed responses may have been the result of cross-induced immunity . The impact of environmental ( myco ) bacteria on induction of T-cell immunity needs further study [41] . CD8+ T-cells that recognize HLA-E restricted S . typhi derived peptides can produce IFNγ and lyse infected target cells , indicating a role in host defense [9] . Such a functional contribution of HLA class Ib genes to host defense is interesting , particularly in view of the low genetic polymorphism of HLA class Ib genes . Murine studies have shown that the HLA-E homologue Qa-1 can not only be recognized by CTLs but also induce Tregs [42] . Qa-1 knockout mice had increased CD4+ T-cell responses upon infection and vaccination , due to the lack of Qa-1 restricted CD8+ Tregs [42] . Indeed , our CD8+ T-cell lines were also able to inhibit T-cell proliferation via membrane bound TGFβ . In theory , both functions may be the property of a single cell population or alternatively be expressed by 2 subpopulations . Our preliminary experiments show that single-cell derived ‘T-cell clones’ can possess either both regulatory and cytotoxic dual activity or single functionality . This demonstrates that cytotoxic and regulatory activity are not necessarily mediated by , yet can be exerted by the same cellular populations . The role of Tregs in TB infection is debated although some recent studies in murine infection models show that they preclude efficient pathogen clearance and are therefore harmful to the host [43] , [44] . Although direct evidence is lacking , indirect evidence indicates that Tregs are involved in active disease in humans as well . First , CD4+ and CD8+ Tregs were increased at the site of infection , both in mycobacterium induced granulomas [32] , [45] and at sites of extrapulmonary TB compared to the circulation [45]–[48] . Secondly , the frequency and number of Tregs was increased in the circulation of patients with active TB compared to controls , and normalized after treatment [44]–[47] . Depletion of Tregs in vitro increased IFNγ production in response to mycobacterial antigens [47] , [48] . Thus the presence and activity of Tregs is associated with ( and a biomarker of ) active TB disease . In vitro stimulation with peptides containing a HLA-E binding motif resulted in the induction of functionally active regulatory T-cells . If similar activity occurs following vaccination with putative HLA-E binding Mtb peptides , these Tregs may regulate rather than mediate pathogen eradication . In this context , it is interesting to note , however , that it was recently described that Treg activity can also contribute to host immune protection against infectious agents , by allowing a timely entry of effector cells during viral infections [49] . The relative contribution of these different functional T-cell properties remains to be clarified in future studies . Another open question is whether peptide recognition in the context of HLA-E is an exclusive gateway for CD8+ Treg induction . To decipher if HLA-E preferentially activates T-cells with regulatory properties compared to classically restricted T-cells , more detailed quantitative comparisons will be needed between classically and non-classically restricted T-cells regarding the frequency of Tregs within these populations . Until these analyses have been completed we can only conclude that HLA-E restricted cells can have regulatory properties . Previously we have described CD8+ Tregs upon in vitro stimulation with live BCG [32] , but we have not confirmed or excluded a role for HLA-E in antigen presentation to these CD8+ Tregs . However these cells had a different phenotype and used a different mechanism of suppression ( i . e . via CCL4 ) , thus suggesting that the CD8+ T ( reg ) -cell response to mycobacteria is heterogeneous and includes multiple subsets with regulatory activity . At this stage , we can only speculate why T-cells that recognize foreign , pathogen-derived peptides in the context of HLA-E might have dual ( cytolytic and regulatory ) functions . The low level of variation in HLA-E proteins suggests a similarity with pattern recognition in innate immunity , possibly with a primary default effector T-cell response as a consequence . Natural ligands of HLA-E are signal sequences derived from other HLA class I molecules; in all cases the immune system aims to avoid harmful immunity to these self-antigens . HLA-E is amongst the few HLA molecules expressed in the human throphoblast , which invades the maternal part ( the decidua ) of the placenta [50] . Expression of HLA class I alleles is necessary to evade immune surveillance by NK cells , but recognition of allo-antigens by the maternal immune system is undesirable for the fetus . Increased Treg numbers are observed in the placenta , mostly at the site of feto-maternal contact [51] , [52] , perhaps as a result of HLA-E presented peptides . Pathogen derived peptides might have hijacked this mechanism in order to be able to induce pathogen specific Tregs that can down regulate host immunity . This balance between effector and regulatory immunity in the context of HLA-E might allow partial clearance of pathogen from the host , thus providing sufficient levels of protection while avoiding excessive inflammation and pathology , but at the expense of pathogen persistence and chronic infection . Future studies need to dissect this “primordial” host immune response pathway , and to determine the relative importance of effector vs . immunoregulatory activities within the HLA-E based antigen presentation system in infection and other human diseases .
Human participation in this research was according to the U . S . Department of Health and Human Services and good clinical practice guidelines . This included protocol approval by the Leiden University Medical Center Ethics Committee and the University of Cape Town research Ethics Committee and written ( parental ) informed consent by all donors . Anonymous buffy coats from healthy blood bank donors were only used if donors had consented scientific use of blood products . A set of open reading frames corresponding to all ( predicted ) proteins from the Mtb ( H37Rv ) genome was scanned using a semi-quantitative scoring matrix to search for nonameric peptides with the potential to bind HLA-E . The matrix was adapted by a motif change and inclusion of a quantitative matrix , derived from the results of Miller et al [20] . The top scoring set of peptides was combined with a set of legacy peptides of known provenance and a set of other available peptides as potential negative controls ( peptides 1–43 ) . The resulting set of peptides was synthesized and tested in in vitro binding assays . This data was used to create a quantitative discriminant model [25]; ranking peptides in terms of their likelihood of binding HLA-E , yielding a second set of peptides ( peptides 44–50 ) . The ranked set of Mtb peptides was then subjected to diversity analysis using Z score descriptors for each position [26] , [53] , [54] to generate a third set of peptides which were synthesized and tested for HLA-E binding and immunogenicity ( peptides 51–69 ) . Peptides were made on a Syro II peptide synthesizer ( MultiSyntech , Witten , Germany ) using TentagelS AC resins ( Rapp , Tübingen , Germany ) in combination with Fmoc chemistry [55] , [56] . The purity of the peptides was checked on reverse phase C18 HPLC ( Vydac 218TP5415 , Grace , Deerfield , IL , USA ) . As the standard peptide VMAPC ( Fl ) TLLL was used . This peptide is derived from the human HLA-B*0801 leader peptide VMAPRTLLL . Fluorescence labeling of the cysteine in the precursor peptide was performed with 4- ( iodoacetamido ) fluorescein ( Fluka Chemie AG , Buchs , Switzerland ) in a mixture of 250 µl Na-phosphate buffer 0 . 15 M , pH 8 . 0 and 150 µl acetonitrile [55] . Recombinant HLA-E*0103 ( kind gift of Dr . V . M . Braud , Université de Nice-Sophia , Valbonne , France ) was overexpressed in E . coli and purified as described previously for HLA-A*0201 [57] , and then dissolved in 8M urea and stored in stock solutions ( 50 µM ) at −20°C until use . The integrity of the protein was confirmed by TOF-MALDI mass spectrometry . Human β2-microglobulin was purchased from Sigma ( St . Louis , MO ) and dissolved in H2O . HLA-E*0103 was titrated in the presence of 100 fmol fluorescent standard peptide to determine the recombinant HLA concentration necessary to bind 20–50% of the total fluorescent signal . All subsequent inhibition assays were then performed at this concentration . HLA-E*0103 was incubated in 96-well plates ( polypropylene , serocluster , Costar ) at RT ( pH7 ) for 24 h with 15 pmol β2M and 100 fmol fluorescent labeled standard peptide in assay buffer ( 100 mM Na-phosphate , 75 mM NaCl , 1 mM CHAPS ) , protease inhibitor mixture ( 1 µM chymostatin , 5 µM leupeptin , 10 µM pepstatin A , 1 mM EDTA , 200 µM pefabloc ) and 2 µl of the peptides of which HLA-E binding capacity was to be determined . As a standard peptide VMAPC ( FL ) TLLL was used . The HLA-peptide complexes were separated from free peptide by gel filtration on a Synchropak GPC 100 column ( 250mm × 4 . 6mm; Synchrom , Inc . , Lafayette , Indiana ) . Fluorescent emission was measured at 528 nm on a Jasco FP-920 fluorescence detector ( B&L Systems , Maarssen , The Netherlands ) . As HPLC running buffer , assay buffer containing 5% CH3CN was used . The percentage of peptide bound was calculated as the amount of fluorescence bound to MHC divided by total fluorescence . The concentration of peptide yielding 50% inhibition was deduced from the dose-response curve . Each peptide was tested in at least two separate experiments . Anonymous buffy coats were collected from healthy blood bank donors ( Dutch , adults ) that all had signed informed consent . No clinical information is available for the donors other than that they were healthy , and had no chronic viral infections or other contraindications for donating blood . BCG in The Netherlands is only administered to people at risk for TB exposure and the TB incidence in the Netherlands is extremely low , such that the vast majority of our donors ( >95% ) is highly unlikely to have been vaccinated with BCG , or to have had exposure to Mtb . PBMCs were isolated by density centrifugation and directly tested in a lymphocyte stimulation test to analyze their reactivity to Mtb derived antigens . PBMCs ( 5×10e5/well ) were stimulated with PHA ( 2 µg/ml , Remel , Oxoid , Haarlem , The Netherlands ) and PPD ( 5 µg/ml , Statens Serum Institute , Copenhagen , Denmark ) in Iscove's modified Dulbecco's medium ( IMDM , Invitrogen , Breda , The Netherlands ) containing 10% pooled human serum . After 6 days supernatants were tested in an IFNγ ELISA ( U-CyTech , Utrecht , The Netherlands ) . IFNγ production ≥100 pg/ml was considered a positive response [58] . Ten donors that had IFNγ responses ( ≥100 pg/ml ) to PPD stimulation as well as 10 donors that did not respond were selected for the immunogenicity screening of peptides with predicted HLA-E binding motifs . The average IFNγ production in response to PPD was 2067 pg/ml , median 1213 pg/ml ( range 275–10 , 000 pg/ml ) . Anonymous umbilical cord blood ( UCBs ) cells were provided by Dr . S . Scherjon ( dept . of Obstetrics , Leiden University Medical Center ) , all were derived from full term-pregnancies and delivery by caesarian section . As a second group we analysed BCG vaccinated infants , to examine whether BCG vaccination could elicit response against predicted HLA-E binding epitopes . Similar proliferation experiments were performed using cells from 10 week old infants ( n = 12 , from South Africa , with informed consent from their parents ) that had received BCG vaccination at birth . Peptides were selected after the screening in healthy Dutch adults was completed and included the 4 peptides selected for detailed analysis , the top 3 peptides recognized by Dutch donors and 3 high affinity HLA-E binding peptides , irrespective of recognition by Dutch donors . PBMCs were CFSE labeled ( 5 µM , Invitrogen ) , 1×10e5 cells were stimulated with predicted HLA-E binding peptides at a concentration of 10 µg/ml in IMDM with 10% human serum in the presence of 5 ng/ml IL-7 ( Peprotech , Rocky Hill , NJ ) . Positive and negative controls were included in each assay and included PHA ( 2 µg/ml ) , PPD ( 5 µg/ml ) , ESAT-6 and CFP-10 ( 10 µg/ml each ) or culture medium only . On day 7 of culture , IL-2 was added to a final concentration of 10 U/ml ( Cetus , Emeryville , CA ) . On day 10 , supernatants were collected and stored at −20°C . Cells were harvested , replicates ( n = 6 ) pooled and stained using CD3-PerCP , CD8-APC and CD56-PE ( all BD Biosciences , Alphen aan de Rijn , The Netherlands ) before acquisition on a FACS Calibur flowcytometer using CellQuest Pro software ( BD Biosciences ) or on a LSRII flowcytometer using FACS Diva software ( BD Biosciences ) . To analyze proliferation , cells were gated on lymphocytes , followed by gating on CD3+CD8+CD56− cells . The percentage of proliferation was calculated using geometric means by subtracting the geometric mean of all cells from the geometric mean of the undivided population . Subsequently the percentage was calculated by: ( delta geo mean of sample- delta geo mean of negative control ) / delta geo mean of maximal proliferation . To demonstrate that putative HLA-E binding Mtb peptide directed CD8+ T-cell proliferation in PBMC cultures can indeed be induced by HLA-E peptide presentation we performed the following experiment . CD8+ cells were purified from PBMCs from 3 donors ( 2 , 4 and 6 ) by positive selection using magnetic beads ( MACS , Milteny Biotec , Auburn , CA ) and labeled with CFSE ( 5 µM , Invitrogen ) , the CD8− fraction was irradiated at 30 Gy and added to the culture as feeders . Peptides ( 25 µg/ml ) were loaded onto K562 cells with or without HLA-E*0103 ( kind gift of Dr . E . Weiss , Ludwig-Maximilians-Universität , Munich , Germany ) [59] during an overnight period at 26°C , followed by stabilization at 37°C for at least 2 hours . After washing to remove any free peptide , peptide loaded target cells were irradiated at 50 Gy and HLA-E expression was checked by FACS . CFSE labeled CD8+ cells ( 1×104 ) were co-cultured with peptide loaded K562 cells ( 5×103 ) with or without HLA-E in the presence of irradiated self CD8− cells ( 2 , 5×104 ) in 96 well roundbottom plates ( 12 wells per condition ) . Cells were cultured in IMDM+10% human serum supplemented with 5 ng/ml IL-7 and with costimulatory antibodies ( CD28 ( 1 µg/ml , CLB , Amsterdam , The Netherlands ) & CD49d ( 1 µg/ml , BD Biosciences ) ) . On day 7 of co-culture , IL-2 was added to a final concentration of 10 U/ml ( Cetus , Emeryville , CA ) . On day 10 , cells were harvested and stained using CD3-Pe-Cy7 , CD8-APC and CD56-Alexa 700 ( all BD Biosciences , Alphen aan de Rijn , The Netherlands ) before acquisition on a LSRII flowcytometer using FACS Diva software ( BD Biosciences ) . For analysis , cells were gated on CD8+CD56− and CFSE proliferation was analysed . PBMCs were stimulated with single predicted HLA-E binding peptides ( 10 µg/ml ) in IMDM with 10% pooled human serum and 5 ng/ml recombinant human IL-7 . On day 6 of culture IL-2 was added to a final concentration of 25 U/ml . CD8+ T-cells were isolated from peptide induced T-cell lines at day 13 by magnetic bead separation ( MACS , Milteny Biotec , Auburn , CA ) . For restimulation , feeder cells were pre-pulsed with 10 µg/ml of the specific peptide for 4 hours , washed and irradiated ( 30 Gy ) , 1×10e5 prepulsed feeder cells were added to 2×10e4 T-cells in the presence of 125 U/ml IL-2 ( Cetus ) in IMDM ( Invitrogen ) with 10% pooled human serum . T-cell clones were generated by limiting dilution of T-cell lines of donor 2 which were directed against peptide 62 . Cells were stimulated with peptide pre-pulsed ( 10 µg/ml for 3 hours ) , irradiated ( 30Gy ) allogeneic feeders in the presence of IL-2 ( 125 U/ml; Cetus ) . Cell growth in the 0 . 1 cell/well conditions was considered clonal , although formal clonality assessment was not performed . Cells were maintained in IL-2 and tested for suppression and cytolytic activity at the end of the stimulation cycle . T-cell lines were analyzed in detail by flowcytometry both directly from culture as well as after overnight restimulation with αCD3/28 beads ( Dynal T-cell expander beads , Invitrogen ) . Antibodies used for staining included CD3-Pacific blue/ PE-Cy7 , CD8-Am Cyan , CD25-APC-Cy7 , CD56-PE-Cy5 , CD94-PE , CD16-Pacific blue , TCRαβ-FITC , IFNγ-Alexa 700 ( all BD Biosciences ) . Moreover , we used NKG2A-PE from Immunotech ( Mijdrecht , The Netherlands ) , NKG2C-PE and NKG2D-PE ( R&D systems , Abingdon , UK ) . In addition cells were stained for LAG-3 ( 17B4 kind gift of Dr . F . Triebel , Immutep S . A . , Chatenay-Malabry , France ) in combination with goat-anti-mouse-PE ( Dako Cytomation , Heverlee , Belgium ) ; CCL4-FITC ( R&D systems ) , FoxP3-Pe-Cy5 ( Ebioscience , San Diego , CA ) , Granzyme B-APC ( CLB , Amsterdam , The Netherlands ) , rabbit anti-human granulysin ( kind gift of Dr . A . Krensky , Stanford , CA ) in combination with goat-anti-rabbit FITC ( BD Biosciences ) . Membrane bound TGF-β1 was detected using the PE conjugated monoclonal TB21 ( IQproducts , Groningen , The Netherlands ) . Intracellular staining was done after overnight incubation with Brefeldin A ( 3 µg/ml , Sigma ) using Intrastain reagents ( Dako Cytomation ) . Samples were acquired on a LSRII flowcytometer and analyzed using FACSDiva software ( BD Biosciences ) . Scoring in Figure 3B was based on the percentage of cells that expressed the particular marker , markers expressed by 10% of the cells , or more , were considered positive . Single HLA-E expressing cells lines ( K562 cells ) were used to test the capacity of the T-cell lines to lyse target cells that present the peptide in HLA-E ( kind gift of Dr . E . Weiss , Ludwig-Maximilians-Universität , Munich , Germany ) [59] . Lines were made by transfection of HLA-E ( G variant = HLA-E*0103 ) in K562 cells , a leukemic line that does not express any HLA-class I molecules as previously described [59] . In order to obtain stable surface expression of HLA-E one of its natural ligands needs to be co-expressed; in this case this was achieved by cotransduction of the signal peptide of HLA-B7 . HLA-E expression in HLA-E transfectants was inducible by culturing the cells at 26°C for at least one day . Subsequently predicted HLA-E binding Mtb peptides ( 10 µg/ml ) were added for 16 hours at 26°C and HLA-E-peptide dependent expression was stabilized by a final incubation at 37°C for 2 hours . Transfectants were cultured in Iscove's modified Dulbecco's medium ( Invitrogen , Breda , The Netherlands ) supplemented with 10% FCS ( Greiner Bio-One B . V . , Alphen aan de Rijn , The Netherlands ) and 200 µg/ml G418 ( Invitrogen ) . Transfectants were always checked for HLA-E expression prior to experiments using FACS staining for HLA-class I ( W6/32 ) and more specifically for HLA-E ( 3D12 , kind gift of Dr . D . Geraghty , Fred Hutchinson Cancer Research Center , Seattle , WA ) . Peptide loaded HLA-E transfectants were labeled with 1 µCi 51Cr before co-culture with different ratios of T-cell lines . After 4 to 5 hours of co-culture 51Cr release was measured and specific lysis calculated using the following formula: ( sample lysis – medium ) / ( maximum lysis – medium ) [60] . To demonstrate processing and recognition of naturally presented epitopes . T-cell lines were tested for recognition of BCG infected monocytes . Allogeneic PBMCs , completely mismatched for HLA-A , B & C ( including supertypes ) for all T-cell donors , and derived from 2 different PBMC donors , were plated for 7 days ( 150 . 000 cells/ well in 96 well plates ) to obtain adherent monocytes . After washing cells were infected with live BCG ( MOI 5 , Montreal strain [32] ) and labeled with 1 µCi 51Cr over night . After washing , cells were co-cultured for 5 hours with T-cell lines and assessment of 51Cr release . A control cytotoxic CD8+ T cell clone , reactive with the male HY antigen presented by HLA-A2 was included to control for aspecific lysis of infected monocytes ( kindly provided by the laboratory of Prof E . Goulmy , department of Immunohematology and Blood Transfusion , LUMC , Leiden , previously described in [61] ) . HLA-A2 positive B-cells expressing the male HY antigen were used as natural targets for this CTL clone . T-cell lines specific for predicted HLA-E binding peptides were tested for their capacity to inhibit proliferation of a well characterized Th1 responder clone ( Rp15 1-1 ) [33] . T-cell lines were added in various doses to co-cultures containing 1×104 Th1 cells , 5×104 HLA-DR3 matched , irradiated PBMCs as APCs ( 20 Gy ) and 0 . 5 µg/ml specific Mtb hsp65 p3–13 peptide . After 3 days , proliferation was measured by [3H] TdR incorporation . To test if the reduced thymidine uptake by the responder T-cell clone ( Rp15 1-1 ) was not the consequence of cell lysis we performed a CFSE labeling experiment , which included a control CD4+ T-cell clone ( R2F10 , HLA-DR2 restricted , specific for M . leprae hsp65 p418–427 ) as indicator for the input signal . Rp15 1-1 cells were labeled with a low dose CFSE ( 0 . 005 µM ) , whereas R2F10 was labeled with a high dose of CFSE ( 5 µM ) . Both CFSE labeled responder and control clones were co-cultured with the Mtb hsp65 p3–13 peptide , HLA-DR3 matched APCs and different concentrations of Tregs . After 16 hours ( before divisions of the responder clone occur ) cells were harvested , stained for CD3 , CD4 and CD8 and analyzed by flowcytometry on a BD LSRII . The number of cells of both responder and control T-cell clones were compared: a ratio close to 1 indicates that the responder clone is not lysed by the Tregs , a ratio below 1 indicates lysis of the responder clone . Lysis of both responder and control T-cell clone can be excluded by comparison of conditions with and without Tregs . To decipher the mechanism of suppression mediated by the HLA-E restricted Tregs , we performed inhibition experiments with the Latency Associated Peptide ( LAP ) [38] . LAP binds to TGFβ1 and thereby limits its activity . In these experiments , Tregs were titrated onto the Rp15 1-1 responder T-cell clone , in the presence of peptide ( 0 . 5 µg/ml ) , APCs and different concentrations of LAP ( R&D systems ) ; [3H] TdR incorporation was assessed after 3 days of co-culture . | Mycobacterium tuberculosis ( Mtb ) has infected about one-third of the world population , resulting in fatal pulmonary tuberculosis ( TB ) in 1 . 5 million people annually . Vaccination against Mtb has decreased disease incidence in young children but does not prevent pulmonary TB in adults . The immune response against Mtb comprises multiple players , one of which is the CD8+ T-cell . CD8+ T-cells recognize infected cells because Mtb derived peptides are presented on HLA class I molecules . Here , we studied the non-classical HLA molecule HLA-E as presenter of Mtb antigens . The Mtb genome contained multiple sequences that can be presented by human HLA-E . These peptides were recognized by CD8+ T-cells from healthy individuals that were sensitized to Mtb , resulting in CD8+ T-cell proliferation . These T-cells lysed mycobacterium infected cells in a HLA-E restricted manner . Additionally , these T-cells also inhibited proliferation of other T-cells in their vicinity , a property of regulatory T-cells . The dual functionality of these T-cells makes them interesting players during infection , probably balancing their function depending on the environment . These findings contribute to our understanding of the immune response towards Mtb and will be helpful in designing new and improved vaccines against TB . | [
"Abstract",
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"Results",
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"Methods"
] | [
"infectious",
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] | 2010 | Mycobacterium tuberculosis Peptides Presented by HLA-E Molecules Are Targets for Human CD8+ T-Cells with Cytotoxic as well as Regulatory Activity |
Chromosome organizations of related bacterial genera are well conserved despite a very long divergence period . We have assessed the forces limiting bacterial genome plasticity in Escherichia coli by measuring the respective effect of altering different parameters , including DNA replication , compositional skew of replichores , coordination of gene expression with DNA replication , replication-associated gene dosage , and chromosome organization into macrodomains . Chromosomes were rearranged by large inversions . Changes in the compositional skew of replichores , in the coordination of gene expression with DNA replication or in the replication-associated gene dosage have only a moderate effect on cell physiology because large rearrangements inverting the orientation of several hundred genes inside a replichore are only slightly detrimental . By contrast , changing the balance between the two replication arms has a more drastic effect , and the recombinational rescue of replication forks is required for cell viability when one of the chromosome arms is less than half than the other one . Macrodomain organization also appears to be a major factor restricting chromosome plasticity , and two types of inverted configurations severely affect the cell cycle . First , the disruption of the Ter macrodomain with replication forks merging far from the normal replichore junction provoked chromosome segregation defects . The second major problematic configurations resulted from inversions between Ori and Right macrodomains , which perturb nucleoid distribution and early steps of cytokinesis . Consequences for the control of the bacterial cell cycle and for the evolution of bacterial chromosome configuration are discussed .
Genomic analyses have revealed that bacterial genomes are dynamic entities that evolve through various processes , including intrachromosome genetic rearrangements , gene duplication , and gene loss or acquisition by lateral gene transfer [1] . Nevertheless , comparison of bacterial chromosomes from related genera revealed a conservation of organization [2] . For example , the genetic maps of E . coli and Salmonella typhimurium that diverged from a common ancestor about 140 million years ago are extensively superimposable [1] . Multiple forces seem to shape the organization of bacterial chromosomes , and the imprinting of these processes on the chromosome is evident at different levels . DNA replication initiated at oriC proceeds bidirectionally until the two replication forks meet . Replication initiation and termination at defined loci result in guanine/cytosine skew between leading and lagging strands due to the mutational differences [3–5] . In wild-type ( wt ) cells , replication arms coincide with the two compositional skewed halves of the chromosome , hence the name of replichore [6] . Initiation of replication occurs at oriC , the origin junction of replichores , and in most cases , the two replication forks are predicted to meet at the terminal junction of replichores where skew changes [7] . Biological processes may exploit these strand-biased sequences defining each replication arm as a target for selection pressure . Two examples of positive selection at the replichore scale have been well documented in bacteria; first , the octamer χ sequence involved in the RecBCD-mediated recombination process is overrepresented 3 . 5 times in one orientation along each replichore [8] . Second , FtsK-Orientating-Polar-Sequences ( KOPS ) are overrepresented on one DNA strand ( [9] , see below ) . Beyond the replichore organization , processes affecting the genome organization at the gene level also shape chromosome structures , and two different parameters might be affected: orientation of gene transcription relative to replication , and location of genes relative to the origin of replication . Since replication and transcription occur simultaneously on the same DNA molecule , both head-on and co-oriented collisions are thought to occur in replicating bacteria . It has been originally proposed that highly expressed genes are preferentially positioned on the leading strand to allow faster DNA replication and reduce transcript losses that occur during head-on collisions [10] . In E . coli , 54% of coding sequences are found on the leading strand , and as for most bacterial species , highly expressed genes such as rRNA operons ( rDNA ) and genes encoding ribosomal proteins are transcribed in the direction of replication . However , at least in E . coli and Bacillus subtilis , essentiality , not expressiveness , selectively drives the gene-strand bias [11] . Another parameter thought to shape chromosome structure at the gene level involves the location of genes relative to the replication origin , and gene dosage effect may constrain this positioning . In fast-growing bacteria , the replication gene dosage effects are mainly associated with the elements of the translation and transcription machinery , i . e . , rDNA , transfer DNA ( tDNA ) , RNA polymerase , and ribosomal protein genes [12] . In bacteria , selection operates to maintain the two replichores of approximately equal length . In most cases , the size of the longest replichore corresponds to 50%–60% of the entire chromosome [13] . In E . coli , the constraint on the size of replication arms is ensured by the presence of ten Ter sites ( TerA–J ) scattered in two oppositely oriented groups in the terminal half of the chromosome ( [14] , Figure 1A ) . Each of the Ter sites binds Tus , the replication terminator protein , with a specific affinity . Each replication fork travels across the five Ter sites in the permissive orientation before it encounters a Ter site in the nonpermissive orientation and is blocked . The forks are thus trapped between oppositely oriented sites , defining a region called the replication fork trap . In conditions in which Tus blocks replication forks at ectopic Ter sites , creating a region impossible to replicate , the RecBCD pathway of homologous recombination and SOS induction are essential for viability [15–17] . The need for a high level of homologous recombination protein RecA and helicase UvrD accounts for the requirement of SOS induction for viability [18] . A detailed study has shown that forks blocked at Ter sites are stable; linear DNA molecules are formed upon arrival of a second round of replication forks and RecBCD-promoted recombination catalyzes the reincorporation of the double-strand DNA ( ds-DNA ) ends made by replication run off [17] . UvrD was proposed to enable replication forks initiated at recombination intermediates to progress across the Ter–Tus barrier [18] . Microscopy observations have shown that circular bacterial chromosomes are organized with a particular orientation within growing cells that preserves the linear order of loci on the DNA [19–23] . The E . coli chromosome consists of four structured macrodomains ( MDs ) and two nonstructured regions [24 , 25] . The Ori MD containing the origin of replication oriC is centered on migS , a centromere-like structure involved in bipolar positioning of oriC [26] . The Ori MD is flanked by two nonstructured ( NS ) regions called NSright and NSleft ( Figure 1A ) . The Ter MD containing the replication fork trap is centered on the terminal replichore junction . The Ter MD is flanked by two MDs called the Right and Left MDs . The existence of the four MDs and two NS regions was deduced from genetic data showing that different MDs do not interact during cell growth , but interact with their adjacent NS regions [25] . Several important processes take place in the Ter MD . First , replication ends in the Ter MD because of the presence of the replication fork trap . Second , the replichore junction diametrically opposed to oriC is the region of the change in compositional skew defining the two replichores [7] . The site-specific recombination site dif is present near the replichore junction and allows the resolution of chromosome dimers into monomers; to be active , dif must be present in a zone of converging KOPS [9 , 27] . KOPS are recognized by FtsK which translocates the DNA directionally in order to align dif sites at the septum where XerCD can resolve chromosome dimers into monomers ( for review , see [28 , 29] ) . Third , the Ter MD contains two Non-Divisible Zones ( NDZ ) refractory to inversions ( [30] , see below ) . Genetic approaches have provided experimental evidence that some chromosome rearrangements are detrimental for growth or , in rare cases , refractory to inversions [30–37] . Using homologous recombination , intrareplichore inversions ( Intra ) of segments with one endpoint located in the 20%–30% region flanking the terminal replichore junction , i . e . , the periphery of the Ter MD , have been shown to be reproducibly highly problematic or prohibited in E . coli ( for review , see [38] ) . However , these regions are not refractory to inversions by the site-specific recombination system used here [25 , 37] . Inversions that split the Ter MD are detrimental for growth and delay cell division [37] . In a previous study , we have generated strains with chromosomes carrying inverted segments using the λ site-specific recombination system [25] . Interestingly , we noticed that strains carrying combinations of partner att sites located in the same regions of the chromosome have similar phenotypes upon inversion , and many of the inversions seemed to affect cell physiology . The results reported here allow us to define extents and limits to plasticity in the E . coli chromosome . The analysis of detrimental rearrangements allowed the identification of two types of chromosome inversions that , by changing MD organization , severely affect the progression of the cell cycle .
By using the site-specific recombination system of bacteriophage λ , we previously developed a genetic system that allows the construction and detection of genetic inversions in the E . coli chromosome [25] . We have constructed several series of strains containing one defined att site at a fixed position and its att partner site inserted at random locations; strains carrying combinations of partner att sites that could give rise to viable recombinants have been selected . Cassettes were designed to detect inversion between att sites: recombination between attL and attR restores lacZ integrity ( Figure 1B ) . By providing a limiting amount of recombinase , we were able to reveal the existence of MDs that correspond to large regions that are insulated from each other in the cell ( Figure 1A ) . By providing a high amount of recombinase , recombination between most of the combinations of att sites can be detected , and there is a good correlation between the frequency of collisions and the frequency of recombinants [25] . There was no correlation between the frequency of inversion and the physiological properties of cells with inverted configuration; inversions occurring at high frequency can be detrimental , whereas those occurring at low frequency can be neutral ( see below ) . We now analyze in detail the properties of strains carrying chromosomes with the different inverted configurations . To unravel the consequences of inverting a chromosomal segment on cell physiology , we performed a number of analyses aimed at detecting defects visible at the colony or cell level . The size of colonies from strains with the inverted chromosome ( lacZ reconstituted , blue colonies ) was compared to that of strains with the wt configuration ( white colonies ) in rich medium . The effect of these inversions on growth was also measured using a coculture assay in which strains with chromosomes in wt and inverted configurations were compared ( Materials and Methods ) . To analyze the consequences of the inversion on the nucleoid morphology , cells grown in exponential phase were stained with DAPI , and nucleoids were observed by fluorescence microscopy ( see Materials and Methods ) . The percentages of cells with different types of nucleoids were numbered according to the cell size . The number of chromosome origins was estimated by fluorescence-activated cell sorting ( FACS ) analysis . Viability of strains was tested in different genetic backgrounds affected in pathways related to DNA metabolism . We used recA mutants and since RecA is required for both homologous recombination and SOS induction , the requirement of SOS induction for viability was tested in lexA ind− ( SOS− Rec+ ) and recA lexAdef ( SOS+ Rec− ) mutants . When RecA was required , we used mutants affected in the two RecA-dependent recombination pathways , i . e . , RecBC and RecFOR , to identify the pathway involved . Measurements of SOS induction were performed in a sfiA background to avoid SfiA-dependent filamentation and inhibition of cell division [39] . SOS induction was quantified in culture by using a plasmid carrying the uidA gene encoding β-glucoronidase under the control of the PsfiA promoter ( see Materials and Methods ) . In addition , the presence of a plasmid carrying a gfp gene under the control of the PSfiA promoter allowed the direct visualization of the induction of the SOS response at the cellular level . tus mutants were used to estimate the defects provoked by inverted Ter sites in various configurations . When recombinant colonies could not be obtained , PCR reactions probing the presence of recombination at the DNA level were used to check for the occurrence of attL–attR inversion and for the presumed lethality conferred by the inversion . Ten Ter sites are found in the E . coli chromosome , which are bound in vitro by Tus with varying efficiencies [14] . The Kobs for Tus binding to the very strong TerB site is about 5 × 10−13 M , and the relative arrest activity was estimated to be around 95% . Although studies were not performed with other Ter sites , the effect of mutations in TerB mimicking the sequence of other Ter sites allows estimation of their respective strength as deduced from Tus–Ter binding affinity measurements and from measure of the replication arrest activity [14] . TerA–E and TerG are predicted to be very strong sites ( arrest activity greater than 50% ) , TerH moderately strong ( arrest activity around 33% ) , and TerF and TerI–J weak sites ( arrest activity less than 20% ) . To estimate the respective strength of Ter sites in vivo in a strain producing wt levels of Tus , we have generated strains in which different Ter sites are inverted , and their properties have been analyzed ( Figures S1 and S2 , and Text S1 ) . Altogether , the results indicate that efficiency of replication arrest at different chromosomal Ter sites correlates with the predictions based on in vitro affinities and on replication arrest activity of TerB mutant sites . They show that in conditions of wt level of Tus protein , blocking the two forks by the strong TerE and TerA sites renders RecBCD-dependent recombination essential for viability , as previously observed with TerA [15] . SOS induction is also essential in these conditions . The effect of the moderate site TerH in the inverted orientation was less severe , but still significant . Inverted weak TerI and TerJ sites do not appear to affect growth , suggesting they do not significantly impede replication ( Figure S1 and Text S1 ) . On the E . coli chromosome , the two replichores are of similar size , suggesting that most replication forks meet within the replication fork trap diametrically opposite to the origin . To evaluate the requirements for the balance of replication arms , we analyzed strains in which inversion endpoints are in each replication arm , asymmetrically relative to oriC ( interreplichore inversion [Inter] , Figure 2A , Table 1 ) . As the inverted region contains oriC , these inversions do not change the orientation of sequences or genes relative to replication . However , because the two endpoints are at different distances from oriC , the size of the replication arms are modified , one becoming greater and one smaller than 50% of the chromosome . Imbalance of 5%–10% for replication arms has no effect on colony morphology: the colonies with inverted configurations are similar to those with wt configuration ( Figure 2B , 47% for the short arm and 53% for the long arm ( 47–53 ) in strain Inter R-L3 ( Table 1 ) , and 42–58 in strain Inter R-L5 ( Table 1 ) ) . The effect of these inversions on growth was also measured using the coculture assay containing strains with either a chromosome in wt or inverted configuration: no defect was associated to this genetic rearrangement as the ratio of inverted to wt cells was close to one after 60 generations ( Figure 2C and unpublished data ) . The cells and nucleoids of strains with either configuration were not distinguishable ( Figure S3 ) . When the imbalance reached 15% ( 36–64 in Figure 2B , strain Inter R-NSleft1 in Table 1 ) , some defects became apparent . The recombinant colonies were smaller than noninverted colonies , and the ratio of inverted to wt cells after 60 generations was affected ( 0 . 13 ± 0 . 02 ) , but the cells and nucleoids of the inverted configuration were similar to those of the wt configuration: only 2% of the cells appeared abnormal ( Figure 2D ) . Around 20% of imbalance ( 30–70 in Figure 2B , strain Inter R-NSleft2 ) , the size of colonies carrying the inversion was affected; in coculture assays , the ratio of cells with inversion to wt configuration was less than 0 . 01 ( Figure 2C ) and microscopic observation showed longer cells with abnormal nucleoids ( 14% of abnormal cells in Inter R-NSleft2 , Figure S3 ) . Above 20% of imbalance ( 23–77 and 18–82 in Figure 2B , strains Inter R-NSleft4 and Inter R-O1 , respectively , in Table 1 ) , colonies were barely visible , and more than 20% of cells displayed condensed nucleoids , i . e . , a par phenotype , or grew as cells with unsegregated nucleoids ( Figure 2D and Figure S3 , respectively ) . Interestingly , we noticed that all strains with an imbalance greater than 20% , i . e . , with a replication arm smaller than 30% and the other larger than 70% , were dependent on RecA for viability ( Figure 2B and Table 1 ) . Recombinant colonies could be obtained in a recFOR background , but not in conditions inhibiting either RecBC DNA recombination or SOS induction , indicating that the RecBC homologous recombination pathway is required for viability in the presence of an imbalance of replication arms greater than 20% ( Table 1 ) . The dependence on RecA for viability was suppressed by a tus deletion , indicating that the impediment of replication forks by Tus at Ter sites is responsible for lethality in a recA background ( Figure 2B ) . Finally , a 2- to 4-fold SOS induction was apparent in strains that required recA for viability ( Table 1 ) . The analysis performed with inverted Ter sites indicated that in cells expressing wt levels of Tus , the replication forks are stopped at the first strong Ter site in the nonpermissive orientation ( [15] and Text S1 ) . It implies that , when the imbalance is smaller than 20% , the two forks of a same replication round can progress to the replication fork trap . In contrast , RecBC-dependent recombination is solicited to restart the first fork that reaches a Ter site before the other fork can reach it when the imbalance of replication arms is larger than 20% . We propose that , in the conditions used , when the shorter replication arm is less than half the longer one , it is fully replicated twice before completion of replication of the longer arm , leading to the formation of DNA double-stranded ends . These double-stranded ends induce the SOS response and are lethal in the absence of RecARecBC-dependent homologous recombination . Many natural inversions in bacterial genomes are symmetrical with respect to replication origins and termini . Scatter plots of the conserved sequences between related species produce an X-shaped pattern , called X-alignment [2] . These rearrangements reveal that selection operates to maintain replichores of similar lengths; in most genomes , the size of the longest predicted replication arm does not exceed 60% of the chromosome [13] . By moving the position of the replication fork trap on the genetic map , we have been able to analyze the effect of varying the imbalance of replication arms . Remarkably , we did not observe negative effects when the imbalance was around 10% , in total agreement with the observed size distribution of replichores in different species . Some defects appeared when the imbalance reached 15% , and recombinational rescue of replication forks was required above 20% . The analysis of interreplichore inversions affecting at the same time two MDs revealed that making hybrid MDs while keeping the wt replichore junction unaffected was well tolerated ( Figure S3 ) . We noticed that for similar levels of imbalance less than 20% , inversions involving endpoints located either in the NS regions or in the Left , Right , and Ter MDs ( Figure S3 and Table 1 ) behave similarly: the growth of recombinant colonies was slightly affected , and recombinants were viable in a recA background ( Table 1 ) . It is only when the imbalance exceeded 20% that recombinant colonies were affected and their formation recA-dependent ( Table 1 ) . Altogether , these results suggest that in the context of interreplichore inversions , the effect of MD disorganization for the Left , Right , and Ter MDs can be well tolerated by the cell . We noticed that large inversions inside a replichore ( intrareplichore ) with one endpoint in the Ori MD and the other in the NSright region gave rise to recombinants with no strong defects . Three examples of strains with such rearrangements are shown in Figure 3 . These inversions encompass 916 , 927 , and 668 kb corresponding to 826 , 828 , and 607 genes , including four , three , and one rDNA operons , respectively ( Figure 3A , strains Intra O-NSright1 to −3 in Table 1 ) . Similar outcomes were obtained in the left replichore . For example , the inversion of a 982-kb–long segment that changes the orientation of 942 genes , including two rDNA operons and 34 ribosomal protein genes ( strain Intra L-NSleft1 in Table 1 ) had no detectable detrimental effects ( Figure 3B and unpublished data ) . The colonies of strains with the rearranged chromosome had the same size as those with the wt configuration ( Figure 3B ) . The diagram shown in Figure 3C indicates that even the largest inversion has no detectable effect on nucleoid morphology . No strong defect was associated with these genetic rearrangements , because the ratio of inverted to wt configuration was above 0 . 75 after 60 generations in coculture assays ( Figure 3D ) . Finally , colonies carrying these inverted configurations were viable in a recA background , indicating the absence of important DNA damage ( Figure 3B ) . Altogether , these results indicate that the direction of replication can be inverted through hundred of genes , including rDNA genes , without important consequences for growth . Furthermore , the results show that inversions between Ori MD and the NS regions are well tolerated . Similar conclusions were obtained from the analyses of intrareplichore inversions between NS regions and the flanking Right or Left MD in the absence of active Ter sites ( Figure S4 and Table 1 ) . Therefore , gene orientation , gene dosage , and sequence skews appear to operate only as long-term positive selection determinants . Our results are in agreement with the evidence [11 , 40] that weakens the proposed influence of replication on gene orientation [41 , 42] . However , given the large size of bacterial populations , slightly deleterious effects that can be accredited to positioning rDNA and ribosomal protein genes on the lagging strand are most likely sufficient to eliminate such configurations from the population in long-term evolution . In contrast to well-tolerated inversions described above , two types of intrareplichore inversions were highly detrimental for the cell: the first type involved endpoints located in the Ter and the Right MDs , and provokes the separation of the replication fork trap from the wt replichore junction . The second type involved inversion between endpoints located in the Ori MD and in the Right MD . Features of these two detrimental configurations are described in detail below . Intrareplichore inversions with endpoints in the Right and Ter MDs ( Figure 4A , strains Intra R-T1 to −3 in Table 2 ) generate a hybrid Right-Ter MD in which the orientation of TerA , TerD , and TerE is modified , creating a replication arms imbalance close to 35%–65% ( see intra R-T1 in Figure 4B ) . These strains carry two zones of converging KOPS ( Figure 4C ) : the normal one corresponding to the wt replichore junction , and a new one associated with the replication fork trap in the hybrid Right-Ter MD . Inversion severely affected the growth of colonies ( Figure 4D ) . The observation of cells with the inverted configuration revealed the occurrence of a high proportion of abnormal cells: 27% of cells showed a par phenotype , 15% formed cells with unsegregated DNA , and 1% of cells were anucleate ( strain Intra R-T1 in Figures 4E , 4F , and S5 ) . Cells larger than 10 μm with a high amount of nonsegregated nucleoids were observed . FACS analyses indicated that the number of chromosomes in the large cells ranged from 16 to 32 ( unpublished data ) . Other strains with intrareplichore inversions between Right and Ter MDs ( Intra R-T2 and −3 in Figure 4 and Table 2 ) showed the same features ( unpublished data ) . The origin of the detrimental phenotypes caused by this chromosomal configuration was analyzed by testing different genetic backgrounds ( Figure 4G ) . It was not possible to obtain viable recombinants in a lexA ind− background , i . e . , in SOS-defective conditions . SOS induction was directly visualized by the use of a plasmid expressing gfp under the control of PSfiA promoter ( Figures 4F and S5 ) . Homologous recombination was also required because recombinants with the inverted configuration could not be obtained in a recA , recBC , or in a recA lexAdef background ( i . e . , in conditions of constitutive SOS induction , but in the absence of RecA-dependent recombination ) . The phenotype and RecA-independence of interreplichore Right-Ter inversions ( Figure S3 , strains Inter R-T1 to −4 in Table 1 ) suggests that intermingling Right and Ter MDs cannot by itself be responsible for the growth defects of strains Intra R-T1 to −3 in the inverted configuration . Growth defects and RecA dependence for viability were suppressed in a tus background , indicating that the position of the displaced replication fork trap is responsible for the growth defects ( Figure S5E ) . The detrimental effects can not originate only from imbalance of replication arms because the imbalance of replication arms is close to 35–65 , a level that does not render RecA essential for viability in interreplichore inversions ( Figure 2 and Table 1 ) . Three other hypotheses that might account for the growth defects were tested below: the positioning of dif outside of the replication fork trap , the presence of two zones of converging KOPS , and the merging of replication forks far away from the wt replichore junction region . In these intrareplichore Right-Ter inversions , the replication fork trap is separated from dif . It was previously reported that the dif site does not need to be present in the replication fork trap to be fully active because the insertion of a ectopic TerA* site near TerA , moving the replication fork trap away from the dif region , did not affect dif activity [43] . dif is active in any new replichore junction formed after inversion [9 , 27] . After deletion of dif from its normal position , we reinserted a 28-bp fragment corresponding to dif in the new replication fork trap , in the region where KOPS converge , far away from the wt replichore junction ( Figure 4C , strain Intra R-T2 Δdif difRFT in Table 2 ) . Strains carrying this inverted configuration still showed strong detrimental defects and were not obtained in a recA background ( Table 2 ) even though insertion of dif in the new replication fork trap improved nucleoid distribution in a way suggesting dif activity , i . e . , by removing a 12%–15% fraction of filaments ( 47% of abnormal cells instead of 64% in the absence of dif , and 50% when dif is present at its normal location; unpublished data ) . These results indicate that the absence of dif from the new replication fork trap is not responsible for the observed defects . These results were corroborated by the viability of Inter Right-Ter inversions in a recA background when dif was deleted ( strain Inter R-T2 Δdif in Table 2 ) , confirming that the RecA dependence for viability of Intra Right-Ter inversions does not result from the lack of dif in the replication fork trap . To analyze the defects provoked by forming two zones of converging KOPS and by positioning the replication fork trap far from the dif region but without Right and Ter MDs intermingling , we constructed strains with an inversion that positioned the replication fork trap at the limit between the Ter and the Right MDs ( Intra T1 in Figure 4C , and strains Intra T1 and Intra T2 in Table 2 ) . Colony formation was not affected; cells and nucleoids were similar to those of the wt configuration , and strains carrying inversions were viable in a recA background ( Figure 4C and 4D ) . These results indicate that as long as sequences belonging to the Ter MD remain together , merging of replication forks far away from the wt replichore junction in the presence of two zones of converging KOPS does not provoke important growth defects . To determine whether merging of replication forks outside the Ter MD may be responsible for the detrimental effects of intrareplichore Right-Ter inversions , we generated two different genetic inversions in the Right MD ( strain Intra R3 in Figure 4C , and strains Intra R3 and Intra R4 in Table 2 ) that inverted TerE in a strain in which TerA and TerD are deleted; inversion of the TerE region provoked replication to end in the Right MD , and generated two zones of converging KOPS ( Figure 4C ) and an imbalance of replication arms close to 35–65 ( Table 2 ) . Recombinant colonies were slightly affected compared to those with a wt configuration ( Figure 4D ) ; cells and nucleoids from both configurations were similar ( unpublished data ) , and recombinants were viable in a recA background ( Figure 4D ) . These results indicate that replication forks can merge in the Right MD without affecting viability . Because none of the simple modifications in the chromosome structure can , by itself , account for the growth defect of intrareplichore Right-Ter inversions , we tested whether the defect was dependent on the length of the Right MD that separates the replication fork trap from the wt replichore junction in the Intra R-T1 configuration . We constructed strain Intra R-T4 ( Figure 4C and Table 2 ) . In this strain , the chromosome configuration is similar to Intra R-T1 , Intra R-T2 and Intra R-T3 configurations , but the extent of sequences belonging to the Right MD that are embedded in the Ter MD is reduced ( 170 kb compared to 420 kb ) . Recombinants showed fewer defects; only a fraction of cells ( 13% ) showed a par phenotype , and less than 1% formed cells with unsegregated nucleoids ( Figure S6 ) . Importantly , strains in the inverted configuration were viable in a recA background ( Figure 4D ) . These results are in agreement with the hypothesis that the extent of Right MD DNA that separates the Ter region where fork merge from the replichore junction region is responsible for the observed defects . The combination of the embedding of Ter sequences in the Right MD and finishing replication within these Ter sequences is responsible for the deleterious effect . The shortening of the region of Right MD that separate the replication fork trap from the wt replichore junction region suppresses the defects . Together with the observed viability of recA- interreplichore inversions involving the Right and the Ter MDs ( strains Inter R-T1 and R-T2 in Figure S3 and Table 1 ) , these observations support the hypothesis that the requirement of RecA for the viability of intrareplichore Right-Ter inversions results from the separation of the replichore junction region from the region in the Ter MD where replication ends . It is therefore tempting to speculate that in deleterious configurations resulting from intrareplichore inversion , replication ends in the displaced part of the Ter MD , activities normally associated to the wt replichore junction region cannot be performed , and the cell cycle is affected . Altogether , these results suggest the existence of a tight temporal and/or spatial coupling between the end of DNA replication in the Ter MD and an unknown activity near the replichore junction region required to progress in the cell cycle . Further work will be required to determine whether proteins known to function near the terminal replichore junction , FtsK [44] and TopoIV [45] , are involved in this coupling . The second class of detrimental inversions corresponds to intrareplichore inversions that combine Ori MD with the Left or Right MD . Because most of the inversions between the Left and Ori MDs also induce a high imbalance of replication arms , we focused our study on the inversion between Ori and Right MDs . The detrimental effects of intermingling Ori and Right MDs were revealed by combining attL and attR sites inserted at various positions in the Right ( 14′ , 17′ , 19′ , and 22′ ) and Ori ( 0 . 7′ , 97′ , 95′ , 94′ , 92′ , 88′ , 87′ , and 86′ ) MDs ( Figure 5A , Table 2 , and unpublished data ) . Viable recombinants could be obtained only when the inversion involved sites located between 92′ and 0 . 7′ in the Ori MD , and they all showed strong growth defects ( proximal Ori–Right combinations , strains Intra O-R1 to −5 in Figure 5A [indicated in grey] and 5B ) . In contrast , viable recombinants could not be obtained when the inverted fragment extended from the Right MD to 88′ , 87′ , or 86′ ( distal Ori-Right combinations , strains Intra O-R6 to −8 indicated in black in Figure 5A ) . Proximal inversions invert the TerHI sites as inversions between Right MD and NSright region ( control c in Figure 5A , Intra R-NSright3 in Table 1 ) , whereas distal inversions invert both TerHI sites and the centromere-like sequence migS found at 89′ . migS did not seem to be responsible for the difference observed between the two types of combinations since distal combinations did not give rise to viable recombinants in the absence of migS ( unpublished data ) . In the absence of both TerH and TerI ( ΔTerHI in Figure 5C ) , viable recombinant colonies with no strong growth defects could be obtained for proximal inversions , and they are viable in a recA background ( strains Intra O-R1 to −3 in Figure 5C and Text S1 ) . Distal inversions remained lethal on rich medium when both TerH and TerI were deleted ( Figure 5D and unpublished data , and strains Intra O-R6 to −8 in Table 2 ) , but viable colonies could be obtained on minimal growth medium ( Figure 5D ) . These recombinants showed growth defects in minimal medium supplemented with casamino-acids ( Figure 5D ) and could not be propagated in rich medium ( unpublished data ) . The proximal Ori–Right inversions that gave rise to viable colonies were used for microscopy analysis ( strains Intra O-R1 to −3 in Table 2 ) . In the presence of TerH and TerI , we observed a predominant filamentation with DNA accumulating in nonsegregated nucleoids ( e . g . , 39% of filaments and 10% of par-like cells in the inverted configuration of Intra O-R3 in Figure 5E , and unpublished data ) . Analysis of the nucleoids of recombinant colonies deleted for TerH and TerI revealed a high percentage of normal cells . However , in the inverted configurations , a significant proportion of cells formed filaments ( 5% , 9% , and 22% , according to the strain , Figure 5F and 5G , and unpublished data ) . Remarkably , these filaments were different from those observed in all other rearrangements described in this study; they showed apparently segregated nucleoids with no division septa between DNA bodies ( Figure 5G ) . To visualize the defects responsible for the absence of viability in distal Ori–Right combinations in rich medium , cells obtained in minimal medium were grown in liquid rich medium and observed at different time points . After 180 min , cells with inverted configurations accumulated a fraction of abnormal cells ( 16% of filaments not observed in wt cells; Figure 5H and unpublished data ) , whereas after 300 min , most of the cells were elongated , with improperly compacted and segregated nucleoids ( Figure 5H and 5I ) . Altogether , these results indicate that intermingling Right and Ori MDs interferes with nucleoid management and formation of the division septum . For both proximal and distal combinations involving Ori–Right MDs , the presence of longer cells with an increased number of segregated nucleoids indicates an inhibition in the formation of a septum of division . It is striking to note that inversions that move the Ori MD close to the Right MD ( less than 50 kb from the Right MD; strain Intra O-NSright4 in Table 1 , Figure S7 ) slightly affect cell physiology . It is therefore likely that the origin of the defect results from an antagonism between Ori and Right MDs rather than from simply moving Ori sequences on the genetic map . The observation that distal inversions were more problematic than proximal ones suggests that the deleterious effects are proportional to the length of the MD . In B . subtilis , the chromosome partitioning and sporulation protein Spo0J binds eight parS sites scattered in the 800-kb region flanking oriC [46] . Our results suggest the presence of similar specific sequences in the Ori MD . Imbedding of such putative sequences in the Right MD could be the reason for growth defects . In this regard , it is interesting to note that the NSright region separates Ori and Right MDs and could play a buffer role . We would like to speculate that mixing Ori and Right MD sequences would perturb proper segregation of Ori and Right MD , a step necessary to establish septum division . Further experiments will be necessary to determine whether the perturbation of the spatial control of cytokinesis affected by this type of inversion involves SfiA [39] , MinCDE [47] , SlmA [48] , or other unidentified proteins . Altogether , the results reported here give an important insight into the role of MDs in cell cycle control by chromosome configuration in E . coli ( Figure 6 ) . The Ter MD is involved in a process that spatially and/or temporally couples the end of replication in the Ter MD with a subsequent step near the replichore junction region . The antagonistic Ori and Right MDs are involved in a process coupling chromosome segregation and cytokinesis . Identification of determinants or factors that specify MDs should help us understand how MDs are involved in the control of these processes .
E . coli K12 strains are all derivatives of MG1655 . Standard transformation and transduction procedures used were as described before [25] . Plasmids and strains with relevant genotypes are described in Table S1 . Conditions for inversion formation were as described previously [25] . SOS response was quantified by measuring the amount of β-glucoronidase [49] in sfiA cells transformed by a pBAD18-derived plasmid carrying the uidA gene fused to the sfiA promoter . Similar results were obtained in a sfiA+ background , but results were less variable in a sfiA background . SOS induction was visualized by using cells transformed by a P15A derivative carrying gfp under the control of the sfiA promoter ( pZA-PsfiA-gfp ) . The cultures were grown to optical density ( OD ) 0 . 2 at 30 °C and then processed for microscopy or flow cytometry . For the microscopy analysis , the cells were processed as described before [45] . For flow cytometry analysis , chromosome numbering was estimated by counting the number of replication origins using a rifampicin/cephalexin replication run-out [50]; aliquots were taken every 10 min over a period of 200 min . Cells were fixed in a 75% ethanol–PBS 1× solution , then washed in PBS 1× , treated with RNaseA , and the DNA was then stained with propidium iodide . The cells were analyzed on a Partec PASIII flow cytometer . Strains carrying the inverted configurations were grown in coculture with the same strain carrying the wt configuration . A 1:1 mixture of the two strains was grown in serial cultures in LB medium at 30 °C for up to 70 generations . Every 10 generations , the relative numbers of both configurations were determined by plating . Experiments were performed in triplicate . | Genomic analyses have revealed that bacterial genomes are dynamic entities that evolve through various processes including intrachromosome genetic rearrangements , gene duplication , and gene loss or acquisition by gene transfer . Nevertheless , comparison of bacterial chromosomes from related genera revealed a conservation of genetic organization . Most bacterial genomes are circular molecules , and DNA replication proceeds bidirectionally from a single origin to an opposite region where replication forks meet . The replication process imprints the bacterial chromosome because initiation and termination at defined loci result in strand biases due to the mutational differences occurring during leading and lagging strands synthesis . We analyze the strength of different parameters that may limit genome plasticity . We show that the preferential positioning of essential genes on the leading strand , the proximity of genes involved in transcription and translation to the origin of replication on the leading strand , and the presence of biased motifs along the replichores operate only as long-term positive selection determinants . By contrast , selection operates to maintain replication arms of similar lengths . Finally , we demonstrate that spatial structuring of the chromosome impedes strongly genome plasticity . Genetic evidence supports the presence of two steps in the cell cycle controlled by the spatial organization of the chromosome . | [
"Abstract",
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] | 2007 | Chromosome Structuring Limits Genome Plasticity in Escherichia coli |
Mutations in Nesprin-1 and 2 ( also called Syne-1 and 2 ) are associated with numerous diseases including autism , cerebellar ataxia , cancer , and Emery-Dreifuss muscular dystrophy . Nesprin-1 and 2 have conserved orthologs in flies and worms called MSP-300 and abnormal nuclear Anchorage 1 ( ANC-1 ) , respectively . The Nesprin protein family mediates nuclear and organelle anchorage and positioning . In the nervous system , the only known function of Nesprin-1 and 2 is in regulation of neurogenesis and neural migration . It remains unclear if Nesprin-1 and 2 regulate other functions in neurons . Using a proteomic approach in C . elegans , we have found that ANC-1 binds to the Regulator of Presynaptic Morphology 1 ( RPM-1 ) . RPM-1 is part of a conserved family of signaling molecules called Pam/Highwire/RPM-1 ( PHR ) proteins that are important regulators of neuronal development . We have found that ANC-1 , like RPM-1 , regulates axon termination and synapse formation . Our genetic analysis indicates that ANC-1 functions via the β-catenin BAR-1 , and the ANC-1/BAR-1 pathway functions cell autonomously , downstream of RPM-1 to regulate neuronal development . Further , ANC-1 binding to the nucleus is required for its function in axon termination and synapse formation . We identify variable roles for four different Wnts ( LIN-44 , EGL-20 , CWN-1 and CWN-2 ) that function through BAR-1 to regulate axon termination . Our study highlights an emerging , broad role for ANC-1 in neuronal development , and unveils a new and unexpected mechanism by which RPM-1 functions .
The mammalian Nuclear Envelope Spectrin repeat proteins ( Nesprins ) ( also called Syne-1/Enaptin and Syne-2/NUANCE ) mediate the anchorage of nuclei in multinucleated cells such as muscle [1] , [2] , and mediate nuclear movement and positioning in mononuclear cells [3] , [4] . The orthologs of Nesprin-1 and 2 are called MSP-300 in Drosophila and abnormal nuclear Anchorage 1 ( ANC-1 ) in C . elegans . MSP-300 and ANC-1 also function in nuclear anchorage , and regulate positioning of organelles including mitochondria and the endoplasmic reticulum [5] , [6] . Nesprin family members are attached to the nuclear envelope by the SUN proteins ( SUN 1 and 2 ) , which together compose the Linker of the Nucleoskeleton and Cytoskeleton ( LINC ) complex [1] , [6] . A C-terminal Klarsicht/ANC-1/Nesprin homology ( KASH ) domain anchors Nesprin-1 and 2 in the outer nuclear membrane by binding to SUN1 and 2 , which are localized to the inner nuclear membrane . C . elegans has two SUN family proteins: Uncoordinated 84 ( UNC-84 ) ( retains ANC-1 in the nuclear membrane; expressed in most somatic cells ) and SUN-1 ( functions in the germ line and early embryo ) . Tandem calponin homology domains at the N-terminus of the Nesprins mediate binding to the actin cytoskeleton . Nesprin-1 and 2 have functions outside of their role in nuclear anchorage . Nesprin-1 and 2 regulate centrosome orientation in migrating cells and ciliogenesis [3] , [4] and regulate formation and trafficking of the Golgi [7] . Importantly , mutations in Nesprin-1 and 2 are associated with numerous diseases including: autism [8] , [9] , cerebellar ataxia [10] , Emery Dreifuss muscular dystrophy [11] , cancer [12] , arthrogryposis [13] , and cardiomyopathy [14] . Genome-wide association studies have also identified single nucleotide polymorphisms in Nesprin-1 that are associated with schizophrenia [15] and bipolar disorder [16] , [17] . Nesprin-1 and 2 perform several functions at the neuromuscular junction ( NMJ ) and in the central nervous system ( CNS ) . At the NMJ , multiple nuclei are anchored in clusters directly adjacent to the postsynaptic terminal . Nesprin-1 is enriched on these postsynaptic nuclei [18] , and required for their clustering [19] . Nesprin-1 is required for axon termination of motor neurons that innervate the diaphragm [2] . Nesprin-1 and 2 are also expressed in neurons of the CNS [18] , where they function in neuronal migration and neurogenesis by mediating connections between the nucleus and the cytoskeleton [4] , [20] . Of particular note , Nesprin-1 shows extremely strong , broad expression in the adult murine CNS , which suggests that Nesprin-1 is likely to have an important function in neurons beyond the role it plays in neural precursor migration and neurogenesis ( Allen Brain Atlas: http://mouse . brain-map . org ) [21] . This is consistent with the observation that an extremely small splice variant of Nesprin-1 called Candidate Plasticity Gene 2 ( CPG2 ) regulates synaptic plasticity [22] . At present , it remains unclear if Nesprin family members play broader roles in neuronal function and development outside of their roles in very early developmental events such as neurogenesis , and neural migration . The role of Nesprin-1 and 2 in signal transduction has begun to be explored , but remains relatively poorly understood . In vascular smooth muscle cells , small isoforms of Nesprin-2 regulate Erk MAP kinase signaling [23] . Studies using a keratinocyte cell line showed that Nesprin-2 binds to α- and β-catenin , and regulates the nuclear localization of β-catenin [24] . While these studies demonstrate that Nesprin-2 has the potential to regulate signal transduction , the broader functional consequences of these activities remain unclear . Further , it remains unknown if Nesprin-1 and/or Nesprin-2 mediate signal transduction in neurons . Members of the Pam/Highwire/RPM-1 ( PHR ) protein family are large signaling proteins that include: human Pam ( also called MYCBP2 ) , murine Phr1 , zebrafish Phr1 ( Esrom ) , Drosophila Highwire , and C . elegans Regulator of Presynaptic Morphology 1 ( RPM-1 ) [25] . The PHR proteins are important regulators of neuronal development that function in axon outgrowth and termination [26]–[28] , axon guidance [29]–[31] , and synapse formation [32]–[34] . PHR proteins also function in axon regeneration [35] , [36] and axon degeneration following damage [37] , [38] . The PHR proteins regulate several conserved signal transduction pathways [39]–[45] . However , it is poorly understood if PHR protein activity is linked to signaling by extracellular guidance cues , morphogens , or adhesion molecules . Work in Drosophila and C . elegans has shown that Highwire negatively regulates BMP signaling [46] , and RPM-1 negatively regulates Slit and Netrin signaling [29] . However , it remains unclear if PHR protein activity converges with extracellular cues on common signaling targets . It is also uncertain if the PHR proteins have the ability to positively regulate , modify or enhance signals generated by extracellular cues . Using a proteomic approach in C . elegans , we have identified ANC-1 as an RPM-1 binding protein . Similar to rpm-1 , anc-1 functions in both axon termination and synapse formation . Our analysis indicates that anc-1 functions in a genetic pathway with beta-catenin/armadillo related protein 1 ( bar-1 ) downstream of rpm-1 . Further , we identify the Wnt signaling mechanisms that regulate BAR-1 to control axon termination . Our observations provide the first evidence of a link between RPM-1 signaling and the Wnt ligands that regulate neuronal development .
To better understand the mechanism of how RPM-1 functions in neuronal development , we previously performed a proteomic screen to identify RPM-1 binding proteins [42] . Briefly , RPM-1 fused with Green Fluorescent Protein ( GFP ) was transgenically expressed using the native rpm-1 promoter . This construct was purified from whole worm lysate using an anti-GFP antibody , and RPM-1 binding proteins were identified using mass spectrometry and de novo peptide sequencing . To date , our screen has successfully identified three functional RPM-1 binding proteins: Gut Granule Loss ( GLO-4 , a putative Rab GEF ) [42] , RNA Export factor 1 ( RAE-1 , a microtubule binding protein ) [43] , and Protein Phosphatase Mg2+/Mn2+ dependent 2 ( PPM-2 , a PP2C phosphatase ) [47] . Our screen also identified the F-box Synaptic Protein 1 ( FSN-1 ) [42] , which was previously discovered using a genetic approach [45] . Importantly , GLO-4 , RAE-1 , and PPM-2 are not targets of RPM-1 ubiquitin ligase activity . Thus , our proteomic screen preferentially identified RPM-1 binding proteins that are not degraded by RPM-1 , and are stable interaction partners . Another RPM-1 binding protein identified in our proteomic screen was ANC-1 , a gigantic protein that is composed of 8545 amino acids and has an approximate molecular weight of 956 kDa . ANC-1 consists mostly of predicted coiled regions ( including six repeats that are nearly identical at the nucleotide level ) , two N-terminal calponin-homology ( CH ) domains that bind to actin , and a C-terminal KASH domain that targets ANC-1 to the nuclear envelope ( Figure 1A ) . Previous work showed that ANC-1 is present at the nuclear envelope and in the cytoplasm of all post-embryotic somatic cells [6] . Our proteomic analysis identified 10 peptides that covered 6 . 3% of the total ANC-1 protein sequence ( Figure S1 ) . The majority of peptide sequence identified was from the ANC-1 specific repeats , presumably because repeat sequence is present at 6-fold molar excess over other regions of ANC-1 . To confirm the biochemical interaction between ANC-1 and RPM-1 we utilized coimmunoprecipitation ( coIP ) from whole worm lysates generated from transgenic animals . A prior study developed polyclonal anti-ANC-1 antibodies that recognize endogenous ANC-1 , which was detected as multiple high molecular weight bands in immunoblots [6] . We used these anti-ANC-1 antibodies in coIP experiments with transgenic animals expressing RPM-1::GFP ( juIs58 ) . When RPM-1::GFP was immunoprecipitated using an anti-GFP antibody , coprecipitating ANC-1 was detected as multiple high molecular weight bands ( Figure 1B , juIs58 ) . Further examples of this coIP are shown in Figure S2 . Coprecipitating bands were absent or strongly reduced in intensity in juIs58; anc-1 mutants demonstrating that these bands represent endogenous ANC-1 ( Figure 1B ) . Coprecipitating ANC-1 was not detected in precipitates from non-transgenic animals ( Figure 1B , N2 ) . Thus , ANC-1 did not bind non-specifically to the agarose beads or the anti-GFP antibody , which demonstrates that the interaction between ANC-1 and RPM-1 is specific . These biochemical results confirm that RPM-1 binds to ANC-1 , or a protein complex that contains ANC-1 . Previous studies have shown that rpm-1 regulates synapse formation in the GABAergic dorsal D ( DD ) motor neurons [32] . The DD motor neurons innervate , and inhibit the dorsal muscles of the worm ( Figure 2A , schematic ) . The presynaptic terminals of DD neurons can be visualized in living animals using the transgene juIs1 , which uses a cell specific promoter ( Punc-25 ) to express a fusion protein of Synaptobrevin-1 and GFP ( SNB-1::GFP ) [48] . In wild type animals , SNB-1::GFP localized to evenly sized puncta that were uniformly positioned along the dorsal nerve cord ( Figure 2A ) . In rpm-1 ( ju44 ) mutants , SNB-1::GFP puncta in the dorsal nerve cord were abnormally aggregated ( Figure 2A , arrowheads ) , and there were regions of the cord lacking any puncta ( Figure 2A , arrows ) . Quantitation showed that the number of SNB-1::GFP puncta in rpm-1 mutants was significantly lower than wild-type animals ( compare 11 . 9±0 . 4 SNB-1::GFP puncta/100 µm for rpm-1 with 21 . 9±0 . 4 puncta/100 µm for wild type , Figure 2B ) . These findings are consistent with results from previous studies [32] . Importantly , previous electron microscopy studies showed that the defects in SNB-1::GFP puncta localization in rpm-1 mutants reflect defects in synapse formation , rather than defects in the formation of presynaptic terminals or the trafficking of synaptic vesicles [32] , [39] . Subsequent studies have also shown that milder defects in organization of SNB-1::GFP puncta , such as those that occur in fsn-1 mutants , are also due to defects in both pre and postsynaptic terminals [49] . Our observation that ANC-1 binds to RPM-1 led us to hypothesize that anc-1 might function in synapse formation similar to rpm-1 . To test this hypothesis , we analyzed two alleles of anc-1 , e1873 and e1753 . DNA sequencing confirmed that e1873 is a nonsense mutation that results in a severely truncated protein ( Figure 1A ) [6] . The molecular nature of the lesion in e1753 remains unknown . However , a previous study showed that antibodies raised against the repeat region of ANC-1 fail to detect all isoforms of ANC-1 greater than 175 kDa in both anc-1 ( e1873 ) and anc-1 ( e1753 ) mutants [6] . Thus , e1873 and e1753 are likely to be molecular null alleles of anc-1 . Previous studies have shown that anc-1 loss of function ( lf ) results in abnormal nuclear anchorage ( Anc phenotype ) of the nuclei in the syncytial cells that form the hypodermis of C . elegans [6] . The hypodermal nuclei can be visualized using a transgene , kuIs54 , which expresses Suppressor of Activated LET-60 Ras ( SUR-5 ) ::GFP [50] . In wild-type animals , the hypodermal nuclei are anchored to the cytoskeleton , and distributed in a well organized , even pattern ( Figure 1C , arrowheads ) . Consistent with previous results , we observed that anc-1 mutants display an Anc phenotype , in which the nuclei are no longer anchored and aggregate dramatically ( Figure 1C , arrows ) . With regard to synapse formation , anc-1 mutants had normal spatial distribution and number of SNB-1::GFP puncta ( Figure 2A and B ) . We also constructed double mutants of anc-1 with two members of the RPM-1 pathway , fsn-1 and glo-4 . FSN-1 is an F-box protein that binds to RPM-1 and mediates RPM-1 ubiquitin ligase activity [45] . GLO-4 binds to RPM-1 , and is a putative guanine nucleotide exchange factor for a Rab pathway [42] . We found that fsn-1; anc-1 and glo-4; anc-1 double mutants had enhanced defects in synapse formation compared to single mutants ( compare 14 . 0±0 . 5 puncta/100 µm for fsn-1; anc-1 ( e1873 ) with 18 . 3±0 . 3 for fsn-1 , Figure 2A and B ) . rpm-1; anc-1 double mutants had similar defects to those observed in rpm-1 single mutants ( Figure 2A and B ) . Additionally , we constructed rpm-1 ( +/− ) ; anc-1 animals and found enhanced defects in synapse formation compared to rpm-1 ( +/− ) animals ( Figure 2A and B ) . These results are consistent with several conclusions . First , because we used null alleles , we conclude that anc-1 functions in a parallel genetic pathway to both fsn-1 and glo-4 to regulate synapse formation . Second , our observation that rpm-1; anc-1 double mutants were not enhanced , in a phenotypic assay that is not saturated [45] , suggests that anc-1 functions in the same genetic pathway as rpm-1 . This conclusion is further supported by our observation that rpm-1 ( +/− ) ; anc-1 animals had enhanced defects in synapse formation . Third , these results support the conclusion that ANC-1 is not negatively regulated by RPM-1 and , therefore , is unlikely to be a target of RPM-1 ubiquitin ligase activity . If this were the case , we would expect to see suppression of synapse formation defects in anc-1; rpm-1 double mutants similar to what was shown for Dual Leucine Zipper-bearing Kinase 1 ( DLK-1 ) , a known target of RPM-1 ubiquitin ligase activity [39] . We next sought to dissect the mechanism of how anc-1 regulates synapse formation . A previous study found that Nesprin-2 ( a mammalian ortholog of ANC-1 ) regulates nuclear localization of β-catenin , thereby potentially regulating canonical Wnt signaling [24] . When Wnt signaling is not active , β-catenin is degraded; when Wnt signaling is activated , β-catenin accumulates , enters the nucleus , and interacts with T Cell Specific Transcription Factor ( TCF ) /Lymphoid Enhancer Binding Factor ( LEF ) family transcription factors to promote gene expression [51] . Previous work in C . elegans has shown that Wnt signaling regulates neuronal development [52]–[54] . In addition , Abnormal Cell Lineage 23 ( LIN-23 ) , an F-box protein that negatively regulates β-catenin in C . elegans , regulates the abundance of postsynaptic glutamate receptors in the ventral nerve cord [55] , as well as axon termination in mechanosensory neurons [56] . Both of these developmental events are also regulated by rpm-1 [27] , [57] . Based on this prior work , we hypothesized that anc-1 may function as a genetic link between rpm-1 and β-catenin signaling . To test this hypothesis , we started by determining if β-catenin regulates synapse formation in the DD motor neurons . In C . elegans , there are four β-catenins that have diverged to perform separate functions [58] , [59] . The canonical Wnt pathway operates through the β-catenin homolog BAR-1 and a single TCF homolog , Posterior Pharynx Defect 1 ( POP-1 ) [60] . To test the role of bar-1 in synapse formation , we analyzed a null allele of bar-1 , ga80 [61] . Consistent with a prior study , we observed that small , consistent sections of the dorsal cord were absent in bar-1 ( lf ) mutants ( data not shown ) [62] . However , we were able to analyze synapse formation in sections of the dorsal cord that formed normally . bar-1 mutants showed a distribution and number of SNB-1::GFP puncta that were similar to wild-type animals ( Figure 3A and B ) . In contrast , fsn-1; bar-1 double mutants showed enhanced defects in synapse formation ( compare 14 . 1±0 . 4 SNB-1::GFP puncta/100 µm for fsn-1; bar-1 with 18 . 3±0 . 3 puncta/100 µm for fsn-1 , Figure 3A and B ) . The enhanced phenotype in fsn-1; bar-1 double mutants was similar to what was observed for fsn-1; anc-1 double mutants ( Figure 3B ) . This result suggested that anc-1 and bar-1 may function in the same genetic pathway . To test this possibility , we constructed double and triple mutants between bar-1 , anc-1 and fsn-1 . anc-1; bar-1 double mutants were not enhanced compared to bar-1 and anc-1 single mutants ( Figure 3A and B ) . Likewise , fsn-1; anc-1; bar-1 triple mutants were not enhanced compared to fsn-1; bar-1 or fsn-1; anc-1 double mutants ( Figure 3A and B ) . To determine if BAR-1 regulates synapse formation by acting through a canonical signaling pathway that includes the TCF transcription factor POP-1 , we analyzed the role of pop-1 in synapse formation . Because null alleles of pop-1 are lethal , we opted to analyze a hypomorphic allele of pop-1 , q645 . The POP-1 protein produced by q645 shows reduced interaction with BAR-1 due to a mutation in the β-catenin binding domain [63] . Similar to our findings with bar-1 , pop-1 ( lf ) animals were largely wild type , but pop-1; fsn-1 double mutants had enhanced defects in synapse formation that were of similar severity to fsn-1; bar-1 double mutants ( compare 14 . 6±0 . 4 SNB-1::GFP puncta/100 µm for pop-1; fsn-1 with 18 . 2±0 . 3 puncta/100 µm for fsn-1 , Figure 3A and B ) . To determine if bar-1 functions in the same pathway as rpm-1 , we constructed bar-1; rpm-1 double mutants . We observed no change in the severity of synapse formation defects in bar-1; rpm-1 double mutants compared to rpm-1 single mutants ( Figure 3B ) . As a whole , our results support several conclusions . First , anc-1 , bar-1 and rpm-1 function in the same genetic pathway to regulate synapse formation . Second , the anc-1/bar-1 pathway acts in parallel to fsn-1 to regulate synapse formation . Finally , bar-1 is likely to regulate synapse formation by functioning through a canonical Wnt signaling pathway that includes the TCF transcription factor pop-1 . Previous work showed that rpm-1 regulates axon termination in the mechanosensory neurons that sense soft touch [27] . The posterior lateral microtubule ( PLM ) and the anterior lateral microtubule ( ALM ) mechanosensory neurons are an excellent system in which to study axon termination . Each PLM and ALM neuron extends a single axon that terminates extension at a precise anatomical location [64] . In addition , these neurons are easily visualized using a transgene ( muIs32 ) that expresses GFP specifically in the mechanosensory neurons [65] . C . elegans contains two PLM neurons that sense soft touch in the posterior of the animal's body . Each PLM neuron has a single axon that terminates extension prior to the cell body of the ALM neuron ( Figure 4A , schematic ) . In rpm-1 ( lf ) mutants , PLM axons fail to terminate extension properly , grow past the ALM cell body and hook towards the ventral side of the animal ( Figure 4A ) [27] , [42] . This defect , which we refer to as a “hook” , is highly penetrant in rpm-1 mutants ( 87 . 6±1 . 5% , Figure 4B ) . Likewise , rpm-1 mutants have highly penetrant axon termination defects in the ALM neurons ( Figure S3A ) [27] , [42] . To determine whether anc-1 also functions in axon termination , we analyzed anc-1 animals as well as double mutants of anc-1 and members of the rpm-1 signaling pathway . Both anc-1 ( e1873 ) and anc-1 ( e1753 ) showed PLM hook defects that were significant compared to wild-type animals , but occurred with extremely low penetrance compared to rpm-1 mutants ( 3 . 4±0 . 8% for anc-1 ( e1873 ) and 1 . 9±0 . 8% for anc-1 ( e1753 ) , Figure 4B ) . fsn-1; anc-1 double mutants showed enhanced penetrance of defects compared to single mutants ( compare 33 . 2±2 . 4 for fsn-1; anc-1 ( e1873 ) and 27 . 5±4 . 6% for fsn-1; anc-1 ( e1753 ) with 9 . 1±1 . 1% for fsn-1 , Figure 4B ) . Likewise , glo-4; anc-1 double mutants also showed enhanced penetrance of defects compared to single mutants ( compare 36 . 9±1 . 9% for glo-4; anc-1 ( e1873 ) with 17 . 7±4 . 3% for glo-4 , Figure 4B ) . We observed similar results when the function of anc-1 was analyzed with regard to axon termination in the ALM neurons ( Figure S3A ) . These results demonstrate that anc-1 regulates axon termination by functioning in a parallel pathway to both fsn-1 and glo-4 . We also analyzed rpm-1; anc-1 double mutants , which had the same penetrance of defects as rpm-1 single mutants . This result suggests that anc-1 functions in the same genetic pathway as rpm-1 ( Figure 4B ) . Consistent with this conclusion , we observed enhanced axon termination defects in both the PLM and the ALM neurons of rpm-1 ( +/− ) ; anc-1 animals ( Figure 4B and Figure S3A ) . Having established that anc-1 regulates axon termination by functioning in the same pathway as rpm-1 , we wanted to test if anc-1 functions cell autonomously in the PLM neurons to regulate axon termination . Due to the enormous size of the anc-1 gene , our first approach was to transgenically overexpress a dominant negative fragment of ANC-1 . Previous work by Starr and Han showed that a C-terminal fragment of ANC-1 that contains the KASH domain ( see Figure 1A , C-terminal domain aa 8200–8546 ) acts as a dominant negative by blocking binding of endogenous ANC-1 to the SUN domain protein UNC-84 , thereby preventing ANC-1 localization to the nuclear envelope [6] . We engineered fsn-1 mutants with transgenic extrachromosomal arrays that used a strong , pan-neuronal promoter ( Prgef-1 ) to overexpress either the dominant negative ANC-1 , or mCherry as a negative control . We observed enhanced penetrance of axon termination defects in fsn-1 mutants expressing the ANC-1 dominant negative ( 44 . 9±4 . 6% ) compared to animals expressing mCherry ( 8 . 9±2 . 6% , Figure 4C ) . Transgenic overexpression of dominant negative ANC-1 in wild-type animals did not give a significant phenotype ( Figure 4C ) . These results are consistent with ANC-1 functioning in neurons to regulate axon termination . To more directly address cell autonomy , we used a cell specific promoter , Pmec-3 , to transgenically overexpress the ANC-1 dominant negative specifically in the mechanosensory neurons . In fsn-1 mutants , when the mec-3 promoter was used to overexpress the ANC-1 dominant negative we observed enhanced penetrance of axon termination defects ( 38 . 8±3 . 3% ) compared to fsn-1 mutants that transgenically overexpressed mCherry ( 17 . 9±2 . 5% , Figure 4C ) . Thus , transgenic overexpression of dominant negative ANC-1 , specifically in the mechanosensory neurons , enhances fsn-1 ( lf ) to levels that are similar to what we observed in fsn-1; anc-1 double mutants ( Figure 4C ) . To provide further evidence that anc-1 functions cell autonomously in the mechanosensory neurons , we generated fsn-1; anc-1 double mutants that carried a transgenic extrachromosomal array containing an anc-1 mini-gene and a promoter , Pmec-7 , that drives expression specifically in the mechanosensory neurons . In this case , we observed a strong , but partial rescue of the enhanced axon termination defects in fsn-1; anc-1 double mutants ( compare 20 . 5±1 . 9% for fsn-1; anc-1+Pmec-7::ANC-1 with 37 . 6±3 . 2% for fsn-1; anc-1 , Figure 4C ) . Transgenic expression of ANC-1 in the surrounding muscle cells using the myosin 3 ( myo-3 ) promoter did not rescue defects in fsn-1; anc-1 double mutants ( Figure 4C ) . Our transgenic analysis supports several conclusions . First , the dominant negative and rescue experiments demonstrate that ANC-1 functions cell autonomously in the mechanosensory neurons to regulate axon termination . Second , our results with the dominant negative indicate that ANC-1 needs to be associated with the nuclear envelope via its C-terminal KASH domain in order to regulate axon termination . Finally , the lesion in anc-1 causes the enhanced axon termination defects observed in fsn-1; anc-1 double mutants . Our genetic analysis indicated a role for bar-1 in synapse formation , and we next sought to determine if bar-1 also functions in axon termination of the mechanosensory neurons , similar to anc-1 . While axon termination defects were observed at very low penetrance in the PLM neurons of bar-1 ( lf ) mutants , these defects did not reach statistical significance ( Figure 5A ) . However , the penetrance of axon termination defects was enhanced in bar-1; fsn-1 double mutants ( 25 . 8±2 . 5% hook ) compared to fsn-1 single mutants ( 9 . 1±1 . 1% , Figure 5A ) . This level of enhancement was similar to what we observed in fsn-1; anc-1 double mutants , which suggested that bar-1 might function in the same pathway as anc-1 to regulate axon termination . To test this , we generated anc-1; bar-1 double mutants and fsn-1; anc-1; bar-1 triple mutants . As shown in Figure 5A , anc-1; bar-1 double mutants did not show enhanced PLM axon termination defects . Similarly , fsn-1; anc-1; bar-1 triple mutants were not enhanced compared to fsn-1; bar-1 double mutants and fsn-1; anc-1 double mutants ( Figure 5A ) . Similar results were observed for bar-1 regarding axon termination in the ALM neurons ( Figure S3B ) . Thus , bar-1 and anc-1 regulate axon termination by functioning in the same genetic pathway . Given that BAR-1 regulates the transcription factor POP-1 , we also tested if pop-1 functions in axon termination . Similar to bar-1 , pop-1 ( lf ) mutants had axon termination defects that occurred with very low penetrance , and pop-1; fsn-1 double mutants were enhanced compared to single mutants ( compare 47 . 7±4 . 3% for pop-1; fsn-1 with 9 . 1±1 . 1% for fsn-1 ) . Importantly , pop-1; bar double mutants were not enhanced consistent with pop-1 and bar-1 functioning in the same genetic pathway ( Figure 5A ) . Consistent with the results from PLM neurons , ALM axon termination defects were enhanced in pop-1; fsn-1 double mutants , but failed to show enhancement in pop-1; bar-1 double mutants ( Figure S3B ) . These results are consistent with bar-1 functioning through pop-1 to regulate axon termination . To address if bar-1 and rpm-1 function in the same genetic pathway to regulate axon termination , we generated rpm-1; bar-1 double mutants . As shown in Figure 5A , rpm-1; bar-1 double mutants had similar penetrance of hook defects to rpm-1 single mutants . Given that ANC-1 functions cell autonomously in the mechanosensory neurons , we also wanted to determine if BAR-1 functions cell autonomously . To do so , we generated fsn-1; bar-1 double mutants that transgenically express BAR-1 using a cell specific promoter . We performed our analysis on fsn-1; bar-1 double mutants because the penetrance of axon termination defects were higher than in bar-1 single mutants . When BAR-1 was expressed using a mechanosensory neuron specific promoter , Pmec-7 , the axon termination defects in fsn-1; bar-1 double mutants were significantly reduced ( compare 12 . 1±2 . 0% for fsn-1; bar-1+Pmec-7::BAR-1 with 29 . 6±3 . 1% for fsn-1; bar-1 , Figure 5C ) . In contrast , expression of BAR-1 with the myo-3 promoter ( expressed in body wall muscles surrounding the PLM axon ) did not rescue defects in fsn-1; bar-1 double mutants ( Figure 5C ) . Overall , these findings support several conclusions . First , bar-1 regulates axon termination in the mechanosensory neurons by functioning in the same genetic pathway as rpm-1 , anc-1 , and pop-1 . Second , bar-1 functions in a parallel genetic pathway to fsn-1 . Finally , bar-1 regulates axon termination by functioning cell autonomously in the mechanosensory neurons . Our genetic analysis indicated that rpm-1 , anc-1 and bar-1 function in the same pathway . We next sought to determine whether bar-1 functions up or downstream of anc-1 and rpm-1 . We chose to perform our epistasis analysis using bar-1 for two reasons . First , the existing knowledge of how BAR-1 functions allowed the design and implementation of highly informative experiments . Second , a previous study in mammalian cells on Nesprin-2 and β-catenin ( the orthologs of ANC-1 and BAR-1 , respectively ) suggested that bar-1 might function downstream of anc-1 [24] . Therefore , axon termination defects caused by loss of function in rpm-1 and enhanced axon termination defects in fsn-1; anc-1 double mutants might be due , in part , to decreased BAR-1 activity . If this model is correct , we anticipated that excess BAR-1 activity might suppress the axon termination defects caused by loss of function in rpm-1 , and suppress the enhanced axon termination defects in fsn-1; anc-1 double mutants . We chose to address this question using a genetic approach that utilized a loss of function mutation in apc related 1 ( apr-1 ) , as GFP expressed by muIs32 was greatly reduced ( preventing proper visualization of mechanosensory neurons ) when BAR-1 was transgenically overexpressed at high levels ( data not shown ) . APR-1 is the C . elegans ortholog of human Adenomatous Polyposis Coli ( APC ) [66] . In the vertebrate canonical Wnt signaling pathway , APC forms a complex with the scaffold protein Axin and Glycogen Synthase Kinase 3β ( GSK3β ) to phosphorylate β-catenin and target it for destruction [51] . APR-1 interacts with the functional axin ortholog Polyray 1 ( PRY-1 ) , and has been shown to negatively regulate the β-catenin BAR-1 during vulva development and neuroblast migration [67] . Thus , APR-1 can negatively regulate BAR-1 signaling , and apr-1 mutants are likely to have increased levels of BAR-1 which is consistent with findings from other organisms on Wnt signaling and APC function . Because axon termination defects in anc-1 mutants occur with relatively low penetrance , we chose to analyze fsn-1; anc-1 double mutants , which have enhanced penetrance of defects . As shown in Figure 5B , fsn-1; anc-1; apr-1 triple mutants had significantly reduced penetrance of hook defects ( 21 . 4±1 . 9% ) compared to fsn-1; anc-1 double mutants ( 33 . 2±2 . 4% ) . Importantly , fsn-1; apr-1 double mutants did not show reduced penetrance of defects ( Figure 5B ) . These results show that increased BAR-1 activity suppresses anc-1 ( lf ) , which is consistent with bar-1 functioning downstream of anc-1 . To test if bar-1 functions downstream of rpm-1 we took a similar genetic approach using apr-1 . As shown in Figure 5B , rpm-1; apr-1 double mutants showed suppressed penetrance of axon termination defects when compared to rpm-1 single mutants ( compare 65 . 1±3 . 9% hook for rpm-1; apr-1 with 87 . 6±1 . 5% for rpm-1 ) . These results demonstrate that bar-1 is likely to function downstream of rpm-1 . Our observation that transgenic overexpression of the ANC-1 KASH domain in fsn-1 mutants results in enhanced axon termination defects suggested that ANC-1 needs to be at the nuclear envelope in order to regulate axon termination ( Figure 4C ) . To further support this concept , we examined the role of UNC-84 in axon termination . UNC-84 is a conserved SUN domain protein that is localized to the inner nuclear membrane . UNC-84 binds to ANC-1 , thereby tethering ANC-1 in the nuclear envelope and mediating formation of the LINC complex [6] , [68] . Therefore , we hypothesized that if ANC-1 needs to be localized to the nuclear membrane to function in axon termination , then unc-84 should regulate axon termination similar to anc-1 . In unc-84 ( lf ) mutants we observed a very mild penetrance of hook defects , similar to anc-1 ( lf ) animals ( Figure 6A ) . fsn-1; unc-84 double mutants had enhanced penetrance of defects compared to fsn-1 single mutants ( compare 24 . 9±3 . 1% hook for fsn-1; unc-84 with 9 . 1±1 . 1% for fsn-1 , Figure 6A ) . In contrast , anc-1; unc-84 double mutants did not show a significant increase in penetrance compared to single mutants ( Figure 6A ) . Likewise , the penetrance of defects in fsn-1; anc-1; unc-84 triple mutants was not increased compared to fsn-1; anc-1 and fsn-1; unc-84 double mutants ( Figure 6A ) . The unc-84 allele we used , e1410 , is a hypomorph that results in loss of function in the SUN domain of UNC-84 , and has nuclear anchorage defects [68] . Previous studies have shown that the SUN domain of UNC-84 is required for recruitment of ANC-1 to the nuclear envelope [6] . Thus , unc-84 ( e1410 ) is predicted to lack nuclear localization of ANC-1 . This is consistent with our genetic data showing that loss of function in unc-84 does not enhance loss of function in anc-1 . Thus , unc-84 regulates axon termination by functioning in the same genetic pathway as anc-1 , and in a parallel pathway to fsn-1 . These findings are also consistent with ANC-1 regulating axon termination by functioning at the nuclear envelope . It was previously shown that Emerin binds to and facilitates the nuclear export of β-catenin thereby antagonizing β-catenin function [69] . Emerin also binds to Nesprin-1 and Nesprin-2 [70] , [71] . These prior observations suggested that ANC-1 might regulate BAR-1 by functioning through Emerin homolog 1 ( EMR-1 ) in C . elegans . To test this , we analyzed the genetic relationship between anc-1 and emr-1 using gk119 , an allele that deletes the entire emr-1 coding sequence [72] . Although PLM axon termination defects occurred with very low penetrance in anc-1 single mutants , defects in anc-1; emr-1 double mutants were significantly suppressed ( compare 3 . 4±0 . 8% hook for anc-1 with 0 . 6±0 . 6% for anc-1; emr-1 , Figure 6B ) . Consistent with this result , we also observed that the enhanced penetrance of axon termination defects in fsn-1; anc-1 double mutants was suppressed in fsn-1; anc-1; emr-1 triple mutants ( compare 33 . 2±2 . 2% hook for fsn-1; anc-1 with 20 . 5±1 . 5% for fsn-1; anc-1; emr-1 , Figure 6B ) . Notably , fsn-1; emr-1 double mutants were mildly enhanced rather than being suppressed ( Figure 6B ) . This result explains why fsn-1; anc-1; emr-1 triple mutants were only suppressed to the level of defect present in fsn-1; emr-1 double mutants . Our findings demonstrate that anc-1 and emr-1 function in the same genetic pathway , and that ANC-1 is a negative regulator of EMR-1 . Our observation that RPM-1 and ANC-1 function through BAR-1 , which mediates canonical Wnt signaling , prompted us to test which Wnt ligands regulate axon termination in the ALM and PLM mechanosensory neurons . To do so , we analyzed loss of function alleles in four Wnt ligands: c . elegans wnt family 1 ( cwn-1 ) , cwn-2 , egg laying defective 20 ( egl-20 ) , and lin-44 . The fifth Wnt , more of ms 2 ( mom-2 ) , was not analyzed because loss of function is lethal . With regard to the ALM neurons , loss of function in individual Wnt ligands did not result in significant defects in axon termination ( Figure S3C ) . Analysis of double mutants with fsn-1 showed that only fsn-1; cwn-2 double mutants had enhanced axon termination defects ( Figure S3C ) . In the PLM neurons , the situation was more complex . cwn-1 and cwn-2 single mutants did not show defects in PLM axon termination . While fsn-1; cwn-2 double mutants failed to show enhancement , fsn-1; cwn-1 double mutants showed a small but significant enhancement ( compare 13 . 6±1 . 3% hook defects for fsn-1; cwn-1 with 9 . 1±1 . 1% for fsn-1 , Figure 7 ) . In the case of egl-20 , single mutants did not have a significant defect , but mild enhancer effects were observed in fsn-1; egl-20 double mutants ( compare 18 . 8±2 . 3% hook defects for fsn-1; egl-20 with 9 . 1±1 . 1% for fsn-1 , Figure 7 ) . Consistent with previous work , we observed that axon polarization was abnormal in the PLM neurons of lin-44 mutants ( data not shown ) [54] , [73] . However , axon polarization defects were not completely penetrant in lin-44 mutants , which allowed us to analyze axon termination in neurons with normal polarity . Using this approach , we observed that lin-44 mutants had significant defects in PLM axon termination ( compare 18 . 9±4 . 7% hook for lin-44 with 0% defects for wild-type , Figure 7 ) . Further , fsn-1; lin-44 double mutants were significantly enhanced compared to fsn-1 or lin-44 single mutants ( compare 55 . 1±4 . 8% hook for fsn-1; lin-44 with 18 . 9±4 . 7% for lin-44 , Figure 7 ) . Consistent with LIN-44 functioning through BAR-1 to regulate axon termination , we observed no further enhancement of defects in fsn-1; lin-44; bar-1 triple mutants ( Figure 7 ) . Notably the more posterior the location of expression for a Wnt ligand the stronger the phenotypes and/or enhancer effects observed , e . g . lin-44 mutants had the strongest PLM axon termination defects and enhancer effects , and LIN-44 is the most posteriorly expressed Wnt [74] . Thus , because the ALM and the PLM neurons terminate axon extension in anatomically distinct locations , different Wnt ligands control this process for each type of neuron . Our results show that only cwn-2 regulates ALM axon termination . In contrast , a combination of cwn-1 , egl-20 and lin-44 regulate PLM axon termination with lin-44 functioning most prominently .
Our proteomic screen for RPM-1 binding proteins identified the nuclear anchorage protein ANC-1 , which was confirmed using coIP . Consistent with these findings , our genetic analysis indicated that anc-1 and rpm-1 function in the same genetic pathway to regulate synapse formation in the GABAergic motor neurons , and axon termination in the mechanosensory neurons . Transgenic analysis indicated that anc-1 functions cell autonomously in the mechanosensory neurons to regulate axon termination , similar to rpm-1 . Our observation that defects in anc-1; rpm-1 double mutants were not suppressed also suggests that RPM-1 positively regulates ANC-1 . While we were unable to provide definitive evidence that anc-1 functions downstream of rpm-1 , our analysis indicates that this is likely to be the case . We show that bar-1 functions downstream of both anc-1 and rpm-1 , and that these three genes function in the same genetic pathway . Because similar enhancer effects are observed in fsn-1; bar-1 and fsn-1; anc-1 double mutants , and the penetrance of these defects is less than rpm-1 ( lf ) mutants , it is probable that anc-1 , like bar-1 , functions downstream of rpm-1 . The PHR protein family is highly conserved with orthologs in Drosophila , zebrafish and mice [25] . To date , all of the proteins identified in our proteomic screen for RPM-1 binding proteins ( including FSN-1 , GLO-4 , and RAE-1 ) are evolutionarily conserved [42] , [43] . Therefore , RPM-1 and its downstream signaling pathways are likely to function through conserved mechanisms . ANC-1 is also conserved with orthologs in Drosophila ( MSP-300 ) and mammals ( Nesprin-1 and 2 ) . Thus , the function of ANC-1 in axon termination and synapse formation during development is also likely to be conserved . This is supported by the observation that the branches of phrenic nerves are overgrown in Nesprin-1−/− knockout mice , indicating possible axon termination defects [2] . Further addressing if the role of ANC-1 in axon and synapse development is evolutionarily conserved remains an important goal . Our genetic results showed that the β-catenin bar-1 functions in synapse formation in the GABAergic motor neurons , and axon termination in the mechanosensory neurons . Prior studies showed that BAR-1 regulates glutamate receptor trafficking , and axon extension in motor neurons [55] , [75] . Our results show that BAR-1 plays a more expansive role in neuronal development than originally thought . We also provide significant insight into the mechanism of how BAR-1 is regulated by showing that a novel pathway containing RPM-1 and ANC-1 functions upstream of BAR-1 . Our functional genetic and transgenic findings are consistent with a prior study on a keratinocyte cell line , which showed that Nesprin-2 regulates the nuclear localization of β-catenin [24] . Our results do not rule out the possibility that ANC-1 regulates the ubiquitination or phosphorylation of BAR-1 . We used epifluorescent microscopy to investigate if BAR-1::GFP localization to the nucleus was altered in anc-1 mutants , but found no obvious changes ( data not shown ) . However , it is possible that detecting such changes may require more sensitive methods . In the canonical Wnt signaling pathway , BAR-1 activates POP-1 ( the C . elegans TCF/LEF transcription factor ) [58] . Our observation that defects in axon termination and synapse formation were enhanced in both fsn-1; pop-1 and fsn-1; bar-1 double mutants , and that defects were not enhanced in pop-1; bar-1 double mutants is consistent with bar-1 and pop-1 functioning in the same genetic pathway . Because fsn-1; pop-1 double mutants displayed more penetrant defects than fsn-1; bar-1 double mutants , it is possible that another β-catenin may also function through POP-1 to regulate axon termination . Humpback 2 ( HMP-2 ) is an unlikely candidate as it functions in a cadherin-catenin complex and does not act through POP-1 [60] . Symmetrical Sister Cell and Gonad Defect 1 ( SYS-1 ) and Worm Armadillo 1 ( WRM-1 ) are plausible candidates as they regulate transcriptional activation and nuclear export of POP-1 , respectively [76] , [77] . In the case of synapse formation in the GABAergic motor neurons the situation is simpler . Phenotypes were similar in fsn-1; pop-1 and fsn-1; bar-1 double mutants suggesting that only BAR-1 is likely to function through POP-1 to regulate synapse formation in GABAergic motor neurons . In vertebrates , β-catenin plays a broad and important role in neurons by regulating a range of processes including: neuronal differentiation [78] , synaptic vesicle assembly [79] , dendrite morphogenesis and plasticity [80] , [81] , neurite extension [82] , and axon arborization and targeting in retinal ganglion cells ( RGC ) [83] . β-catenin also regulates the morphology of the NMJ and phrenic nerve growth [84] . Notably similar to the function of β-catenin , Phr1 regulates RGC axon arborization and targeting [31] and NMJ morphology [30] , [33] , and both Phr1 and Nesprin-1 regulate axon growth in phrenic nerves [2] , [33] . While these studies hinted at a possible functional link between the PHR proteins , Nesprins and β-catenin , our finding that RPM-1 , ANC-1 and BAR-1 function in the same pathway provides the first mechanistic explanation for these phenotypic relationships in mammals . Thus , the functional relationship between RPM-1 , ANC-1 and BAR-1 is likely to be evolutionarily conserved . Further , our study outlines a signaling network that links two central and important regulators of neuronal development , the PHR proteins and β-catenin , through the function of ANC-1 . ANC-1 regulates nuclear anchorage by binding to the nuclear envelope via its C-terminal KASH domain , and binding to the actin cytoskeleton via its N-terminal calponin homology domains [6] . The SUN domain protein UNC-84 mediates binding of ANC-1 to the nuclear envelope . Several of our findings demonstrate that ANC-1 needs to be anchored to the nuclear envelope in order to regulate axon termination . First , we found that transgenic overexpression of dominant negative ANC-1 , which acts by inhibiting recruitment of endogenous ANC-1 to the nuclear envelope [6] , enhances the axon termination defects caused by fsn-1 ( lf ) . Second , loss of function in either anc-1 or unc-84 enhances the axon termination defects caused by fsn-1 ( lf ) . Third , our results demonstrate that anc-1 and unc-84 function in the same genetic pathway to regulate axon termination . Finally , axon termination defects in anc-1 single mutants and fsn-1; anc-1 double mutants are suppressed by loss of function in emr-1 , a nuclear envelope associated protein . Collectively , these findings support the conclusion that ANC-1 regulates axon termination by functioning at the nuclear envelope . Previous studies showed that RPM-1 is localized to the perisynaptic zone of presynaptic terminals [32] , [85] . However , a transgenically expressed fusion protein of RPM-1 and GFP that rescues rpm-1 ( lf ) phenotypes is also found at low levels in the cell body and excluded from the nucleus of the mechanosensory neurons ( Opperman and Grill , in press ) and the motor neurons ( Figure S4 ) [32] . In addition , RPM-1 binds to RAE-1 , a protein that localizes to the nuclear envelope as well as presynaptic terminals [43] . These prior observations combined with our findings here that ANC-1 binds to RPM-1 , that both genes function in the same pathway , and that ANC-1 regulates axon termination by functioning at the nucleus suggest that RPM-1 may play a novel signaling function in the neuronal cell body , possibly at the nuclear envelope . This possibility is further supported by immunohistochemistry in mammals , which has shown that the rat ortholog of RPM-1 ( called MYCBP2 or Pam ) is present in the cell bodies of neurons in the spinal cord and throughout the brain [86] , [87] . An emerging question is whether the PHR proteins , or the signaling pathways they control , are regulated or integrated with signals originating from outside the cell . Extracellular guidance cues , adhesion molecules and morphogens , such as Wnts , play roles in both axon guidance and synapse formation [88] . A previous study in zebrafish noted that loss of function in Phr1 and Wnt4a/Ryk causes abnormal axon stopping at the medial habenula hinting at a possible link between PHR protein function and Wnt signaling [89] . We now provide genetic evidence linking the PHR protein RPM-1 to the canonical β-catenin BAR-1 and Wnt signaling . Specifically , we show that several Wnt ligands regulate axon termination by functioning coordinately with FSN-1 , a key component of RPM-1 signal transduction . In the anterior of the animal , we found that only CWN-2 regulates ALM axon termination . Our finding is consistent with previous studies showing that CWN-2 regulates axon polarization in ALM neurons , and anterior axon guidance in neurons of the nerve ring [54] , [90] . Further , CWN-2 regulation of ALM axon termination is consistent with CWN-2 being the most anteriorly expressed Wnt ligand [74] . With regard to the PLM neurons in the posterior of the animal , three Wnts are involved in decreasing order of importance: LIN-44 , EGL-20 , and CWN-1 . These findings are consistent with several previous observations . First , this same combination of Wnt ligands was shown to regulate axon polarization in the PLM neurons [54] , [73] . Second , the hermaphrodite specific motor neuron ( HSN ) cell bodies are in roughly the same relative anterior-posterior location as the sites of PLM axon termination , and the same three Wnt ligands we have found that control PLM termination also regulate HSN migration [53] . Our genetic analysis indicates that RPM-1 and ANC-1 positively regulate BAR-1 . In mammals , Nesprin-2 binds to a complex of α- and β-catenin [24] . Thus , RPM-1 may mediate formation of an ANC-1/BAR-1 complex that regulates nuclear levels of BAR-1 ( Figure 8 ) . In vitro biochemical and tissue culture experiments have shown that Nesprin-1 and Nesprin-2 bind to the nuclear membrane protein Emerin , and Emerin regulates nuclear export of β-catenin [69] , [71] . We have found that loss of function in emr-1 suppressed anc-1 ( lf ) in the context of axon termination , which is consistent with ANC-1 inhibiting EMR-1 . Thus , excess EMR-1 activity in anc-1 mutants presumably results in increased nuclear export of BAR-1 , thereby mimicking loss of function in bar-1 . Taken collectively , prior findings and our results here are consistent with a model in which canonical Wnt signaling controls BAR-1 protein levels and nuclear import , while RPM-1 and ANC-1 function in a linear pathway to inhibit EMR-1 and nuclear export of BAR-1 ( Figure 8 ) .
The N2 isolate of C . elegans was propagated using standard procedures . Alleles used in this study included; anc-1 ( e1873 ) , anc-1 ( e1753 ) , apr-1 ( ok2970 ) , pop-1 ( q645 ) , fsn-1 ( gk429 ) , glo-4 ( ok623 ) , rpm-1 ( ju44 ) , bar-1 ( ga80 ) , unc-84 ( e1410 ) , emr-1 ( gk119 ) , cwn-1 ( ok546 ) , cwn-2 ( ok895 ) , lin-44 ( n1792 ) , and egl-20 ( hu120 ) . The strain MH1870 , which contains the transgene kuIs54 , and anc-1 ( e1873 ) were kind gifts from Dr . Daniel Starr . All mutants were constructed using standard procedures , and were confirmed using PCR or by the associated visible phenotypes . Heterozygous analysis with rpm-1 was done by linking rpm-1 ( ju44 ) with dpy-11 ( e224 ) , and using unc-42 ( e270 ) as a balancer . Other homozygous alleles were isolated as necessary and non-dpy non-unc ( rpm-1 dpy-11/unc-42 ) animals were scored . The transgenic strains used in this study were: muIs32 [Pmec-7GFP] , juIs1 [Punc-25SNB-1::GFP] , and kuIs54 [Psur-5SUR-5::GFP] . Transgenic animals were generated using standard microinjection procedures . Transgenes were constructed by injection of plasmid DNA or DNA generated by PCR with plasmid encoding Pttx-3RFP ( 50 ng/µL ) and pBluescript ( 50 ng/µL ) . Dominant negative ANC-1 was cloned as a genomic fragment ( bp 36523–37778 ) . For rgef-1 promoter lines , the plasmid pBG-GY360 was amplified by PCR and injected at 10 ng/µL . For the mec-3 promoter lines , the plasmid pBG-GY370 was amplified by PCR and injected at 5 ng/µL . The anc-1 mini-gene was constructed by ligating together 4 fragments: a cDNA fragment from 1–4154 bp using an engineered ApaI site and BamHI , a BamHI to NotI genomic fragment ( 14 , 808–24 , 000 ) , a NotI to KpnI genomic fragment ( 24 , 001–36 , 849 ) , and a KpnI to 3′ UTR fragment containing an engineered SacII site ( 36 , 850–38 , 921 ) . For anc-1 rescue analysis , a plasmid encoding Pmec-7ANC-1 ( mini-gene , pBG161 ) was injected at 10–40 ng/µL , and a plasmid encoding Pmyo-3ANC-1 ( mini-gene , pBG-183 ) was injected at 20 ng/µL . For bar-1 rescue analysis , a plasmid encoding Pmec-7BAR-1 ( genomic clone , pBG-GY318 ) was injected at 1 ng/µL . Analysis was carried out on live animals at 40× magnification using a Nikon epifluorescent microscope and a Q-imaging camera . Animals were anesthetized using 1% ( v/v ) 1-phenoxy-2-propanol in M9 buffer . Synapse formation defects were quantified by collecting images of juIs1 ( Punc-25SNB-1::GFP ) and manually scoring puncta numbers in Adobe Photoshop . Dorsal cord lengths were determined in µmeters using Q-imaging software . For each genotype 20 or more worms were analyzed from at least 3 independent experiments . Both bar-1 and pop-1 mutants displayed stereotyped , reproducible gaps in their dorsal cords ( data not shown ) . Care was taken to avoid collecting images at these locations . Axon termination defects were visualized using muIs32 ( Pmec-7GFP ) and manually scored . For all genetic analysis on axon termination , averages are shown for data collected from 5–8 independent counts of 20–30 PLM neurons from young adult worms . For transgenic analysis , data shown is an average of 4 or more transgenic lines for each genotype . Proteomic analysis of RPM-1 binding proteins , including ANC-1 , was described previously [42] . For biochemical analysis of RPM-1 binding to ANC-1 , worms were grown in liquid culture , harvested by centrifugation , frozen in liquid N2 , ground with a mortar and pestle under liquid N2 , and extracted using 0 . 1% NP-40 lysis buffer as described previously [42] . CoIPs were performed from 40 mg of total protein extract . RPM-1::GFP was precipitated using a mouse monoclonal antibody ( 3E6 , MP Biomedical ) and protein G agarose beads ( Roche ) . For immunoblotting , precipitates were run on an SDS-PAGE gel ( 3–8% Tris Acetate , Invitrogen ) , and proteins were wet transferred to PVDF membrane in Tris acetate transfer buffer ( 30 volts for 24–30 hours ) . Blots were blocked with part-skim milk in TBST , and probed with an anti-GFP antibody ( mouse monoclonal , Roche ) or purified anti-ANC-1 polyclonal antibodies that were used previously [6] . Primary antibodies were detected with secondary antibodies coupled to HRP , Supersignal FemtoWest enhanced chemiluminescent reagent ( Pierce ) , and autoradiography . | The molecular mechanisms that underpin synapse formation and axon termination are central to forming a functional , fully connected nervous system . The PHR proteins are important regulators of neuronal development that function in axon outgrowth and termination , as well as synapse formation . Here we describe the discovery of a novel , conserved pathway that is positively regulated by the C . elegans PHR protein , RPM-1 . This pathway is composed of RPM-1 , ANC-1 ( a Nesprin family protein ) , and BAR-1 ( a canonical β-catenin ) . Nesprins , such as ANC-1 , regulate nuclear anchorage and positioning in multinuclear cells . We now show that in neurons , ANC-1 regulates neuronal development by positively regulating BAR-1 . Thus , Nesprins are multi-functional proteins that act through β-catenin to regulate neuronal development , and link the nucleus to the actin cytoskeleton in order to mediate nuclear anchorage and positioning in multi-nuclear cells . | [
"Abstract",
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] | 2014 | The Nesprin Family Member ANC-1 Regulates Synapse Formation and Axon Termination by Functioning in a Pathway with RPM-1 and β-Catenin |
During infection , pathogens must utilise the available nutrient sources in order to grow while simultaneously evading or tolerating the host’s defence systems . Amino acids are an important nutritional source for pathogenic fungi and can be assimilated from host proteins to provide both carbon and nitrogen . The hpdA gene of the dimorphic fungus Penicillium marneffei , which encodes an enzyme which catalyses the second step of tyrosine catabolism , was identified as up-regulated in pathogenic yeast cells . As well as enabling the fungus to acquire carbon and nitrogen , tyrosine is also a precursor in the formation of two types of protective melanin; DOPA melanin and pyomelanin . Chemical inhibition of HpdA in P . marneffei inhibits ex vivo yeast cell production suggesting that tyrosine is a key nutrient source during infectious growth . The genes required for tyrosine catabolism , including hpdA , are located in a gene cluster and the expression of these genes is induced in the presence of tyrosine . A gene ( hmgR ) encoding a Zn ( II ) 2-Cys6 binuclear cluster transcription factor is present within the cluster and is required for tyrosine induced expression and repression in the presence of a preferred nitrogen source . AreA , the GATA-type transcription factor which regulates the global response to limiting nitrogen conditions negatively regulates expression of cluster genes in the absence of tyrosine and is required for nitrogen metabolite repression . Deletion of the tyrosine catabolic genes in the cluster affects growth on tyrosine as either a nitrogen or carbon source and affects pyomelanin , but not DOPA melanin , production . In contrast to other genes of the tyrosine catabolic cluster , deletion of hpdA results in no growth within macrophages . This suggests that the ability to catabolise tyrosine is not required for macrophage infection and that HpdA has an additional novel role to that of tyrosine catabolism and pyomelanin production during growth in host cells .
A number of pathogenic microbes reside within phagocytic cells of the host innate immune system , a strategy that minimizes exposure to the adaptive immune response but which requires subversion or escape from the cytotoxic capacity of innate immune cells . The acquisition of nutrients for growth is a key challenge faced by these intracellular pathogens , which have to scavenge nutrients from the relatively nutrient poor environment of the macrophage . Expression profiling studies using a number of intracellular pathogens has revealed distinct changes to metabolism upon phagocytosis , specifically for genes involved in carbon assimilation from host proteins and amino acids [1 , 2 , 3 , 4] . Many fungi can readily assimilate amino acids as both carbon and nitrogen sources and proteins are relatively abundant in host cells . There is good evidence that amino acids are an important nutritional source for pathogenic fungi . For example , genes required for tyrosine catabolism are induced under infection conditions in the fungal pathogens Paracoccidioides brasiliensis , Histoplasma capsulatum , Penicillium marneffei ( recently renamed Talaromyces marneffei ) and Aspergillus fumigatus , suggesting that tyrosine may provide an important source of carbon and/or nitrogen during infectious growth [2 , 4 , 5 , 6] . Tyrosine is catabolized via a pathway that is conserved across the kingdoms . Garrod originally identified this pathway in his classic work on inborn errors of metabolism in humans and the full pathway was uncovered by elegant studies in the model fungus Aspergillus nidulans [7 , 8] . The catabolism of tyrosine provides the fungus with nitrogen from a transamination reaction with α-ketoglutarate to produce glutamate and carbon via production of fumarate and acetoacetate , which is further catabolised to acetyl-CoA , which feed into the TCA cycle [7] . As well as enabling the fungus to acquire carbon and nitrogen intermediates from proteins within the host , tyrosine is also an important precursor in the formation of two different types of melanin . Melanins are a large group of pigment macromolecules which although chemically diverse , display similar chemical properties and function to protect cells from environmental stress . Melanins are broadly classified into one of three groups: pheomelanins , eumelanins and allomelanins ( pyomelanins and DHN-melanins ) [9] . Tyrosine can be hydroxylated by tyrosinases and/or laccases to produce DOPA that can then be used for the formation of the eumelanin DOPA melanin ( L-3 , 4-dihydroxyphenylalanine melanin ) , while catabolism of tyrosine produces the pathway intermediate homogentisate that can be used to generate the allomelanin pyomelanin through oxidation and polymerization [10 , 11] . The two most commonly identified melanins in fungi are DOPA-melanin and DHN-melanin ( 1 , 8-dihydroxynaphthalene melanin ) ; the latter being synthesised from polyketides made from acetate precursors . Both DHN- and DOPA-melanin have been shown to protect fungal cells from RNS and ROS derived from host macrophages [12 , 13 , 14 , 15] . Mutants which block the biosynthesis of DOPA-melanin in Cryptococcus neoformans and DHN-melanin in A . fumigatus , P . marneffei and Wangiella dermatitidis show reduced virulence in murine models of infection [12 , 13 , 14 , 15 , 16] . Melanisation has also been shown to influence phagocytosis , phagolysosomal maturation and the release of proinflammatory cytokines during infection [17 , 18 , 19 , 15 , 20 , 21 , 22] . A number of important human fungal pathogens are dimorphic and switch from a non-pathogenic multicellular hyphal form found outside the host to a unicellular yeast growth form during infection . Microarray-based expression profiling in P . marneffei , P . brasiliensis and H . capsulatum , to identify differential expression between the two growth types , has revealed that genes required for tyrosine catabolism are induced specifically in the pathogenic cell type at 37°C [2 , 4 , 6] . The melanin synthesized ex vivo by these pathogens has been previously postulated to be either or both of the two most commonly identified melanins , DHN-melanin and DOPA-melanin [23 , 24 , 25 , 26] . However , this raises the possibility that these fungal pathogens may be producing the third type of melanin , pyomelanin , via the oxidation and polymerization of homogentisate produced during tyrosine catabolism [10 , 11] . To investigate this possibility , and the role of tyrosine catabolism during infectious growth , the role of genes required for the catabolism of tyrosine was investigated in P . marneffei . The genes required for tyrosine catabolism are located within a conserved gene cluster . This study shows that the expression of genes within the cluster is both positively and negatively regulated by a gene ( hmgR ) encoding a C6 binuclear cluster DNA binding motif transcription factor , present within the cluster , in response to nitrogen source availability . The expression of tyrosine catabolic cluster genes is also under the regulation of the global GATA-type transcription factor AreA . Deletion of genes of the tyrosine catabolic cluster reveals the requirement of the cluster for growth on tyrosine as both a nitrogen and carbon source at both 25°C during hyphal growth and at 37°C during yeast growth . This study also shows that P . marneffei produces pyomelanin when grown on medium containing tyrosine at 37°C and this melanisation requires genes of the tyrosine catabolic cluster . Deletion of the genes of the tyrosine catabolic cluster , with the exception of hpdA , does not affect infectious growth in macrophages . This result suggests that tyrosine catabolism and pyomelanin formation are not required for the initial stages of infection , however , HpdA has an additional novel role during ex vivo infectious growth of P . marneffei .
Microarray-based expression profiling in P . marneffei revealed that hpdA , encoding 4-hydroxyphenylpyruvate dioxygenase which catalyses the conversion of 4-hydroxyphenylpyruvate to homogentisate during tyrosine catabolism , is induced specifically in the pathogenic cell type at 37°C [6] . To assess if hpdA is required for ex vivo yeast growth , wildtype conidia were used to infect murine J774 macrophages in the absence and presence of the 4-hydroxyphenylpyruvate dioxygenase ( HpdA ) chemical inhibitor NTBC ( 2- ( 2-nitro-4-trifluoromethylbenzoyl ) -cyclohexane-1 , 3-dione ) and examined 24 hours post-infection [2 , 27 , 28] . After 24 hours , wildtype conidia within macrophages have completed isotropic expansion followed by elongation into a cylindrical yeast cell . Macrophages infected with wildtype conidia contain numerous yeast cells , some of which are dividing by fission ( Fig . 1A and B ) . In contrast , predominately ungerminated conidia were observed in macrophages in the presence of NTBC ( Fig . 1A and B ) . This suggests that tyrosine catabolism plays an important role during ex vivo yeast cell production in P . marneffei . Tyrosine is assimilated via a conserved catabolic pathway that provides the fungus with both nitrogen , from a transamination reaction with α-ketoglutarate to produce glutamate , and carbon via production of fumarate and acetoacetate , which is used to generate acetyl-CoA that can then feed into the TCA cycle ( Fig . 2A ) . Microarray-based expression profiling identified the hpdA gene as highly upregulated in yeast cells compared to hyphal cells [6] . Annotation of the 30Kb genomic region which encompasses P . marneffei hpdA shows the genes encoding additional enzymes required for the catabolism of tyrosine ( hmgA , maiA and fahA ) are located in a gene cluster ( Fig . 2B ) . Tyrosine catabolism genes are also clustered in Aspergillus species [7 , 8 , 11] . The P . marneffei gene cluster also contains a gene ( hmgR ) encoding a Zn ( II ) 2-Cys6 binuclear cluster transcription factor which has been shown to be required for expression of genes required for tyrosine catabolism in A . fumigatus , and a conserved gene , hmgX , which is postulated to act as an accessory factor to HpdA [5] ( Fig . 2B ) . The P . marneffei tyrosine cluster contains an additional ORF PMAA031970 , we have named hypW , which is absent in the Aspergilli and other dimorphic pathogens . hypW is predicted to encode a hypothetical protein of unknown function which lacks any predicted domains ( Fig . 2B ) . The P . marneffei tyrosine catabolism gene cluster also contains an additional gene , mfpA ( PMAA_032010 ) , encoding a putative alpha-1 , 2-mannosidase family protein which is conserved in other fungi but located elsewhere in the genome ( Fig . 2B ) . Blast searches of the P . marneffei genome also revealed the presence of paralogous genes scattered throughout the genome . A comparison of other fungal genomes revealed this is common to many species ( Table 1 ) . The P . marneffei genome contains a hpdA paralogue ( PMAA_089170 ) , four hmgA paralogues ( PMAA_102000 , PMAA_054730 , PMAA_035450 and PMAA_080510 ) and two fahA paralogues ( PMAA_080500 and PMAA_099050 ) . To investigate if this cluster is conserved in other fungi , approximately 30Kb of the genomic region which encompasses the orthologue of hpdA , from P . marneffei’s closest sexual relative T . stipitatus , from the dimorphic fungal pathogens P . brasiliensis , Coccidioides immitis , H . capsulatum and Blastomyces dermatitidis , and the filamentous fungi A . nidulans and A . fumigatus was compared to that of P . marneffei ( Fig . 2B ) . The homologues of hmgA , maiA , fahA and hmgX were within close proximity of hpdA in all species except H . capsulatum and B . dermatitidis in which the catabolic cluster was split into two and three , respectively , different genomic locations ( Fig . 2B ) . T . stipitatus fahA and maiA are misannotated in the database as a single fused gene named fahA ( TSTA_065590 ) . The cluster in T . stipitatus also contains a homologue of hypW and mfpA ( Fig . 2B ) . P . brasiliensis , C . immitis , H . capsulatum and B . dermatitidis also lacked a homologue of the Zn ( II ) 2-Cys6 binuclear cluster transcription factor encoded by hmgR . To investigate the expression of genes in the tyrosine catabolic cluster , RNA was isolated from cells grown in liquid culture for 2 days at 25°C ( hyphal cells ) or 6 days at 37°C ( yeast cells ) then transferred into medium containing ammonium or tyrosine as the sole nitrogen source at 25°C or 37°C for 4 hours . At both 25°C and 37°C , expression of hmgA , hmgX , fahA and maiA was low in the presence of ammonium and high in tyrosine suggesting that expression is induced in the presence of tyrosine ( Fig . 3 ) . Although hpdA showed the same pattern of expression at 25°C , at 37°C expression in ammonium and tyrosine was almost equivalent suggesting that unlike the other genes in the cluster , hpdA expression at 37°C is not repressed in the presence of a preferred nitrogen source ( Fig . 3 ) . The hmgR gene was expressed in the presence of both ammonium and tyrosine at both 25°C and 37°C , with expression only slightly higher in tyrosine ( Fig . 3 ) . Interestingly , expression of mfpA , the gene within the tyrosine metabolic cluster only in P . marneffei and T . stipitatus which is not predicted to have a role in tyrosine catabolism , was induced on tyrosine at both 25°C and 37°C ( Fig . 3 ) . This suggests that the cluster may also be under a more global level of regulation such as that mediated by chromatin effects and any gene captured within this genomic region may consequently come under it’s regulation . The analysis of hypW expression using RT-PCR primers spanning the predicted intron showed an expression pattern similar to the other genes of the tyrosine catabolic cluster ( S3A Fig ) . However , the size of the RT PCR product suggested the predicted intron is not spliced . Examination of RNAseq data for this region confirmed the lack of this predicted intron and indicated that the predicted start site is incorrect ( H . Weerasinghe and A . Andrianopoulos , personal communication ) ( S3B Fig ) . The small number of the RNA seq reads were also restricted to the 5’ region which is in close proximity to hmgX ( 163bp ) ( S3B Fig ) . Overall , this suggests that this ORF is not expressed under the conditions tested and the expression observed by RT PCR is due to read through from hmgX . Expression of the paralogues of tyrosine catabolic genes could not be detected under these conditions . To investigate if the tyrosine-induced expression of the genes in the cluster is a result of a general response to limiting nitrogen levels or a specific induction by tyrosine , hpdA , maiA and fahA expression was also assessed in alanine at 25°C and 37°C . Expression of these genes was higher in alanine compared to ammonium but lower than tyrosine at both temperatures . This suggests that the increased expression observed in tyrosine is not solely due to derepression of tyrosine catabolic genes in response to general limiting nitrogen levels but rather specific induction due to the presence of tyrosine ( Fig . 4A and B ) . A gene encoding a Zn ( II ) 2-Cys6 binuclear transcription factor , hmgR , is conserved in the tyrosine metabolic cluster across many fungi . This gene has been shown to be essential for tyrosine-induced expression of hppD ( hpdA orthologue ) , hmgA , hmgX , fahA and maiA in A . fumigatus [5] . To investigate the role of hmgR in P . marneffei , the orthologous gene ( PMAA_032020 ) was cloned and a deletion strain ( ΔhmgR::pyrG+ ( G825 ) ) was generated by transforming P . marneffei strain G816 ( ΔligD niaD1 pyrG1 ) . To generate a complemented strain ( ΔhmgR hmgR+ ( G867 ) ) , a ΔhmgR pyrG- ( G864 ) strain was transformed with a 5 . 1 kb hmgR fragment targeted to the P . marneffei pyrG locus . In contrast to A . fumigatus , deletion of hmgR in P . marneffei resulted in only the partial loss of induction of hpdA , hmgA , hmgX , fahA and maiA in medium with tyrosine as the sole nitrogen source at both 25°C and 37°C ( Fig . 3 ) . This suggests that this transcription factor is required , but not essential , for tyrosine-induced expression in P . marneffei . Interestingly , deletion of hmgR resulted in increased expression of hpdA at 25°C and hmgA , hmgX , fahA and maiA at both 25°C and 37°C in medium containing ammonium as the sole nitrogen source ( Fig . 3 ) . Therefore , in addition to inducing the expression of tyrosine catabolism genes in the presence of tyrosine , this transcription factor is also required to repress their expression in the presence of a preferred nitrogen source . Nitrogen metabolite repression is a regulatory mechanism utilized by microbes to allow the preferential use of readily assimilated ( preferred ) nitrogen sources . The areA gene encodes a positively-acting GATA-type transcription factor which regulates the global response to limiting nitrogen conditions in P . marneffei and other fungi [29 , 30] . To investigate the contribution of AreA regulation to the tyrosine catabolic cluster , the expression of hpdA , maiA and fahA was examined in wildtype , ΔhmgR and ΔareA at 25°C and 37°C . RNA was isolated from cells grown in liquid culture for 2 days at 25°C or 6 days at 37°C and transferred into medium containing ammonium , alanine or tyrosine as the sole nitrogen source at 25°C or 37°C for 4 hours . In wildtype , expression of maiA and fahA was barely detectable in ammonium , increased to a low level in alanine and strongly induced in tyrosine at both 25°C and 37°C ( Fig . 4A and B ) . Expression levels of hpdA were similarly dependent on the nitrogen source at 25°C but constitutive at 37°C ( Fig . 4A and B ) . Compared to wildtype , the expression of hpdA , maiA and fahA in the ΔhmgR mutant at 25°C was partially derepressed in ammonium and alanine and full induction in tyrosine was not observed ( Fig . 4A ) . Surprisingly unlike wildtype and the ΔhmgR mutant , the expression of hpdA , maiA and fahA on either ammonium , alanine or tyrosine was equivalent in the ΔareA mutant at 25°C ( Fig . 4A ) . This was unexpected given that AreA usually acts as a positive regulator of gene expression under conditions of limiting nitrogen . Likewise at 37°C , the expression of maiA and fahA was equivalent on either ammonium , alanine or tyrosine in the ΔareA mutant ( Fig . 4B ) . Deletion of areA did not affect the highly constitutive expression of hpdA at 37°C ( Fig . 4B ) . These results suggest that AreA is not acting to positively regulate expression of the tyrosine catabolic cluster in the presence of tyrosine . In addition , the increased expression of these genes in the ΔareA mutant on ammonium and alanine suggests that AreA is negatively regulating expression of the cluster in the absence of tyrosine which is contradictory to the current model of AreA function . To investigate if genes of the tyrosine catabolic cluster are under nitrogen metabolite repression mediated by AreA , the expression of hpdA , maiA and fahA was assessed on tyrosine and both tyrosine and ammonium at 25°C in both wildtype , ΔhmgR and ΔareA ( Fig . 4C ) . In wildtype , the levels of hpdA , maiA and fahA are lower on tyrosine and ammonium medium compared to tyrosine alone suggesting that these genes are under nitrogen metabolite repression ( Fig . 4C ) . Although the overall level of induction is reduced in the ΔhmgR mutant , the levels of hpdA , maiA and fahA are lower on tyrosine and ammonium medium compared to tyrosine alone suggesting that HmgR is not playing a role in nitrogen metabolite repression ( Fig . 4C ) . In contrast , in the ΔareA mutant the levels of hpdA , maiA and fahA expression are equivalent between tyrosine and ammonium medium compared to tyrosine alone indicating that areA is required during nitrogen metabolite repression to repress expression of the cluster in the presence of ammonium ( Fig . 4C ) . To determine the role of genes present in the tyrosine catabolic cluster , hpdA , hmgA , hypW , hmgX , maiA and mpfA were cloned and deleted . Due to the fact that an intermediate in the tyrosine catabolism pathway , homogentisate , can be oxidized and polymerized to form the brown pigment pyomelanin , the wA gene required for the synthesis of DHN melanin was also cloned and deleted for comparison . As expected , the ΔwA strain appears white due to the absence of DHN melanin in asexual spores ( conidia ) ( Fig . 5 ) . To confirm the mutant phenotype was a result of the gene deletion events , deletions strains were complemented with the wildtype gene targeted to either pyrG or niaD . Tyrosine can be utilized as both nitrogen and carbon sources for growth . In A . fumigatus , an L-amino oxidase is thought to catalyse the conversion of tyrosine to the α-keto acid , liberating ammonium as a nitrogen source . However , the P . marneffei genome lacks genes encoding L-amino oxidases so it is likely that a transamination reaction of tyrosine with α-ketoglutarate to produce glutamate as a nitrogen source is the first catabolic step . The enzyme that performs this reaction is currently unknown . The catabolism of tyrosine also produces fumarate and acetoacetate , which is further catabolised to acetyl-CoA , which feed into the TCA cycle to provide carbon ( Fig . 2A ) . Phenylalanine is also catabolised via the tyrosine catabolism pathway to provide both nitrogen and carbon , although the enzymatic steps required to convert it to 4-hydroxyphenylpyruvate remain unclear . Growth of the wildtype , ΔwA , ΔhpdA , ΔhmgA , ΔhmgX and ΔmaiA strains was assessed at 25°C on a range of media in which the sole carbon source was either glucose , tyrosine or phenylalanine and the sole nitrogen source was either ammonium , tyrosine or phenylalanine or on medium lacking both a nitrogen and carbon source . Wildtype and the ΔwA strain grew well on tyrosine or phenylalanine as a sole nitrogen or carbon source ( Fig . 5 ) . Compared to wildtype and the ΔwA strain , the ΔhpdA , ΔhmgA , ΔhmgX and ΔmaiA strains showed reduced growth on tyrosine and phenylalanine as the sole nitrogen source which suggests a feedback loop regulates the nitrogen-liberating first step in the catabolism of tyrosine ( Fig . 5 ) . Unlike wildtype and the ΔwA strain , the ΔhpdA , ΔhmgA , ΔhmgX and ΔmaiA strains showed no growth on tyrosine or phenylalanine as the sole carbon source ( Fig . 5 ) . The medium became pigmented when the ΔhmgA mutant was grown on tyrosine or phenylalanine as the sole nitrogen or carbon source ( Fig . 5 ) . This suggests that accumulated homogentisate is being oxidized to produce pyomelanin , as has been observed in the Aspergilli [10 , 11 , 31] . Reintroduction of the wildtype gene to generate the ΔhmgA hmgA+ , ΔhmgX hmgX+ , ΔmaiA maiA+ complemented strains restored growth on tyrosine and phenylalanine as the sole nitrogen and carbon source at 25°C but to a level slightly below that of wildtype ( S1A Fig ) . The ΔhpdA hpdA+ strain did not show the same extent of restoration of growth on tyrosine and phenylalanine as the sole carbon source , indicating either the complementation construct may lack some regulatory sequences required for full expression or the location of the gene in the cluster , as opposed to an ectopic site , is important for it’s regulation ( S1A Fig ) . The growth phenotypes of the ΔhpdA and ΔhmgA strains suggests that there is no functional overlap between these genes and the paralogues located elsewhere in the genome with respect to this catabolic pathway . To confirm that these paralogues do not have a role in tyrosine catabolism , hpdB was cloned and deleted . This gene was selected as hpdA has only a single paralogue in the genome , whereas , hmgA has four paralogues . Unlike ΔhpdA , the ΔhpdB strain was indistinguishable from wildtype on tyrosine or phenylalanine as either the sole nitrogen or carbon source at 25°C and 37°C supporting the hypothesis that there is no functional overlap between hpdA and hpdB ( S1B Fig and S4B Fig ) . Accumulation of intermediates of tyrosine catabolism results in cellular toxicity due to the production of intermediary metabolites or their spontaneous degradation products ( 4-hydroxyphenlpyruvic acid in hpdA mutants , homogentistic acid in hmgA mutants and succinylacetone and succinylacetoacetate from fumarylacetoacetate in fahA mutants ) [7 , 8 , 10 , 32 , 33] . To investigate if toxic metabolites are accumulating in the P . marneffei tyrosine catabolism mutants , the wildtype , ΔhpdA , ΔhmgA , ΔhmgX and ΔmaiA strains were grown on tyrosine medium also containing the non-repressive carbon sources sorbitol , lactose , acetate or proline . In contrast to wildtype , the ΔhpdA and ΔhmgA strains showed no growth under these conditions indicating that toxic intermediates are accumulating in these strains ( S2 Fig ) . Growth of the ΔhmgX strain was less than wildtype but greater than the ΔhpdA and ΔhmgA mutants . This suggests that the levels of toxic intermediates are lower in this strain compared to ΔhpdA and ΔhmgA ( S2 Fig ) . Growth of the ΔmaiA was unaffected on tyrosine medium also containing the non-repressive carbon sources suggesting that toxic intermediates are not accumulating in this strain ( S2 Fig ) . These results are in contrast to A . nidulans in which deletion of hpdA and maiA , but not hmgA , results in cellular toxicity [10 , 32 , 33] . These results suggest putative differences between the metabolites derived from homogentisate and 4-malelyacetate in P . marneffei versus A . nidulans . To assess if the genes within the cluster that are not conserved in other species also have a role in tyrosine catabolism in P . marneffei , growth of the ΔmfpA and ΔhypW strains was assessed at 25°C on glucose , tyrosine or phenylalanine as the sole carbon source , ammonium , tyrosine or phenylalanine as the sole nitrogen source or on medium lacking both a nitrogen and carbon source . As expected , the ΔmfpA strain was indistinguishable from wildtype under these conditions indicating that despite being transcriptionally regulated in response to the presence of tyrosine mfpA is not required for tyrosine catabolism ( S1B Fig ) . However , unexpectedly , the ΔhypW strain showed reduced growth on tyrosine and phenylalanine as the sole nitrogen or carbon source compared to wildtype ( S3C Fig ) . Due to the low transcriptional activity in this region and the ORF’s close proximity to hmgX , we hypothesized that deletion of this ORF may be affecting hmgX expression . To assess this , hmgX expression was evaluated in the ΔhypW strain at 25°C in ammonium and tyrosine as the sole nitrogen source . Deletion of hypW reduced expression of hmgX under both conditions suggesting that the reduced growth on tyrosine as the sole nitrogen source is a consequence of reduced hmgX expression rather than indicating a role for hypW in tyrosine catabolism ( S3D Fig ) . In support of this hypothesis , the introduction of a construct containing both the hypW and hmgX wildtype genes complemented the phenotype of the ΔhypW strain at both 25°C and 37°C , whereas , introduction of a construct containing wildtype hypW and a truncated hypX lacking the start codon , did not complement this growth phenotype ( S3C Fig ) . Therefore , hypW is not required for tyrosine catabolism To further investigate the role of regulatory genes on the tyrosine catabolic cluster , the growth of the ΔhmgR and ΔareA strains was assessed at 25°C on glucose , tyrosine or phenylalanine as the sole carbon source , ammonium , tyrosine or phenylalanine as the sole nitrogen source or on medium lacking both a nitrogen and carbon source . Compared to wildtype , the ΔhmgR strain showed reduced growth on tyrosine and phenylalanine as the sole nitrogen or carbon source ( Fig 5 ) . Reintroduction of the wildtype gene in the ΔhmgR hmgR+ complemented strain restored growth on tyrosine and phenylalanine as the sole nitrogen and carbon source at 25°C ( S2A Fig ) . This growth reduction correlates with the reduced expression of tyrosine catabolic genes on tyrosine as a sole nitrogen source in the ΔhmgR mutant ( Fig . 3 ) . To assess if the growth reduction of the ΔhmgR strain was specific to tyrosine and phenylalanine , the ΔhmgR strain was also grown on a variety of amino acids as the sole nitrogen source at 25°C and 37°C . No reduction in growth was observed on any other amino acids tested . The ΔareA strain showed reduced growth on tyrosine or phenylalanine as the sole nitrogen source but was unaffected on tyrosine and phenylalanine as a carbon source ( Fig . 5 ) . As AreA does not directly regulate expression of the tyrosine catabolic genes in the gene cluster ( Fig . 4 ) , this result suggests that the deamination step is AreA dependent and in it’s absence there can be no metabolites to flux through the pathway . To assess if the tyrosine catabolic gene cluster is required for yeast-phase growth on tyrosine at 37°C , the wildtype , ΔwA , ΔhpdA , ΔhmgA , ΔhmgX and ΔmaiA strains were grown on glucose , tyrosine or phenylalanine as the sole carbon source and ammonium , tyrosine or phenylalanine as the sole nitrogen source at 37°C . Growth of wildtype on tyrosine or phenylalanine as a sole nitrogen or carbon source at 37°C is relatively robust when compared to growth on the preferred nitrogen and carbon sources of ammonium and glucose , respectively ( Fig . 6 ) . Compared to wildtype and the ΔwA , the ΔhpdA and ΔhmgX strains showed reduced growth on tyrosine and phenylalanine as the sole nitrogen source at 37°C ( Fig . 6 ) . However unlike 25°C , the ΔhmgA and ΔmaiA strains showed no growth reduction on tyrosine and phenylalanine as the sole nitrogen source at 37°C . This difference suggests that the feedback regulation of the pathway observed at 25°C is not operating at 37°C , possibly because of a higher demand for intermediate metabolites such as homogentisate for pyomelanin production ( Fig . 6 ) . In contrast to wildtype and the ΔwA , the ΔhpdA , ΔhmgA , ΔhmgX and ΔmaiA strains showed no growth on tyrosine or phenylalanine as the sole carbon source ( Fig . 6 ) . In the absence of growth on tyrosine as a carbon source , the un-utilized tyrosine in the medium crystalizes ( Fig . 6 ) . The ΔhmgA hmgA+ , ΔhmgX hmgX+ , ΔmaiA maiA+ complemented strains displayed growth almost to the same extent as wildtype on tyrosine and phenylalanine as the sole nitrogen and carbon source at 37°C ( S4A Fig ) . Like at 25°C , the ΔhpdA hpdA+ strain did not show a complete restoration of wildtype growth supporting the hypothesis that the position of the gene in the cluster is important for it’s regulation ( S4A Fig ) . To assess if mfpA has a role in tyrosine catabolism in P . marneffei at 37°C , growth of the ΔmfpA strain was assessed on glucose , tyrosine or phenylalanine as the sole carbon source and ammonium , tyrosine or phenylalanine as the sole nitrogen source . As expected , the ΔmfpA strain was indistinguishable from wildtype under these conditions indicating that it is not required for tyrosine catabolism ( S4B Fig ) . To investigate the role of regulatory genes on the tyrosine catabolic cluster at 37°C , the growth of the ΔhmgR and ΔareA strains was assessed at 37°C on glucose , tyrosine or phenylalanine as the sole carbon source and ammonium , tyrosine or phenylalanine as the sole nitrogen source . Compared to wildype , the ΔhmgR strain showed reduced growth on tyrosine and phenylalanine as the sole nitrogen or carbon source at 37°C and reintroduction of the wildtype gene restored growth ( Fig . 6 and S4 Fig ) . This result is consistent with the reduced expression of tyrosine catabolic genes on tyrosine as a sole nitrogen source in the ΔhmgR mutant at 37°C ( Fig . 3 ) . The ΔareA strain showed reduced growth on tyrosine or phenylalanine as both the sole nitrogen and carbon source ( Fig . 6 ) . Tyrosine is an important precursor in the formation of two different types of melanin; DOPA melanin via the metabolism of tyrosine to form DOPA and pyomelanin through the oxidation and polymerization of homogentisate during tyrosine catabolism [10 , 11] . To assess if P . marneffei can produce melanin from tyrosine , the wildtype strain was grown on medium containing ammonium or tyrosine as the sole nitrogen source , medium containing both ammonium plus tyrosine or tyrosine plus alanine as nitrogen sources or on L-DOPA at 37°C . P . marneffei was unpigmented on medium containing ammonium as a sole nitrogen source ( Fig . 7A ) . In contrast , a brown pigment was evident on medium containing tyrosine as the sole nitrogen source , suggesting that this may be a melanin produced from tyrosine ( Fig . 7A ) . This pigment was confirmed to be melanin by testing resistance to chemical degradation by boiling in acid ( Materials and Methods ) [23] ( S5A Fig ) . If both ammonium and tyrosine are present the amount of melanization is greatly reduced suggesting that some of the genes required for production of this melanin are under nitrogen metabolite repression ( Fig . 7A ) . The amount of melanization on tyrosine increases with the addition of another non-preferred nitrogen source such as alanine ( Fig . 7C ) . Wildtype P . marneffei also becomes pigmented on L-DOPA medium indicating that P . marneffei is capable of producing DOPA melanin ( Fig . 7B ) . To assess if deletion of genes of the tyrosine catabolic cluster affects the production of DOPA melanin , the wildtype , deletion and complementation strains were grown at 37°C on L-DOPA . The production of DOPA melanin was indistinguishable amongst all of the strains except ΔareA , which showed a decrease in DOPA melanin production ( S5C Fig ) . To investigate if pyomelanin production contributes to the melanization observed on tyrosine medium , the wildtype , ΔwA , ΔhpdA , ΔhmgA , ΔhmgX and ΔmaiA strains were examined after growth at 37°C on both tyrosine as a sole nitrogen source ( Fig . 6 ) and tyrosine plus alanine as nitrogen sources ( Fig . 7C ) . In contrast to wildtype and ΔwA , the ΔhpdA and ΔhmgX strains produced no visible melanin at 37°C when grown on tyrosine ( Fig . 5 and 6C ) . This suggests that although capable of producing DOPA melanin from tyrosine , the major melanin produced by P . marneffei on medium containing tyrosine is the allomelanin pyomelanin produced during tyrosine catabolism . This was further confirmed by testing if melanin particles could be isolated from the ΔhpdA mutant grown on tyrosine as the sole nitrogen source for 14 days at 37°C ( Materials and Methods ) . In contrast to wildtype under these growth conditions , no melanin particles were observed in the ΔhpdA mutant after boiling in acid ( S5A Fig ) . This confirms that the melanin produced by P . marneffei on tyrosine is pyomelanin . The ΔmaiA strain showed a mild reduction in pyomelanin production compared to the wildtype ( Fig . 6 and 7C ) . The ΔhmgA strain showed increased pyomelanin production on tyrosine as the sole nitrogen source ( Fig . 6 ) . The ΔhmgA strain failed to grow on medium containing both tyrosine and alanine ( Fig . 7C ) , despite being able to grow on alanine or tyrosine when provided as sole nitrogen sources ( Fig . 6 ) . This suggests that like at 25°C toxic metabolites are accumulating in the ΔhmgA mutant , when grown with an additional non-repressing carbon source such as alanine . The ΔhpdA and ΔhmgX strains also showed reduced growth compared to wildtype on this medium suggesting some cellular toxicity , however this was to a lesser extent than at 25°C ( Figs . 7C and S2 Fig ) . This suggests that fewer toxic metabolites accumulate in these mutants at 37°C possibly as a result of an increased demand for pyomelanin production via homogentisate . Reintroduction of the wildtype gene in the ΔhpdA hpdA+ , ΔhmgA hmgA+ , ΔhmgX hmgX+ and ΔmaiA maiA+ complemented strains restored pyomelanin production on tyrosine as a sole nitrogen source ( S4A Fig ) . To investigate if genes within the cluster which do not play a role in tyrosine catabolism and gene paralogues outside the cluster are required for pyomelanin production , the ΔmfpA and ΔhpdB strains were also grown on medium containing tyrosine as the sole nitrogen source and medium with both tyrosine and alanine as the nitrogen sources at 37°C . Consistent with no role in tyrosine catabolism , the ΔmfpA and ΔhpdB mutants were indistinguishable from wildtype under these conditions ( S4B Fig ) . Wildtype P . marneffei appears pigmented on the standard medium of brain heart infusion ( BHI ) from growth at 37°C . BHI is comprised of bovine brain and heart tissue that is postulated to be rich in phenolic compounds [23] . To assess if pyomelanin contributes to pigmentation on BHI , the wildtype , ΔwA , ΔhpdA , ΔhmgA , ΔhmgX , ΔmaiA and complemented control strains were grown at 37°C on BHI for 5 days . The ΔhpdA and ΔhmgX strains showed a large reduction in melanisation on BHI medium , whereas , the ΔhmgA strains showed an increase in melanisation ( S5B Fig ) . The ΔmaiA strain displayed a minor decrease in melanisation and the complemented strains were indistinguishable from wildtype ( S5 Fig ) . This suggests that pyomelanin contributes to some , but not all , of the melanisation observed when P . marneffei is grown on BHI medium . As expected , the ΔwA strain was also comparable with wildtype , suggesting that DHN melanin is not contributing to the melanisation observed on BHI medium ( S5B Fig ) . Deletion of hmgR results in reduced expression of the tyrosine catabolic cluster genes in the presence of tyrosine at both 25°C and 37°C ( Figs . 3 and 4 ) . To assess if this decreased expression translates into a visible decrease in pyomelanin production , the ΔhmgR and ΔhmgR hmgR+ strains were grown on medium containing both alanine and tyrosine at 37°C . Compared to wildtype and the ΔhmgR hmgR+ complemented strain , the ΔhmgR mutant showed decreased pyomelanin production suggesting that hmgR is required to positively regulate pyomelanin production in the presence of tyrosine ( Fig . 7C ) . The ΔhmgR mutant also showed a reduction of melanization on BHI medium ( S5 Fig ) . Deletion of hmgR also leads to partial derepression of tyrosine catabolic cluster genes in the presence of ammonium ( Figs . 3 and 4A and C ) . To investigate if this derepression results in inappropriate pyomelanin production , the wildtype , ΔhmgR and ΔhmgR hmgR+ strains were also grown on ammonium plus tyrosine medium at 37°C . Compared to wildtype and the ΔhmgR hmgR+ complemented strain , the ΔhmgR mutant produced increased pyomelanin production on ammonium plus tyrosine medium at 37°C consistent with the partial derepression of gene expression ( Fig . 7D ) . Deletion of areA also leads to derepression of tyrosine catabolic cluster genes in the presence of ammonium at 25°C and 37°C and a complete loss of nitrogen metabolite repression in ammonium plus tyrosine medium at 25°C ( Fig . 4A , B and C ) . To investigate if this derepression also results in inappropriate pyomelanin production in these strains , the wildtype and ΔareA strains were grown on ammonium plus tyrosine medium at 37°C . In contrast to the ΔhmgR mutant , no increase in pyomelanin production was observed in the ΔareA strain on ammonium plus tyrosine medium ( Fig . 7D ) . Rather , the ΔareA strain produced less pyomelanin under this growth condition ( Fig . 7D ) . The ΔareA strain also appeared slightly less melanised on BHI medium ( S5 Fig ) . Similarly , deletion of areA lead to reduced pyomelanin production on alanine plus tyrosine medium ( Fig . 7C ) . These results support the previous hypothesis that the deamination step is AreA dependent and in a ΔareA mutant there is reduced metabolic flux through the pathway . Both DHN- and DOPA-melanin have been shown to protect fungal cells from reactive oxygen species ( ROS ) produced by host innate immune cells [12–15 , 20] . To investigate if pyomelanin plays a similar role , the wildtype , ΔwA , ΔhpdA , ΔhmgA , and complemented control strains were grown for 6 days at 37°C on medium containing tyrosine as the nitrogen source and varying concentrations of hydrogen peroxide ( H2O2 ) ( Materials and Methods ) . The ΔhpdA and ΔhmgA strains showed increased sensitivity to H2O2 compared to the wildtype , ΔhpdA hpdA+ and ΔhmgA hmgA+ complemented strains ( Fig . 8 ) . The wA gene , which encodes the polyketide synthase required for DHN melanin production in conidia , is required for resistance to H2O2 in A . fumigatus [15 , 20] . However , only a very mild sensitivity to H2O2 was observed in the P . marneffei ΔwA strain and this was much lower than the ΔhpdA and ΔhmgA strains ( Fig . 8 ) . The addition of the HpdA inhibitor NTBC prevented P . marneffei yeast cell production during macrophage infection suggesting hpdA is required for ex vivo growth ( Fig . 1 ) . To confirm the required for hpdA during ex vivo growth and to assess if the other genes of the tyrosine catabolic cluster are also required for ex vivo growth , conidia of the wildtype , ΔwA , ΔhpdA , ΔhmgA , ΔhypW , ΔhmgX , ΔmaiA , ΔmfpA and ΔhmgR strains were used to infect murine J774 macrophages and examined 24 hours post-infection . A control of wildtype conidia incubated in macrophage medium alone was also performed . After 24 hours , macrophages infected with wildtype conidia contain numerous yeast cells dividing by fission ( Fig . 9A ) . In contrast , only 2 . 67 ± 0 . 70% of conidia had germinated in the macrophage medium control . This suggests that any growth of P . marneffei observed within macrophages is due to the derivation of nutrients from the host macrophage . In contrast to wildtype and consistent with the NTBC experiments , ungerminated conidia were predominately observed in macrophages infected with conidia of the ΔhpdA mutant 24 hours post-infection ( Fig . 9A and B ) . In macrophages infected with the ΔhpdA strain , 82 . 2±6 . 78% of conidia remained ungerminated and only 17 . 4±6 . 57% germinated to produce yeast cells ( compared to 10 . 6±4 . 65% ungerminated conidia and 89 . 5±4 . 65% yeast cells for wildtype ) ( Fig . 9B ) . This phenotype was complemented by reintroduction of hpdA+ in the ΔhpdA hpdA+ strain ( 10 . 9±0 . 41% ungerminated conidia and 88 . 4±0 . 66 yeast cells ) . No differences in germination rates were observed between the wildtype and ΔhpdA strain in vitro . Despite the ΔhpdA and ΔhmgX mutants having indistinguishable growth and pigmentation phenotypes on tyrosine , the ΔhmgX strain produced normal numbers of yeast cells ex vivo ( Fig . 9A and B ) . The ΔhmgA strain showed a small increase in the number of ungerminated conidia ( 29 . 0±5 . 78% ) and consequently a small decrease in the number of yeast cells ( 69 . 2±5 . 72% ) produced at 24 hours post-infection ( Fig . 9A and B ) . This phenotype was complemented by reintroduction of hmgA+ in the ΔhmgA hmgA+ strain ( 10 . 4±1 . 75% ungerminated conidia and 89 . 6±1 . 75% yeast ) . The production of yeast cells in macrophages infected with ΔwA , ΔhypW , ΔhmgX , ΔmaiA , ΔmfpA and ΔhmgR conidia was indistinguishable from wildtype , suggesting that tyrosine and phenylalanine catabolism as a source of carbon is not required for the initial stages of infection ( Fig . 9B ) . Interestingly , the ΔwA strain produced numerous yeast cells at levels equivalent to wildtype after 24 hours infection ( 85 . 2±4 . 97% compared to 89 . 5±4 . 65% for wildtype ) despite a previous report that suggested decreased expression of this gene using RNAi results in a decrease in virulence in a mouse model of infection ( Fig . 9B ) [16] . To observe if ΔhpdA conidia germinate with a longer period of incubation , conidia of the wildtype , ΔhpdA and ΔhpdA hpdA+ strains were used to infect murine J774 macrophages and examined 48 hours post-infection . After 48 hours , macrophages infected with wildtype and ΔhpdA hpdA+ conidia contain prolific numbers of yeast cells dividing by fission ( wildtype 1 . 43±0 . 98% ungerminated conidia , 98 . 6±0 . 98% yeast cells and ΔhpdA hpdA+ 0 . 75±0 . 48 ungerminated conidia and 99 . 3±0 . 48% yeast cells ) . Conidia of the ΔhpdA strain have begun to germinate into yeast cells by 48 hours but show reduced cellular proliferation ( 38 . 5±5 . 71% ungerminated conidia , 61 . 3±5 . 63% yeast ) . Consistent with this observation , ΔhpdA conidia remain viable in macrophages . Plating the contents of lysed ΔhpdA infected macrophages at 37°C in vitro resulted in the growth of ΔhpdA colonies . To assess if deletion of genes of the tyrosine catabolic cluster affects the phagocytosis of conidia by macrophages , conidia of the wildtype , ΔhpdA , ΔhmgA , ΔhmgX and ΔmaiA strains were used to infect murine J774 macrophages and the macrophages examined 2 hours post-infection . No differences in phagocytosis were observed ( average number of conidia per 100 macrophages: wildtype 76 . 7±4 . 70 , ΔhpdA 67 . 3±9 . 80 , ΔhmgA 70 . 0±9 . 90 , ΔhmgX 75 . 0±6 . 70 and ΔmaiA 79 . 3±8 . 20 ) . The wildtype , ΔhpdA , ΔhmgA and ΔhmgX strains were also used to infect human THP-1 macrophages . The ΔhpdA , ΔhmgA and ΔhmgX phenotypes in human macrophages were identical to those described for infection of murine macrophages ( S6 Fig ) .
The expression of genes within the P . marneffei tyrosine catabolic gene cluster is subject to a complex network of regulatory control with four different levels of regulation; pathway specific regulation by the Zn ( II ) 2-Cys6 binuclear transcription factor HmgR , cluster specific regulation by location , global regulation in response to nitrogen source by AreA and post-transcriptional feedback by pathway intermediates . The complex regulation of the tyrosine catabolism gene cluster reflects the multiple roles played by genes in this cluster , not only in adapting to the nutritional sources available but also the production of protective melanin . An additional , unique role for HpdA in P . marneffei , and probably other dimorphic fungi with an intracellular ( host cell ) phase , is also suggested . Genes of the tyrosine catabolic cluster are regulated specifically in response to the presence of tyrosine by the Zn ( II ) 2-Cys6 binuclear transcription factor HmgR encoded within the cluster . HmgR is required for full induction of these genes in the presence of tyrosine as the sole nitrogen source and deletion results in reduced and no growth on tyrosine as a sole nitrogen and carbon source , respectively . The orthologue in A . fumigatus , hmgR , is also required for tyrosine-induced expression of genes in the cluster , suggesting this protein plays a conserved role [5] . However , unlike A . fumigatus , the P . marneffei ΔhmgR mutation results in only a partial loss of tyrosine-induced expression , suggesting that an additional factor contributes to positively regulating expression of the tyrosine catabolic cluster in response to tyrosine . Zn ( II ) 2-Cys6 binuclear transcription factors have been shown to positively regulate the expression of many nitrogen metabolic pathways in fungi [5 , 34 , 35 , 36 , 37] . Unexpectedly , deletion of hmgR in P . marneffei also resulted in partial derepression on ammonium suggesting that HmgR also has a negative role in regulating gene expression . The genes of the tyrosine catabolic cluster are also under specific regulation by location . The expression of mfpA , the gene within the tyrosine metabolic cluster only in P . marneffei and T . stipitatus which does not appear to have a role in tyrosine catabolism , was induced on tyrosine at both 25°C and 37°C even though it does not share a promoter with another cluster gene . This suggests that any gene captured within this genomic region may consequently come under its regulation . Similar effects have been described for other gene clusters ( for example spoCI in A . nidulans ) [38] . In addition , the complementation strains which had the wildtype gene reintroduced at the pyrG or niaD loci , did not show complete restoration of wildtype growth on tyrosine and phenylalanine , also indicating that the position of the gene in the cluster is important for it’s regulation . AreA is a positively-acting GATA-type transcription factor which regulates a large number of genes to effect nitrogen metabolite repression; the global response to limiting nitrogen conditions [30 , 39] . We originally hypothesized that the additional factor contributing to positively regulating expression of the tyrosine catabolic cluster in response to tyrosine could be AreA . In A . nidulans , AreA remodels chromatin in cooperation with Zn ( II ) 2-Cys6 binuclear transcription factors in response to specific nitrogen sources [36 , 40] . However , deletion of areA in P . marneffei did not reduce induction of the tyrosine catabolic cluster genes in the presence of tyrosine but rather unexpectedly lead to a loss of repression in the presence of ammonium . This suggests that contrary to the paradigm , AreA is acting negatively on the tyrosine catabolic cluster genes under nitrogen repressing conditions . To our knowledge , only two examples exist of AreA acting as a negative regulator in fungi despite examples of a related human GATA factor acting as both an activator and repressor [41 , 42 , 43] . In A . nidulans , AreA negatively regulates the expression of nadA , an adenine deaminase encoding gene required for purine degradation , though unlike the P . marneffei tyrosine cluster genes , nadA is also expressed on ammonium [41] . There is only 4 bp between the unique UaY ( positively acting Zn ( II ) 2-Cys6 binuclear transcription factor ) and AreA binding sites in the nadA promoter and AreA acts in part to negate UaY-mediated induction by directly competing with UaY binding [41] . However , the identification of potential AreA binding sites in the promoters of P . marneffei tyrosine catabolic cluster genes ( HGATAR ) shows that no predicted sites are in close proximity to the potential HmgR binding site predicted by RSAT analysis . AreA also acts as a repressor of genes required for arginine catabolism in A . nidulans [43] . Similar to the P . marneffei tyrosine gene cluster , genes required for arginine catabolism in A . nidulans are not expressed in ammonium , are induced by arginine and this induction is dependent on the ArcA Zn ( II ) 2-Cys6 binuclear transcription factor [35] . Also similar to tyrosine , arginine can be utilized as both a nitrogen and carbon source . Under carbon repressing conditions ( 1% glucose ) an areA loss of function mutant results in a loss of nitrogen metabolite repression of the arginine catabolism genes [43] . Tyrosine catabolism is also regulated by post-transcriptional feedback by pathway intermediates . Deletion of genes of the tyrosine catabolic cluster ( hpdA , hmgA , hmgX and maiA ) resulted in reduced growth on tyrosine as a sole nitrogen source . The proposed transamination reaction of tyrosine with α-ketoglutarate to produce glutamate as a nitrogen source occurs prior to the reactions catalysed by HpdA , HmgA and MaiA . This is indicative of a negative feedback loop regulating the catabolism of tyrosine in P . marneffei . This regulatory mechanism appears to be conserved as the hmgR and hmgX deletions in A . fumigatus also show reduced growth on tyrosine or phenylalanine as the sole nitrogen source [5] . Dimorphism is intricately linked to pathogenicity and therefore differentially expressed genes , particularly those that display yeast-specific expression , are likely to play a role in the establishment and progression of infection . Based on this hypothesis , yeast-specific genes in P . marneffei were identified using a microarray-based expression profiling [6] . This profiling revealed that hpdA , encoding the enzyme 4-hydroxyphenylpyruvate dioxygenase ( HpdA ) required during the catabolism of tyrosine , was induced specifically in the pathogenic yeast cell type at 37°C [6] . Intriguingly , chemical inhibition of HpdA activity using NTBC or deletion of hpdA resulted in a severe defect in the production of yeast cells during macrophage infection , suggesting that HpdA is required for P . marneffei pathogenicity . Therefore the question arose as to whether this was a result of reduced nutrient acquisition , reduced melanin protection against oxidative stress or a combination of both . Interestingly , chemical inhibition of HpdA resulted in more significant delay in conidial germination compared to deletion of hpdA ( Figs . 1 and 9 ) . This is likely due to the inhibition of the host 4-hydroxyphenylpyruvate dioxygenase , as well as that of the fungus , thus leading to a further decrease in available nutrients for growth . Deletion of hpdA , as well as other cluster genes encoding enzymes required for tyrosine catabolism ( hmgA , hmgX and maiA ) and the gene encoding the regulatory transcription factor ( hmgR ) resulted in reduced growth on tyrosine as the sole nitrogen or carbon source at both 25°C and 37°C , showing that these genes are required for tyrosine catabolism and that P . marneffei can utilize tyrosine as both a nitrogen and carbon source for growth . However , unlike ΔhpdA , the ΔhmgX , ΔmaiA and ΔhmgR strains did not show reduced yeast cell production in macrophages . The ΔhmgA mutant showed slightly reduced yeast cell production but this is likely to be attributed to reduced conidial germination due accumulation of homogentisate and consequent hypermelanization . This result suggests that nitrogen or carbon acquisition via tyrosine catabolism is not essential for the early stages of infection in macrophages and that the phenotype of the ΔhpdA mutant is not due to reduced nutrient acquisition . In addition , the lack of conidial germination in the ΔhpdA in macrophages is unlikely to be due to a toxic build-up of tyrosine catabolic intermediates , as the ΔhmgA mutant , which showed a greater reduction in growth than the ΔhpdA mutant due to toxicity in vitro , displayed only a mild reduction in yeast cell production ex vivo . Somewhat unexpectedly , ΔhmgX did not show the same phenotype as ΔhpdA during macrophage infection , despite the hypothesis that HmgX acts as an accessory protein to HpdA and the ΔhpdA and ΔhmgX strains showing an equivalent growth reduction on tyrosine as a sole nitrogen or carbon source at both 25°C and 37°C and an equivalent lack of pyomelanin production in vitro . It is possible that HmgX is only partially required for HpdA function under certain conditions . This hypothesis is supported by the reduced accumulation of toxic intermediates of the tyrosine catabolic pathway compared to ΔhpdA and ΔhmgA . The ex vivo phenotype of the ΔhpdA mutant is unlikely to be a result of the inability to produce pyomelanin , as the ΔhmgX mutant , which like the ΔhpdA mutant cannot produce pyomelanin , produces wildtype levels of yeast cells in macrophages . It is possible that the ΔhmgX mutant can produce a colourless intermediate to the final pigmented pyomelanin which may be sufficient to afford protection in macrophages , however , the ΔmaiA and ΔhmgR mutants , which displayed reduced pyomelanin production at 37°C , also displayed ex vivo growth indistinguishable from wildtype . Instead these results may suggest that HpdA has an additional role to tyrosine catabolism and pyomelanin formation in P . marneffei and may also explain why hpdA is highly constitutively expressed at 37°C , in contrast to all of the other genes in the tyrosine catabolic cluster . Therefore the question arises as to what this additional novel role of hpdA during ex vivo growth could be . It is possible that tyrosine catabolism , and specifically the step catalysed by HpdA , is acting as a developmental signal . The enzyme tyrosine aminotransferase , which catalyses the first step in tyrosine catabolism and which an orthologue is lacking in P . marneffei , has been shown to promote apoptosis and prevent cell proliferation during cancer development in humans [44] . In addition , inhibition of hpd-1 by RNAi in the nematode Caenorhabditis elegans extends lifespan and results in a developmental arrest at the dauer larval stage , which prevents the completion of development when environmental conditions are unfavourable [45] . In C . elegans the link between tyrosine catabolism and development is proposed to occur via the insulin-like daf-2 signalling pathway [46] . Elevated tyrosine has inhibitory affects on DAF-2 signalling [47] . The DAF-2 receptor , orthologous to the insulin receptor in mammals , lies upstream of a phosphatidylinositol 3 signalling cascade which acts to phosphorylate the DAF-16 forkhead transcription factor preventing it’s entry into the nucleus [47] . Daf-16 positively regulates the expression of a number of genes regulating metabolism and lifespan including those encoding enzymes which protect against free radicals such as catalase-1 ( ctl-1 ) and superoxide dismutase ( sod-3 ) and 4-hydroxyphenylpyruvate dioxygenase ( hpd-1 ) [45 , 47] . The P . marneffei genome encodes a daf-16 homologue ( PMAA_055700 ) and the hpdA promoter contains a putative DAF-16 binding site ( ATGTTTGA ) which is conserved in the Aspergilli ( consensus WTGTTTVV ) , suggesting that this regulatory mechanism may be conserved in fungi . Further elucidation of the role played by hpdA during ex vivo growth will clearly provide insight into the progression of conidia into yeast cells during intracellular pathogenic growth of P . marneffei and opens up an exciting new avenue of investigation .
P . marneffei genomic DNA was isolated as previously described [48] . Southern and northern blotting was performed with Amersham Hybond N+ membrane using [a-32P]dATP labeled probe hybridization using standard methods [49] . Sequences of primers are provided in S1 Table . A PCR product encompassing P . marneffei hpdA ( PMAA_031950 ) was generated with primers AA48 and AA49 and cloned into pGemT Easy to generate pAM7072 . To make the deletion construct , pKB7717 , an EcoRI/SalI 5’ PCR product generated with MM70 and MM71 was cloned into EcoRI/SalI pBluescript II SK+ . A BamHI/SpeI 3’ PCR product generated with MM72 and MM73 was then cloned into the BamHI/SpeI sites and subsequently a BamHI/EcoRI pyrGb fragment from pAB4626 was cloned into the BamHI/EcoRI sites . The complementation construct , pKB7682 , was generated by cloning a HindIII/SacI fragment from pAM7072 into pLS7804 ( pyrG targeting pBluescript II SK+ ) . hmgA ( PMAA_031960 ) was PCR amplified using primers MM75 and MM76 and cloned into pGemT Easy to generate pKB7723 . The deletion construct , pKB7724 , was generated by replacing the BglII/StuI fragment from pKB7723 with BamHI/EcoRV pyrGb from pAB4626 . The complementation construct , pKB7791 , was generated cloning a BamHI/XhoI digested PCR product generated using OO78 and OO79 into BamHI/XhoI pLS7804 . maiA ( PMAA_032000 ) was cloned by PCR amplification with primers PP18 and PP19 and cloned into pGemT Easy to generate pKB7786 . The deletion construct , pKB7805 , was generated by replacing the BglII/XhoI fragment from pKB7786 with a BamHI/XhoI fragment from pAB4626 . The maiA complementation construct , pKB858 , was generated by cloning a PstI/EcoRV fragment from pKB7786 into PstII/EcoICRI pHB7615 ( niaD targeting pBluescript SK+ ) . hypW ( PMAA_031870 ) was cloned by PCR amplification with primers KK37 and KK38 and cloned into pGemT Easy to generate pSM7504 . The deletion construct , pSM7546 , was generated by ligating an inverse PCR product generated using primers KK71 and KK72 to EcoRV/SmaI pyrGb from pAB4626 . Likewise , hmgX ( PMAA_031980 ) was cloned by PCR amplification with primers KK39 and KK40 and cloned into pGemT Easy to generate pSM7505 . The deletion construct , pSM7651 , was generated by ligating an inverse PCR product generated using primers KK73 and KK74 to EcoRV/SmaI pyrGb from pAB4626 . The hypW and hmgX complementation construct , pKB7801 , was generated by cloning a SpeI/StuI fragment from pSM7505 into XbaI/EcoICRI pLS7804 . The complementation construct containing hypW and a truncated hmgX allele , pKB7940 , was generated by cloning a PstI/HindIII fragment from pSM7505 into PstI/HindIII pHB7615 . The regulatory gene hmgR ( PMAA_032020 ) was cloned by ligating the PCR product generated using primers LL13 and LL14 into EcoRV digested pBluescript II SK+ , to generate pSH7541 . The deletion construct , pSH7578 , was generated by cloning an EcoRV/SmaI pyrGb fragment from pAB4626 into an inverse PCR product of pSH7541 with primers LL56 and LL57 . The complementation construct , pKB7683 , was generated by cloning in the KpnI/SpeI fragment from pSH7541 into KpnI/XbaI pLS7804 . mfpA ( PMAA_032010 ) was PCR amplified using primers QQ50 and QQ52 and cloned into pGemT Easy to generate pLS7828 . The deletion construct , pLS7852 , was generated using a Gateway method as described in [50] using the primers QQ57 and QQ58 . hpdB ( PMAA_089170 ) was amplified using primers PP14 and PP15 and cloned into pGemT Easy to generate pLS7785 . The deletion construct , pLS7845 , was generated using the Gateway method using the primers QQ59 and QQ60 . wA ( PMAA_082120 ) was amplified using primers HH45 and HH46 and cloned into pGemT Easy to generate pSJ7351 . The deletion construct , pHW7445 , was generated by replacing the BglII/EcoRV fragment of pSJ7351 with the BamHI/EcoRV pyrGb fragment from pAB4626 . Transformation was performed using the previously described protoplast method [48] . Strains used in this study are listed in Table 2 . The ΔhpdA pyrG+ strain ( G827 ) was generated by transformation of strain G832 ( pkuA TK barA niaD1 pyrG1 ) with linearised pKB7717 which removes sequences from -11 to +1517 of hpdA and selecting for pyrG+ . The ΔhmgA::pyrG+ ( G823 ) , ΔhypW::pyrG+ ( G854 ) , ΔhmgX::pyrG+ ( G856 ) , ΔmaiA::pyrG+ ( G895 ) , ΔmfpA::pyrG+ ( G946 ) and ΔhpdB strains were generated by transforming strain G816 ( ΔligD niaD1 pyrG1 ) with linearised deletion constructs pKB7724 ( which removes from +138 to +717 of hmgA ) , pSM7546 ( which removes from +5 to +1145 of hypW ) , pSM7651 ( which removes from -53 to +929 of hmgX ) , pKB7805 ( which removes from +42 to +249 of maiA ) and pLS7852 ( removes from -22 to +2741 of mfpA ) , pLS7845 ( which removes from -39 to +1127 of hpdB ) and pSH7578 ( which removes from -278 to +2601 of hmgR ) , and selecting for pyrG+ or glufosinate resistance . The ΔwA::pyrG+ ( G748 ) strain was generated by transforming strain G147 ( niaD1 pyrG1 ) with pHW7445 and selecting for pyrG+ . Deletion was confirmed by genomic Southern blot analysis . pyrG- strains ( Table 2 ) were generated by plating on medium containing 1 mg mL-1 5-fluoroorotic acid ( 5-FOA ) supplemented with 10 mM NH4SO4 and 5 mM uracil to select for the loss of the pyrG marker . These strains are unable to grow in the absence of 5 mM uracil . The deletion mutants were complemented by transformation of pyrG- strains with pKB7682 ( hpdA ) , pKB7791 ( hmgA ) , pKB7800 ( maiA ) , pKB7801 ( hypW+ hmgX+ ) , pKB7940 ( hypW+ hmgXtrun ) , pKB7801 ( hmgX ) , pKB7683 ( hmgR ) and selecting for pyrG+ or transformation of G895 with pKB7858 ( maiA ) and selecting for niaD+ . Integration of the complementation constructs at pyrG or niaD was confirmed by Southern blot analysis of genomic DNA . To test growth on tyrosine or phenylalanine as a sole nitrogen source , strains were grown at 25°C and 37°C on A . nidulans minimal medium ( ANM ) supplemented with 1% glucose and 10 mM ammonium sulphate ( ( NH4 ) 2SO4 ) , 10 mM phenylalanine , or 10 mM tyrosine [51 , 52] . To test growth on tyrosine or phenylalanine as a sole carbon source , strains were grown at 25°C and 37°C for 14 days on A . nidulans minimal medium ( ANM ) supplemented with 10 mM ammonium sulphate ( ( NH4 ) 2SO4 ) and 50 mM phenylalanine or 10 mM tyrosine [51 , 52] . NTBC ( 2- ( 2-nitro-4-trifluoromethylbenzoyl ) -cyclohexane-1 , 3-dione ) was added for HpdA inhibition experiments at a final concentration of 400μg mL-1 . To assess pyomelanin production , strains were grown at 37°C for 14 days on 1% glucose A . nidulans minimal medium ( ANM ) supplemented with either 10 mM ammonium sulphate ( NH4SO4 ) , 10 mM tyrosine , both 10 mM NH4SO4 and 10 mM tyrosine or both 10 mM tyrosine and 10 mM alanine . Strains were also grown at 37°C on BHI for 5 days . The growth of the ΔhmgR strain was assessed after 14 days on 10 mM alanine , arginine , asparagine , cysteine , glutamate , glycine , histidine , isoleucine , leucine , lysine , methionine , phenylalanine , proline , serine , threonine , tryptophan , tyrosine or valine as the sole nitrogen source at both 25°C and 37°C . To test the effect of accumulating toxic tyrosine catabolism intermediates on growth , the wildtype ( WT ) and ΔhpdA , ΔhmgA and ΔhmgX strains were grown on carbon-free medium containing 10mM Gaba and either 10mM sorbitol , 1% lactose , 10mM NaAc or 10mM proline with or without 10mM tyrosine for 14 days at 25°C . L-DOPA medium was prepared by making a 50 mL dH20 solution containing 0 . 5 g L-asparagine , 0 . 5 g glucose , 1 . 5 g KH2PO4 , 0 . 125 g MgSO4-7H20 and 100 mg L-DOPA and adjusting the pH to 5 . 6 . Subsequently , 0 . 5 mg thiamine-HCL and 0 . 5 mL of 1M biotin solution were added and the solution filter sterilized . This solution was added to 500 mL of sterilized 2% agar solution . Strains were grown at 37°C for 14 days . Oxidative stress was tested by plating on ANM + Supps medium supplemented with 10 mM tyrosine and 0 mM , 0 . 25 mM , 0 . 5 mM , 0 . 75 mM and 1 mM of H2O2 . Plates were inoculated with 10 μL drops of 10 fold serial dilutions of a 1x 107 conidia mL-1 suspension and were incubated at 37°C for 14 days . The wildtype and ΔhpdA mutant were grown on ANM + Supps medium supplemented with 10 mM tyrosine for 14 days at 37°C . Cells were suspended in 0 . 1M sodium citrate and 1M sorbitol , pH 5 . 5 plus 10mg/mL lytic enzyme overnight at 30°C . Cells were pelleted and resuspended in 4M guanidine thiocyanate solution and incubated at room temperature overnight . Cells were then washed in PBS , resuspended in 6 . 6M HCL and boiled for 1 . 5 hours . The remaining particles ( none were visible for the ΔhpdA mutant ) were centrifuged and the pellets washed 3x in PBS and resuspended ( in PBS ) . J774 murine macrophages ( 1 x 105 ) or THP-1 human macrophages were seeded into each well of a 6 well microtitre tray containing one sterile coverslip and 2 mL of complete Dulbecco’s Modified Eagle Medium for J774 ( complete DMEM: DMEM , 10% fetal bovine serum , 8 mM L-glutamine and penicillin-streptomycin ) or 2 mL of RPMI ( complete RPMI: RPMI , 10% fetal bovine serum , 2 mM L-glutamine and penicillin-streptomycin ) for THP-1 . Macrophages were incubated at 37°C for 24 hours before activation with 0 . 1 μg mL-1 lipopolysaccharide ( LPS ) from E . coli ( Sigma ) and THP-1 macrophages differentiated with phorbol 12-myristate 13-acetate . Macrophages were incubated a further 24 hours at 37°C , washed in phosphate buffered saline ( PBS ) and 2 mL of complete DMEM or RPMI medium containing 1 x 106 conidia was added . A control lacking conidia was also performed . Macrophages were incubated for 2 hours at 37°C ( to allow conidia to be engulfed ) , washed once in PBS ( to remove free conidia ) and incubated a further 24 or 48 hours at 37°C . Macrophages were fixed in 4% paraformaldehyde and stained with 1 mg mL-1 fluorescent brightener 28 ( calcofluor—CAL ) to observe fungal cell walls . Mounted coverslips were examined using differential interference contrast ( DIC ) and epifluorescence optics for cell wall staining and viewed on a Reichart Jung Polyvar II microscope . Images were captured using a SPOT CCD camera ( Diagnostic Instruments Inc ) and processed in Adobe PhotoshopTM . The numbers of ungerminated conidia , germlings or yeast cells were recorded in a population of approximately 100 in three independent experiments . Mean and standard error of the mean values were calculated using GraphPad Prism3 . 400μg mL-1 of NTBC was added at the time of infection for HpdA inhibition experiments . Liquid cultures of FRR2161 ( wildtype ) , ΔhmgR and 2206areA strains grown for 2 days at 25°C in ANM plus 10 mM ( NH4 ) 2SO4 and 6 days at 37°C in BHI were used to inoculate ANM plus 10 mM ( NH4 ) 2SO4 , 10 mM alanine or , 10 mM tyrosine as the sole nitrogen source at 25°C and 37°C and RNA was isolated after 4 hours . Liquid cultures of FRR2161 ( wildtype ) , ΔhmgR and ΔareA strains grown for 2 days at 25°C in ANM plus 10 mM ( NH4 ) 2SO4 were used to inoculate ANM plus 10 mM tyrosine or 10 mM tyrosine and 10 mM ( NH4 ) 2SO4 at 25°C and RNA was isolated after 4 hours . 2 day 25°C liquid cultures of ΔhypW grown in ANM with 10 mM ( NH4 ) 2SO4 , was used to inoculate ANM with 10 mM ( NH4 ) 2SO4 or 10 mM tyrosine as the sole nitrogen source at 25°C and RNA was isolated after 4 hours . RNA was extracted using TRIzol Reagent ( Invitrogen ) and a MP FastPrep-24 bead beater according to the manufacturer’s instructions . At least two biological repeats were performed . Illumina RNA sequencing was performed on RNA isolated from liquid cultures of FRR2161 ( wildtype ) grown for 6 days at 37°C in BHI . RNA was DNAase treated ( Promega ) prior to RT PCR analysis . For every gene in this study , 3 increasing cycle numbers were used on two biological repeats to ensure the product was not saturated and the result was representative . H3 ( histone H3 ) was used as a loading control . Expression was determined by RT PCR using primers CC53 and CC54 ( hpdA ) , DD13 and DD14 ( hmgA ) , MM64 and MM65 ( hmgX ) , MM32 and MM33 ( hypW ) , DD19 and DD20 ( fahA ) , DD17 and DD18 ( maiA ) , II94 and II95 ( mfpA ) , II88 and II89 ( hmgR ) , and GG4 and GG5 ( H3 ) . | Fungi that infect humans are a major health problem , especially for those with compromised immune systems . Many fungal infections are extremely difficult to cure and if left untreated are fatal . For successful infection to occur , the fungal pathogen must be able to grow by acquiring and utilising the available nutrient sources within the host whilst evading or tolerating the host’s defence systems . Expression profiling in several pathogenic fungal species has revealed that genes required for tyrosine catabolism are induced specifically in the pathogenic cell type at 37°C . As well as enabling the fungus to acquire carbon and nitrogen intermediates from proteins within the host , tyrosine is also an important precursor in the formation of two different types of melanin , which protects cells against the host’s defence systems . This study shows that the ability to catabolise tyrosine and produce tyrosine derived melanin is not required for the initial stages of fungal infection . However , a novel role for hpdA , which encodes the enzyme which catalyses the second step of tyrosine catabolism , was identified during growth in host cells . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Intracellular Growth Is Dependent on Tyrosine Catabolism in the Dimorphic Fungal Pathogen Penicillium marneffei |
Cornelia de Lange Syndrome ( CdLS ) is the founding member of a class of multi-organ system birth defect syndromes termed cohesinopathies , named for the chromatin-associated protein complex cohesin , which mediates sister chromatid cohesion . Most cases of CdLS are caused by haploinsufficiency for Nipped-B-like ( Nipbl ) , a highly conserved protein that facilitates cohesin loading . Consistent with recent evidence implicating cohesin and Nipbl in transcriptional regulation , both CdLS cell lines and tissues of Nipbl-deficient mice show changes in the expression of hundreds of genes . Nearly all such changes are modest , however—usually less than 1 . 5-fold—raising the intriguing possibility that , in CdLS , severe developmental defects result from the collective action of many otherwise innocuous perturbations . As a step toward testing this hypothesis , we developed a model of nipbl-deficiency in zebrafish , an organism in which we can quantitatively investigate the combinatorial effects of gene expression changes . After characterizing the structure and embryonic expression of the two zebrafish nipbl genes , we showed that morpholino knockdown of these genes produces a spectrum of specific heart and gut/visceral organ defects with similarities to those in CdLS . Analysis of nipbl morphants further revealed that , as early as gastrulation , expression of genes involved in endodermal differentiation ( sox32 , sox17 , foxa2 , and gata5 ) and left-right patterning ( spaw , lefty2 , and dnah9 ) is altered . Experimental manipulation of the levels of several such genes—using RNA injection or morpholino knockdown—implicated both additive and synergistic interactions in causing observed developmental defects . These findings support the view that birth defects in CdLS arise from collective effects of quantitative changes in gene expression . Interestingly , both the phenotypes and gene expression changes in nipbl morphants differed from those in mutants or morphants for genes encoding cohesin subunits , suggesting that the transcriptional functions of Nipbl cannot be ascribed simply to its role in cohesin loading .
Cohesin is a multi-protein complex that associates with the chromosomes of all eukaryotic cells , and mediates sister chromatid cohesion , ensuring appropriate segregation of chromosomes during cell division [1] , [2] . Recent work suggests that cohesin also acts during interphase to regulate gene expression [3]–[5] . Studies on a set of human birth defect syndromes recently termed “cohesinopathies , ” along with work in Drosophila and in cell culture , point to roles for cohesin in various processes such as long-range promoter/enhancer communication , insulator action , and gene activation in the presence of polycomb silencing activity ( reviewed by [1] ) . Many of these studies have focused on a protein known variously as Scc2 ( in yeast and Xenopus ) , Nipped-B ( in Drosophila ) , or Nipped-B-like ( Nipbl , in most vertebrates ) , which is not itself a cohesin subunit , but is required for the loading of cohesin onto DNA [6]–[9] . Haploinsufficiency for NIPBL is the most frequent cause of Cornelia de Lange Syndrome ( CdLS ) ( OMIM #122470 ) , the most common of the cohesinopathies [10]–[13] . CdLS is a multi-organ birth defects syndrome characterized by low birth weight , short stature , and variably penetrant structural abnormalities of the skeleton , heart , gut , kidney , genitalia , eyes , and teeth , together with abnormalities in cognition and behavior [14]–[20] . A recently developed mouse model of Nipbl haploinsufficiency displays many of these abnormalities , along with nearly 80% perinatal mortality [21] . Interestingly , in both man and mouse , Nipbl heterozygotes show only a ∼30% reduction in Nipbl mRNA and protein [21]–[23] , presumably a result of autoregulation of the wild-type allele . This implies an extraordinary sensitivity of developmental events to small changes in the levels of this molecule . Indeed , clinical data suggest that a mere 15% decrease in NIPBL levels produces a recognizable phenotype [22] . The fact that such small changes in the levels of NIPBL/Nipbl/Nipped-B expression have little or no effect on chromosome cohesion [21] , [24]–[26] has led to the hypothesis that developmental abnormalities in CdLS are the result of dysregulated gene expression . Human and mouse studies indicate that hundreds of genes are expressed abnormally in NIPBL/Nipbl/Nipped-B heterozygotes in any given cell type or organ [21] , [23] . Yet with few exceptions , the changes in gene expression are modest , nearly always less than 2-fold , and typically less than 1 . 5-fold . Similarly small changes in gene expression are seen in Drosophila and mammalian cell lines when Nipped-B/NIPBL expression is knocked down using RNAi [5] , [27] . Are such small effects , perhaps collectively , the cause of pervasive developmental abnormalities ? Or are there some much larger effects of NIPBL deficiency on the expression of critical developmental genes ( cf . [27] ) that have just not yet been detected ( e . g . , because such genes are expressed at early stages , for limited time periods , or in limited groups of cells ) ? Answering this question is critical for more than just an understanding of cohesinopathies and cohesin function: the structural abnormalities in CdLS include some of the most common , clinically significant , isolated ( non-syndromic ) birth defects in humans , such as abnormalities of cardiac septum development [15]–[17] , [28] . If such common defects can be reliably caused by the collective actions of many small changes in gene expression , it suggests a model for birth defects very different from the single-gene-centered models that are supported by much of the experimental literature . To address this question definitively , one must be able to manipulate Nipbl or cohesin levels and quantitatively monitor phenotypic and gene expression changes from the earliest stages of development . Here we achieve this goal in the zebrafish , employing morpholino ( MO ) -mediated knockdown of Nipbl and several of its putative targets . We find that Nipbl knockdown produces heart , gut , and laterality defects with similarities to those seen in CdLS . Analysis of gene expression suggests that early , modest changes in expression of key regulatory genes involved in endodermal differentiation , migration , and left-right ( L/R ) patterning are likely to be the primary causes of such defects . Quantitative analyses of the expression and functional knockdown of two endodermal determinants , sox17 and foxa2 , supports the idea that developmental defects in CdLS can arise from the synergistic effects of changes in expression of Nipbl target genes .
BLAST searches using the amino acid sequence of human NIPBL identified two zebrafish genes on chromosomes 10 and 5 , referred to as nipbla and nipblb , respectively . We cloned full-length cDNAs for both from wild-type zebrafish ( AB strain ) by RT-PCR and 5′-RACE . The nipbla and nibplb cDNAs contained long open-reading frames encoding proteins of 2 , 876 and 2 , 381 amino acids in length , respectively ( Figures 1A , S1 ) . Predicted amino acid sequences of Nipbla and Nipblb are 70% identical to each other and 66% identical to that of human NIPBL . In both proteins , highly conserved N-terminal and C-terminal regions ( ∼200 and ∼1 , 700 amino acids , respectively ) flank a less-conserved central region that is much shorter in Nipblb than in Nipbla ( Figures 1A , B , S1 ) . Conserved HEAT domains , a putative nuclear localization signal ( NLS ) , and several protein-binding motifs ( for SCC4 [9]; HDAC1/3 [29]; and HP-1 [30] ) , are also found in both proteins ( Figures 1A , S1 ) . Alignment with genome sequences revealed that nipbla and nipblb contain 47 and 45 exons , respectively , and in each case the initial ATG codon is located in exon 2 . Exon-intron structure resembles mammalian orthologs , though nipblb lacks one exon found in nipbla and other orthologs , as well as the intron that separates exons 36 and 37 . Both nipbl genes lie in genomic neighborhoods syntenic to that surrounding human NIPBL: zebrafish orthologs of human SLC1A3 ( which encodes a high-affinity glutamate transporter ) , LOC556181 , and slc1a3a , lie upstream of nipbla and nipblb on chromosomes 10 and 5 , respectively . Other distantly related teleosts such as medaka , Fugu , and stickleback also have two nipbl genes , though one is much more similar to the two zebrafish Nipbls ( Figure 1C ) , raising the possibility that both zebrafish genes arose from a single teleost duplicate . Northern blot analysis revealed nipbla and nipblb transcripts of approximately 10 and 8 . 5 kb , respectively ( Figure 1D ) . Both are detected in the early blastula , 2 . 5 h post fertilization ( hpf , 256-cell ) , before the onset of zygotic gene expression , and expression progressively increases , reaching a peak at late gastrula stages ( 9 hpf , 90% epiboly ) , before decreasing by 26 hpf . While NIPBL mRNAs of multiple sizes have been reported in human tissues [11] , [13] , transcripts of both zebrafish genes were detected as single bands at all stages examined; this was confirmed using two different probes against 5′- and 3′-ends ( Figures 1D , S2 ) . In situ hybridization ( ISH ) revealed that both nipbla and nipblb are expressed in similar spatiotemporal patterns ( Figure 1E ) . Maternal transcripts of both genes were detected throughout the blastoderm , although staining was stronger for nipblb ( consistent with the Northern blotting results ) . Ubiquitous expression continues until early somitogenesis ( 12 hpf ) , after which transcript levels gradually decrease in the trunk ( 15–18 hpf ) , with strong expression becoming restricted to the head by 25 hpf ( Figure 1E ) . These expression patterns are similar to those of cohesin subunits , smc3 [31] and rad21 [32] , [33] . To analyze Nipbl function , we designed pairs of translation-blocking morpholino antisense oligonucleotides ( MOs ) for nipbla ( nipbla-MO1 and MO2 ) and nipblb ( nipblb-MO1 and MO2 ) that target regions in the 5′-UTR of each mRNA ( see Materials and Methods ) . To evaluate MO efficiency and specificity , we made antibodies specific to Nipbla and Nipblb , and used them to quantify protein levels by Western blotting . Both proteins were detected at the predicted sizes , >300 kDa and 260 kDa , respectively ( Figure S3A ) . Injection of 0 . 5 ng/embryo of nipbla-MO1 efficiently depleted Nipbla protein at 10 and 24 hpf , whereas nipbla-MO2 was much less efficient ( Figures 2A , S3B , D ) . Injection of 0 . 5–1 ng of nipblb-MO1 partially depleted Nipblb protein at 10 hpf , but almost completely eliminated the protein by 24 hpf ( Figures 2A , S3B , C ) . Higher amounts of nipblb-MO1 did not further deplete Nipblb protein at 10 hpf , nor did nipblb-MO2 ( Figure S3B , D ) , even though both MOs were highly effective at 24 hpf ( Figures 2A , S3C ) . As both MOs completely suppressed EGFP expression when co-injected with nipblb-5′-UTR-EGFP mRNA ( Figure 2B ) , the data suggest that , at 10 hpf , a substantial fraction of Nipblb ( but not Nipbla ) is resistant to translational knockdown ( e . g . , there may be a relatively stable pool of maternal Nipblb protein ) . Embryos co-injected with 0 . 75 ng each of 5-base mismatch control MOs ( referred to as “control” embryos below ) to either gene were indistinguishable from uninjected embryos ( Figure 2C ) . Embryos injected with either nipbla-MO1 ( 0 . 75 ng ) alone ( nipbla-morphants ) or together with nipblb-MO1 ( 0 . 75 ng; nipbla/b-morphants ) resembled controls at 18 . 5 hpf , but began to exhibit defects by 24 hpf , including pericardial edema and a short tail ( Figure 2C ) . At 34 hpf , nipbla-morphants had more severe pericardial edema ( Figure 2C , asterisk ) and no blood circulation ( 52 . 7% , n = 55 ) ( Table S2 ) . Some embryos had short tails , often split or branched along the dorsal-ventral axis ( 49 . 1% ) , which became more obvious at 52 hpf ( Figure 2C , arrowheads ) . Embryos injected with nipblb-MO alone ( 0 . 75 ng; nipblb-morphants ) appeared normal at 34 or 52 hpf , but co-injection of this MO with nipbla-MO1 increased the percentage with pericardial and tail defects ( Figures 2C , S4B , Table S2 ) . Some nipbla/b-morphants also had defects in their urogenital openings at 52 hpf ( 10 . 0% , n = 30 ) ( Table S2 , Figure S4D ) . In addition , Alcian staining at 120 hpf revealed changes in size , but not patterning , of most craniofacial cartilages , with particularly severe reductions of the hyosymplectic cartilage in the dorsal hyoid arch ( pharyngeal arch 2; Figure S5 ) . Many nipbla/b-morphants had heart defects , as visualized by ISH for a cardiac muscle marker , cmlc2 , at 32 hpf ( Figure 3A , Table S2 ) . These were classified into two types: Type A , abnormal jogging/looping; and type B , defective cardiac precursor migration . Type A embryos ( 59% of nipbla/b-morphants ) had beating hearts but showed reduced or no jogging to the left ( Figure 3B , Table S2 ) . Type B embryos ( 33% of nipbla/b-morphants ) never formed a midline heart tube , and nearly half exhibited cardia bifida ( Figure 3A–B , Table S2 ) . This was not simply due to delayed development of morphants , as similar phenotypes were observed at 48 hpf ( Table S2 and Figure S6A ) , and other developmental events , such as the spreading of ath5 expression in the retina , occurred on schedule ( Figure 3A ) . Similar heart defects were also observed in nipbla single morphants but at lower frequency ( Table S2 and Figure 3B ) . Because morphants with abnormal heart morphologies showed circulation defects ( Figure S7A ) , we examined development of their blood/vascular system . O-dianisidine staining ( which reveals differentiated erythrocytes; Figure S7B–E ) and ISH for gata1 ( which labels erythrocyte precursors; Figure S7F , G ) indicated that erythrocytes form normally , but accumulate in the ventral tail . Blood vessels , as marked by fli1a , are also specified normally in morphants ( Figure S7H , I ) . These data suggest that circulation defects , and probably pericardial edema , in nipbla/b-morphants are due to impaired heart function . nipbla/b-morphants also displayed defects in the looping of gut and visceral organs . ISH for the endodermal marker foxa3 revealed a range of phenotypes , which we grouped into three classes ( Types I–III; Figure 3C ) . At 52 hpf , the gut normally loops leftward , and liver and pancreas buds form on the left and right sides , respectively . Of nipbla/b-morphants , 64 . 5% ( n = 96 ) had thin and abnormally looped guts , and a smaller liver and pancreas ( Type I; Figure 3C ) . This included 38 . 5% with partial looping , 25% with no looping , and 1% with reversed looping . In type II embryos ( 11 . 5% ) , both the anterior gut and visceral organs were bifurcated or duplicated bilaterally , whereas in type III embryos ( 18 . 9% ) , the number of foxa3-expressing cells was severely reduced , and anterior gut tubes did not form ( Figure 3C ) . Similar heart and visceral organ defects were also observed in embryos injected with an independent set of MOs ( nipbla-MO2 and nipblb-MO2 ) , although at lower frequency ( Table S2 ) . To gain insight into the earliest effects of nipbl-depletion on gene expression in the zebrafish embryo , we used microarrays to analyze mRNA from uninjected and nipbla/b-morphant embryos at early gastrula stages ( 6 hpf ) , hours before any morphological phenotypes become visible ( Table S3 ) . Partial loss of Nipbl function in mouse and man leads to many alterations in gene expression , most less than 1 . 5-fold [21] , [23] . With effects in this range , large sample sizes ( 10–20 independent samples for each condition ) are typically needed to achieve the statistical power to establish the significance of individual effects [21] . Due to sample limitations , our studies were restricted to three independent pools each of uninjected and morphant embryos . Therefore , we did not seek to infer significance directly from the data , but rather used them to generate ranked lists of candidate genes; up- or down-regulation of these was subsequently tested by quantitative-RT-PCR ( Q-PCR ) . Thus , gene expression changes confirmed in the present study are most likely a subset of those that actually occurred . As shown in Table S3 , two known regulators of endoderm development , sox17 and foxa2 , appeared near the top of the list of potentially down-regulated genes . Q-PCR confirmed that both were significantly down-regulated by nipbla/b-MO1 even at minimum doses of these MOs ( 0 . 5 ng each ) , as well as by nipbla/b-MO2 ( Figure 4A , B ) . The quantitative relationship between nipbla MO1 dose and sox17/foxa2 reduction closely matched that between MO dose and Nipbla protein level ( Figure 4B ) . Interestingly , the partial reduction in Nipblb protein caused by injection of nipblb-MO1 at this stage did not have a significant effect on the expression of these two endodermal genes , even in nipbla-morphants . During gastrulation , sox17 and foxa2 are expressed not only in migrating endoderm , but also in dorsal forerunner cells ( sox17 ) and axial mesoderm ( foxa2 ) . At 6 . 5 hpf ( Figure 4C ) and 8 . 5 hpf ( Figure S8A ) , we consistently observed reduced expression of sox17 and foxa2 by ISH in the endoderm of nipbla/b-morphants ( Figure 4C , arrows ) , but no significant change in their extra-endodermal expression ( arrowheads ) . Thus , nipbl levels specifically affect endodermal expression of these genes . sox17 and foxa2 are part of a pathway for endodermal specification that begins with Nodal signaling [34] , [35] . They are induced by a zebrafish-specific sox gene , sox32 , which is essential for the generation of endodermal cells [36]–[40] . We found that sox32 expression was also decreased in the endoderm of nipbla/b-morphants ( Figure 4C ) , although less severely than sox17 or foxa2 . Two genes are known to lie upstream of sox32 in the endoderm specification pathway , gata5 and bon [41] . By ISH , gata5 expression was found to be slightly reduced at 6 . 5 hpf in nipbla/b-morphants ( Figure 4C ) , and more severely reduced at later stages ( Figure S8A ) ; no change in bon expression was seen ( Figure 4C ) . In contrast , further upstream genes such as the Nodal-relative cyclops ( cyc ) and the essential Nodal receptor co-factor one-eyed-pinhead ( oep ) as well as genes involved in mesodermal development ( no tail , ntl; even-skipped-1 , eve1; and T-box gene 16 , tbx16 ) or expressed ubiquitously ( ribosomal protein L13a , rpl13a and POU domain class 5 transcription factor 1 , pou5f1 ) were all normally expressed in nipbla/b-morphants ( Figure S8B , C ) . Nodal targets were also not among those conspicuously altered in expression in the microarray studies ( Table S3 ) . We next sought to determine whether the decrease in expression of sox17 and foxa2 in nipbla/b-morphants is a direct effect of reduced Nipbl function , or is indirectly mediated by the reduction in sox32 expression . Sox32 lies upstream of sox17 and foxa2 in two distinct ways . First , Sox32 is required for endoderm specification , so that reduced sox32 expression might lower sox17 and foxa2 levels simply by depleting the cells that transcribe these genes . Second , Sox32 is a direct transcriptional activator of sox17 and foxa2 . To test whether the changes in expression of sox17 and foxa2 in nipbla/b-morphants could be accounted for by either of these explanations , two types of experiments were done . First , we directly counted endodermal cells at late gastrula stages in control and nipbla/b-morphant embryos ( Figure 4D ) . Using sox32 ISH as a marker , we observed a 58% decrease in endodermal cells . However , using either sox17 or foxa2 as markers , the numbers of endodermal cells that could be visualized were significantly lower ( 68%–69% , p<0 . 001 ) . These results suggest that endodermal depletion contributes to , but is only part of the explanation for , the overall reduction in sox17 and foxa2 expression . Second , we directly analyzed the transcriptional regulation of sox17 and foxa2 by Sox32 in controls and nipbla/b-morphants ( Figure 5A–D ) . In one set of experiments , we measured the dose-dependence of induction of sox17 and foxa2 in response to injected , exogenous sox32 mRNA . For any gene that Nipbls influence solely via the indirect effect of altering sox32 levels , we would expect to see an identical dose-response relationship , with respect to total , measured Sox32 level , in both controls and morphants ( Figure 5A , left panel ) . For cxcr4a , this is indeed what was observed ( Figure 5B ) . In contrast , for sox17 and foxa2 , the dose-response curves were shifted downward in nipbla/b-morphants ( Figure 5C , D ) . This implies a Nipbl-sensitive input to the expression of these genes , independent of the effect of the level of sox32 ( Figure 5A , right panel ) . In a second set of experiments , we effectively removed endogenous Sox32 with a sox32-MO , and replaced it with an exogenous sox32 mRNA that lacked the MO-binding site ( sox32-9mis ) ( Figure 5E , F ) . As expected , injection of the sox32-MO significantly reduced expression of sox17 , foxa2 , and cxcr4a , and this could be restored by co-injection of sox32-9mis mRNA . For cxcr4a , rescue of expression by sox32-9mis mRNA occurred to about the same degree in nipbla/b-morphants and control embryos . In contrast , for sox17 and foxa2 , exogenous sox32 mRNA restored gene expression about half as well in nipbla/b-morphants as in controls ( Figure 5F ) . These results imply that Nipbls influence the responsiveness of sox17 and foxa2 to transcriptional activation by sox32 . Although it is possible that this effect reflects an influence of Nipbls on the expression of some transcriptional co-regulator of sox17 and foxa2 , the only other well-characterized activator of sox17 in zebrafish is Pou5f1/Oct4 [42]–[44] , and neither microarray analysis ( Table S3 ) nor Q-PCR ( Figure S8C ) showed a change in pou5f1 levels in nipbla/b-morphants . Thus , Nipbls may act directly upon sox17 and foxa2 . Although the heart and gut derive from different germ layers ( mesoderm and endoderm , respectively ) , Nipbls could regulate their development through common mechanisms . For example , mutations that affect early L/R patterning can cause defects in the looping of both heart and gut tubes [45]–[47] , while mutations that affect early endoderm can severely disrupt medial migration of cardiac progenitors ( which use the endoderm as a migratory substrate ) , leading in some cases to cardia bifida [36] , [38] , [48] . Interestingly , we observed that most morphants with type A heart jogging/looping defects later displayed type I gut looping defects ( Figure S9A ) , consistent with both being caused by a common abnormality of L/R patterning . Similarly , most morphants with type B heart fusions , including those with cardia bifida , later displayed type III gut defects ( Figure S9A ) , consistent with both types arising from a deficiency in early endoderm . This idea was further supported by the fact that type A and type B heart phenotypes were obtained at doses of nipbla-MO similar to those that caused type I and type II/III gut phenotypes , respectively ( Figure S9B–D ) . Type B heart and type II/III gut phenotypes required at least 0 . 25 ng of nipbla-MO1 ( Figure S9C , D ) , similar to the doses required for endodermal gene expression defects ( Figure 4B ) . In contrast , looping defects were more sensitive to small changes in Nipbla protein levels ( Figure S9C , D ) , as were circulation and tail defects ( Figure S9E ) . To test the hypothesis that endoderm deficiency is the cause of type B heart and type III gut phenotypes in nipbla/b-morphants , we attempted to rescue these defects by expressing exogenous gata5 or sox32 mRNA , which increases the number of endodermal cells in the gastrula-stage embryo [36] , [37] , [48] . At low levels of gata5 or sox32 mRNA expression , which did not themselves cause substantial heart or gut phenotypes , we observed marked rescue of both type B heart , and type III gut , phenotypes , but no significant rescue of type A ( heart ) or type I ( gut ) phenotypes ( Figure 6A , B ) . These results support the idea that some heart and gut phenotypes in nipbla/b-morphant phenotypes have a common origin in reduced endodermal cell production or survival . Interestingly , the type II gut phenotype ( bifurcation with visceral organ duplication ) was also partially rescued by exogenous gata5 and sox32 ( Figure 6B ) . Gut/visceral organ duplication can arise from delayed medial migration of endoderm [49] , [50] . Moreover , in mice , anterior gut duplications can be caused by loss of Foxa2 [51] , [52] . In zebrafish , however , gut defects have not been reported in foxa2 mutants [53] , but foxa2 morphants do show subtle morphological changes in the liver and pancreas [54] . Upon repeating such studies , we observed a type II phenotype in a small proportion of foxa2-morphants ( 7%; Figure 6C ) . We also observed a type II phenotype in a small proportion ( 5% , Figure 6C ) of sox17-morphants ( sox17 knockdown has not previously been reported , Figure S10 ) . Intriguingly , when sox17 and foxa2 were knocked down simultaneously , the fraction of embryos displaying gut/visceral organ duplication rose to nearly 60% ( Figure 6C ) . Yet even in such embryos , the sizes of liver and intestine were normal , or only slightly reduced ( Figure 6D ) , in contrast to the marked reductions seen in most nipbla/b-morphants ( Figure 3C ) . Thus , reduced expression of sox17 and foxa2 can synergistically reproduce most , but not all , aspects of the type II phenotype . In contrast to other phenotypes , looping defects in heart ( type A ) and gut ( type I ) tubes were not significantly improved by expressing gata5 and sox32 ( Figure 6A , B ) . Since , as mentioned above , these phenotypes could be caused by global defects in laterality , we examined expression of genes involved in L/R patterning [46] , [47] , [55] , [56] . At 18 hpf , expression of the nodal-related gene southpaw ( spaw ) is normally restricted to the left lateral plate mesoderm ( LPM ) along the midline , as well as to a region adjacent to Kupffer's vesicle ( KV ) , a structure involved in the initiation of L/R asymmetric patterning ( Figure 7A ) [57] , [58] . In nipbla/b-morphants , spaw expression in LPM was severely reduced ( Figure 7C ) or present on both left and right sides ( Figure 7D ) ; in both cases , expression around KV was reduced ( Figure 7C , D ) . A second nodal relative , lefty-2 , is downstream of spaw [56] and is normally expressed in left LPM in the heart region at 21 . 5 hpf ( Figure 7E ) . In nipbla/b-morphants , its expression was also severely reduced ( Figure 7G ) or lost ( Figure 7H ) . Ectopic ( right-sided ) lefty-2 expression was also observed in a small number of the morphants ( Figure 7I ) . The expression of spaw in left LPM is induced by signals from KV [58] , and inhibited by the Cerberus-related protein Charon [59] . Although we did not observe morphological abnormalities in KV , or in charon expression ( Figure 7J–M ) , expression of dynein , axonemal , heavy polypeptide 9 ( dnah9 ) was markedly reduced in most nipbla/b-morphants ( Figure 7N–P ) . dnah9 encodes a motor protein required for motility of monocilia in the KV , and its loss is known to impair KV fluid flow and disrupt L/R development [58] . This result suggests that Nipbls may act on L/R patterning by controlling KV function . On the other hand , the fact that the phenotypes of nipbla/b-morphants and dnah9-morphants are qualitatively different—dnah9 has more of an effect on the sidedness , than the level , of lefty-2 and spaw expression [58] , whereas for nipbls , the opposite appears to be the case ( Figure 7 ) —also raises the possibility that Nipbls exert later , more direct effects on the expression of left-side-specific genes . Consistent with this possibility , both nipbla and nipblb expression was detected in cells around KV and in LPM ( both sides ) at 14 hpf ( 10-som ) and 20 hpf ( 22-som ) , respectively ( unpublished data ) . Nipbl/nipped-B/Scc2 was initially characterized as a cohesin-loading factor , and localizes extensively with cohesin on chromosomes ( e . g . , [60] ) . Whereas NIPBL mutations are responsible for more than half of CdLS cases , recent work has shown that CdLS can also be caused by mutations in the genes encoding cohesin subunits Smc1 and Smc3 [61] , [62] . These and other findings suggest that Nipbl and cohesin work together in regulating gene expression [5] , [18] , [60] , [63] , [64] . Recently , it has been found that mutation and/or morpholino knockdown of cohesin function in the zebrafish produces a variety of gene expression changes , with a phenotype characterized by loss of expression of runx3 , loss of hematopoietic expression of runx1 , and concomitant lack of development of differentiated blood cells [32] , [65] . To address whether developmental effects caused by nipbl deficiency in the zebrafish could be explained by impaired cohesin function , we examined the effects of knocking down expression of cohesin subunit genes smc3 and rad21 ( Figure 8 ) [31] , [65] . Remarkably , expression of the endodermal genes sox32 , sox17 , and foxa2—which are markedly affected in nipbla/b-morphants—was unchanged in smc3 or rad21 morphants ( Figure 8A , B ) , even at levels of MO that produced substantial reduction in cohesin protein level ( Figure 8C ) and caused gross morphological abnormalities ( Figure 8D ) . Conversely , upregulation of p53 and mdm2 , which occurs in smc3- and rad21-morphants ( Figure 8B; [65] ) , was not seen in nipbla/b morphants . For some genes , including myca , ascl1a , and ascl1b , expression was reduced in both types of morphants ( Figure 8B ) . Both p53 and mdm2 are known to be induced by a range of physiological stresses [66] , suggesting that their differential induction in cohesin ( smc3 or rad21 ) - and nipbla/b-morphants might be due to different levels of overall phenotypic severity ( and , thus , non-specific stress ) in the two situations . To investigate this possibility , we injected lower amounts ( 0 . 75 ng/embryo ) of smc3-MO ( Figure S11A , low-smc3-morphants ) and examined gene expression ( Figure S11B ) . At levels at which reduction of both gross morphology and ascl1a and ascl1b expression were comparable between these and nipbla/b-morphants , induction of p53 and mdm2 was still much higher in the smc3-morphants , suggesting specific regulation by cohesin . These results , together with the observation that blood cells develop normally in nipbla/b morphants ( Figure S7 ) , suggest that nipbl and cohesin have overlapping , but distinct , influences on gene expression .
Here we characterize the zebrafish nipbl genes , and show that nipbl-morphant zebrafish display multiple abnormalities in early heart and gut/visceral organ development ( Figure 3 ) . These changes are preceded by reduced expression of genes required for endoderm formation and function , and L/R patterning , both processes being critical for normal heart and gut development ( Figures 4 , 7; Table S2 ) . We demonstrate that at least some of the observed gene expression changes are sufficient , collectively , to produce a subset of the observed morphological abnormalities ( Figure 6C , D ) . These results indicate that heart and gut/visceral organ deficits arise in the context of both abnormal endoderm development and abnormal L/R patterning ( Figure 9 ) . The parallels between nipbl-morphant phenotypes and the birth defects in CdLS [16] are striking . Heart and gut abnormalities are prominent in CdLS , and among the primary causes of morbidity and mortality [16] , [17] , [67] . Marked L/R asymmetry in the severity of defects in CdLS ( e . g . , in limbs; [17] ) , together with the occurrence of intestinal malrotation [16] , suggest that overall L/R patterning is also disturbed in this syndrome . Gut duplications , which are sometimes observed in nipbl-morphant fish ( Figure 3 , Table S2 ) , are also seen , rarely but significantly , in CdLS [68] , [69] . As in CdLS , and in Nipbl-heterozygous mice [21] , Nipbl-deficient fish also exhibit growth retardation and distinctive craniofacial abnormalities ( Figures 2C and S5 ) , the latter including severe reductions of the hyosymplectic cartilage , the homologue of the mammalian stapes [70] . Abnormal development of the stapes and other middle ear bones is reported in CdLS [71] , and our results suggest that defects in embryonic development of the precursors of these bones could account for some aspects of hearing loss in CdLS [16] , [17] and in Nipbl-heterozygous mice [21] . Notwithstanding some major anatomical differences between fish and mammals ( e . g . , fish hearts are not divided by septa ) , these results suggest that developmental alterations caused by Nipbl deficiency in zebrafish may provide mechanistic insight into the origins of human birth defects . For example , the results immediately raise the possibility that heart and gut defects in CdLS originate during gastrulation , much earlier than might have been suspected from the times at which structural abnormalities ( e . g . , septal defects ) are observed [21] . One reason why nipbl-morphant zebrafish may provide such a good model for CdLS is that the early insensitivity of nipblb to MO-mediated knockdown ( Figures 2 , S3 ) may fortuitously make the total decrease in Nipbl function in nipbla/b-morphant embryos approximate what occurs in human and murine NIPBL-haploinsufficiency [21] , [23] . This view is supported by data suggesting that Nipbla and Nipblb have similar functions , e . g . knockdown of nipblb in nipbla-single morphants increases the frequency of heart defects . Yet the fact that Nipblb reduction does not also increase the frequency of endodermal gene expression defects suggests that some functional differences exist between the two genes . Despite the fact that , at late stages , both Nipbla and Nipblb proteins were strongly reduced in nipbla/b-morphants , their phenotypes were much milder than those of smc3 or rad21-morphants , suggesting that only trace amounts of Nipbls are sufficient to maintain normal chromosomal cohesion . Consistent with this view , nipbla/b-morphants display at most a small increase in premature sister chromatid separation ( unpublished observations ) , similar to what is seen in NIPBL-heterozygous human and mouse cells [21] , [23] , [25] . It is rapidly emerging that Nipbl and cohesin play global , if still poorly understood , roles in the regulation of gene expression ( e . g . , [1] , [5] , [21] , [23] , [27] , [72]–[74] ) . In human , mouse , and fly cells , partial reduction in NIPBL/Nipped-B function produces modest changes in the expression of large numbers of genes [21] , [23] , [27] . In nipbla/b-morphant zebrafish , we also observed modest changes in the expression of multiple zygotic genes ( Figures 4 , 7 , S8; Table S3 ) . Because these changes were detected at early gastrula stages—only hours after the onset of zygotic transcription—the probability that some of them are directly caused by reduced Nipbl function should , a priori , be greater than in studies using other organisms , in which many gene expression changes could be secondary effects of other gene expression changes . Interestingly , a subset of the genes identified by microarray experiments as potentially affected by Nipbl depletion in zebrafish ( Table S3 ) also show changes in Nipbl heterozygous mouse fibroblasts ( pitx2 , cebpd , jag1 [21] ) , embryonic mouse brain ( alcam , cnot8 , cxcl12 , hlcs , myc , neo1 , nos1 , notch2 , vldlr [21] ) or human lymphoblastoid cells ( alcam , myc , bmi1 , ctage5 , id3 , nsun2 , pccb , psme1 , ptma , rab2a , robo1 , rora , sfrs1 , snrp70 , snx3 , ube2g1 [23] ) . Whether such overlap is functionally significant is difficult to assess given the different tissues and stages examined in different organisms . Interestingly , we found some of the genes dysregulated in nipbl-morphants to be members of known gene-regulatory networks: e . g . , gata5 , sox32 , sox17 , and foxa2 , controlling endoderm development; and dnah9 , spaw , and lefty2 , controlling L/R patterning . By examining the effects of graded sox32 misexpression as well as replacement of endogenous with exogenous Sox32 , we showed that changes in sox17 and foxa2 expression due to Nipbl deficiency are only partially explained by altered levels of sox32 , their common upstream activator ( Figures 4–5 ) , and presented evidence that Nipbls influence the transcriptional responsiveness of sox17 and foxa2 . Consistent with this , preliminary chromatin immunoprecipitation studies suggest that Nipbla binds near the transcriptional start site of sox17 ( unpublished observations ) . Whether Nipbls regulate responsiveness of sox17 and foxa2 solely to Sox32 , or to other transcriptional effectors as well , is not known , but specificity is suggested by the fact that extra-endodermal patterns of expression of sox17 and foxa2 are largely insensitive to Nipbl depletion ( Figures 4C , 7A ) . foxa2 expression in floor plate , for example , is regulated by Nodal and Shh , but independent of Sox32 [53] , and is normal in nipbla/b-morphants ( Figure S8 and unpublished observations ) . The mechanisms by which Nipbl regulates gene expression are currently unknown . Drosophila Nipped-B and cohesin occupy largely overlapping sites in the genome [60] . In mammalian cells , most cohesin localizes to binding sites for the insulator protein CTCF , which requires cohesin for function [75]–[78] , but a recent study of mammalian cells [5] indicates that Nipbl preferentially occupies non-CTCF sites at promoter regions , at which complexes exist between cohesin and the Mediator complex ( which mediates transcriptional transactivation ) . Our finding that nipbl-knockdown and cohesin-knockdown produce distinctive phenotypic and gene-expression effects in the zebrafish ( Figure 8 ) strongly suggests that only some of the functions of Nipbl are attributable to a general role in promoting cohesin function . In Drosophila , manipulation of Nipped-B and cohesin expression has also been observed to have overlapping , different , or even opposite effects , depending upon the experimental circumstances [24] , [27] , [64] . In man , the mild forms of CdLS that have been linked to mutations in cohesin subunit genes ( Smc1 and Smc3 [61] , [62] ) appear to be caused by specific , rare missense mutations ( i . e . , very likely not simple loss-of-function alleles ) , also consistent with the idea that the cause of CdLS is not simply a reduced level of cohesin function , but rather the selective disruption of specific functions in which Nipbl and cohesin work together . Such functions could be related to the ability of Nipbl to interact directly with molecules such as histone deacetylases , HP-1γ , and chromatin remodeling factors ( e . g . , [29] , [30] , [79] ) . Indeed , given its large size and conserved , multi-domain structure , it may turn out to be more appropriate to envision Nipbl as a cohesin-associated scaffold protein , rather than a cohesin-loading factor . Endoderm development and L/R patterning are crucial events in normal heart and gut development ( Figure 9 ) , and both are clearly affected in nipbla/b-morphants . Endoderm development appears to be impaired at more than one level , consistent with altered expression of multiple genes ( sox32 , sox17 , foxa2 , gata5 ) in the pathway controlling early endoderm specification and formation of the gut . Previous work suggests that gata5 and sox32 are required for specification of endodermal cell fates , whereas sox17 and foxa2 act downstream at the level of endodermal differentiation [37]–[39] , [41] , [48] . We hypothesize that reduced gata5 and/or sox32 expression is the cause of type III gut phenotypes ( reduced gut/visceral organ tissue ) in nipbla/b-morphants; consistent with this , injection of exogenous gata5 or sox32 mRNA rescues this phenotype ( Figure 6B ) . A defective urogenital opening—which is also seen in nibpla/b-morphants ( Figure S4 ) —is also a known consequence of a deficiency of endoderm [36] , [80] . Exogenous gata5 and sox32 mRNA also rescued type B heart phenotypes ( failure to form a normal midline heart tube; Figures 6A , S6 ) , consistent with the known dependence of cardiac progenitor migration on the endoderm . Moreover , most aspects of the type II gut phenotype ( anterior bifurcation/duplication ) were efficiently produced by simultaneous MO-knockdown of sox17 and foxa2 ( Figure 6C , D ) . These results are consistent with sox17 and foxa2 acting downstream of endoderm specification to influence endodermal cell behaviors , such as migration ( Figure 9 ) . In contrast , defects in nipbla/b-morphants characterized by abnormal or reversed looping ( type I gut and type A heart defects ) were not significantly rescued by gata5 or sox32 mis-expression , and only weakly reproduced by combined sox17/foxa2 knockdown ( Figure 6 and unpublished data ) . Given the nature of these phenotypes , we suspect they are primarily caused by a common defect in L/R patterning ( Figure 7 ) . Interestingly , these different phenotypes correlate with different degrees of depletion of Nipbla protein levels; i . e . , endoderm-related ( type B heart and type II/III gut ) phenotypes require more significant reduction of Nipbla protein than L/R patterning-related ( type A heart and type I gut ) phenotypes ( Figure S9 ) . However , since we examined protein levels in whole embryos , we cannot exclude the possibility that effects of MO on protein levels differ between cell types , and a higher amount of nipbla-MO may be required for significant reduction of Nipbla protein levels in endodermal cells . Taken together , our findings imply a mechanistic model in which reduced Nipbl function has modest , quantitative effects on the expression of multiple genes; some of these changes lead to quantitative functional deficits; and these in turn contribute collectively to the appearance of developmental defects ( Figure 9 ) . For at least one set of genes ( sox17 and foxa2 ) , the mode of gene interaction is synergistic; i . e . , quantitative alterations in the levels of both genes seem to be necessary to produce at least one aspect of the nipbl-morphant phenotype ( Figure 6C ) . If phenotypes in nipbl-morphant zebrafish are indeed explained by the cooperative effects of quantitative changes in gene expression , it not only provides a framework for understanding specific classes of birth defects syndromes , such as CdLS and the cohesinopathies; it also suggests a mechanism by which non-syndromic birth defects ( which are far more common ) might naturally arise out of combinations of quantitative genetic variants in the human population .
All animals were handled in strict accordance with good animal practice as defined by the relevant national and/or local animal welfare bodies , and all animal work was approved by the University of California , Irvine Institutional Animal Care and Use Committee . Zebrafish ( AB strain ) were maintained and staged as described [81] , [82] . By searching Genbank and Sanger DNA/protein databases using the human NIPBL protein sequence , we found entries for nipbla and nipblb genes . Although the Ensembl database had only partial sequences for both genes ( ENSDART00000086861 and ENSDART00000086653 [nipbla]; ENSDART00000111663 and ENSDART00000108661 [nipblb] ) , these differed from those in the NCBI database ( XM_001919812 [nipbla]; XM_001920168 [nipblb] ) even within overlapping regions . We therefore cloned and sequenced cDNAs for both genes by RT-PCR , using cDNA from wild-type zebrafish embryos ( 9 hpf ) . cDNA fragments containing 5′-UTR were amplified by 5′ RACE , subcloned into the pCRII-TOPO vector ( Invitrogen ) , and sequenced . Morpholino antisense oligos ( MO ) were designed to block translation or splicing ( Gene Tools , Inc . ) including: nipbla-MO1 , 5′-ACGTGGACGCACAGGTTGCTCAGTG-3′; nipbla-MO2 , 5′-TCGCTGCTCACTGATCCACCTTTAC-3′; nipblb-MO1 , 5′-TGACGGCTGGGCACAGAAGTCTAAC-3′; nipblb-MO2 , 5′-GCACACAGAGATCCACAGAGATATT-3′; 5mis-nipbla-MO1 , 5′-ACcTcGACGgACAGcTTcCTCAGTG-3′; 5mis-nipblb-MO1 , 5′-TGACGcCTcGGCAgAGAAcTgTAAC-3′; sox17-MO , 5′-CCATGACTTACCTATAAACAGAACA-3′; foxa2-MO , 5′-CCTCCATTTTGACAGCACCGAGCAT-5′ [53]; sox32-MO , 5′-CAGGGAGCATCCGGTCGAGATACAT-5′ [38] , smc3-MO , 5′-GTACATGGCGGTTTATGCACAAAAC-3′ [31]; rad21-MO , 5′- AGGACGAAGTGGGCGTAAAACATTG-3′ [32] . MOs were prepared at 20 mg/ml and diluted in 1× Danieau buffer ( 58 mM NaCl , 0 . 7 mM KCl , 0 . 4 mM MgSO4 , 0 . 6 mM Ca ( NCO3 ) 2 , 5 mM HEPES ( pH 7 . 6 ) ) and stored at −20°C . To construct nipbla-5′-UTR-EGFP and nipblb-5′-UTR-EGFP reporter genes , double stranded oligo DNAs encoding 5′-UTR of nipbla or nipblb containing target sites of MOs were fused with EGFP cDNA by subcloning both into the pCS2+ vector . Full-length cDNAs of gata5 and sox32 were amplified by RT-PCR and cloned into pCS2+ . sox32-9mis cDNA was prepared by RT-PCR using the cloned sox32 cDNA as a template with primers designed to introduce mutations within the sox32-MO target sequence located at 5′ end of ORF . Mutations in sox32-9mis are introduced within the first 25 bases of ORF: ATGTActTgGAtaGaATGtTgCCaG ( mutations are shown in lower cases and do not change amino acid sequence ) . Effective translation of sox32-9mis in the presence of sox32-MO was examined by reporter assay using fusion reporter constructs , in which the first 30 bases of sox32 and sox32-9mis ORF was fused in frame with EGFP cDNA ( Figure 5E ) . Capped mRNA was synthesized using mMESSAGE mMACHINE kit ( Ambion ) . MOs and in vitro synthesized mRNA were injected into yolk of embryos at the 1–4-cell stage . Whole mount ISH was performed using digoxigenin ( DIG ) -labeled antisense RNA probes [83] . For Northern blotting , total RNA was prepared using TRIzol ( Invitrogen ) . RNA ( 10 µg per lane ) was separated on formaldehyde gels , transferred to nylon membranes , and probed with two different DIG-labeled antisense RNA probes for nipbla and nipblb . Hybridized probes were detected by using alkaline phosphate-conjugated anti-DIG antibody and visualized with NBT/BCIP ( Roche ) . Plasmids for expression of 6x-His-tagged fusions of amino acid residues 523–948 of Nipbla and residues 232–593 of Nipblb ( corresponding to regions poorly conserved between the two proteins ) were constructed by subcloning partial cDNA fragments of nipbla and nipblb into the pET15b vector . The proteins were expressed in E . coli ( Rosetta-gami2 [Novagen] ) , purified by nickel-column chromatography , and used to immunize rabbits at Open Biosystems , Inc . For Western blotting , total protein was extracted from embryos as described [84] . Briefly , chorions were removed , yolks punctured by pipetting in 1/2× Ginzburg Fish Ringer without calcium ( 55 mM NaCl , 1 . 8 mM KCl , 1 . 25 mM NaHCO3 ) , and cells collected by centrifugation and lysed by boiling in Laemmli buffer . Total protein ( 5–10 embryos per lane ) was separated by SDS-PAGE and subjected to Western blotting with anti-Nipbla ( 1∶1 , 000 ) , anti-Nipblb ( 1∶400 ) , and anti-α-Tubulin ( Sigma , 1∶2 , 000 ) , and detected by chemiluminscence ( SuperSignal , Pierce ) . Protein levels were quantified using ImageJ software . Gene expression was measured by Q-PCR using SYBR green and analyzed using iQ5 and CFX software ( BioRad ) . cDNA was synthesized using Superscript III first strand synthesis ( Invitrogen ) . All data were normalized to ef-1a as a reference , and rpl13a [85] served as a negative control for MO specificity ( neither ef-1a nor rpl13a transcript levels were altered in microarray analysis of nipbla/b-morphants; unpublished data ) . At least three replicates were examined for each primer set and data were averaged over three independent experiments ± S . E . M . p values were calculated from ΔCt values by paired t test . Primers for Q-PCR are shown in Table S1 . RNA was prepared from 30–40 each of uninjected embryos or embryos co-injected with 2 ng each of nipbla-MO1 and nipblb-MO1 . Total RNA was isolated at 6 hpf ( Shield stage ) using TRIzol and further purified using the RNeasy kit ( Qiagen ) . cRNA preparation , hybridization , and scanning were done at the microarray facility at the University of California , Irvine using Affymetrix GeneChip Zebrafish Genome Arrays . Hybridization was performed in triplicate using cRNA from three biologically independent samples . Data were analyzed with Gene Pattern Web software ( http://www . broadinstitute . org/cancer/software/genepattern/ ) , and ranked by permutation analysis . Embryos were fixed at 120 hpf and craniofacial cartilages were visualized by staining with Alcian blue as described previously [86] . | Although best known for its role in chromatid cohesion , cohesin is increasingly seen as a regulator of gene expression . In Cornelia de Lange Syndrome ( CdLS ) , partial deficiency for NIPBL , which encodes a cohesin regulator , is associated with small changes in the expression of many genes ( similar effects are seen in Nipbl-deficient mice and flies ) . Are such changes responsible for pervasive developmental defects in CdLS ? To address this , we used morpholino oligonucleotides to quantitatively reduce levels of Nipbl protein and Nipbl target genes in zebrafish embryos . Combined knockdown of both zebrafish nipbl genes produced heart and gut defects with similarities to those observed in CdLS . Nipbl-deficient embryos showed quantitative changes in the expression of several genes involved in the specification of endoderm , which both gives rise to gut and provides a substrate for cardiac precursor migration , as well as genes that regulate left-right asymmetry . Functional studies of these putative targets suggest that changes in their expression collectively , and in some cases synergistically , contribute to the observed phenotypes . These findings suggest that birth defects in CdLS result from combinatorial , quantitative effects of NIPBL on gene expression , and suggest that cardiac and visceral organ defects in CdLS arise during early embryonic development . | [
"Abstract",
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"Results",
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... | 2011 | Multifactorial Origins of Heart and Gut Defects in nipbl-Deficient Zebrafish, a Model of Cornelia de Lange Syndrome |
Malaria in pregnancy is exquisitely aggressive , causing a range of adverse maternal and fetal outcomes prominently linked to Plasmodium-infected erythrocyte cytoadherence to fetal trophoblast . To elucidate the physiopathology of infected erythrocytes ( IE ) sequestration in the placenta we devised an experimental system for intravital placental examination of P . berghei-infected mice . BALB/c females were mated to C57Bl/6 CFP+ male mice and infected with GFP+ P . berghei IE , and at gestational day 18 , placentas were exposed for time-lapse imaging acquisition under two-photon microscopy . Real-time images and quantitative measurements revealed that trophoblast conformational changes transiently restrain blood flow in the mouse placental labyrinth . The complex dynamics of placental microcirculation promotes IE accumulation in maternal blood spaces with low blood flow and allows the establishment of stable IE-trophoblast contacts . Further , we show that the fate of sequestered IE includes engulfment by both macrophagic and trophoblastic fetal-derived cells . These findings reinforce the current paradigm that IE interact with the trophoblast and provide definitive evidence on two novel pathogenesis mechanisms: ( 1 ) trophoblast layer controls placental microcirculation promoting IE sequestration; and ( 2 ) fetal-derived placental cells engulf sequestered IE .
Infection with Plasmodium parasites during pregnancy is one of the leading causes of maternal and perinatal morbidity and mortality in malaria endemic areas and is particularly severe in regions of unstable transmission [1] . Women infected with Plasmodium falciparum experience a range of adverse pregnancy outcomes including abortions , stillbirths , premature delivery and low infant birth weight . Early descriptions of marked accumulation of infected erythrocytes ( IE ) in the placental intervillous spaces [2] are currently explained by the cytoadherence of P . falciparum-infected erythrocytes to low-sulfated chondroitin 4-sulfate ( C4S or CSA ) proteoglycan present predominantly in the intervillous space of the placenta [3] and on the syncytiotrophoblast lining [4] . Binding of infected cells to placental C4S proteoglycan requires interaction of P . falciparum erythrocyte membrane protein 1 ( PfEMP1 ) molecule , namely VAR2CSA expressed on the surface of IE [5] , [6] . The current placental malaria ( PM ) pathogenesis paradigm stipulates that accumulation of IE in human placenta elicits an inflammatory response [7]–[9] that is presumably responsible for pathological changes observed in the placental barrier ( interhaemal membrane ) , which ultimately has a negative impact on fetal growth and viability [10]–[12] . To a large extent , this pathogenesis model is based on seminal findings that correlate human placental pathology with in vitro IE adhesion properties [13] . It is still unclear whether placental microcirculation contributes to establishment of in vivo IE-throphoblast interactions . IE cytoadherence in the placenta is seen as a strategy of the parasite to circumvent host immunity and propagate the blood stage infection . Little is known of the fate of sequestered IE and the role of fetal components in the subsequent inflammatory response . Although mouse PM develops within a narrow time window of mouse pregnancy with reduced accumulation of P . berghei-IE as compared to human acute PM , pathological findings suggest that the disease involves a strong inflammatory response leading to extensive placental tissue remodeling [14] , [15] . P . berghei-IE display placental tissue adhesion that is partially dependent on placental extracellular C4S [14] , but it is uncertain whether placental sequestration in the mouse is dependent on IE surface antigens mimicking the var2CSA adhesion mechanisms . Observations in experimental mouse models of severe malaria might not have direct correlation to human disease [16] . It should be noted that mouse placental tissue organization and vasculature architecture show marked differences compared to the human placenta [17] . In addition , the exposure time to the parasite is reduced to a few days , conditioning the extent of inflammatory infiltration and immune response observed in mouse PM . Nevertheless , this experimental PM model provides an opportunity to evaluate in vivo placenta-IE interactions and track the fate of IE in the placenta , a research that is not amenable in the human placenta . We used intravital microscopy methods to visualize IE in the placental maternal blood space and analyze the dynamics of IE-placenta interactions . We observed that unexpected maternal blood flow dynamics in the placenta labyrinth favor stable contacts of IE with the fetal trophoblast layers and promote phagocytosis of IE by the fetal cells in the placenta .
The mouse model of placental malaria is based on infection of pregnant mice at gestational day ( G ) 13 with P . berghei ANKA [14] . Placenta observations were performed on G18 in the labyrinth – the inner region of the placenta composed of fetal vasculature and fetal-derived trophoblast layers , the cytotrophoblast and syncytiotrophoblast ( Figures 1A and 1B ) . Together with fetal endothelia , the trophoblast layers in the labyrinth provide a trichorial barrier between maternal and fetal blood circulation , the interhaemal membrane ( IM ) ( Figure 1C ) through which nutrients and oxygen are transferred both by diffusion and active transport [18] . In this model , IM thickening is a prominent consequence of P . berghei infection during pregnancy [14] , [19] ( Figure 1D ) and congenital infection does not occur , as infected erythrocytes ( IE ) are restricted to maternal circulation and were never observed in fetal blood vessels [14] ( Figure 1E ) . To discriminate fetal placental tissue , infected erythrocytes and maternal blood in the infected placenta with fluorescence microscopy we combined three separate tags: i ) a label for fetal-derived tissue by crossing BALB/c females with actin-CFP C57Bl/6 ( B6 . Cyan ) males; ii ) infection of pregnant mice with P . berghei GFP labeled parasites; iii ) a label for the maternal blood fluid by Dextran-Rhodamine injection ( when applicable ) ( Figure S1 ) . Using this experimental system we performed intravital placental imaging by means of a novel two-photon microscopy staging technique that allowed visualization of the placental labyrinth displaying CFP+ placental components of fetal origin ( except for fetal erythrocytes that were CFP− ) , GFP+ IE and Rhodamine+ maternal blood spaces . Identification of CFP+ fetal-derived tissue ( Figure 2A ) was found using intravital imaging of the placenta in the transversal plane . The movement of the blood cell mass in Rhodamine+ areas revealed that maternal blood flow was highly heterogeneous in the labyrinth ( Video S1 ) . Time-lapse imaging showed maternal blood spaces with high flow rates but also various regions with low flow rates or no perfusion as evidenced by an uneven Dextran-Rhodamine distribution , suggesting that infected placenta display local impairments in microcirculation and tissue perfusion . These heterogeneous blood flow patterns allowed identification of areas with different flow within one microscope field ( Figures 2B and 2C ) . At higher magnification , blood flow in the labyrinth showed unexpected dynamics . Within the intravital observation period ( 300 s ) , blood flow appeared to be interrupted by transient occlusion of the maternal blood space lumen . Figure 2D shows sequential images of a continuous 6 min image acquisition ( Video S2 ) where trophoblast ( in blue ) and maternal blood space ( black areas with GFP+ IE ) are readily identified . These images demonstrate that blood flow was interrupted after 2 minutes of acquisition as the blood space was occluded by trophoblast conformational changes . Strong co-localization of the 2 images at observation time-points 72 and 360 s indicates that the modifications occurred in the same focal plane and are highly dynamic ( Pearson's r = 0 . 84; Spearman's r = 0 . 86 - “Pearson-Spearman correlation colocalization” ( PSC ) test ) ( Figure 2E ) . Topological trophoblast changes could involve the cytotrophoblastic bridges ( “Coan-Burton bridges” ) that cross the maternal blood spaces ( Figure 2F ) , which are abundant during the last third of pregnancy , as recently described by Coan et al . [20] . Further , remodeling of maternal blood spaces appears to be transient and seems to operate by reversible constriction . This is illustrated by comparison of sequential images in which trophoblast undergo conformational changes leading to either the “sealing-off” of lumen or “opening up” of other blood spaces ( Figure 3A; Video S3 ) . Intermittent passage of individual infected cells within maternal blood spaces suggests that trophoblast can also operate by temporarily “constricting” and “relaxing” the lumen of maternal blood area , thereby controlling blood flow ( Figures 3B and 3C; Video S4 ) . We propose that trophoblast topological changes act as highly dynamic and reversible septa that transiently occlude the maternal blood spaces and deflect blood flow ( Figure 3C ) , presumably promoting fetal-maternal exchanges and contributing to heterogeneous maternal blood flow rate patterning across the labyrinth . Maternal blood flow patterns were evaluated in the labyrinth of non-infected placentas at late stage gestation ( G18 ) . Intravital imaging with a 5 min time-period at 600× magnification showed that Dextran-Rhodamine labeled maternal blood exhibited different flow rates ( Figure 4A; Video S5 ) . To quantify the relative blood flow in selected regions we used the Dextran-Rhodamine signal as a reference and analyzed the variance of mean pixel value ( MPV ) at all time-points in visually defined areas of low , intermediate and high flow ( Figure 4B ) . For this analysis we applied a custom imaging stabilization algorithm that minimizes background movements of the living tissue in the final resulting image sequences ( see materials and methods ) . As expected , areas of low blood flow showed decreased MPV standard deviation ( SD ) compared to high and intermediate flow areas due to a more constant and lower flow dynamics ( Figure 4C ) . Conversely , the intermediate flow areas had the highest MPV SD , which results from a more heterogeneous flow pattern across the acquisition time-period ( very often related to “stop and go” flow ) . On the other hand , in areas of high flow the MPV SD was increased compared to low flow and decreased compared to areas of intermediate flow as a result of a more constant but highly dynamic flow pattern . These observations confirm that maternal blood flow heterogeneity in the labyrinth is a physiological characteristic of the microcirculation in the mouse placenta . We evaluated whether IE movement in infected placentas paralleled blood flow heterogeneity as ascertained by labeling maternal blood with Dextran-Rhodamine ( Figure 5A and Video S6 ) . Sequential time-points revealed that IE movement was dictated by blood flow rate . In areas of high blood flow no stationary IE were observed whereas in low flow regions IE remained stationary . Moreover , compilation of images across the z axis confirmed the conserved positioning of IE in low flow areas ( as detected by single signals with high intensity ) contrasting with the multiple signals of lesser intensity observed in regions of high blood flow ( Figure 5B ) . As our observations suggested that blood flow rate impacted on the speed of IE travelling in the labyrinth we evaluated whether the IE burden within blood spaces differed according to flow rates . From Video S2 , we visually identified three different blood flow rates within the same microscopic field ( Figures 6A–C ) and counted the number of IE per area during an observation period of 6 min ( Figure 6D ) . Time-lapsed analysis showed that the number of IE ( events/mm2 ) was consistently higher in regions of lower blood flow rate ( RII ) . This was particularly apparent after blood space occlusion ( Figure 2D; Video S2 ) , indicating a preferential IE accumulation in this region compared to RI and RIII ( Figure 6D ) . Individual IE tracking was performed in RI and RIII during the entire observation period whereas in RII IE were followed during the first 75 s of acquisition – the period before blood space occlusion ( Figures 6E–G ) . In RI IE traveled at relatively high speed and could be observed during a period of 10–15 s ( Figure 6E ) whereas in an intermediate flow region ( RIII ) IE were observed during 30–40 s ( Figure 6G ) . In contrast , in the RII region IE were present for a longer period ( 70 s ) and velocity was significantly lower compared to RI and RIII ( Figure 6F and H ) . The average velocity of parasitized cells ( Figure 6H ) confirmed that blood flow rates were sharply distinct amongst these regions . Taken together these observations indicate that transient blood flow alterations in the labyrinth determine the accumulation of IE in areas of low but not high blood flow , possibly favoring IE interactions with the trophoblast layers . These data also suggest that the parasite takes advantage of the heterogeneous placental blood flow pattern to accumulate in the mouse placenta . Cytoadherence and sequestration of P . falciparum-IE in the human placenta via interaction with C4S on trophoblast surface was initially proposed by Fried and Duffy [13] as a key event in placental malaria pathogenesis . In vitro studies in PM mouse models also showed binding of P . berghei parasitized cells to non-infected placental tissue [14] , [19] . By examining intravital images of infected placentas , we observed distinct IE-trophoblast interaction patterns in low blood flow areas . Stable contacts were observed when IE freely travelled in the maternal blood space and encountered the trophoblast . In a typical case the IE experiences a sharply decreased velocity , possibly caused by a rolling contact with the trophoblast membrane , and subsequently attains a steady position . This position was maintained for at least 100 s , until the end of the observation period ( Figures 7A and 7D; Video S7 ) . In other cases IE remained stationary , seemingly nested in the trophoblast , suggesting intimate contact with the tissue while other infected cells travelled freely in the blood with velocities varying between 1 and 6 µm/s ( Figure 7B and 7E; Video S8 ) . These observations indicate that IE-trophoblast contacts resist the forces of the surrounding blood flow , suggesting strong attachment mechanisms that maintain the IE immobilized on trophoblast membrane . We also observed transient interactions when IE engaged in short-term contact with the trophoblast ( aprox . 80 s ) followed by disengagement ( Figure 7C and 7F; Video S9 ) , possibly representing a failed IE adhesion event . In many instances , images showed interactions occurring in cytotrophoblast discontinuities ( “holes” described by Coan et al . [18] ) , suggesting direct contacts of IE with the underlying trophoblast layers ( Figure 7B ) . By using the same imaging acquisition set-up , we observed that IE do not accumulate on , or interact with , blood vessel walls of the popliteal lymph node in an infected non-pregnant female ( Figure S2 and Video S10 ) , thereby validating that IE interact with the placental tissue in distinct manners as compare to peripheral vessels . This data demonstrates that the IE adhere to placental tissue and establish stable interactions that immobilize IE on the trophoblast layers in vivo and possibly elicit interhaemal membrane responses . It is assumed that sequestered IE burst and release infective merozoites , however the fate of stationary IE after attachment to the trophoblast has not yet been addressed . We repeatedly observed that immobilized IE are subjected to further interactions with placental tissue . Image sequences showed that IE were immobilized on the trophoblast for approximately 250 s , abruptly traversed into a neighbor maternal space , and subsequently remained stationary ( Figures 8A , 8B and Video S11 ) . This behavior was also observed in Video S6 ( see arrowheads ) . Furthermore , intravital imaging of highly infected placenta provided evidence that stationary IE are targeted by fetal phagocytic cells . Sequential images showed that CFP+ cells actively migrate and target IE that are then engulfed and presumably undergo intra-phagocytic destruction ( Figure 8C and Video S12 ) over a time period of approximately 5 minutes . Furthermore , analysis of infected placenta sections showed that fetal-derived macrophages ( Mac-1+ cells ) protrude into the maternal blood space ( Figure 8D ) . Both fetal-derived Mac-1+ and Mac-1− cells showed engulfed IE and contained parasite-derived material ( Figures 8E and F ) . Microscopic examination also suggested that the majority of cells containing parasite material did not express the myeloid Mac-1 marker . These observations suggest that fetal-derived cells in the labyrinth are actively involved in IE uptake ( Figures 8E and F ) , a finding that is consistent with previous reports indicating that murine placental macrophages and trophoblast lineage cells have phagocytic capacity and can ingest IE [21] , [22] . This study provides first ever evidence in the living animal that trophoblast conformational changes modulate blood flow in the mouse placental labyrinth promoting accumulation of infected cells and establishment of intimate IE-throphoblast contacts that lead to IE sequestration . These findings support the notion that P . berghei-IE adhere in vivo to the trophoblast and highlight the fact that fetal-derived placental cells play a role in the response to placental malaria .
Here we provide an intravital description of the Plasmodium-infected placenta in the mouse . Our findings reveal novel placental microcirculatory attributes that favor PM pathology and reinforce the current paradigm that IE establish intimate interactions with trophoblasts and elicit fetal-derived cellular responses in the placenta . Specifically , this study demonstrates that unique characteristics of placental microcirculation driven by trophoblast conformational changes favor intra-placental IE accumulation . Furthermore , we show that fetal-derived cells in the placenta are involved in phagocytosis of sequestered IE in vivo . Two-photon microscopy was used to unveil the microcirculatory dynamics in the infected placental labyrinth . The technical procedure for in vivo imaging requires anesthesia and surgical exposure of the placenta unit that may influence the organ hemodynamics . Nevertheless our observations are compelling in revealing that placental microcirculation is , at least partially , governed by the fetal-derived trophoblast conformational changes . The trophoblast topology imposes heterogeneous blood flow pattern across the placenta , which is evident in absence of infection . We visualized areas of transient low-level flow or stasis providing intravital evidence that blood flow rate in the placental microcirculation is not controlled by the maternal arterial blood pressure . We propose that transient constrictions of maternal blood spaces generated by reversible trophoblast conformational changes are at the basis of an exquisite mechanism to control blood flow warranting prolonged contact of maternal blood with the interhaemal membrane . It did not escape our attention that some dynamic conformational changes may involve the “Coan-Burton bridges” as these structures were observed and described in an ultra-structural study as cytotrophoblastic prolongations connecting separate sides of maternal blood space lumen [20] . Such bridges could originate transient blood space obliteration and control maternal blood flow as evidenced by our intravital observations and illustrated in Figure 3C . Nevertheless , without a detailed 3D description of the labyrinth architecture at microscopic scale it is difficult to envisage how this flow-control mechanism could be coordinated at placental organ level . Also , a full three-dimensional reconstruction of the placenta would provide further information about the impact of Plasmodium infection on microcirculation , correlating the alterations in placental haemodynamics with pathological findings . It is plausible that such hemodynamic alterations would lead to increased uterine and umbilical artery vascular resistance as reported in pregnant women with P . falciparum infection [23] , [24] . Rhodamine negative maternal blood areas that contained stationary IE were frequently observed , indicating impaired perfusion . The uneven pattern of Rhodamine distribution in these maternal blood spaces is compatible with blood flow interruption ( or low perfusion ) by fibrin deposition leading to clot formation and subsequent thrombosis . Significant fibrin deposits in the intervillous space have been documented in placental malaria [25]–[27] . In P . falciparum-infected placenta fibrin clots narrowed and plugged intervillous spaces [27] . Fibrin thrombi formation and hemorrhage were also observed in placentas of aborted mice that were infected at G0 with P . chabaudi [28] . A recent study showed that intrauterine growth restriction was associated with compromised maternal circulation displaying slow intervillous blood flow , intermittent stops in perfusion as well as unperfused regions [29] . In our experimental system we frequently observe fibrin deposition in histological sections ( Figure S3 ) that raises the possibility that micro-thrombotic events can lead to low perfusion and contribute to PM pathology . Our observations demonstrate that low blood flow favors accumulation of IE in maternal blood spaces and promotes the establishment of stable contacts with the trophoblast . Apart from the CS4 enriched environment , we propose that the presence of low and intermediate flow regions in the placental labyrinth favors IE sequestration . Nevertheless , we noted that the IE encounters with trophoblast structures did not always lead to stable contact since transient IE-trophoblast interactions were also observed . As opposed to in vitro binding assays , the intravital images suggest that IE adhesion to the trophoblast is not limited to passive receptor-ligand interaction . The trophoblast appears to actively react to the presence of IE at the margins of maternal blood spaces , as is the case of sequestered IE that were carried across neighboring maternal blood regions ( Figure 8A and Video S11 ) . The observations that stationary IE are targeted by fetal macrophages and trophoblast provide evidence for an active reaction to the presence of IE . Our study is in line with findings showing that fetal placenta cells such as the trophoblast [21] , [28] , [30]–[33] and placental macrophages [22] respond to parasite components . The exact mechanisms linking fetal-derived placental cellular response against the parasite to the pro-inflammatory environment and angiogenic impairments observed in infected placenta [34]–[36] are yet to be determined . Our findings lead us to propose that placental hemodynamics as well as trophoblast responses to sequestered IE contribute to PM pathogenesis in the mouse . Despite the marked differences in microanatomy of the mouse and human placenta it cannot be excluded that impairments in microcirculation and pro-inflammatory responses from fetal-derived placental tissue also play a role in human Plasmodium placental infection . Nevertheless , it should be noted that the mouse model here studied represents an aggressive and acute form of placental infection that is observed in only a fraction of pregnant women with malaria [37] .
All procedures involving laboratory mice were in accordance with national ( Portaria 1005/92 ) and European regulations ( European Directive 86/609/CEE ) on animal experimentation and welfare and were approved by the Instituto Gulbenkian de Ciência Ethics committee and the Direção-Geral de Veterinária is the Official National Entity that regulates the use of laboratory animals in Portugal . Eight to twelve week-old BALB/c female and B6-Tg ( CAG-ECFP ) ( B6-Cyan ) male mice were obtained from the Instituto Gulbenkian de Ciência animal facility . Mice were bred and maintained under specific-pathogen free ( SPF ) conditions . All infection experiments made use of P . berghei ANKA constitutively expressing green fluorescent protein under the eef-1 promoter ( ANKA-GFP ) [38] , [39] . Infection inocula consisted of infected erythrocyte preparations ( IE ) obtained after one in vivo passage of a frozen parasite stock that was injected intra-peritoneally in BALB/c mice and collected when parasitemia reached approximately 10% . BALB/c females were mated to B6-Cyan males to obtain placentas where fetal components expressed cyan fluorescent protein ( CFP ) . The day that the pair was separated was considered gestational day 1 ( G1 ) . Pregnancy was monitored every other day by weighing the females . Body weight gain of 3 to 4 g at G10 to G13 indicated successful fertilization . Pregnant mice were intravenously ( i . v . ) infected on G13 with 106 P . berghei ANKA-GFP IE [14] . Intravital imaging was performed on G18 . Intravital images of lymph-node blood vessels from non-pregnant females were acquired 7 days after infection with 106 ANKA-GFP+ IE . Placentas from infected and non-infected pregnant mice , sacrificed on G18 , were fixed in 10% formalin and embedded in paraffin . Non-consecutive 5 µm sections were stained with hematoxylin-eosin ( HE ) and examined under light microscope ( Leica DM LB2 , Leica Microsystems ) . Placentas from P . berghei-GFP+ infected BALB/c females that were mated to B6 . Cyan males were fixed overnight in 4% formalin/6% sucrose , embedded in Tissue Tek OCT compound ( Sakura ) , snap frozen in liquid nitrogen and cut in 7 µm-thick slices using a Leica 3050S cryostat ( Leica Microsystems , Germany ) . Sections were rehydrated for 10 min in PBS 1X , stained with rat anti-mouse Mac-1 ( M1/70 ) biotinilated antibody and developed with streptavidin-HRP Cy3 conjugate . All incubations were performed at room temperature with PBS1x/10% FCS/0 . 1% azide/5% mouse serum . Slides were mounted in 2 . 5% 1 , 4-Diazabicyclo ( 2 , 2 , 2 ) octane ( pH 8 . 6 ) in 90% glycerol in PBS and images were acquired in a DMRA2 Leica microscope ( Leica Microsystems ) using 63X and 100X objectives . Mice were anaesthetized with 150 mg ketamine and 12 mg xylazine per kg body weight and kept on a warm pad at 37°C . For placental imaging , an incision on the lower abdomen was performed and one feto-placental unit of the uterus exposed . Uterine membrane was gently incised and fetus and placenta liberated so that placenta could be exposed in its entirety whilst still attached to the uterus by the decidua , so not compromising tissue irrigation . The fetus was covered with gauze immersed in PBS to avoid drying . Mouse was restrained in a bespoke apparatus with a sliding lid and the placenta was immobilized with a clip to display the fetal side upwards . The placenta was stabilized for observation by covering with a metal platform with an orifice in the middle which holds a cover slip . Tissue hydration was assured by surrounding the tissue with 2% low melting agarose and temperature was monitored with a sensor placed in contact with the tissue . This procedure is described in more detail by Zenclussen et al [40] . Preparation of popliteal lymph nodes was performed as previously described [41] Briefly , hind legs were shaved , the animal restrained on a warm pad at 37°C and an incision on the back of the hind leg near the “K” shaped artery was performed . The lymph node was exposed and immobilized using a metal strap with a small orifice . For blood flow detection , mice were injected i . v . with 1 mg of Rhodamine B isothicyanate ( Dextran-Rhodamine ) ( Sigma-Aldrich ) diluted in PBS , immediately before imaging . A Praire Ultima two-photon microscope on an Olympus BX-51 base with x-y translation stage equipped with two sets of conventional galvanometer-based scanners , fitted with a 2P Coherent Chameleon Laser tuned to 900–910 nm was used throughout . Rhodamine signal was separated from both GFP and CFP fluorescence emission using a dichroic mirror of 565 nm . The GFP/CFP fluorescent protein signals were split by a 495 nm dichroic mirror . Filters used were 500–550 ( GFP ) , 435–485 ( CFP ) and 570–620 nm ( Rhodamine ) . Time-lapse imaging of a single focal plan was performed . Data were acquired using PraireView software and 2D T-series imaging was performed in 512×512 pixel size frame at a rate of 1 . 8 s/frame . Objectives used were 20X ( 1 . 0 NA 2 mm working distance ) and 60X ( 0 . 90 NA 2 mm working distance ) . Images were processed and data analyzed using Fiji/ImageJ 1 . 46a software ( http://pacific . mpi-cbg . de ) . Cell velocity and distance travelled by infected cells were calculated using the Fiji/ImageJ Manual Tracking plug-in . Due to endogenous motion , such as that caused by intestinal peristaltic movement , we performed a software-based post-processing step to stabilize the intravital image sequences over time in order to better quantify the blood flow hemodynamics inside the labyrinth . For this , we developed a custom software algorithm that is conceptually simple and efficient and that has the capability to stabilize a full 3D two-photon microscopy image sequence . In particular , our software ( to be published elsewhere ) performs a cross-correlation based image registration between two consecutive z-image stacks and provides the optimal displacement ( x , y , z ) , such that the global pixel overlap between these two image stacks is maximum . Simply stated , the image drift in all dimensions ( x , y , and z planes ) is minimized in order to achieve a stable and better movie quality . In this study , only 2D image acquisitions were performed . Nonetheless , we were able to use our software to stabilize these image sequences with the same principles described above . In non-infected placentas , areas were selected by visual inspection based on apparent flow rate . A constant region of interest ( ROI ) was defined and the mean pixel value ( MPV ) was calculated for each frame in the image sequence . Also , the standard deviation ( SD ) of the MPV/frame was calculated . In infected placentas , an output image was generated from the merging of all images along the z axis containing the maximum pixel values over all images in the stack . This image analysis procedure was performed using Fiji/ImajeJ 1 . 46a software . Data were presented as mean values +/− SEM . Unpaired t test or ANOVA with Tukey's or Dunnet's post-test were performed using the GraphPad Prism 4 . 0 software . Data were considered significant for p<0 . 05 . Pearson and Spearman correlation coefficients were calculated using a specific plug-in ( http://www . cpib . ac . uk/~afrench/coloc ) [42] of Fiji/ImageJ 1 . 46a image processing software . | Malaria in pregnancy is exquisitely aggressive , causing a range of adverse effects impacting maternal and fetal health . Many of those effects are thought to derive from placental sequestration of red blood cells infected with the malaria parasite ( Plasmodium falciparum ) eliciting a placental inflammatory response that impairs maternal-fetal exchanges . We developed an experimental system for intravital microscopy to directly observe the course of placental infection in a mouse model of pregnancy-associated malaria . We found that microcirculation in infected placentas showed areas of low blood flow that promote sequestration of infected red blood cells . Furthermore , we observed that sequestered infected red blood cells are targeted and phagocytosed by fetal-derived cells in the materno-fetal interface . This work provides the first ever in vivo evidence that unique placental microcirculatory features promote infected red blood cell sequestration , implying a vascular component in placental malaria pathogenesis . Moreover , we reinforce the notion that fetal-derived cells contribute to the placental response against sequestered infected red blood cells . | [
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] | 2013 | Intravital Placenta Imaging Reveals Microcirculatory Dynamics Impact on Sequestration and Phagocytosis of Plasmodium-Infected Erythrocytes |
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